From 2a7730a5a511153503932f0dc8199ef8aa45ba59 Mon Sep 17 00:00:00 2001 From: silvermilight Date: Thu, 23 Jul 2026 00:14:35 +0800 Subject: [PATCH 1/4] feat(lab5): finish inference and quantization framework --- src/lab5/.python-version | 1 + src/lab5/README.md | 248 +++++++- src/lab5/datasets/calibration-256.jsonl | 256 ++++++++ src/lab5/datasets/performance_public.jsonl | 5 + src/lab5/datasets/performance_small.jsonl | 4 + src/lab5/datasets/quality_public.jsonl | 62 ++ src/lab5/main.py | 38 -- src/lab5/modules/__init__.py | 22 - src/lab5/modules/attention.py | 151 ----- src/lab5/modules/config.py | 73 --- src/lab5/modules/layer.py | 73 --- src/lab5/modules/mlp.py | 53 -- src/lab5/modules/model.py | 562 ---------------- src/lab5/modules/rope.py | 99 --- src/lab5/pyproject.toml | 31 + src/lab5/results/bf16-public-quality.json | 57 ++ src/lab5/scripts/evaluate_quality.py | 344 ++++++++++ src/lab5/scripts/quantize.py | 5 + src/lab5/scripts/run_generation_queue.py | 193 ++++++ src/lab5/simple_test.py | 222 ------- src/lab5/src/hpc101_infer/__init__.py | 17 + src/lab5/src/hpc101_infer/config.py | 35 + src/lab5/src/hpc101_infer/data/__init__.py | 9 + src/lab5/src/hpc101_infer/data/jsonl.py | 58 ++ .../src/hpc101_infer/data/tokenization.py | 80 +++ src/lab5/src/hpc101_infer/engine.py | 423 +++++++++++++ .../src/hpc101_infer/evaluation/__init__.py | 7 + .../src/hpc101_infer/evaluation/quality.py | 126 ++++ src/lab5/src/hpc101_infer/layers/__init__.py | 0 src/lab5/src/hpc101_infer/layers/attention.py | 274 ++++++++ src/lab5/src/hpc101_infer/layers/linear.py | 201 ++++++ src/lab5/src/hpc101_infer/layers/norm.py | 18 + src/lab5/src/hpc101_infer/layers/rotary.py | 87 +++ src/lab5/src/hpc101_infer/models/__init__.py | 0 src/lab5/src/hpc101_infer/models/config.py | 80 +++ src/lab5/src/hpc101_infer/models/gemma4.py | 194 ++++++ src/lab5/src/hpc101_infer/models/loader.py | 165 +++++ .../src/hpc101_infer/quantization/__init__.py | 23 + .../hpc101_infer/quantization/calibration.py | 121 ++++ .../hpc101_infer/quantization/checkpoint.py | 195 ++++++ src/lab5/src/hpc101_infer/quantization/cli.py | 137 ++++ .../src/hpc101_infer/quantization/config.py | 53 ++ .../quantization/methods/__init__.py | 7 + .../hpc101_infer/quantization/methods/base.py | 7 + .../hpc101_infer/quantization/methods/gptq.py | 215 +++++++ .../hpc101_infer/quantization/methods/rtn.py | 99 +++ .../src/hpc101_infer/quantization/packing.py | 61 ++ .../src/hpc101_infer/quantization/pipeline.py | 599 ++++++++++++++++++ .../quantization/recipes/__init__.py | 3 + .../quantization/recipes/gemma4.py | 19 + .../src/hpc101_infer/quantization/registry.py | 24 + .../src/hpc101_infer/quantization/types.py | 101 +++ src/lab5/src/hpc101_infer/runner.py | 119 ++++ src/lab5/src/hpc101_infer/runtime/__init__.py | 11 + src/lab5/src/hpc101_infer/runtime/batch.py | 23 + src/lab5/src/hpc101_infer/runtime/device.py | 8 + src/lab5/src/hpc101_infer/runtime/kv_cache.py | 158 +++++ src/lab5/src/hpc101_infer/runtime/metrics.py | 39 ++ src/lab5/src/hpc101_infer/sampling.py | 100 +++ .../src/hpc101_infer/scheduler/__init__.py | 32 + src/lab5/src/hpc101_infer/scheduler/base.py | 49 ++ .../hpc101_infer/scheduler/static_batch.py | 91 +++ src/lab5/src/hpc101_infer/types.py | 108 ++++ src/lab5/utils/utils.py | 90 --- 64 files changed, 5353 insertions(+), 1412 deletions(-) create mode 100644 src/lab5/.python-version create mode 100644 src/lab5/datasets/calibration-256.jsonl create mode 100644 src/lab5/datasets/performance_public.jsonl create mode 100644 src/lab5/datasets/performance_small.jsonl create mode 100644 src/lab5/datasets/quality_public.jsonl delete mode 100644 src/lab5/main.py delete mode 100644 src/lab5/modules/__init__.py delete mode 100644 src/lab5/modules/attention.py delete mode 100644 src/lab5/modules/config.py delete mode 100644 src/lab5/modules/layer.py delete mode 100644 src/lab5/modules/mlp.py delete mode 100644 src/lab5/modules/model.py delete mode 100644 src/lab5/modules/rope.py create mode 100644 src/lab5/pyproject.toml create mode 100644 src/lab5/results/bf16-public-quality.json create mode 100644 src/lab5/scripts/evaluate_quality.py create mode 100644 src/lab5/scripts/quantize.py create mode 100644 src/lab5/scripts/run_generation_queue.py delete mode 100644 src/lab5/simple_test.py create mode 100644 src/lab5/src/hpc101_infer/__init__.py create mode 100644 src/lab5/src/hpc101_infer/config.py create mode 100644 src/lab5/src/hpc101_infer/data/__init__.py create mode 100644 src/lab5/src/hpc101_infer/data/jsonl.py create mode 100644 src/lab5/src/hpc101_infer/data/tokenization.py create mode 100644 src/lab5/src/hpc101_infer/engine.py create mode 100644 src/lab5/src/hpc101_infer/evaluation/__init__.py create mode 100644 src/lab5/src/hpc101_infer/evaluation/quality.py create mode 100644 src/lab5/src/hpc101_infer/layers/__init__.py create mode 100644 src/lab5/src/hpc101_infer/layers/attention.py create mode 100644 src/lab5/src/hpc101_infer/layers/linear.py create mode 100644 src/lab5/src/hpc101_infer/layers/norm.py create mode 100644 src/lab5/src/hpc101_infer/layers/rotary.py create mode 100644 src/lab5/src/hpc101_infer/models/__init__.py create mode 100644 src/lab5/src/hpc101_infer/models/config.py create mode 100644 src/lab5/src/hpc101_infer/models/gemma4.py create mode 100644 src/lab5/src/hpc101_infer/models/loader.py create mode 100644 src/lab5/src/hpc101_infer/quantization/__init__.py create mode 100644 src/lab5/src/hpc101_infer/quantization/calibration.py create mode 100644 src/lab5/src/hpc101_infer/quantization/checkpoint.py create mode 100644 src/lab5/src/hpc101_infer/quantization/cli.py create mode 100644 src/lab5/src/hpc101_infer/quantization/config.py create mode 100644 src/lab5/src/hpc101_infer/quantization/methods/__init__.py create mode 100644 src/lab5/src/hpc101_infer/quantization/methods/base.py create mode 100644 src/lab5/src/hpc101_infer/quantization/methods/gptq.py create mode 100644 src/lab5/src/hpc101_infer/quantization/methods/rtn.py create mode 100644 src/lab5/src/hpc101_infer/quantization/packing.py create mode 100644 src/lab5/src/hpc101_infer/quantization/pipeline.py create mode 100644 src/lab5/src/hpc101_infer/quantization/recipes/__init__.py create mode 100644 src/lab5/src/hpc101_infer/quantization/recipes/gemma4.py create mode 100644 src/lab5/src/hpc101_infer/quantization/registry.py create mode 100644 src/lab5/src/hpc101_infer/quantization/types.py create mode 100644 src/lab5/src/hpc101_infer/runner.py create mode 100644 src/lab5/src/hpc101_infer/runtime/__init__.py create mode 100644 src/lab5/src/hpc101_infer/runtime/batch.py create mode 100644 src/lab5/src/hpc101_infer/runtime/device.py create mode 100644 src/lab5/src/hpc101_infer/runtime/kv_cache.py create mode 100644 src/lab5/src/hpc101_infer/runtime/metrics.py create mode 100644 src/lab5/src/hpc101_infer/sampling.py create mode 100644 src/lab5/src/hpc101_infer/scheduler/__init__.py create mode 100644 src/lab5/src/hpc101_infer/scheduler/base.py create mode 100644 src/lab5/src/hpc101_infer/scheduler/static_batch.py create mode 100644 src/lab5/src/hpc101_infer/types.py delete mode 100644 src/lab5/utils/utils.py diff --git a/src/lab5/.python-version b/src/lab5/.python-version new file mode 100644 index 00000000..86f8c02e --- /dev/null +++ b/src/lab5/.python-version @@ -0,0 +1 @@ +3.13.5 diff --git a/src/lab5/README.md b/src/lab5/README.md index 02dfce51..9aad2270 100644 --- a/src/lab5/README.md +++ b/src/lab5/README.md @@ -1,55 +1,245 @@ -# 实验五: TinyLLM +# 实验五：Gemma4 端到端推理优化 -## 实验框架结构 +本目录包含 Lab 5 的推理框架、公开数据集和实验脚本。实验主要分为两部分： -实验框架的目录结构如下： +1. 使用 GPTQ 将 Gemma4-12B 从 BF16 量化为 W4A16； +2. 在 10 GiB GPU 环境中优化端到端生成吞吐量。 + +完整的实验背景、算法原理和提交要求请阅读实验手册。本文只说明如何使用脚本和运行实验。 + +## 1. 目录说明 ```text . -├── main.py # 简单推理脚本 -├── modules -│ ├── attention.py # TODO: 完成 Qwen3Attention 的实现 -│ ├── config.py # Qwen3 模型配置类 -│ ├── __init__.py -│ ├── layer.py # Qwen3 Decoder Layer 的实现 -│ ├── mlp.py # TODO: 完成 Qwen3FFN 的实现 -│ ├── model.py # Qwen3 模型的实现 -│ └── rope.py # RoPE 位置编码的实现 -├── README.md -├── simple_test.py # 单元测试脚本 -└── utils +├── datasets/ +│   ├── calibration-256.jsonl # 参考校准数据集 +│   ├── performance_public.jsonl # 公开性能评测数据集 +│   ├── performance_small.jsonl # 小规模性能测试数据集 +│   └── quality_public.jsonl # 公开精度评测数据集 +├── results/ +│ └── bf16-public-quality.json # BF16 公开精度基准 +├── scripts/ +│ ├── quantize.py # W4A16 量化入口 +│ ├── evaluate_quality.py # NLL 精度评测入口 +│ └── run_generation_queue.py # 端到端生成与性能测试入口 +└── src/hpc101_infer/ # 推理与量化框架 +``` + +下文命令均假设当前目录为仓库中的 `src/lab5/`。 + +## 2. 准备环境 + +项目使用 `uv` 管理 Python 环境和依赖： + +```bash +uv sync ``` -你需要完成 `modules/attention.py` 中的 `Qwen3Attention` 类和 `modules/mlp.py` 中的 `Qwen3FFN` 类的实现。 +确认三个入口脚本可以正常启动： -我们提供了一个简单的推理脚本 `main.py`，你可以通过运行该脚本来测试你的模型是否能够输出正常的人类可读文本。你也可以通过修改 `main.py` 中的生成配置来探索不同的生成效果（如调整不同的参数、使用 `` 标签强制模型思考等），欢迎同学们将自己的尝试和结果分享在实验报告中。 +```bash +uv run python scripts/quantize.py --help +uv run python scripts/evaluate_quality.py --help +uv run python scripts/run_generation_queue.py --help +``` + +准备原始 Gemma4-12B Hugging Face checkpoint，并记住其目录。下文使用环境变量简化命令： + +```bash +export MODEL_DIR=/path/to/gemma-4-12b +export QUANT_DIR=/path/to/gemma-4-12b-gptq-w4a16 +``` -我们也提供了测试用例，你需要确保你的实现能够通过测试。你可以使用以下命令来运行测试： +`MODEL_DIR` 中应包含模型配置、tokenizer 和 safetensors 权重。脚本按本地 checkpoint 加载模型，不会自动下载缺失文件。 + +在 MIG 环境中，可通过 `CUDA_VISIBLE_DEVICES` 指定分配到的 GPU： ```bash -python simple_test.py +export CUDA_VISIBLE_DEVICES= ``` -## 开发环境设置 +## 3. 运行量化实验 -本次实验使用 [UV](https://docs.astral.sh/uv/) 进行环境管理。UV 是一个现代化的 Python 环境管理工具，支持多种 Python 版本和依赖包管理。 +### 3.1 使用 RTN 检查量化流程 -首先，你需要安装 UV。参考 UV 文档中的 [安装手册](https://docs.astral.sh/uv/getting-started/installation/) 使用以下命令来安装 UV： +RTN 不需要校准数据，适合先检查 checkpoint 的读取、打包和保存流程： ```bash -curl -LsSf https://astral.sh/uv/install.sh | sh +uv run python scripts/quantize.py \ + --model "$MODEL_DIR" \ + --output /path/to/gemma-4-12b-rtn-w4a16 \ + --algorithm rtn \ + --group-size 128 \ + --scale-dtype float16 \ + --device cpu ``` -本次实验提供了一个预配置的 UV 环境，你可以通过以下命令来加载环境: +### 3.2 使用 GPTQ 生成提交模型 + +完成 `src/hpc101_infer/quantization/methods/gptq.py` 后，使用公开校准集运行 GPTQ： ```bash -source /river/hpc101/2025/lab5/env/bin/activate +uv run python scripts/quantize.py \ + --model "$MODEL_DIR" \ + --output "$QUANT_DIR" \ + --algorithm gptq \ + --calibration-file datasets/calibration-256.jsonl \ + --calibration-limit 256 \ + --calibration-micro-batch-size 1 \ + --max-calibration-tokens 16384 \ + --group-size 128 \ + --gptq-block-size 128 \ + --gptq-damp-percent 0.01 \ + --scale-dtype float16 \ + --device cuda \ + --verbose ``` -加载后直接使用 `python main.py` 命令即可运行实验代码。 +常用量化参数： + +| 参数 | 作用 | 默认值 | +| --- | --- | --- | +| `--algorithm` | 量化方法，可选 `rtn`、`gptq` | `rtn` | +| `--group-size` | 每组包含的输入列数，可选 `64` 或 `128` | `128` | +| `--asymmetric` | 使用非对称量化；不指定时使用对称量化 | 关闭 | +| `--scale-dtype` | scale 的数据类型 | `float16` | +| `--calibration-limit` | 最多读取的校准样本数 | 不限制 | +| `--max-calibration-tokens` | 每个 Linear 最多收集的有效 token 数 | `4096` | +| `--gptq-block-size` | GPTQ 分块处理的列数 | `128` | +| `--gptq-damp-percent` | Hessian 阻尼比例 | `0.01` | +| `--max-shard-size-mib` | 输出权重分片的大小上限 | `1024` | + +量化完成后，`QUANT_DIR` 中会保存 packed INT4 权重、量化配置和 manifest。不要覆盖原始 BF16 checkpoint。 + +如果量化时显存不足，优先减小 `--max-calibration-tokens`，并保持 `--calibration-micro-batch-size 1`。修改参数后应重新进行精度评测。 + +## 4. 运行精度评测 + +公开精度评测使用 teacher forcing 计算平均 NLL。框架已提供同一数据集上的 BF16 结果： + +```text +results/bf16-public-quality.json +``` + +直接评测量化模型并与 BF16 基准比较： + +```bash +uv run python scripts/evaluate_quality.py \ + --model "$QUANT_DIR" \ + --dataset datasets/quality_public.jsonl \ + --output results/gptq-public-quality.json \ + --reference results/bf16-public-quality.json \ + --max-delta-nll 0.2 \ + --linear-backend int4_reference \ + --device cuda \ + --dtype bfloat16 \ + --max-sequence-length 2048 \ + --chunk-size 128 +``` + +重点检查输出 JSON 中的字段： + +- `mean_nll`：量化模型在公开数据集上的平均 NLL； +- `delta_nll`：量化模型与 BF16 基准的 NLL 差值； +- `perplexity_ratio`：量化模型与 BF16 基准的困惑度比值； +- `passed`：`delta_nll` 是否不超过 `--max-delta-nll`。 + +实验要求公开测试集上的 `delta_nll < 0.2`。当 `passed` 为 `false` 时，脚本会以非零状态退出。 + +调试时可以使用 `--limit N` 只评测前 `N` 条记录；正式记录结果时不要限制样本数量。`--chunk-size` 只控制每次产生 logits 的 token 数，可在显存不足时适当减小。 + +精度数据集为 JSONL，每行可以提供文本或冻结后的 token 序列： + +```json +{"text": "Zhejiang University is located in Hangzhou."} +{"input_ids": [2, 123, 456, 789, 1]} +``` + +## 5. 运行端到端性能实验 + +使用量化 checkpoint 和公开性能请求运行生成脚本： + +```bash +uv run python scripts/run_generation_queue.py \ + --model "$QUANT_DIR" \ + --input datasets/performance_public.jsonl \ + --output results/gptq-public-generation.jsonl \ + --device cuda \ + --dtype bfloat16 \ + --batch-size 1 \ + --max-sequence-length 2048 \ + --seed 0 +``` + +性能数据集采用 JSONL 格式，每行是一条请求： + +```json +{"prompt": "Hello", "max_new_tokens": 32} +{"input_ids": [2, 3, 4], "max_new_tokens": 16, "stop_token_ids": [1]} +``` + +每条请求必须提供 `prompt` 或 `input_ids`。如果请求没有 `max_new_tokens`，脚本使用命令行参数 `--max-new-tokens`，默认值为 `32`。 + +常用生成参数： + +| 参数 | 作用 | 默认值 | +| --- | --- | --- | +| `--input` | 输入 JSONL；使用 `-` 时从标准输入读取 | 必填 | +| `--output` | 输出 JSONL；使用 `-` 时写入标准输出 | `-` | +| `--batch-size` | 调度 batch size，同时决定 KV cache 的 batch 容量 | `1` | +| `--max-sequence-length` | prompt 与生成 token 的最大总长度 | `4096` | +| `--max-new-tokens` | 请求未指定时的默认生成长度 | `32` | +| `--seed` | 采样随机种子 | `0` | +| `--no-progress` | 关闭进度条 | 关闭 | +| `--no-synchronize-metrics` | 关闭各指标区间前后的设备同步 | 关闭 | + +输出请求结果写入 `--output` 指定的 JSONL 文件。整体性能摘要写入标准错误，包括： + +- `elapsed_s`：处理全部请求的总时间； +- `requests_per_s`：每秒完成的请求数； +- `generated_tokens_per_s`：每秒生成的 token 数； +- `batch_size`：本次运行使用的 batch size。 + +测试不同 batch size 时，建议保留完整日志： + +```bash +uv run python scripts/run_generation_queue.py \ + --model "$QUANT_DIR" \ + --input datasets/performance_public.jsonl \ + --output results/generation-bs4.jsonl \ + --batch-size 4 \ + --max-sequence-length 2048 \ + 2> results/generation-bs4.log +``` + +`--no-synchronize-metrics` 会改变细分指标的计时语义。进行正式对比时，应保持所有实验的该选项一致，且不得通过修改计时区间跳过必要工作。 + +如果在 `performance_public.jsonl` 数据集上测试时显存不足，可以先使用 `performance_small.jsonl` 进行测试。 + +## 6. 推荐实验流程 + +1. 先用 RTN 跑通量化、加载和精度评测闭环； +2. 实现 GPTQ，并生成 W4A16 checkpoint； +3. 在 `quality_public.jsonl` 上确认 `delta_nll < 0.2`； +4. 用 `performance_public.jsonl` 记录未优化版本的性能基线； +5. 逐项实现显存、算子或调度优化； +6. 每次修改后重新检查精度，并使用相同参数重复性能测试； +7. 保存最终精度 JSON、生成结果 JSONL 和性能日志，用于实验报告与提交。 + +为了让性能结果可比较，每组实验应固定模型 checkpoint、数据集、GPU、`batch-size`、`max-sequence-length`、随机种子和计时选项。建议先预热一次，再重复运行多次并报告稳定结果。 + +## 7. 常见问题 + +### CUDA out of memory + +- 减小性能实验的 `--batch-size` 或 `--max-sequence-length`； +- 减小量化时的 `--max-calibration-tokens`； +- 确认只使用分配到的 MIG 实例，且没有残留进程占用显存。 + +### 精度评测提示 reference 不匹配 -## 提交作业 +`evaluate_quality.py` 会检查数据集哈希、长度 buckets 和评测配置。请使用未修改的 `datasets/quality_public.jsonl`，并保持与 BF16 reference 相同的 buckets。 -本实验需要在 GPU 平台上运行，你可以参考 [提交作业 - 提交 GPU 任务](https://hpc101.zjusct.io/guide/job/#%E6%8F%90%E4%BA%A4gpu%E4%BB%BB%E5%8A%A1) 来申请 GPU 资源并提交你的任务。 +### 无法加载量化 checkpoint -需要注意的是，你需要确保在运行前加载 UV 环境。你可以在 SBATCH 提交脚本中执行加载环境的命令，或者在使用 `srun` 命令前确保已经加载了 UV 环境。 +确认量化流程完整结束，输出目录中包含 `quantization_config.json`、manifest、模型配置、tokenizer 和全部权重分片。 diff --git a/src/lab5/datasets/calibration-256.jsonl b/src/lab5/datasets/calibration-256.jsonl new file mode 100644 index 00000000..377a46d2 --- /dev/null +++ b/src/lab5/datasets/calibration-256.jsonl @@ -0,0 +1,256 @@ +{"text": " Agriscience ( 1918 ) "} +{"text": " The Hammond was a key instrument in progressive rock music . Author Edward Macan thinks this is because of its versatility , allowing both chords and lead lines to be played , and a choice between quiet and clean , and what Emerson described as a \" tacky , aggressive , almost distorted , angry sound . \" Emerson first found commercial success with the Nice , with whom he used and abused an L @-@ 100 , putting knives in the instrument , setting fire to it , playing it upside down , or riding it across stage in the manner of a horse . He continued to play the instrument in this manner alongside other keyboards in Emerson , Lake and Palmer . Other prominent Hammond organists in progressive rock include the Zombies ' and Argent 's Rod Argent , Yes 's Tony Kaye and Rick Wakeman , Focus 's Thijs van Leer , Uriah Heep 's Ken Hensley , Pink Floyd 's Rick Wright , Kansas 's Steve Walsh , and Genesis 's Tony Banks . Banks later claimed he only used the Hammond because a piano was impractical to transport to gigs . "} +{"text": " = Foster @-@ Payne House = "} +{"text": " Though Mauritius had previously been visited by Arab vessels in the Middle Ages and Portuguese ships between 1507 and 1513 , they did not settle on the island . The Dutch Empire acquired the island in 1598 , renaming it after Maurice of Nassau , and it was used from then on for the provisioning of trade vessels of the Dutch East India Company . To the sailors who visited Mauritius from 1598 and onwards , the fauna was mainly interesting from a culinary standpoint . The dodo was sometimes considered rather unpalatable , but the red rail was a very popular gamebird for the Dutch and French settlers . The reports dwell upon the varying ease with which the bird could be caught according to the hunting method and the fact that when roasted it was considered similar to pork . "} +{"text": " Varchasva , a three @-@ day @-@ long event held towards the end of September every year is the annual Cultural and Sports festival of IIM Lucknow . A classical cultural night \" Aarohan \" is organised by the IIM Lucknow chapter of SPIC MACAY in the month of August . It showcases live shows of classical arts including carnatic , hindustanic , folk , instrumental and various classical Indian dances . The institute 's dance club \" Random Walk \" organises dance workshops regularly and performs stage shows during major festivals . The dramatics club \" Abhivyakti \" performs stage plays and street plays in Hindi and English . The Intracollege sports event called as Sikander is held throughout the year in a league format . The institute sports team participates in most of the popular college sports meets including the Inter @-@ IIM Meet ( Sangharsh ) held during January every year . "} +{"text": " Prime formed a partnership with the Nine Network ( affiliated in Australia to competitor WIN Television ) , giving its owner , PBL Media the option to purchase a 50 % share of Prime Television New Zealand in return for access to original programming , and cross @-@ promotion in PBL 's New Zealand magazine titles . Following this , Prime Television New Zealand began to take on branding and programming similar to that of the Nine Network . In November 2005 , Prime Television New Zealand was purchased by subscription television provider SKY Network Television for $ NZ30 million , completed after approval by New Zealand 's Commerce Commission in February 2006 . "} +{"text": " UB @-@ 42 was laid down on 3 September 1915 . As one of six U @-@ boats selected for service in the Mediterranean while under construction , UB @-@ 42 was broken into railcar @-@ sized components and shipped overland to the Austro @-@ Hungarian port of Pola . Shipyard workers from Weser assembled the boat and her five sisters at Pola , where she was launched on 4 March 1916 . "} +{"text": " Friedman won the Nobel Memorial Prize in Economic Sciences , the sole recipient for 1976 , \" for his achievements in the fields of consumption analysis , monetary history and theory and for his demonstration of the complexity of stabilization policy . \" "} +{"text": " Evans enjoyed an executive position at Apple until 1969 , when Allen Klein was hired as a manager to reorganise the whole company . Evans was fired by Klein the next year , because Klein complained to Lennon that Aspinall and Evans were \" living like kings ; like fucking emperors \" . Evans was later reinstated after McCartney , Harrison and Starr complained . On 13 September 1969 , Evans accompanied Lennon , Yoko Ono , Klaus Voorman , Alan White and Eric Clapton to Toronto , Canada , for the Toronto Rock and Roll Revival concert . He later wrote : \" I was really enjoying myself . It was the first show I had roadied for three years and I was really loving every minute of plugging the amps in and setting them up on stage , making sure that everything was right . Everyone wanted the show to go particularly well because Allen Klein , who had flown over , had organised for the whole of John 's performance to be filmed . This was on top of it being video @-@ taped by Dan Richter . \" "} +{"text": " finished in the top @-@ five at the NCAA Championships 9 times "} +{"text": " = = Reception = = "} +{"text": " = = Technical requirements = = "} +{"text": " As the first chorus begins , earlier scenes from the video are intercut with each other ; Rihanna is shown commanding her troops , firing a machine gun , and dancing in the bunker . As the second verse begins , Rihanna is seen with slicked @-@ back hair wearing a metal bikini top , rolling in mud and surrounded by troops on a stone structure before a mountainous backdrop . She is shown gambling with ( and winning against ) soldiers in another bunker . "} +{"text": " = = History = = "} +{"text": " Candice Michelle "} +{"text": " Route 109 in Cape May "} +{"text": " While the band worked on The Notorious Byrd Brothers album throughout late 1967 , there was increasing tension and acrimony between the members of the group , which eventually resulted in the departures of both Crosby and Clarke . McGuinn and Hillman became increasingly irritated by what they saw as Crosby 's overbearing egotism and his attempts to dictate the band 's musical direction . In addition , during the Byrds ' performance at the Monterey Pop Festival on June 17 , 1967 , Crosby gave lengthy in @-@ between @-@ song speeches on controversial subjects , including the JFK assassination and the benefits of giving LSD to \" all the statesmen and politicians in the world \" , to the intense annoyance of the other band members . He further irritated his bandmates by performing with rival group Buffalo Springfield at Monterey , filling in for ex @-@ member Neil Young . His reputation within the band deteriorated even more following the commercial failure of \" Lady Friend \" , the first Byrds ' single to feature a song penned solely by Crosby on its A @-@ side . "} +{"text": " = = Themes and analysis = = "} +{"text": " = = = Liechtenstein = = = "} +{"text": " Ashdod has seen much of its growth as the result of absorption of immigrants . The first settlers were Jewish immigrants from Morocco and Egypt . In the 1960s Ashdod accepted a large number of immigrants from Romania , followed by a large number from Georgia ( then part of the Soviet Union ) in the 1970s . More than 60 @,@ 000 Russian Jews from the former Soviet Union who immigrated to Israel in the 1990s following the collapse of the Soviet Union settled in Ashdod . Recent demographic figures suggest that about 32 % of the city 's population are new immigrants , 85 % of whom are originally from the former Soviet Union . During the 1990s the city absorbed a large number of Beta Israel immigrants from Ethiopia , and in more recent years Ashdod absorbed a large number of immigrants from the United States , United Kingdom , France , Argentina , and South Africa . Many of the 60 @,@ 000 Marathi @-@ speaking Bene Israel from Maharashtra , India who moved to Israel also settled there . Ashdod also receives a significant amount of internal migration , especially from the Gush Dan region . "} +{"text": " Elstow first formed in the area known as Lakeview . Administrative affairs were handled from 1905 to 1909 by the Local Improvement District # 17 @-@ A3 and then by rural municipality Blucher No. 343 . The station of Fountain was on the rail line and Provincial Highway 14 in 1907 . Lanigan received steel in 1907 . Lanigan was a CPR junction point and five rail lines served the area from Lanigan , as well as Provincial highway 14 ( the Yellowhead ) and Hwy 20 . Humboldt and Watson were served to the north , and Nokomis to the south as well as the main Saskatoon Winnipeg line . The rail came to Wynyard in 1909 , and three years later the area incorporated as a town . Wynyard was the CPR divisional point . Laxdal received its post office in 1907 , and renamed to Mozart in 1909 . "} +{"text": " The Canucks announced that they would be celebrating 100 years of hockey in British Columbia during the season . To that end , they would wear a special patch on their jerseys with the logo of the Vancouver Millionaires , the first professional team in the city . They also would wear replica jerseys of the Millionaires for their March 16 game against the Detroit Red Wings . "} +{"text": " Ethnic violence continued to escalate when Serb paramilitaries abducted and killed five Croatian civilians from Lovinac in southern Lika on 5 August , and a police checkpoint in Žuta Lokva was attacked . In early August , the JNA moved the 236th Proletarian Motorized Brigade ( a brigade in name only , the 236th was actually only a company @-@ sized unit ) and a portion of the motorised battalion of the 4th Armoured Brigade , previously deployed to the Ljubovo pass , to Gospić as reinforcements for its garrison in the town . Other movements in that month include the assault and capture of the Plitvice police station on the 30th by SAO Krajina forces . "} +{"text": " On November 22 , 1997 , the Fiji Meteorological Service 's Regional Specialized Meteorological Center in Nadi , Fiji ( RSMC Nadi ) and the Naval Pacific Meteorology and Oceanography Center ( NPMOC ) started to monitor a tropical depression that had developed about 465 km ( 290 mi ) to the northeast of the Northern Cook Island : Manihiki . Over the next two days the depression gradually developed further , as it slowly moved southwards due to a weakness in the subtropical ridge of high pressure . At 1200 UTC on November 23 , the NPMOC reported that the depression had become equivalent to a tropical storm and assigned it the designation 06P . Twelve hours later RSMC Nadi named the system Osea after it had developed into a Category 1 tropical cyclone on the Australian tropical cyclone intensity scale . Thereafter , Osea started moving towards the southeast , as an area of high pressure started to develop to the south of the system . "} +{"text": " In June 1919 , V. Everit Macy gave the property , buildings , and $ 7 @,@ 500 for remodeling to the Teachers College Country Club ; Macy was chairman of the college 's board of trustees at the time . The club was founded on October 4 , 1919 and initially used a building called Hill Cottage , which it outgrew shortly thereafter . The club opened there on September 1 , 1919 and existed there until at least 1927 . Presidents included Henry C. Pearson ( 1919 – 21 ) , Jesse C. Williams ( 1921 – 23 ) , Walter H. Eddy ( 1923 – 25 ) , and Edward S. Evanden ( 1925 ) . Walter W. Law gave the club 's members ( which numbered 200 in 1920 ) the right to use Briarcliff Manor 's 9 @-@ hole golf course , the present @-@ day Trump National Golf Club . "} +{"text": " These insults culminated in an altercation between the two on March 16 , 2003 , at Pride 25 . Following a victory over Kevin Randleman , Jackson was given the microphone and started talking directly to Silva , who was sitting at ringside , saying \" I want you boy . It 's gonna be me and you . \" Silva immediately jumped to his feet and made his way into the ring . He was now face @-@ to @-@ face with Jackson , who continued by saying , \" You have my belt , you 're keeping it warm for me . \" Silva responded by yelling \" MY BELT ! \" and giving Jackson a forceful shove . Jackson , visibly upset , advanced towards Silva as dozens of Pride officials intervened . Jackson stood still as he was surrounded by officials , while Silva had to be constantly restrained from charging at Jackson . Jackson later stated that he was instructed by Pride executives to make these comments . He added that Silva 's shove infuriated him , and he was ready to fight Silva immediately . "} +{"text": " The Lafayette Activities Forum is a student @-@ run organization that strives to promote campus interaction and student relations by incorporating programs and entertainment that reflect the interests of the general student body . LAF is made up of three committees : Live Entertainment , Campus Culture , and Marketing & Public Relations . They are in charge of planning events such the Spring Concert , Fall Fest , the Spot Underground , Open Mic nights , and Live Comedy . "} +{"text": " In January 2016 , Welsh Water acquired both Llanishen and Lisvane reservoirs from CELSA Group on a 999 year lease . The campaign against development of the site led by the Reservoir Action Group welcomed the move and hoped that the site could be reopened to the public in future . "} +{"text": " I was at Tulkaram today , and went along the Nablus road . It is strewn with broken lorries , wagons , dead Turks , horses and oxen ; mostly killed and smashed by our bombing aeroplanes . The same bombing of fugitives , on crowded roads , continues today . I think I ought to capture all the Turks ' guns and the bulk of his Army . "} +{"text": " It was also around this time that , in Brazil , footballer Petronilho de Brito would achieve notoriety for his bicycle kicks — locally receiving credit for the move 's invention . During a 1922 match between clubs from São Paulo and Rio de Janeiro , Petronilho notably scored twice from a bicycle kick — or bicicleta , as it was locally known . Amid the nascent football of the Río de la Plata , the bicycle kick was associated with other skills ( such as the back @-@ heel volley and the diving header ) as the nucleus of what newspaper El Gráfico in 1928 praised as a uniquely Argentine style of football ; according to that newspaper , the creative striker Pedro Calomino of Boca Juniors invented the bicycle kick . "} +{"text": " The grey @-@ necked rockfowl breeds either alone or in small colonies averaging two to five nests in size , though nearly fifty nests are present at one site . It is monogamous and therefore does not breed with rockfowl other than its mate . It is believed to breed cooperatively in Equatorial Guinea , as four different birds were observed feeding one nest . The rockfowl 's courtship displays are unknown . The timing of the egg laying in a colony is not synchronized , leading to various stages of development of nestlings within the colony . It has been suggested that this is to promote cooperative breeding . The laying dates also vary by region , typically coinciding with a few weeks before the onset of the wet season ; in areas where the wet season is bimodal , two different breeding seasons occur . However , in mountainous regions such as Mount Cameroon , it breeds during the dry season to avoid the frequent mists of the wet season . Birds in Nigeria lay their eggs between August and November , birds in Gabon lay between November and April , birds in western Cameroon lay between March and November with peaks of June , July , and October , and birds in southern Cameroon have two breeding seasons , a main one from October to December and a secondary one lasting from April to May . In Equatorial Guinea , nesting occurs in mid @-@ February . "} +{"text": " After the 2010 season , an NCAA investigation was launched into rules violations by Ohio State , which included allegations that Buckeyes head coach Jim Tressel knowingly played ineligible players during several 2010 games , including the Michigan game . The NCAA investigation , which resulted in Tressel 's resignation , is ongoing and may lead to sanctions ; Ohio State preemptively in July 2011 imposed internal sanctions , one of which was vacating all of Ohio State 's 2010 victories , including the game over Michigan . For this reason , the Columbus Dispatch reported in May 2011 that Ohio State had delayed the award of its traditional \" gold pants \" charms for the 2010 victory over Michigan . "} +{"text": " Months earlier ( before Ellie meets Joel ) , Ellie 's friend Riley ( Yaani King ) surprises Ellie at their boarding school , returning unannounced after many weeks . After revealing that she has become a member of the Fireflies — a revolutionary militia group — Riley takes Ellie to a nearby abandoned shopping mall , discovering that the electricity is functioning . As the two girls play around in the mall , Ellie frequently seeks the reason behind Riley bringing her there . Though initially hesitant to answer , Riley reveals that she has been assigned to another group of Fireflies in a different city , and wished to see Ellie again despite being forbidden to . After fighting over this revelation , Ellie eventually tells her that she supports her decision , because it is something that she has wanted for a long time . Before parting , Riley takes Ellie 's walkman and plugs it into the sound system , encouraging her to dance with her one last time . Although she initially gets into it , Ellie is unable to maintain her pretence , and tearfully begs Riley not to leave . Without hesitation , Riley rips her dog tags off , deciding to choose Ellie instead of the Fireflies . In response , Ellie impulsively kisses her , which Riley returns . Drawn by the noise of their activities , the Infected pursue Ellie and Riley ; the two attempt to escape , but are bitten . They consider suicide , but choose to spend their final hours together . "} +{"text": " = = Circus = = "} +{"text": " In 1715 , during the Jacobite Uprising the supporters of the Old Pretender were marching on Preston . General Charles Wills wrote to Minister Wood of Atherton Chapel asking him to raise a force to be at Cuerden Green the following day , 12 November . Minister Wood led a force of Chowbent men who were given the job of guarding the bridge over the River Ribble at Walton @-@ le @-@ Dale and a ford at Penwortham , which they defended successfully . The Highlanders were routed , and for his efforts Parson James Wood was given £ 100 annuity ( equivalent to £ 14 @,@ 100 in 2015 ) by Parliament and the title \" The General \" by his congregation . "} +{"text": " In 2006 , Lionsgate released a Blu @-@ ray of the film that is presented in a slightly washed @-@ out 1080p transfer and included no special features and a DTS 5 @.@ 1 audio track from the DVDs instead of a lossless audio track . On May 19 , 2009 , Lionsgate re @-@ released the film on Blu @-@ ray with an enhanced and improved video transfer , as well as a THX certified DTS @-@ Master Audio 6 @.@ 1 audio . This \" SkyNet Edition \" with a runtime of 156 minutes also saw a limited collector 's edition encased in an Endoskull . The limited collector 's edition includes the 2009 Blu @-@ ray , as well as the Extreme Edition and Ultimate Edition DVDs and a digital copy of the film . "} +{"text": " The courtship display is poorly recorded . Ornithologist J. Denis Summers @-@ Smith observed a display in which the male hopped beside the female in a tree , drooping its wings and ruffling the chestnut @-@ coloured feathers on its back . Groups of two or more males have been observed chasing a female . In the house sparrow a similar display exists , in which a female who is not ready to copulate is chased by her mate , who is joined by other males . It is not known if the display in the Cape sparrow has a similar significance . When ready to mate , the female crouches in solicitation and is mounted by the male . Instances of hybridisation with the house sparrow , the southern grey @-@ headed sparrow , and the Sudan golden sparrow have been reported . "} +{"text": " In April 1940 Mackinolty became Director of Supply , reporting to the Director @-@ General of Supply and Production on the Air Board , the RAAF 's controlling body . Responsible for all stores and equipment , Mackinolty also represented his service at the Federal Contract Board , Department of Supply , and Oil Board . He was promoted to temporary group captain in June that year . With the formation in 1942 of Allied Air Forces Headquarters , which assumed the operational functions of the Chief of the Air Staff , the Air Board was reorganised . The offices of Air Member for Organisation and Equipment , once held by Air Marshal Richard Williams , and Director @-@ General of Supply and Production , a civilian position , were dissolved and replaced by those of the Air Member for Supply and Equipment ( AMSE ) and Air Member for Engineering and Maintenance ( AMEM ) to focus on the two key logistical functions of supply and engineering , respectively . Promoted to acting air commodore , Mackinolty became the inaugural AMSE in June . Air Commodore Ellis Wackett became AMEM at the same time . Norman Ashworth , in How Not to Run an Air Force ! , observed that splitting the logistical functions of the Air Board in this manner appeared to be a \" uniquely Australian \" experiment , and it was not inconceivable that the organisation had been \" tailored \" to suit these \" two very capable officers \" . Serving as AMSE for the remainder of the war , Mackinolty was credited with successfully managing the supply requirements of personnel and aircraft for an organisation that by 1945 had grown by a factor of fifty from its pre @-@ war size , to become the world 's fourth largest air force . "} +{"text": " While the vocational proposal would not survive Khrushchev 's downfall , a longer @-@ lasting change was a related establishment of specialized high schools for gifted students or those wishing to study a specific subject . These schools were modeled after the foreign @-@ language schools that had been established in Moscow and Leningrad beginning in 1949 . In 1962 , a special summer school was established in Novosibirsk to prepare students for a Siberian math and science Olympiad . The following year , the Novosibirsk Maths and Science Boarding @-@ School became the first permanent residential school specializing in math and science . Other such schools were soon established in Moscow , Leningrad , and Kiev . By the early 1970s , over 100 specialized schools had been established , in mathematics , the sciences , art , music , and sport . Preschool education was increased as part of Khrushchev 's reforms , and by the time he left office , about 22 % of Soviet children attended preschool — about half of urban children , but only about 12 % of rural children . "} +{"text": " A group of thirty Imperial Aramaic documents from Bactria were recently discovered , and an analysis was published in November 2006 . The texts , which were rendered on leather , reflect the use of Aramaic in the 4th century BCE Achaemenid provinces of Bactria and Sogdiana . "} +{"text": " In 1982 the MDC entered into negotiations with London @-@ based developers Arrowcroft in order to secure much needed private sector investment . On a visit to the site , Arrowcroft 's chairman Leonard Eppel spoke of how the buildings \" talked to him \" and upon his return to London set about persuading the company 's board to take on the project . In September 1983 a deal was signed between Arrowcroft plc and the MDC leading to the creation of the Albert Dock Company , which could now start the process of regenerating the Albert Dock . "} +{"text": " Mulder , determining that Wayne is a Czech immigrant by the name of Ivan Veles , reaches the conclusion that Wayne is a demon who is trying to have a normal baby , and suspects that he terminates pregnancies when the fetus exhibits demon @-@ like traits . Betsy has a dream similar to Laura 's , but recognizes the dream @-@ demon as her husband . Nevertheless , she loses her baby and tracks down Mulder and Scully . The two seek out Weinsider and catch him digging in Betsy 's backyard . After a short conversation , the sheriff arrives at the house and shoots Weinsider . He is taken to hospital and placed in a bed next to Laura . A mist passes from Weinsider 's body into Laura 's , at which point Weinsider dies and Laura recovers . "} +{"text": " In February 2001 , underworld dons \" Vellai \" Ravi and Chera , who terrorised the city of Madras ( now Chennai ) in the 1990s , abandoned a life of crime and took up social work . The then @-@ DCP of Flower Bazaar Shakeel Akhter presided over the oath @-@ taking ceremony and welcomed the rehabilitation programme . During the making of his film Alli Arjuna ( 2002 ) , director Saran came across a newspaper article carrying this piece of news and was fascinated . Shortly afterwards in March 2001 , Saran announced his next directorial venture would be inspired by the incident . Titled Erumugam ( meaning \" upward mobility \" ) , the project was scheduled to enter production after the completion of Alli Arjuna . "} +{"text": " Females reach sexual maturity between one @-@ and @-@ a @-@ half to three years of age . They typically average one litter every two to three years throughout their reproductive lives , though the period can be as short as one year . Females are in estrus for about 8 days of a 23 @-@ day cycle ; the gestation period is approximately 91 days . Females are sometimes reported as monogamous , but this is uncertain and polygyny may be more common . Copulation is brief but frequent . Chronic stress can result in low reproductive rates when in captivity as well as in the field . "} +{"text": " = = = Climbing = = = "} +{"text": " Two large basins have been carved from the stone foundation of the compound . Both constructed under Ranavalona I , one was a pool built in honor of the wives of soldiers of the ennobled Hova Tsimahafotsy clan of Ambohimanga , while the other was built for the wives of members of the elite military corps known as \" the 500 \" . The pools were strictly forbidden for public bathing or drinking and contained fish from Lake Itasy and specially consecrated water . Ranavalona I and her successors Radama I and Rasoherina used the larger royal pool for ritual purification during the annual fandroana new year festival . "} +{"text": " = = = Reigns of Louis XV and Louis XVI ( 1723 – 1789 ) = = = "} +{"text": " As in all mushroom @-@ producing fungi , the fruit bodies ( sporocarps ) are the reproductive structures that are produced from fungal mycelium when the appropriate environmental conditions of temperature , humidity and nutrient availability are met . Hydnellum peckii is a stipitate hydnoid fungus , meaning that it has a cap atop a stipe ( stem ) , and a form resembling a Hydnum — characterized by a teeth @-@ like hymenium , rather than gills or pores on the underside of the cap . Fruit bodies growing closely together often appear to fuse together ( this is called \" confluence \" ) . They can reach a height of up to 10 @.@ 5 cm ( 4 @.@ 1 in ) . Fresh fruit bodies exude a striking , thick red fluid when they are moist . "} +{"text": " Allgame called F @-@ Zero X as \" certainly not up to Nintendo 's usual standards \" in terms of detail and texture quality . GameSpot also criticized the game 's graphical detail , calling the low polygon count on the vehicles \" particularly uninspiring \" and saying that the \" track detail is also very limited , giving the track a spartan feel to it \" . Although the optimizations are strict , critics exalted the game for managing a steady rate of 60 frames per second , which some thought made up for the lack of graphical detail with little room for improvement . The Electric Playground found the framerate to give \" the game a major boost in the feel department \" making it \" seem like your vehicle is bursting through the sound barrier \" . According to GameSpot , F @-@ Zero X became the first racing game to run at 60 frames per second with up to 30 vehicles on screen at the same time , but in order to keep the frame rate , polygon counts on the vehicles , textures and track detail are sacrificed . "} +{"text": " The absence of distinct currencies also theoretically removes exchange rate risks , although the imposition of transfer restrictions in 2012 – 13 Cypriot financial crisis means that the situation is not quite so simple . The risk of unanticipated exchange rate movement has always added an additional risk or uncertainty for companies or individuals that invest or trade outside their own currency zones . Companies that hedge against this risk will no longer need to shoulder this additional cost . This is particularly important for countries whose currencies had traditionally fluctuated a great deal , particularly the Mediterranean nations . "} +{"text": " = = Critical reception = = "} +{"text": " We have already discussed the Pythagorean proof , which was a proof by rearrangement . The same idea is conveyed by the leftmost animation below , which consists of a large square , side a + b , containing four identical right triangles . The triangles are shown in two arrangements , the first of which leaves two squares a2 and b2 uncovered , the second of which leaves square c2 uncovered . The area encompassed by the outer square never changes , and the area of the four triangles is the same at the beginning and the end , so the black square areas must be equal , therefore a2 + b2 = c2 . "} +{"text": " The song received generally mixed reviews from most of the critics , who noted that Gaga 's featured appearance failed to add anything to the tune . The original version charted in the lower regions of the charts of Australia and its Urban chart , Spain , United Kingdom and on the US R & B charts . The remixed version charted at sixty @-@ five on the Billboard Hot 100 , and topped the Hot Dance Club Songs chart . It also charted in Australia and its Urban chart , the Czech Republic , New Zealand , while it bubbled under the main charts of Belgium . "} +{"text": " The first and second encounters of humanity with the monolith have visual elements in common ; both apes , and later astronauts , touch the monolith gingerly with their hands , and both sequences conclude with near @-@ identical images of the Sun appearing directly over the monolith ( the first with a crescent moon adjacent to it in the sky , the second with a near @-@ identical crescent Earth in the same position ) , both echoing the Sun – Earth – Moon alignment seen at the very beginning of the film . The second encounter also suggests the triggering of the monolith 's radio signal to Jupiter by the presence of humans , echoing the premise of Clarke 's source story \" The Sentinel \" . "} +{"text": " Much of the material written about her was aimed at children , and tended to focus on her childhood and early career . Much of it was also highly romanticised and not always historically accurate . She has been referenced in several historical novels , most notably in The French Lieutenant 's Woman ( 1969 ) by John Fowles , who was critical of the fact that no British scientist had named a species after her in her lifetime . As her biographer , Shelley Emling , noted , this contrasted with some of the prominent geologists who had used her finds , such as William Buckland and Roderick Murchison , who ended up with multiple fossil species named after them . The only person who did name a species after her during her lifetime was the Swiss @-@ American naturalist , Louis Agassiz . In the early 1840s , he named two fossil fish species after her — Acrodus anningiae , and Belenostomus anningiae — and another after her friend Elizabeth Philpot . Agassiz was grateful for the help the women had given him in examining fossil fish specimens during his visit to Lyme Regis in 1834 . After her death , other species , including the ostracod Cytherelloidea anningi , and two genera , the therapsid reptile genus Anningia , and the bivalve mollusc genus Anningella , were named in her honour . In 2012 , the plesiosaur genus Anningasaura was named for her , and the species Ichthyosaurus anningae was named for her in 2015 . "} +{"text": " = = Origins = = "} +{"text": " = = = Camp fields = = = "} +{"text": " Discussing her character , Barlow described Mavis as a \" soppy thing \" , adding \" she had great integrity , and she was a very loyal person . She would stick by her morality . She was a mouse to begin with , and then she met [ her future husband ] Derek and ... she learnt to be quite tough . \" In Daran Little 's book The Coronation Street Story Mavis is described as a \" very creative person \" . She often met men through her creative endeavours , but her status as a spinster often led to feelings of bitterness , and she resented that her friend Emily had married two men before she had married once . Her lack of male attention led Mavis to be labelled the \" eternal virgin \" of Coronation Street . The serial 's New Zealand broadcaster TVNZ described her as \" flighty and romantic \" . "} +{"text": " In its original broadcast , \" Mother Simpson \" finished 45th in ratings for the week of October 30 - November 5 , 1995 , with a Nielsen rating of 10 @.@ 0 , equivalent to approximately 9 @.@ 6 million viewing households . It was the fourth highest @-@ rated show on the Fox network that week , following Beverly Hills , 90210 , The X @-@ Files and Melrose Place . "} +{"text": " = = = Production = = = "} +{"text": " Lawton is primarily centered on government , manufacturing , and retail trade industries . Lawton MSA ranks fourth in Oklahoma with a gross domestic product of $ 4 @.@ 2 billion produced in 2008 , with a majority ( $ 2 @.@ 1 billion ) in the government sector . Fort Sill is the largest employer in Lawton , with over 5 @,@ 000 full @-@ time employees . In the private sector , the largest employer is Goodyear Tire and Rubber Company with 2 @,@ 400 full @-@ time employees . Major employers in the Lawton area also include : Lawton Public Schools , Comanche County Memorial Hospital , City of Lawton , and Cameron University . Lawton includes two major industrial parks . One is located in the southwest region of town , while the second is located near the Lawton @-@ Fort Sill Regional Airport . "} +{"text": " The earliest version of the pyramid was built at the end of the Middle Preclassic ; it was raised upon a 1 @.@ 42 @-@ metre ( 4 @.@ 7 ft ) deep infill that levelled the natural terrain . This earliest phase of construction has not survived in its entirety but is believed to have possessed three stepped levels with stairways on all four sides . It measured 23 @.@ 46 metres ( 77 @.@ 0 ft ) north @-@ south and stood 2 @.@ 94 metres ( 9 @.@ 6 ft ) high . "} +{"text": " = = = = \" Property \" = = = = "} +{"text": " Harvey has one older brother , Saul , and four nephews through him . She said in 1995 that she would love to have children , stating : \" I wouldn 't consider it unless I was married . I would have to meet someone that I wanted to spend the rest of my life with . That 's the only person who I would want to be the father of my children . Maybe that will never happen . I obviously see it in a very rational way but I 'd love to have children . \" "} +{"text": " At the burnings of Isabella Condon in 1779 and Phoebe Harris in 1786 , the sheriffs present inflated their expenses ; in the opinion of Simon Devereaux they were probably dismayed at being forced to attend such spectacles . Harris 's fate prompted William Wilberforce to sponsor a bill which if passed would have abolished the practice , but as one of its proposals would have allowed the anatomical dissection of criminals other than murderers , the House of Lords rejected it . The burning in 1789 of Catherine Murphy , a counterfeiter , was impugned in Parliament by Sir Benjamin Hammett . He called it one of \" the savage remains of Norman policy \" and subsequently , amidst a growing tide of public disgust at the burning of women , Parliament passed the Treason Act 1790 , which for women guilty of treason substituted hanging for burning . It was followed by the Treason Act 1814 , introduced by Samuel Romilly , a legal reformer . Influenced by his friend , Jeremy Bentham , Romilly had long argued that punitive laws should serve to reform criminal behaviour and that far from acting as a deterrent , the severity of England 's laws was responsible for an increase in crime . When appointed the MP for Queensborough in 1806 he resolved to improve what he described as \" Our sanguinary and barbarous penal code , written in blood \" . He managed to repeal the death penalty for certain thefts and vagrancy , and in 1814 proposed to change the sentence for men guilty of treason to being hanged until dead and the body left at the king 's disposal . However , when it was pointed out that this would be a less severe punishment than that given for murder , he agreed that the corpse should also be decollated , \" as a fit punishment and appropriate stigma . \" This is what happened to Jeremiah Brandreth , leader of a 100 @-@ strong contingent of men in the Pentrich rising and one of three men executed in 1817 at Derby Gaol . As with Edward Despard and his confederates the three were drawn to the scaffold on sledges before being hanged for about an hour , and then on the insistence of the Prince Regent were beheaded with an axe . The local miner appointed to the task of beheading them was inexperienced though , and having failed with the first two blows , completed his job with a knife . As he held the first head up and made the customary announcement , the crowd reacted with horror and fled . A different reaction was seen in 1820 , when amidst more social unrest five men involved in the Cato Street Conspiracy were hanged and beheaded at Newgate Prison . Although the beheading was performed by a surgeon , following the usual proclamation the crowd was angry enough to force the executioners to find safety behind the prison walls . The plot was the last crime for which the sentence was applied . "} +{"text": " After releasing their compilation album Empire Mates State of Mind , EME acts toured the United States . The tour kicked off on 4 July 2012 and ended on 2 September 2012 . EME acts performed in several different cities , including Houston , Dallas , Toronto , Vancouver , New York City , Providence , Calgary , Atlanta , Washington DC , and Chicago . Wizkid toured London in 2012 and performed at the HMV Apollo on 4 June 2012 . On 12 November 2012 , he performed at the BBC Radio 1Xtra Live 02 Academy in Brixton alongside Trey Songz , Tulisa , Kendrick Lamar and Angel . On 17 October 2013 , Wizkid performed at the 2013 edition of Felabration , a yearly concert dedicated to the late Fela Kuti . On 2 November 2013 , he performed at the Guinness Colorful World of More concert alongside D 'banj , Tiwa Savage , P @-@ Square , Davido , Ice Prince , Burna Boy , Olamide and Phyno , among others . The concert took place at the Eko Convention Centre of the Eko Hotels and Suites . On 23 November 2013 , Wizkid performed at the Guinness Big Eruption Concert . Wizkid was invited as a special guest for some shows in Tinie Tempah 's tour of the UK in 2014 . Wizkid joined Chris Brown on his \" One Hell of a Nite \" world tour in May 2016 . "} +{"text": " By noon the next day they had covered 311 miles ( 501 km ) from the Cape Denison hut . Mertz was ahead on skis , breaking trail . Mawson sat on the first sledge ; Ninnis walked beside the second . In his diary that night , Mertz recounted : "} +{"text": " = = Productions = = "} +{"text": " The heavy rainfall caused flooding in Camagüey that damaged about 5 @,@ 700 houses and destroyed 200 . The flooding also damaged 15 schools and hospitals , as well as several crop buildings . The floods left widespread areas without electricity or communications . Six bridges were destroyed in central and western Cuba , which , in addition to damaged roads and rail lines , severely disrupted the country 's transportation infrastructure . A total of 131 roads were unpassable due to the flooding , and 55 rail lines were damaged . The flood waters prompted officials to evacuate 65 @,@ 000 residents in low lying areas , including using helicopters and amphibious vehicles . A tornado was reported in the city of Camagüey , destroying five Soviet planes and several buildings . By the day after the depression dissipated , the Cuban government reported nine deaths , although the death toll was later finalized at 37 . The depression also killed thousands of livestock . Following the severe flooding , the Red Cross sent aid to the victims of Tropical Depression One in Cuba . The Red Cross had sent medical units , tents , blankets , and other necessary item to the victims by plane . Overall about 90 @,@ 000 people were affected . "} +{"text": " On July 29 , 2012 , two days after his Haskell win , Paynter developed a fever , pneumonia , and diarrhea . He was hospitalized at the nearby Mid @-@ Atlantic Equine Medical Center in New Jersey and then shipped on August 10 , 2012 to Belmont Park where he trained for several days . He was then shipped to Saratoga Race Course , home to the Travers Stakes , to recover further . However , he had no public workouts and missed the Travers . He was scheduled to be flown back to Baffert 's home stable in California on August 27 , but Paynter again developed a fever and diarrhea . He was sent to the Upstate Equine Medical Center in Schuylerville , NY where he was diagnosed with colitis , an inflammation of the colon . Paynter 's condition quickly deteriorated and was deemed life @-@ threatening . Ahmed and Justin Zayat began to tweet regular updates on his condition to the public . His care was supervised by Dr. Laura Javsicas , an Equine Internal Medicine specialist . Her devotion to Paynter was admired by both Zayat and Baffert , who later said that Javsicas should receive a special Eclipse Award . In turn , Paynter , normally characterized as an \" aggressive \" horse , was very cooperative toward his caregivers and accepted treatment without causing problems . "} +{"text": " The ride , though indoors , would close in any type of rain . Because of leaks in the structure , water pooled in the trough , warranting a shutdown . Typically , the ride would remain shut down after a period of rain as the crew would have to cycle several trains through the circuit in order for it to dry . Although the ride was enclosed , the storage track remained outdoors with a large door that opened when the storage track was needed . "} +{"text": " Davies moved into writing adult television dramas in 1994 . His early scripts generally explored concepts of religion and sexuality among various backdrops : Revelations was a soap opera about organised religion and featured a lesbian vicar ; Springhill was a soap drama about a Catholic family in contemporary Liverpool ; The Grand explored society 's opinion of subjects such as prostitution , abortion and homosexuality during the interwar period ; and Queer as Folk , his first prolific series , recreated his experiences in the Manchester gay scene . His later series include Bob & Rose , which portrayed a gay man who fell in love with a woman ; The Second Coming , which focused on the second coming and deicide of Jesus Christ from a mostly non @-@ religious point of view ; Mine All Mine , a comedy about a family who discover they own the entire city of Swansea ; and Casanova , an adaptation of the Venetian lover 's complete memoirs . "} +{"text": " = = Sino @-@ Japanese War = = "} +{"text": " = = = Media coverage = = = "} +{"text": " = = History = = "} +{"text": " At his first NCAA Outdoor Championships , Dix became FSU 's first winner at the championships since 1980 , and the first to do so as a freshman . He won the 100 m in 10 @.@ 21 seconds , beating the defending NCAA champions DaBryan Blanton and Tyson Gay to the title . After recording a personal best of 20 @.@ 18 seconds in the semifinals , he managed a fourth @-@ place finish in the 200 m race . Dix competed at his first US senior championships that year and , as the only amateur to reach the 100 m final , he finished fourth . The only athletes to beat him were Leonard Scott , reigning Olympic Champion Shawn Crawford and the eventual world champion Justin Gatlin . "} +{"text": " By the end of World War II , Cedar Point was in need of financial help . The wood of the Cyclone roller coaster was rotting , the boardwalk was cracked in many places , and the fishing dock was in need of repair . In 1946 , Cedar Point 's oldest still @-@ existing ride , the Midway Carousel , was installed . By 1951 , the Cyclone coaster was razed because of its poor condition , leaving the resort without a roller coaster . While the Cyclone was departing , the Laff @-@ in @-@ the @-@ Dark , Rocket Ships , and Loop @-@ A @-@ Plane were newly installed . Cedar Point Causeway was built in 1957 , and is still in use . The president of Cedar Point , Bernie Zeiher , was replaced by George Roose around 1958 , and Emile Legros was elected chairman that same year . "} +{"text": " Chris Doyon ( alias \" Commander X \" ) , a self @-@ described leader of Anonymous , was arrested in September 2011 for a cyberattack on the website of Santa Cruz County , California . He jumped bail in February 2012 and fled across the border into Canada . "} +{"text": " \" This Is England \" , much like the rest of the album that came out later that year , had been drastically re @-@ engineered by Rhodes , with synths and football @-@ style chants added to Strummer 's incomplete recordings . Although Howard was an adept drummer , drum machines were used for virtually all of the percussion tracks . For the remainder of his life , Strummer largely disowned the album , although he did profess that \" I really like ' This Is England ' [ and album track ] ' North and South ' is a vibe . \" In early 1986 , the Clash disbanded . Strummer later described the group 's end : \" When the Clash collapsed , we were tired . There had been a lot of intense activity in five years . Secondly , I felt we 'd run out of idea gasoline . And thirdly , I wanted to shut up and let someone else have a go at it . \" "} +{"text": " = = Ecology = = "} +{"text": " = = = Series 6 = = = "} +{"text": " 1580 – birth of Jannon "} +{"text": " = = Service history = = "} +{"text": " In 1922 Reich began working in Freud 's psychoanalytic outpatient clinic , known as the Vienna Ambulatorium , which was opened on 22 May that year at Pelikangasse 18 by Eduard Hitschmann . Reich became the assistant director under Hitschmann in 1924 and worked there until his move to Berlin in 1930 . "} +{"text": " Britney Spears as Lucy Wagner "} +{"text": " By Nell Gwyn ( 1650 – 1687 ) "} +{"text": " Like other common water additives such as chlorine , hydrofluosilicic acid and sodium silicofluoride decrease pH and cause a small increase of corrosivity , but this problem is easily addressed by increasing the pH . Although it has been hypothesized that hydrofluosilicic acid and sodium silicofluoride might increase human lead uptake from water , a 2006 statistical analysis did not support concerns that these chemicals cause higher blood lead concentrations in children . Trace levels of arsenic and lead may be present in fluoride compounds added to water , but no credible evidence exists that their presence is of concern : concentrations are below measurement limits . "} +{"text": " World Wrestling Entertainment "} +{"text": " Despite the overwhelming negative reviews , the film did receive some positive feedback . Roger Ebert of the Chicago Sun Times and Gene Siskel of the Chicago Tribune both gave Speed 2 three out of four stars , and wrote the film 's only two positive reviews , respectively , on Rotten Tomatoes . On their film review TV series Siskel & Ebert , they collectively gave Speed 2 a positive rating of \" Two Thumbs Up \" , calling it a \" truly rousing ocean liner adventure story \" , although Ebert criticized Bullock 's more limited role in the sequel while Patric \" stole all the action sequences \" . Since his original review , Ebert claimed that he enjoyed Speed 2 more than Bullock , and wrote an article in 2013 that his favorable review of the film \" inspired more disbelief \" than any other he had written and was frequently cited as an example of him being a poor film critic . At the Conference on World Affairs in 1999 , Ebert spoke about the difficulty of making films such as Speed 2 and defended his review by offering a \" Speed 3 \" contest for anyone to create a five @-@ minute short film that takes place on something that cannot stop moving . "} +{"text": " Most modern species attach to hard surfaces by means of a cylindrical pedicle ( \" stalk \" ) , an extension of the body wall . This has a chitinous cuticle ( non @-@ cellular \" skin \" ) and protrudes through an opening in the hinge . However , some genera such as the inarticulate Crania and the articulate Lacazella have no pedicle , and cement the rear of the \" pedicle \" valve to a surface so that the front is slightly inclined up away from the surface . In a few articulate genera such as Neothyris and Anakinetica , the pedicles wither as the adults grow and finally lie loosely on the surface . In these genera the shells are thickened and shaped so that the opening of the gaping valves is kept free of the sediment . "} +{"text": " Tan , Kevin Y [ ew ] L [ ee ] ( 2011 ) , \" Fundamental Liberties III : Freedom of Expression • Association • Assembly • Religion \" , An Introduction to Singapore 's Constitution ( rev. ed . ) , Singapore : Talisman Publishing , pp. 186 – 203 at 186 – 196 , ISBN 978 @-@ 981 @-@ 08 @-@ 6456 @-@ 9 . "} +{"text": " return a ; "} +{"text": " = = Medieval graffiti = = "} +{"text": " \" Half a Life \" was the first Star Trek credit for writer Peter Allan Fields , who would later co @-@ write the \" The Inner Light \" with Morgan Gendel before becoming a staff writer on Star Trek : The Next Generation during season five . Prior to writing the script , Fields reviewed \" Haven \" and \" Manhunt \" , the two previous Lwaxana Troi episodes , but did not use them as references except to note Lwaxana 's level of privilege . \" I realized that you give her as much as they 'll let her have . She 'll take it \" , he explained . "} +{"text": " = = = Modelling = = = "} +{"text": " Poulton gives examples of mimicry in other insect groups , remarking the \" very imperfect \" resemblance of bee hawk @-@ moths to bees , which totally failed to \" impose on \" a lizard , but noting that the much more convincing mimicry of the hornet clearwing moth was treated with extreme caution by an inexperienced lizard . "} +{"text": " = = Description = = "} +{"text": " = = Performance = = "} +{"text": " United Kingdom "} +{"text": " Most gods were associated with specific aspects of life . For example , Aphrodite was the goddess of love and beauty , Ares was the god of war , Hades the ruler of the underworld , and Athena the goddess of wisdom and courage . Some gods , such as Apollo and Dionysus , revealed complex personalities and mixtures of functions , while others , such as Hestia ( literally \" hearth \" ) and Helios ( literally \" sun \" ) , were little more than personifications . The most impressive temples tended to be dedicated to a limited number of gods , who were the focus of large pan @-@ Hellenic cults . It was , however , common for individual regions and villages to devote their own cults to minor gods . Many cities also honored the more well @-@ known gods with unusual local rites and associated strange myths with them that were unknown elsewhere . During the heroic age , the cult of heroes ( or demi @-@ gods ) supplemented that of the gods . "} +{"text": " Sgt. Charlton , a member of Company C , distinguished himself by conspicuous gallantry and intrepidity above and beyond the call of duty in action against the enemy . His platoon was attacking heavily defended hostile positions on commanding ground when the leader was wounded and evacuated . Sgt. Charlton assumed command , rallied the men , and spearheaded the assault against the hill . Personally eliminating 2 hostile positions and killing 6 of the enemy with his rifle fire and grenades , he continued up the slope until the unit suffered heavy casualties and became pinned down . Regrouping the men he led them forward only to be again hurled back by a shower of grenades . Despite a severe chest wound , Sgt. Charlton refused medical attention and led a third daring charge which carried to the crest of the ridge . Observing that the remaining emplacement which had retarded the advance was situated on the reverse slope , he charged it alone , was again hit by a grenade but raked the position with a devastating fire which eliminated it and routed the defenders . The wounds received during his daring exploits resulted in his death but his indomitable courage , superb leadership , and gallant self @-@ sacrifice reflect the highest credit upon himself the infantry , and the military service . "} +{"text": " = = = \" Dock of the Bay \" = = = "} +{"text": " = = = England = = = "} +{"text": " = = Discovery = = "} +{"text": " The crop produce from these plantations were generally exported to other countries from Singapore with the assistance of Chinese merchants based in that city . From the 1860s onwards , many of these Kangchu chalked up debts and began to sell their property rights to these merchants or to larger business magnates ( Kongsi in Teochew ) based in Singapore , who were known to the locals as Tuan Sungai ( literally Masters of the River ) . The Kangchu then were often hired as supervisors or managers by the merchants to keep watch on the day @-@ to @-@ day operations of the gambier and pepper plantations . Temenggong Abu Bakar began to issue contract @-@ style letters of recognition to these Kangchu ; the letters were known by their Malay name Surat Tauliah . "} +{"text": " = = = Details = = = "} +{"text": " = = = Latent conformation = = = "} +{"text": " = SaGa Frontier 2 = "} +{"text": " = Art Spiegelman = "} +{"text": " Based near Riga at the time of the German attack on the Soviet Union in June 1941 , Kirov was trapped in the Gulf of Riga by the rapid enemy advance . She supported minelaying sorties by Soviet destroyers in the western half of the Irben Strait on the evenings of 24 – 25 and 26 – 27 June . Off @-@ loading her fuel and ammunition to reduce her draft , she passed through the shallow Moon Sound Channel ( between Muhu island and the Estonian mainland ) with great difficulty , and managed to reach Tallinn by the end of June . Kirov provided gunfire support during the defense of Tallinn and served as the flagship of the evacuation fleet from Tallinn to Leningrad at the end of August 1941 . For most of the rest of the war she was blockaded in Leningrad and Kronstadt by Axis minefields and could only provide gunfire support for the defenders during the Siege of Leningrad . She was damaged by a number of German air and artillery attacks , most seriously on 4 – 5 April 1942 when she was hit by three bombs and one 15 @-@ centimeter ( 5 @.@ 9 in ) shell that damaged all six 100 mm AA guns , the aft funnel , the mainmast , and killed 86 sailors and wounded 46 . Repairs took two months during which her catapult was removed ; a lighter pole mainmast was fitted and her anti @-@ aircraft armament increased . After Leningrad was liberated in early 1944 , Kirov remained there , and took no further part in the war except to provide gunfire support for the Soviet Vyborg – Petrozavodsk Offensive in mid – 1944 . "} +{"text": " During the rewrite of the final scenes , the studio executives clashed with Roddenberry about the script 's ending , believing that the concept of a living machine was too far @-@ fetched . The executives consulted Asimov : if the writer decided a sentient machine was plausible , the ending could stay . Asimov loved the ending , but made one small suggestion ; he felt that the use of the word \" wormhole \" was incorrect , and that the anomaly that the Enterprise found itself in would be more accurately called a \" temporal tunnel \" . "} +{"text": " The film 's opening song and recurring theme , \" The Only One \" was composed by Paul Mac and recorded by Mac , Bertie Blackman and the Sydney Children 's Choir . The Sydney Children 's Choir 's contributions to the soundtrack were Randall 's own idea , inspired by the children 's chorus covers from The Langley Schools Music Project . The original score was composed by Guy Gross . "} +{"text": " = = = Music video = = = "} +{"text": " Inserting appropriate values into the left sides of these rules , one can produce 0 on the right sides : "} +{"text": " Personnel "} +{"text": " Due to illness , Skorodumov was replaced after only a month by his chief of staff , Lieutenant @-@ General Boris Shteifon , who was said to have had \" warm and friendly relations with [ Milan ] Nedić . \" The Russian Corps was initially designated the \" Independent Russian Corps \" before being renamed the \" Russian Defense Corps \" on 2 October . It was envisaged by the Germans as a 3 @,@ 000 @-@ strong unit organized into three regiments . By late 1941 , it had 1 @,@ 500 members . Recruitment was carried out by Major General Kreyter , a White Russian émigré in German service who was the head of the Russian Intelligence Office ( German : Vertrauensstelle ) in Serbia . The Corps initially consisted of a single regiment , organized into four battalions . Major General Egorov commanded the 1st Battalion , Colonel Shatilov the second , Colonel Endrzheevskiy the third and Colonel Nestrenko the fourth , respectively . A second regiment was set up on 18 October , commanded by Colonel Zhukov . "} +{"text": " The underwater ancient city off the coast of Bet Dwarka has been proposed to be developed as a scuba diving site . This project is a joint initiative of Adventure Sports Ltd ( ASL ) and the Government of Gujarat , with investment of ₹ 13 crore . This is believed to be the first effort anywhere in the world to exploit a submerged city for tourism . Water and beach sports are also being promoted by the state government , and there are proposals to further promote aero and submarine sports . "} +{"text": " The mean annual rainfall is greater than 2 @,@ 000 millimetres ( 79 in ) . Frequent cloudy cover limits the amount of sunlight that is available to plants . The mean annual temperature is 13 ° C ( 55 ° F ) but the temperature varies considerably during the course of a day , reaching as high as 27 ° C ( 81 ° F ) during the day time , and dipping as low as 5 ° C ( 41 ° F ) at night . During the southwest Monsoon season , the wind speed sometimes reaches gale force . Although some rain falls throughout the year , a dry season occurs from January – March . The ground frost is common in February . Mist can persist in the most of the day during the wet season . Many pools and waterfalls can be seen in the park , and Horton Plains is considered the most important watershed in Sri Lanka . The Horton Plains are the headwaters of important rivers such as the Mahaweli , Kelani , and Walawe . The plains also feeds Belihul Oya , Agra Oya , Kiriketi Oya , Uma Oya , and Bogawantalawa Oya . Due to its high elevation , fog and cloud deposit a considerable amount of moisture on the land . Slow moving streams , swamps , and waterfalls are the important wetland habitats of the park . "} +{"text": " American recording artist Adam Lambert performed at this venue on 5 March 2016 as part of his The Original High Tour . "} +{"text": " Before the event aired live on pay @-@ per @-@ view , a dark match between Rob Van Dam and Shelton Benjamin took place . Van Dam won the match by pinning Benjamin after performing a Five @-@ star frog splash . "} +{"text": " Bordagaray led the PCL in batting average ( .373 ) during the 1931 season . He publicly held out from the Senators for more money during the offseason , which became an annual tradition for Bordagaray . In the 1932 season , he batted .322 in games played . He followed this up by batting .351 in 117 games during the 1933 season . "} +{"text": " The Rolling Stones were among the first British rock groups to record modern electric blues songs . In 1964 , they recorded \" Little Red Rooster \" with original member Brian Jones , a key player in the recording . Their rendition , which remains closer to the original arrangement than Cooke 's , became a number one record in the UK and continues to be the only blues song to reach the top of the British chart . The Stones frequently performed it on television and in concert and released several live recordings of the song . \" Little Red Rooster \" continues to be performed and recorded , making it one of Willie Dixon 's best @-@ known compositions . "} +{"text": " The measurements use highly polished spheres of silicon with a mass of one kilogram . Spheres are used to simplify the measurement of the size ( and hence the density ) and to minimize the effect of the oxide coating that inevitably forms on the surface . The first measurements used spheres of silicon with natural isotopic composition , and had a relative uncertainty of 3 @.@ 1 × 10 − 7 . These first results were also inconsistent with values of the Planck constant derived from watt balance measurements , although the source of the discrepancy is now believed to be known . "} +{"text": " However , vegetables often also contain toxins and antinutrients which interfere with the absorption of nutrients . These include α @-@ solanine , α @-@ chaconine , enzyme inhibitors ( of cholinesterase , protease , amylase , etc . ) , cyanide and cyanide precursors , oxalic acid and others . These toxins are natural defenses , used to ward off the insects , predators and fungi that might attack the plant . Some beans contain phytohaemagglutinin , and cassava roots contain cyanogenic glycoside as do bamboo shoots . These toxins can be deactivated by adequate cooking . Green potatoes contain glycoalkaloids and should be avoided . "} +{"text": " = = = = Battle of Leyte Gulf = = = = "} +{"text": " Pope Gregory XIII excommunicated him in March 1583 , and the Chapter deposed him , by electing in his place the 29 @-@ year @-@ old canon , Ernst of Bavaria , brother of the pious William V , Duke of Bavaria . Ernst 's election ensured the involvement of the powerful House of Wittelsbach in the coming contest . "} +{"text": " Although primates are well studied in comparison to other animal groups , several new species have been discovered recently , and genetic tests have revealed previously unrecognised species in known populations . Primate Taxonomy listed about 350 species of primates in 2001 ; the author , Colin Groves , increased that number to 376 for his contribution to the third edition of Mammal Species of the World ( MSW3 ) . However , publications since the taxonomy in MSW3 was compiled in 2003 have pushed the number to 424 species , or 658 including subspecies . "} +{"text": " = = = Music = = = "} +{"text": " Book reviewers found the criticisms constructive and the writing sincere . His style focuses less on numbers and statistics , and more on connecting decisions by UN officials and western diplomats to consequences on the ground in Africa . His eyewitness accounts are said to be candid and emotional . The book spent seven weeks at # 1 on The Globe and Mail 's Nonfiction Bestseller List . A second edition was released in June 2006 . The Canadian Booksellers Association awarded its Libris Award for non @-@ fiction book of the year to Race Against Time and its Author of the Year Award to Lewis in 2006 . "} +{"text": " State slogan : \" Great Faces . Great Places . \" "} +{"text": " In January 2009 , Washington , D.C. Mayor Adrian Fenty took then @-@ President @-@ elect Barack Obama to eat at Ben 's as part of his welcome to the city . "} +{"text": " = = Charts and certifications = = "} +{"text": " In April 2007 , Bolton Museum held an exhibition of photos of Sudanese refugees resettled in the town under the programme . A film , titled Moving to Mars was made about two ethnic Karen families resettled from Burma to Sheffield under the Gateway Protection Programme . The film opened the Sheffield International Documentary Festival in November 2009 and was aired on the television channel More4 on 2 February 2010 . One ethnic Karen refugee resettled with his family in Sheffield in 2006 , Kler Heh , signed a professional contract to play football for Sheffield United F.C. in March 2015 . "} +{"text": " = Battle of Suoi Chau Pha = "} +{"text": " The emperor 's death released the Hungarians from their alliance with East Francia . On their way back from Italy they expanded their rule over Pannonia . Furthermore , according to Liutprand of Cremona , the Hungarians \" claimed for themselves the nation of the Moravians , which King Arnulf had subdued with the aid of their might \" at the coronation of Arnulf 's son , Louis the Child in 900 . The Annals of Grado relates that the Hungarians defeated the Moravians after their withdrawal from Italy . Thereafter the Hungarians and the Moravians made an alliance and jointly invaded Bavaria , according to Aventinus . However , the contemporary Annals of Fulda only refers to Hungarians reaching the river Enns . "} +{"text": " = The Structure and Distribution of Coral Reefs = "} +{"text": " distributing a book or promoting the sale of a book for not less than its commercial value if the book was planned to be published regardless of whether there was to be an election ; "} +{"text": " Nelson 's titles , as inscribed on his coffin and read out at the funeral by the Garter King at Arms , Sir Isaac Heard , were : "} +{"text": " Tropical Storm Barry was a strong tropical storm that made landfall on the Florida Panhandle during August 2001 . The third tropical cyclone and second named storm of the 2001 Atlantic hurricane season , Barry developed from a tropical wave that moved off the coast of Africa on July 24 . The wave entered the Caribbean on July 29 and spawned a low @-@ pressure area , which organized into Tropical Storm Barry on August 3 . After fluctuations in intensity and track , the storm attained peak winds of 70 mph ( 110 km / h ) over the Gulf of Mexico . Barry headed northward and moved ashore along the Gulf Coast before dissipating on August 7 . "} +{"text": " = = Early life = = "} +{"text": " Dylan Marlais Thomas ( 27 October 1914 – 9 November 1953 ) was a Welsh poet and writer whose works include the poems \" Do not go gentle into that good night \" and \" And death shall have no dominion \" ; the ' play for voices ' Under Milk Wood ; and stories and radio broadcasts such as A Child 's Christmas in Wales and Portrait of the Artist as a Young Dog . He became widely popular in his lifetime and remained so after his premature death at the age of 39 in New York City . By then , he had acquired a reputation , which he encouraged , as a \" roistering , drunken and doomed poet \" . "} +{"text": " = = Gallery = = "} +{"text": " Delays in the shooting schedule pushed filming into the autumn , which required the set decorators to spray @-@ paint the autumn leaves green . The uncooperative weather then took a different turn , plunging from oppressive heat to down near 40 ° F ( 4 ° C ) , causing frigid conditions for the famous swimming scene in October . The crew wore warm coats , gloves , and boots . Swayze and Grey stripped down to light summer clothing , to repeatedly dive into the cold water . Despite her character 's enjoyment , Grey later described the water as \" horrifically \" cold , and she might not have gone into the lake , except that she was \" young and hungry \" . "} +{"text": " = = = Lava types = = = "} +{"text": " Nasr 's campaigns and reforms consolidated Muslim rule over Khurasan and much of Transoxiana , but his success was fragile : the native princes resented their loss of autonomy and the increasing assimilation of their people by their Arab conquerors and sent embassies to the Chinese court for aid , while the rivalry between the Mudari and Yamani tribal groups , evident across the Muslim world , still divided the Arabs themselves . On the accession of the pro @-@ Yemenite caliph Yazid III in 744 , the Khurasani Yemenites supported Juday ' al @-@ Kirmani 's candidature as governor , and when this did not come about , they rebelled . Consequently , Nasr felt it necessary to bring Harith and his adherents back , to both strengthen his own position — Harith and his followers had a long history of enmity towards al @-@ Kirmani — and remove a potential source for another foreign invasion . Nasr secured a full pardon for Harith and his supporters from Yazid . Their confiscated property was returned , and the Caliph even promised to rule \" according to the Book and the sunna \" . "} +{"text": " = = Publications = = "} +{"text": " Riefenstahl was portrayed by Dutch actress Carice van Houten in Race , a sports drama film directed by Stephen Hopkins about Jesse Owens . It was released in North America on February 19 , 2016 . To make her sympathetic portrayal acceptable for an American audience , the film dramatizes her quarrels with Goebbels over her direction of the film , Olympia , especially about filming the African American star who is proving to be a politically embarrassing refutation of Nazi Germany 's claims of Aryan athletic supremacy . "} +{"text": " Atangana 's unfailing loyalty and subservience to Germany prevented the French from ever fully trusting him . His first task under the new colonial regime was to supervise gangs of forced road @-@ construction labourers in the town of Dschang . In Atangana 's absence , the French had appointed a Beti headman named Joseph Atemengue as their local representative in Jaunde ( now known by the French spelling , Yaoundé ) . However , Atemengue never enjoyed the popularity Atangana had among the Beti . Atangana tried to secure an alliance with him by sending his 20 @-@ year @-@ old , German @-@ educated daughter , Katerina , to marry him , but she eventually fled from the much older Atemengue and back to her father . Atangana 's work performance convinced the French to let him return to Yaoundé in late 1921 or early 1922 . "} +{"text": " but night was of Nörvi born ; "} +{"text": " = = = Origin of the name = = = "} +{"text": " The village was named after a Wiradjuri word of unknown meaning . The Wiradjuri people were the original inhabitants of the Lachlan Valley , with campsites along river flats , on open land and by rivers . In 1817 John Oxley and George William Evans were the first Europeans to explore the valley . White settlement commenced several years later in the 1830s , leading to violent clashes between the native population and the settlers . "} +{"text": " Barry 's scheme provided two plinths for sculptures on the north side of the square . A bronze equestrian statue of George IV by Sir Francis Chantrey , originally intended to be placed on top of the Marble Arch , was installed on the eastern plinth in 1844 , while the other remained empty until the late @-@ 20th century . There are two other statues on plinths , both installed during the 19th century : General Sir Charles James Napier by George Cannon Adams in the south @-@ west corner in 1855 , and Major @-@ General Sir Henry Havelock by William Behnes in the south @-@ east in 1861 . In 2000 , the Mayor of London , Ken Livingstone , suggested replacing the statues with figures more familiar to the general public . "} +{"text": " Reggie Lucas – producer , guitars , drum programming "} +{"text": " In September 2003 , Jules Engel , Hillenburg 's mentor when he studied experimental animation at the California Institute of the Arts , died . Hillenburg dedicated the film to him : \" He truly was the most influential artistic person in my life . I consider him my ' Art Dad . ' \" "} +{"text": " The series is the first of the Gundam franchise set in the \" Cosmic Era \" in which mankind is divided between normal Earth dwelling humans , known as \" Naturals \" , and the genetically altered super @-@ humans known as \" Coordinators \" . The primary conflict of the story plot derives from jealous hatred by Naturals of the abilities of Coordinators , leading to hate crimes , and eventually the emigration of almost all Coordinators who flee into space to live idyllic lives on giant orbital space colonies called PLANTS of their own design . War eventually breaks out between Earth and the Plants . The Earth is divided between two major factions , the Earth Forces formed from most of the natural born human nations , primarily the Eurasians and the Atlantic Federation , and a natural human supremacist group known as Blue Cosmos with its slogan , \" For the preservation of our blue and pure world \" . The Earth Forces are not a unified alliance , and infighting and mistrust exist between their various nation states . The second major Earth nation is the Orb Union , a staunchly politically neutral and isolationist nation located on small Pacific Ocean islands ruled by a hereditary monarchy and still contains Coordinator citizens . "} +{"text": " \" Seven Ways to Compute the Relative Value of a U.S. Dollar Amount - 1774 to Present \" . Retrieved November 24 , 2010 . "} +{"text": " = = Plays = = "} +{"text": " The common rules do not define a point at which The Game ends . However , some players state that The Game ends when the Prime Minister of the United Kingdom announces on television that \" The Game is up . \" "} +{"text": " Central cord syndrome , almost always resulting from damage to the cervical spinal cord , is characterized by weakness in the arms with relative sparing of the legs , and spared sensation in regions served by the sacral segments . There is loss of sensation of pain , temperature , light touch , and pressure below the level of injury . The spinal tracts that serve the arms are more affected due to their central location in the spinal cord , while the corticospinal fibers destined for the legs are spared due to their more external location . The most common of the incomplete SCI syndromes , central cord syndrome usually results from neck hyperextension in older people with spinal stenosis . In younger people , it most commonly results from neck flexion . The most common causes are falls and vehicle accidents ; however other possible causes include spinal stenosis and impingement on the spinal cord by a tumor or vertebral disk . "} +{"text": " Mayuri Shiina ( 椎名 まゆり , Shiina Mayuri ) "} +{"text": " The episode originally aired on Comedy Central in the United States on June 30 , 2011 , following the Futurama episode \" Ghost in the Machines \" . According to Nielsen Media Research , \" Wet Hot Demonic Summer \" was watched by 1 @.@ 14 million viewers , compared to the 1 @.@ 92 million who watched Futurama . The episode also acquired a 0 @.@ 5 rating among viewers between the ages of 18 and 49 . This means that 0 @.@ 5 % of all 18- to 49 @-@ year @-@ olds viewed the episode . It dropped in viewership compared to the series ' pilot episode , which attracted 2 @.@ 10 million viewers and acquired a 1 @.@ 1 rating . \" Wet Demonic Summer \" also marked a drop in ratings compared to the first season finale , \" The Manbirds \" , which received a 0 @.@ 7 rating . "} +{"text": " The entire route is in Bergen County . All exits are unnumbered . "} +{"text": " The three are unable to find clues to Olivia 's ego , something that William thought would be present if Olivia was looking to be found . Peter realizes that when Olivia is scared , she retreats to somewhere safe , and suggests they search her mind 's version of Jacksonville , her childhood home . As they travel by zeppelin , William tries to encourage Walter that he no longer needs Bell 's guidance . They are soon attacked by a man ( Ulrich Thomsen ) wearing an X @-@ marked t @-@ shirt , who tears open the side of the zeppelin ; Walter is pulled out by the rush of air and falls to his death — waking him back in the real world . "} +{"text": " = = Honors = = "} +{"text": " The Hokies were slightly more effective rushing the ball . In the ground game , seven different players carried the ball at least once , netting 135 rushing yards . Branden Ore finished the game with 116 yards , leading all rushers on both teams . That total put him just eight yards short of his second @-@ straight 1 @,@ 000 @-@ yard season . Ore 's total is even more impressive when one considers that he had been suspended for the entire first quarter of the game , his place taken by backup rusher Kenny Lewis , Jr . Lewis finished the game with four carries for 22 yards , and did not see much action after Ore entered the game at the beginning of the second quarter . "} +{"text": " = = Economy = = "} +{"text": " The remastered visuals were positively received ; reviewers such as The Inquirer 's Chris Martin and The Escapist 's Russ Pitts singled out the graphics @-@ switch button for praise . The Guardian 's Steve Boxer called the feature \" utterly fascinating — a bit like ... archaeology on your console \" , and said that the visual overhaul improved areas where the original game engine was weak , such as rendering outdoor environments . While praising most of the game 's refinements , Watters singled out the Flood as enemies he thought the original game envisioned better , saying \" the simplicity of the classic look feels more sinister and alien \" . Hamza Aziz of Destructoid appreciated the visual updates , but not some of the resulting audio – animation syncing issues . "} +{"text": " \" Verse Chorus Verse \" ( March 1991 , Tacoma rehearsal space ) – 3 : 14 "} +{"text": " In search of someone willing and able to go to Budapest to organize a rescue program for the nation 's Jews , Olsen established contact with a relief committee composed of many prominent Swedish Jews led by the Swedish Chief Rabbi Marcus Ehrenpreis to locate an appropriate person to travel to Budapest under diplomatic cover and lead the rescue operation . One member of the committee was Wallenberg 's business associate Kalman Lauer . "} +{"text": " = = Geography = = "} +{"text": " The original GNP & BR route was extended at both ends in the early 1930s . In the north , a new route was constructed to Wood Green , Southgate and Cockfosters . In the west , the extension from Hammersmith approved in 1913 was finally carried out . The extension paralleled the District line 's route to Acton and Hounslow , and took over the District line 's route to Uxbridge . In 1977 , the Hounslow branch was extended to Heathrow Airport . The Strand branch was closed in 1994 . Today , the GNP & BR 's tunnels form the core of the Piccadilly line 's 73 @.@ 97 @-@ kilometre ( 45 @.@ 96 mi ) route . "} +{"text": " = = = Ratings = = = "} +{"text": " = = = Russo @-@ Japanese War = = = "} +{"text": " As was often the case with militia , who were poorly trained amateur soldiers , there was difficulty maintaining military discipline . The men wasted their rations , and often fired their muskets at wild game , despite orders to the contrary . They were slow to break camp in the mornings , and often failed to take their turn at guard duty . Crawford also proved to be a less capable leader than expected . Rose wrote that Crawford in councils \" speaks incoherent , proposes matters confusedly , and is incapable of persuading people into his opinion . \" The expedition often halted as the commanders debated what to do next . Some volunteers lost heart and deserted . "} +{"text": " Himmler met his future wife , Margarete Boden , in 1927 . Seven years his senior , she was a nurse who shared his interest in herbal medicine and homoeopathy , and was part owner of a small private clinic . They were married in July 1928 , and their only child , Gudrun , was born on 8 August 1929 . The couple were also foster parents to a boy named Gerhard von Ahe , son of an SS officer who had died before the war . Margarete sold her share of the clinic and used the proceeds to buy a plot of land in Waldtrudering , near Munich , where they erected a prefabricated house . Himmler was constantly away on party business , so his wife took charge of their efforts — mostly unsuccessful — to raise livestock for sale . They also owned a dog , Töhle . After the Nazis seized power the family moved first to Möhlstrasse in Munich , and in 1934 to Lake Tegern , where they bought a house . Himmler also later obtained a large house in the Berlin suburb of Dahlem , free of charge , as an official residence . The couple saw little of each other as Himmler became totally absorbed by work . The relationship was strained . The couple did unite for social functions ; they were frequent guests at the Heydrich home . Margarete saw it as her duty to invite the wives of the senior SS leaders over for afternoon coffee and tea on Wednesday afternoons . "} +{"text": " = = Early years = = "} +{"text": " Today , 2001 : A Space Odyssey is widely regarded as one of the greatest and most influential films ever made . In 1991 , it was deemed \" culturally , historically , or aesthetically significant \" by the United States Library of Congress and selected for preservation in the National Film Registry . The critics ' polls in the 2002 and 2012 editions of Sight & Sound magazine ranked 2001 : A Space Odyssey sixth in the top ten films of all time ; it also tied for second place in the directors ' poll of the same magazine . In 2010 , it was named the greatest film of all time by The Moving Arts Film Journal . "} +{"text": " As society became more prosperous and stable in the 11th century , inheritance customs developed that allowed daughters ( in the absence of sons ) to succeed to principalities as well as landed estates . The twelfth @-@ century chronicler Ralph de Diceto noted that the counts of Anjou extended their dominion over their neighbours by marriage rather than conquest . The marriage of Geoffrey to the daughter of a king ( and widow of an emperor ) occurred in this context . It is unknown whether King Henry intended to make Geoffrey his heir , but it is known that the threat presented by William Clito ’ s rival claim to the duchy of Normandy made his negotiating position very weak . Even so , it is probable that , should the marriage be childless , King Henry would have attempted to be succeeded by one of his Norman kinsmen such as Theobald II , Count of Champagne or Stephen of Blois who in the event did seize King Henry ’ s English crown . King Henry ’ s great relief in 1133 at the birth of a son to the couple , described as \" the heir to the Kingdom \" , is understandable in the light of this situation . Following this the birth of a second son raised the question of whether custom would be followed with the maternal inheritance passing to first born and the paternal inheritance going to his brother , Geoffrey . "} +{"text": " The other conference tie @-@ in was with the WAC , which has generally fielded its champion in the game . The WAC has provided a team for the Humanitarian Bowl every year since 2001 . Initially , it was speculated that the WAC championship team , Boise State , would make its fifth appearance in the bowl played in its home stadium . "} +{"text": " = = Service history = = "} +{"text": " Their skulls are fairly narrow and elongated , with small braincases . Their canine teeth are relatively long . Sexual dimorphism of the skull is more pronounced than in corsac foxes , with female red foxes tending to have smaller skulls than males , with wider nasal regions and hard palates , as well as having larger canines . Their skulls are distinguished from those of dogs by their narrower muzzles , less crowded premolars , more slender canine teeth , and concave rather than convex profiles . "} +{"text": " = = Publications = = "} +{"text": " = = = Books = = = "} +{"text": " Most newspaper mentions of West Corum do not report what sorts of cargo she carried , but a January 1922 Associated Press story in The Christian Science Monitor reveals her cargo for one voyage from Argentina . In what the news item said was the first full load of cargo leaving Buenos Aires in nearly a year , West Corum carried 107 @,@ 000 animal hides , estimated to be enough for 1 @,@ 000 @,@ 000 pairs of shoes , along with consignments of wool and linseed . The ship continued calling at Buenos Aires and Santa Fe , Argentina , as late as 1927 , but by 1939 , West Corum had been laid up in a reserve fleet at New Orleans . "} +{"text": " Phoenix is in the southwestern United States , in the south @-@ central portion of Arizona , and about halfway between Tucson to the south and Flagstaff to the north . The metropolitan area is known as the \" Valley of the Sun \" , due to its location in the Salt River Valley . It lies at a mean elevation of 1 @,@ 086 feet ( 331 m ) , in the northern reaches of the Sonoran Desert . "} +{"text": " After a performance opening for evangelist Billy Wayne Arrington in Dublin , Virginia , Arrington invited Bunn and Reedy to join his touring band . Bunn credits Arrington with helping to alter the band 's focus : \" We were playing music for God , but we didn 't really know why we were doing it . It was more so to be ' rock stars ' , per se , and just use God as a way to get there . And so he really helped us flip @-@ flop that and get it the way it should be . \" "} +{"text": " The accompanying music video for \" Don 't Bother \" was directed by Jaume de Laiguana , who had previously collaborated with Shakira on the video for \" No \" , and was released on November 15 , 2005 . The video begins with a man parking his sports car in front of a house and proceeding to enter it . Shakira is seen lying on a bed in the house and the man comes up and lies beside her , indicating that they are a couple . As soon as the man falls asleep , Shakira wakes up and lies atop the man and begins to caress him and bite and whisper in his ear a few times , although he does not wake up . Shakira takes the keys of the man 's car and drives it to a wrecking yard . The video then interludes to scenes of Shakira sexually romancing the man in a shower . The scene then changes back to the wrecking yard , where Shakira begins playing a pink guitar . After the spoken bridge of the song , Shakira places her partner 's car in a car crusher , which soon commences demolishing it . The man reacts in a similar manner to the car being crushed and begins experiencing continuous body jerks and spasms , indicating a connection between his car and his body . After the car is fully destroyed , Shakira leaves the man unconscious on the bed , exits the house , and begins walking on the street triumphantly . Shakira explained the usage of the car symbolism in the video , saying that \" A man 's car is like an extension of their ego and their manhood . I thought this would be a video that would make women say , ' Yeah , yeah ! ' and it would make men feel \" . "} +{"text": " The Maserati MC12 is a limited production two @-@ seater sports car produced by Italian car maker Maserati to allow a racing variant to compete in the FIA GT Championship . The car entered production in 2004 , with 25 cars produced . A further 25 were produced in 2005 , making a total of 50 cars available for customers , each of which was pre @-@ sold for € 600 @,@ 000 . "} +{"text": " Reitz , F.W. , [ J. de Villiers Roos , J.C. Smuts , E.J.P. Jorissen , ] Ein Jahrhundert voller Unrecht : ein Rückblick auf die süd @-@ afrikanische Politik Englands : autorisierte Uebersetzung aus dem Holländischen , veröffentlicht auf Veranlassung und unter Mitwirkung von F.W. Reitz ( Berlin : Walther 1900 ) , 96p . "} +{"text": " Ghirardo et Iseppo Imberti , Venice , 1622 ; "} +{"text": " = Americus ( horse ) = "} +{"text": " While it is difficult to distinguish adult males and females in other eastern rainforest sifakas , such as the Perrier 's sifaka and Milne @-@ Edwards ' sifaka , gender in the silky sifaka is easily discerned due to differences in fur coloration of the upper chest . Females have white fur , while males have a large brown patch of fur that results from scent marking with a gland on the chest and throat ( the sternal gular gland ) . During mating season , the size of the \" chest patch \" increases to cover both the chest and abdomen as a result of increased scent marking . "} +{"text": " The origins of Lahore Fort are obscure and traditionally based on various myths It is unknown who first built a fort there . According to some Hindu myths , its foundation was attributed to Loh , the mythical son of Lord Rama . However , the first historical reference to a fort ever actually existing on that location goes back to the 11th century , during the time of Mahmud of Ghazni . It was a weak mud fort that was subsequently destroyed . The earliest reference for this is that in the 1240s , it was destroyed by Mongols . After nearly 50 years , a new fort was constructed in its place by Balban of Mamluk dynasty of Delhi Sultanate . It was destroyed again around 1399 by the invading forces of Timur only to be rebuilt by Sultan Mubarak Shah Syed after 20 years . In the 1430s , the fort was occupied by Shaikh Ali of Kabul . "} +{"text": " = = Execution = = "} +{"text": " In the 1920s the house was purchased by Mabel Choate , owner of the nearby Naumkeag estate , and moved down into the valley . She and landscape designer Fletcher Steele restored the building , furnished it with 18th century pieces , and designed gardens to Steele 's vision of what a colonial landscape might have been . Choate opened the house as a museum in 1930 , and donated it ( and eventually Naumkeag as well ) to the Trustees of Reservations , who operate both properties as museums . "} +{"text": " Ong Ah Chuan v. Public Prosecutor [ 1980 ] UKPC 32 , [ 1981 ] A.C. 648 , [ 1979 – 1980 ] S.L.R. ( R. ) [ Singapore Law Reports ( Reissue ) ] 710 , Privy Council ( on appeal from Singapore ) . "} +{"text": " Extending from arid eastern Oregon to the cold and rainy Northern California coast , the Klamath River watershed drains parts of three Oregon counties and five counties in California and includes a diversity of landscapes . The northernmost part of the watershed is high desert country drained by the Williamson River and the Sprague River , both which flow generally southwest into Upper Klamath Lake . The middle basin is characterized by extensive wetland , grassland , and agricultural areas , and is partially filled by two major bodies of water : Upper and Lower Klamath Lake . The extensive lower basin , which encompasses over one half of the 15 @,@ 751 @-@ square @-@ mile ( 40 @,@ 790 km2 ) watershed , is composed mainly of rugged mountains , forests and canyons . "} +{"text": " The hurricane executed a slight curve to the northeast later that day , causing it to move inland over the Texas coast . Initially , the major hurricane was analyzed to have made landfall early on August 27 over Port Aransas with winds of 140 mph ( 225 km / h ) , equivalent to a modern @-@ day Category 4 hurricane . However , an reanalysis was conducted on the system , and concluded that it had only attained Category 3 intensity before making landfall at 1200 UTC that day . The reanalysis moved the landfall point closer to Seadrift as well . At the time , the storm had maximum sustained winds confined within an area about 10 miles ( 16 km ) from the hurricane 's center . The reanalysis also concluded that the storm contained a minimum central pressure of 963 millibars ( 28 @.@ 44 inHg ) at landfall . Once inland , the hurricane slowly weakened , but maintained hurricane intensity until 1200 UTC on August 28 . After further weakening to a tropical depression by 0000 UTC the next day , the disturbance dissipated over the Texas interior at 1800 UTC on August 29 . "} +{"text": " Other reviews were mixed . Pitchfork Media 's Tom Breihan wrote that \" Keith himself is responsible for all of the album 's good moments . Even when he 's in unhinged @-@ rant mode , Keith 's imagery often remains lucid . And so the album 's best song is \" Ants , \" where he comes off frantic but omnipotent ... It 's dense and fascinating stuff , a tantalizing glimpse of what might 've happened if Keith hadn 't treated the reemergence of his most popular persona like an easy payday . \" PopMatters writer Michael Frauenhofer wrote that \" The Return of Dr. Octagon is still better than anything Kool Keith 's done in a little while , but apart from its best tracks , it 's not close to the level of his finest work of the past , and it doesn 't really build much on the legend of Dr. Octagon , which still rests most firmly on the good doctor 's debut . \" "} +{"text": " \" Don 't Panic \" "} +{"text": " In 1947 – 48 , Harvey played with younger brothers Neil and Ray in two matches for Victoria , captaining the team in the second of these fixtures . However , his own form began to wane and he was dropped from the team mid @-@ way through the season . In 1948 – 49 , Harvey remained outside the first @-@ choice team , and played in only two first @-@ class matches against Tasmania when Victoria fielded a second @-@ string team . He retired at the end of the season , having played only 22 first @-@ class matches in an interrupted career . Harvey captained his state five times during the post @-@ war phase of his career , standing in when regular captain Lindsay Hassett was away on national duty . "} +{"text": " Burundi was scheduled to participate in a competition in 2007 organised by the Confederation of African Football ( CAF ) in Zanzibar . Nicholas Musonye , the secretary of the Council for East and Central Africa Football Associations ( Cecafa ) , said of the event , \" CAF wants to develop women 's football in this region in recognition of the milestones Cecafa has achieved over the years . CAF appreciates what Cecafa has done despite the hardships the association has gone through , from financial problems to political instability in member states and poor management of associations . Member states in the Cecafa region have not taken women 's football seriously . CAF now wants to sponsor a long @-@ term campaign to attract women from this region into the game . \" The competition was canceled due to lack of funds . "} +{"text": " Durant was a heavily recruited high school prospect . He played one season of college basketball for the University of Texas , where he won numerous year @-@ end awards and became the first ever freshman to be named Naismith College Player of the Year . In the 2007 NBA draft , he was selected with the second overall pick by the Seattle SuperSonics . After his rookie season , the team relocated to Oklahoma City and became the Thunder . Durant helped lead Oklahoma City to the 2012 NBA Finals , losing to the Miami Heat in five games . He played nine seasons for the Thunder organization before joining the Warriors in 2016 . "} +{"text": " Scrum.com Rugby guide "} +{"text": " The Phillies then headed to San Diego to face the Padres in a four @-@ game series , managing two hits in the opener to spoil Jerome Williams ' solid start with a 1 – 0 loss . Although the next night a 5 – 4 loss ( their 82nd ) clinched a losing season , they won game three 5 – 2 behind a \" stellar outing \" by Hamels . The Phillies then lost the fourth game of the series , 7 – 3 , before continuing their West Coast trip with a series against the Oakland Athletics . Despite a good start from David Buchanan in the series ' opener , the Phillies scored once in a 3 – 1 loss . They won the second 3 – 0 , a game \" peppered \" with bright spots , before losing the 10 @-@ inning rubber match of the series 8 – 6 . "} +{"text": " However , during the American leg of the tour , internal friction started to form between the members of the trio . Deborah Frost , writing for Rolling Stone , noticed \" an ever widening personal gulf \" between the band members , and quoted Harvey as saying \" It makes me sad . I wouldn 't have got here without them . I needed them back then – badly . But I don 't need them anymore . We all changed as people . \" Despite the tour 's personal downsides , footage from live performances was compiled and released on the long @-@ form video Reeling with PJ Harvey ( 1993 ) . The band 's final tour was to support U2 in August 1993 , after which the trio officially disbanded . In her final appearance on American television in September 1993 on The Tonight Show with Jay Leno , Harvey performed a solo version of \" Rid of Me . \" As Rid of Me sold substantially more copies than Dry , 4 @-@ Track Demos , a compilation album of demos for the album was released in October and inaugurated her career as a solo artist . In early 1994 , it was announced that U2 's manager , Paul McGuinness , had become her manager . "} +{"text": " Opening track \" Courageous \" , a pop rock and soft rock song , was described by Hall as an \" anthem of encouragement . It ’ s one brother reminding all of God ’ s men we were meant for greatness , and that greatness is defined in contagious and courageous abandonment to Jesus \" . "} +{"text": " As Huskisson and the Duke of Wellington exchanged greetings , some of the crowd saw Rocket , hauling the third of the seven trains on the northern track , approaching in the distance . They shouted \" An engine is approaching , take care gentlemen \" to those people — including Huskisson — standing on the tracks . "} +{"text": " = = = Operation Weserübung = = = "} +{"text": " = = Works = = "} +{"text": " After the British victory in Revolt of 1857 , they confiscated the mosque and stationed their soldiers here.They also wanted to destroy the mosque to punish the people of the city . But due to opposition faced , the demolition was not done . "} +{"text": " The film was released theatrically in Europe and the Middle East in 2001 , and debuted in the United States on March 17 , 2002 , when it was broadcast as part of ABC 's The Wonderful World of Disney series . Both Region 1 and Region 2 DVDs of the film were released . Reviewing the Artisan DVD release for the Combustible Celluloid website , Jeffrey M. Anderson praised its \" gorgeous full @-@ screen transfer \" , featurette , and cast @-@ and @-@ crew biographies , but highlighted that it contained no director 's commentary track . "} +{"text": " = = = Modern history = = = "} +{"text": " The east building wall of the base out of which the tower rises runs at a slant to the Manhattan street grid , following a property line that predated the Commissioners ' Plan of 1811 . The land on which the Chrysler Building stands was donated to The Cooper Union for the Advancement of Science and Art in 1902 . The land was originally leased to William H. Reynolds , but , when he was unable to raise money for the project , the building and the development rights to the land were acquired by Walter P. Chrysler in 1928 . Contrary to popular belief , the Chrysler Corporation was never involved in the construction or ownership of the Chrysler Building , although it was built and designed for the corporation and served as its headquarters until the mid @-@ 1950s . It was a project of Walter P. Chrysler for his children . "} +{"text": " = = Game design = = "} +{"text": " Mount Kenya is the highest mountain in Kenya and the second @-@ highest in Africa , after Kilimanjaro . The highest peaks of the mountain are Batian ( 5 @,@ 199 metres ( 17 @,@ 057 ft ) ) , Nelion ( 5 @,@ 188 metres ( 17 @,@ 021 ft ) ) and Point Lenana ( 4 @,@ 985 metres ( 16 @,@ 355 ft ) ) . Mount Kenya is located in central Kenya , about 16 @.@ 5 kilometres ( 10 @.@ 3 mi ) south of the equator , around 150 kilometres ( 93 mi ) north @-@ northeast of the capital Nairobi . Mount Kenya is the source of the name of the Republic of Kenya . "} +{"text": " More recent dating based on 40Ar @-@ 39Ar , U @-@ Pb ( uranium @-@ lead ) , and coccoliths shows a narrower range of ages from 56 Ma in the south to 50 or 49 Ma in the north . "} +{"text": " He played in 192 games in his career , having officially 55 @.@ 5 quarterback sacks ( 99 when his unofficial totals are added ) . "} +{"text": " = = = 6 April = = = "} +{"text": " = = Theoretic obstacles = = "} +{"text": " = = Legacy = = "} +{"text": " On October 1 , 2011 , Kanye West premiered his women 's fashion label , DW Kanye West at Paris Fashion Week . He received support from DSquared2 duo Dean and Dan Caten , Olivier Theyskens , Jeremy Scott , Azzedine Alaïa , and the Olsen twins , who were also in attendance during his show . His debut fashion show received mixed @-@ to @-@ negative reviews , ranging from reserved observations by Style.com to excoriating commentary by The Wall Street Journal , The New York Times , the International Herald Tribune , Elleuk.com , The Daily Telegraph , Harper 's Bazaar and many others . On March 6 , 2012 , West premiered a second fashion line at Paris Fashion Week . The line 's reception was markedly improved from the previous presentation , with a number of critics heralding West for his \" much improved \" sophomore effort . "} +{"text": " No country recognised the Rhodesian republic . The RF was decisively returned to power in the first election held as a republic , on 10 April 1970 , winning all 50 white seats . Hopes for an Anglo @-@ Rhodesian rapprochement were boosted two months later when the Conservatives won a surprise election victory in the UK . Edward Heath took over as Prime Minister while Douglas @-@ Home became Foreign Secretary . Talks between Douglas @-@ Home and Smith began with a lengthy meeting in Salisbury in April 1971 and continued until a tentative understanding was reached in early November . A UK delegation headed by Douglas @-@ Home and the Attorney General Sir Peter Rawlinson flew to Salisbury on 15 November for negotiations over a new constitution , and after six days of discussion an accord was signed on 21 November 1971 . "} +{"text": " The Ambush of Geary was a skirmish of the American Revolutionary War fought on 14 December 1776 near Ringoes in Amwell Township , Hunterdon County , New Jersey . Cornet Francis Geary , the leader of a company of dragoons , was shot in an ambush set up by local militiamen . "} +{"text": " = = Characteristics = = "} +{"text": " = Kate Winslet = "} +{"text": " Tiring of his position as county archaeologist , finding it \" too safe , pensionable and superannuable \" , in 1978 he returned to Oxfordshire to take up a temporary position as a tutor in archaeology and local studies at Oxford University 's External Studies Department . That year he co @-@ ran his first study tour to Greece with Peter Hardy ; he would continue to run these annually for a number of years , most often visiting Santorini . In 1979 he returned to the West Country as tutor in archaeology at the University of Bristol 's Extra @-@ Mural Department , through which he organised weekend and evening courses throughout the region , introducing thousands of interested people to archaeology . During this period he also authored Interpreting the Landscape ( 1985 ) . "} +{"text": " Revelation features several gameplay enhancements that aid puzzle solving and plot progression . Early in the game , players receive a camera , which can be used to take screenshots or pictures of clues . Players can use an on @-@ screen journal to jot down notes instead of having to write down clues as with previous Myst games . Much of the game 's story is revealed via flashbacks triggered by an amulet that has the power to relay memories attached to objects . Zip mode , the amulet , the camera , and the journal are available via a menu on the bottom of the game screen . "} +{"text": " Irritancy can be measured using the Draize test , where a test substance is applied to an animal 's eyes or skin , usually an albino rabbit . For Draize eye testing , the test involves observing the effects of the substance at intervals and grading any damage or irritation , but the test should be halted and the animal killed if it shows \" continuing signs of severe pain or distress \" . The Humane Society of the United States writes that the procedure can cause redness , ulceration , hemorrhaging , cloudiness , or even blindness . This test has also been criticized by scientists for being cruel and inaccurate , subjective , over @-@ sensitive , and failing to reflect human exposures in the real world . Although no accepted in vitro alternatives exist , a modified form of the Draize test called the low volume eye test may reduce suffering and provide more realistic results and this was adopted as the new standard in September 2009 . However , the Draize test will still be used for substances that are not severe irritants . "} +{"text": " Development of the film began with the cancellation of Spider @-@ Man 4 in 2010 , ending director Sam Raimi 's Spider @-@ Man film series that had starred Tobey Maguire as the titular superhero . Columbia Pictures opted to reboot the franchise with the same production team along with James Vanderbilt to stay on with writing the next Spider @-@ Man film while Alvin Sargent and Steve Kloves helped with the script as well . During pre @-@ production , the main characters were cast in 2010 . New designs were introduced from the comics , such as artificial web @-@ shooters . Using Red Digital Cinema Camera Company 's RED Epic camera , principal photography started in December 2010 in Los Angeles before moving to New York City . The film entered post @-@ production in April 2011 . 3ality Technica provided 3D image processing , and Sony Pictures Imageworks handled CGI . This was also the final American film to be scored by James Horner and released during his lifetime , before his death in 2015 from an aircraft accident . "} +{"text": " \" Evie 's Two Dads \" "} +{"text": " The ketogenic diet is not a benign , holistic or natural treatment for epilepsy ; as with any serious medical therapy , there may be complications . These are generally less severe and less frequent than with anticonvulsant medication or surgery . Common but easily treatable short @-@ term side effects include constipation , low @-@ grade acidosis and hypoglycaemia if there is an initial fast . Raised levels of lipids in the blood affect up to 60 % of children and cholesterol levels may increase by around 30 % . This can be treated by changes to the fat content of the diet , such as from saturated fats towards polyunsaturated fats , and , if persistent , by lowering the ketogenic ratio . Supplements are necessary to counter the dietary deficiency of many micronutrients . "} +{"text": " The flag of Poland consists of two horizontal stripes of equal width , the upper one white and the lower one red . The two colors are defined in the Polish constitution as the national colors . A variant of the flag with the national coat of arms in the middle of the white stripe is legally reserved for official use abroad and at sea . A similar flag with the addition of a swallow @-@ tail is used as the naval ensign of Poland . "} +{"text": " Auditions for the lead role of Sarah began in England in April 1984 . Helena Bonham Carter auditioned for the role , but it was ultimately decided it would be better to cast an American actress . Monthly auditions were held in the U.S. until January 1985 , and Jane Krakowski , Yasmine Bleeth , Sarah Jessica Parker , Marisa Tomei , Laura Dern , Ally Sheedy , Maddie Corman , and Mia Sara all auditioned for the role . Of these , Krakowski , Sheedy and Corman were considered to be the top candidates . 14 @-@ year @-@ old actress Jennifer Connelly was ultimately chosen to play Sarah after her audition on January 29 , 1985 , \" won Jim [ Henson ] over \" and led him to cast her within a week . According to Henson , Connelly was chosen as she \" could act that kind of dawn @-@ twilight time between childhood and womanhood . \" Connelly moved to England in February 1985 in advance of the film 's rehearsals , which began in March . Discussing her understanding of her role with Elle , Connelly said that the film is about \" a young girl growing out of her childhood , who is just now becoming aware of the responsibilities that come with growing up . \" "} +{"text": " Separate awards for Best Hard Rock Performance and Best Metal Performance were introduced in 1990 . Beginning that year , Metallica won three consecutive Grammy Awards for Best Metal Performance for the song \" One \" from ... And Justice for All , their cover of Queen 's \" Stone Cold Crazy \" , and their eponymous album in 1992 . When Metallica won the Grammy in 1992 , drummer Lars Ulrich referenced the previous award by facetiously \" thanking \" Jethro Tull for not putting out an album that year , though they actually had released the album Catfish Rising in 1991 . A decade after Jethro Tull defeated Metallica , Ulrich admitted : \" I 'd be lying if I didn 't tell you I was disappointed . Human nature is that you 'd rather win than lose , but Jethro Tull walking away with it makes a huge mockery of the intentions of the event . \" As of 2010 , Metallica holds the record for the most wins in the metal category , with a total of six . "} +{"text": " = = = Poems = = = "} +{"text": " = = College = = "} +{"text": " The girls ' team went to the state title game every year except for one from 1997 to 2005 . They won in 1998 , 1999 , and had three straight wins in 2003 , 2004 and 2005 . "} +{"text": " Johnston , David A. ; Donnelly @-@ Nolan , Julie , eds . ( 1981 ) . Guides to Some Volcanic Terranes in Washington , Idaho , Oregon , and Northern California . U.S. Geological Survey Circular 838 . United States Geological Survey . Retrieved 2010 @-@ 04 @-@ 10 . "} +{"text": " A blockade of the JNA facilities in and around Varaždin was ordered on 13 September 1991 , which took effect the next day when utilities and access to the barracks were cut . In response , the Corps command informed Croatian forces that they could no longer guarantee peace unless utilities were restored and JNA military vehicles were allowed to move freely . On 15 September , the Corps began preparing for the demolition of all minor military facilities which could not be defended . At the same time , negotiations began between the Corps command and civilian authorities in Varaždin . At 15 : 30 , the Yugoslav Air Force attacked the Varaždin Airfield . The strike was intended to disrupt flights of Antonov An @-@ 2 agricultural aircraft converted to carry 1 @.@ 5 tonnes ( 1 @.@ 5 long tons ; 1 @.@ 7 short tons ) of cargo — transporting weapons from the Hungarian town of Nagykanizsa . The attacking force comprised two planes sortied from Željava Air Base , which broke the sound barrier above Varaždin to produce a sonic boom and bombed the airfield . One bomb destroyed an An @-@ 2 on the ground and damaged the runway , while the second landed in a nearby field . "} +{"text": " The standard definition of \" even number \" can be used to directly prove that zero is even . A number is called \" even \" if it is an integer multiple of 2 . As an example , the reason that 10 is even is that it equals 5 × 2 . In the same way , zero is an integer multiple of 2 , namely 0 × 2 , so zero is even . "} +{"text": " On 22 September 1943 , MacArthur 's General Headquarters ( GHQ ) directed Lieutenant General Walter Krueger 's Alamo Force to secure western New Britain and the surrounding islands . This operation had two goals , the first of which was to establish air and PT boat bases to attack the Japanese forces at Rabaul . The second objective was to secure the Vitiaz and Dampier Straits between New Guinea and New Britain so that convoys could safely pass through them en route to conduct further landings along New Guinea 's north coast and beyond . To this end , GHQ directed that both Cape Gloucester and Gasmata on New Britain 's south coast be captured . This offensive was code @-@ named Operation Dexterity . The 1st Marine Division was selected for the Cape Gloucester operation , and the heavily reinforced 126th Regimental Combat Team from the 32nd Infantry Division was to attack Gasmata . "} +{"text": " The entire route is in Istria County , Croatia . "} +{"text": " The Speaker first asked for comments from government and opposition MPs on the matter , including Peter MacKay , Rob Nicholson , Derek Lee , Jack Harris , Tom Lukiwski , and Jim Abbott . After considering the matter for two weeks , the Speaker ruled on 27 April 2010 that Parliament had a right to ask for uncensored documents . He asked that all House leaders , ministers and MPs to come to a collective solution \" without compromising the security and confidentiality contained . \" The Speaker gave the House until 11 May 2010 to find a common ground . While MPs within the negotiations had to ask The Speaker for an extension of the deadline , it was granted until 14 May , and a deal was reached that morning . A Memorandum of Understanding on the particulars was not established until 16 June , when it was actually tabled in the House of Commons . It was still not agreed to by all political parties : The New Democratic Party refused to endorse the deal . "} +{"text": " During the 2012 – 13 season Walker appeared in twenty games with HC Vítkovice and recorded one assist ; he was loaned to Hokej Šumperk 2003 for three games , where he also had one assist . After six years in the Czech Republic , Walker signed a contract on 8 January 2013 with the Youngstown Phantoms of the United States Hockey League , a junior ice hockey league in the United States . He made his debut for the team on 11 January against the Chicago Steel , recording a goal . After four games with the Phantoms , in which he recorded six points , Walker was named to the USHL / NHL Top Prospects Game on 23 January 2013 ; he replaced his teammate , who was ill . Walker played 29 games for the Phantoms during the regular season and recorded 27 points ( 7 goals , 20 assists ) . His 20 assists were good enough for fifth on the team , even though Walker only joined the team midway through the season . His season ended early due to an injury suffered in a game on 5 April where Walker broke a bone in his neck as a result of a hit from behind ; as a result he missed all the Phantoms playoff games . "} +{"text": " In 480 with the death of the Western Emperor Julius Nepos , Eastern Emperor Zeno became sole Emperor of the empire . Odoacer , now ruler of Italy , was nominally Zeno 's subordinate but acted with complete autonomy , eventually providing support to a rebellion against the Emperor . "} +{"text": " On December 17 , 2011 , the Senate passed legislation to extend the Social Security payroll tax cut which had been previously enacted during the FY2011 budget negotiations . That legislation had reduced the rate from 6 @.@ 2 % to 4 @.@ 2 % for the 2011 calendar year only . The initial 2012 extension was for two months , rather than the full @-@ year extension which had been sought ; the legislation also extended unemployment benefits as well as a measure preventing a drop in rates for Medicare reimbursement ; the spending for these was offset by enacting new fees on Fannie Mae and Freddie Mac . Points of contention included a Democratic plan to fund the tax cut with a new surtax on income over $ 1 million , which was dropped in later stages of negotiation , as well as attempts by Republicans to insert language which would speed the approval of the Keystone XL pipeline , which had recently been delayed by the Obama administration . The bill was initially rejected in the House , whose leaders insisted on a full @-@ year extension , despite the fact that the Senate had already adjourned for the year . However , after criticism from other Republicans that the impasse would harm their prospects in the upcoming 2012 elections , the House leadership on December 23 , 2011 announced that it would pass the Senate bill in return for Democrats promptly beginning negotiations on a full @-@ year extension . The bill , the Middle Class Tax Relief and Job Creation Act of 2011 , was passed by the House and signed by the President later that day . "} +{"text": " = = = Second @-@ class cricket = = = "} +{"text": " In his review , Derek Elley of Variety remarked that the film had \" ... the awkward , issue @-@ driven dialogue and wavering direction , showing influences from both the arty and commercial . [ The ] two leads just about scrape through . \" Although The Hindu review praised the director for \" ... handling ( these ) scenes in an understated , muted fashion ... \" giving \" ... them the power to disturb and haunt you . \" it questioned certain aspects of the film , stating , \" Though the flutters of the heart have been treated with finesse — sometimes a little too prudishly , pandering , perhaps , to middle class morality — we are never entirely convinced that love could blossom between Meenakshi Iyer and Raja Chowdhary . \" Indeed , Sen was criticised for contriving cinematic situation not quite fitting to the real world , \" Can a married woman with a baby in arms fall in love with a total stranger that she meets on a very short bus journey , however extraordinary the situation may have been ? Having decided to drive them to each other 's arms , Sen thinks up situations , which are terribly contrived ... Sen 's story and script are found wanting elsewhere too . The police officer , who plays the good Samaritan , appears so unreal in the world of rancour that Sen creates ... [ She ] , probably in her over enthusiasm , lets her own emotions derail her . \" "} +{"text": " = = Charts = = "} +{"text": " \" Bass Folk Song No. 6 ( Mo Anam Cara ) \" 2 : 41 "} +{"text": " The creation of the Merseyside Development Corporation ( MDC ) in 1981 was part of a new initiative launched by the then Conservative government that earmarked the regeneration of some 800 acres ( 3 @.@ 2 km2 ) of Liverpool 's south docks , by using public sector investment to create infrastructure within an area that could then in turn be used to attract private sector investment . Thus the MDC was not directly responsible for regeneration programmes but rather acted as a spearhead , guiding the development process . Upon its formation it immediately created an initial strategy for the area placing a high priority on restoring those buildings that could be restored & demolishing the rest , restoring a water regime within the dock system ( including the removal of up to 40 ft ( 12 m ) of silt ) and general environmental landscaping . As part of the strategy two flagship schemes were set up : the redevelopment of a site in Otterspool for the International Garden Festival and the regeneration of the Albert Dock . "} +{"text": " The digital effects costs for each episode could stretch the budget thin . In \" Shimmer \" , Bill Millar , special effects supervisor , planned to create an artificial sunset for the closing scene involving Clark and Lana . The effects shot was supposed to last only a couple of seconds . James Marshall , the second unit director for the episode , decided to shoot the entire scene over the shoulders of Clark and Lana , looking at the sunset . The scene called for two greenscreen shots , but when Marshall was finished he had created seventeen greenscreen shots . The seventeen shots , which produced the artificial sunset , cost $ 50 @,@ 000 to produce . By comparison , the entire effects budget costs between $ 65 @,@ 000 and $ 100 @,@ 000 . "} +{"text": " A tropical storm developed east of Nicaragua on May 25 , executing a counterclockwise loop over Central America . After weakening over land , Alice re @-@ intensified over the western Caribbean , moving over western Cuba on May 21 with winds of 50 mph ( 80 km / h ) . On June 1 , it entered the Gulf of Mexico , and later executed another loop off the northwest coast of Cuba . Alice quickly weakened due to a cold front , and advisories were discontinued by June 3 . While near Cuba , Alice produced drought @-@ breaking rainfall that caused flooding and several unofficial drowning deaths . "} +{"text": " Voyager 's Emergency Medical Holographic Program ( EMH ) , \" the Doctor \" ( Robert Picardo ) , is activated due to a red alert . Despite the computer 's ( Majel Barrett ) assertion that nobody is aboard , B 'Elanna Torres ( Roxann Dawson ) soon arrives in sickbay . She informs the Doctor that the ship was attacked by Kazon and that all except herself and Captain Janeway ( Kate Mulgrew ) have abandoned ship . After treating her injuries , Torres transfers the Doctor 's program to the bridge using newly installed holographic emitters there . After treating the captain and then assisting Neelix ( Ethan Phillips ) to defeat a stray Kazon in the mess hall , the Doctor discovers he 's bleeding and feeling pain , neither of which are functions of his program . In sickbay , making inquiries with the computer , the Doctor is told that there are no holographic programs matching his own and that he is actually Lewis Zimmerman ( Picardo ) — whom the Doctor recognizes as his programmer . "} +{"text": " ( a ) to excite organised violence against persons or property in Singapore ; "} diff --git a/src/lab5/datasets/performance_public.jsonl b/src/lab5/datasets/performance_public.jsonl new file mode 100644 index 00000000..4b2a4d30 --- /dev/null +++ b/src/lab5/datasets/performance_public.jsonl @@ -0,0 +1,5 @@ +{"prompt": " scenes depicting deities and people , finely carved and rubbed with vermilion , as well as jade and shell ornaments and ceramic vessels filled with offerings of food and drink . The shrine at the summit of the pyramid has three chambers , each behind the next , with the doorways spanned by wooden lintels fashioned from multiple beams . The outermost lintel is plain but the two inner lintels were carved , some of the beams were removed in the 19th century and their location is unknown , while others were taken to museums in Europe .\n\nTemple II ( also known as the Temple of the Mask ) it was built around AD 700 and stands 38 metres ( 125 ft ) high . Like other major temples at Tikal , the summit shrine had three consecutive chambers with the doorways spanned by wooden lintels , only the middle of which was carved . The temple was dedicated to the wife of Jasaw Chan K 'awil , although no tomb was found . The queen 's portrait was carved into the lintel spanning the doorway of the summit shrine . One of the beams from this lintel is now in the American Museum of Natural History in New York .\n\nTemple III ( also known as the Temple of the Jaguar Priest", "max_new_tokens": 48} +{"prompt": "ries . Regardless , the Boers were surprised and during two hours of heavy fighting , more than 50 were killed and another 40 taken prisoner . Five hundred Queenslanders and the New South Wales Lancers subsequently took part in the Siege of Kimberley in February 1900 .\n\nDespite serious set @-@ backs at Colenso , Stormberg , Magersfontein , and Spion Kop in January — and with Ladysmith still under siege — the British mounted a five division counter @-@ invasion of the Orange Free State in February . The attacking force included a division of cavalry commanded by Lieutenant General John French with the New South Wales Lancers , Queensland Mounted Infantry and New South Wales Army Medical Corps attached . First , Kimberley was relieved following the battles of Modder River and Magersfontein , and the retreating Boers defeated at Paardeberg , with the New South Wales Mounted Rifles locating the Boer general , Piet Cronjé . The British entered Bloemfontein on 13 March 1900 , while Ladysmith was relieved . Disease began to take its toll and scores of men died . Still the advance continued , with the drive to Pretoria in May including more than 3 @,@ 000 Australians . Johannesburg fell on 30 May , and the Boers withdrew from Pretoria on 3 June . The New South Wales Mounted Rifles and Western Australians saw action again at Diamond Hill on 12 June . Mafeking was relieved on 17 May .\n\nFollowing the defeat of the Afrikaner republics still the Boers held out , forming small commando units and conducting a campaign of guerrilla warfare to disrupt British troop movements and lines of supply . This new phase of resistance led to further recruiting in the Australian colonies and the raising of the Bushmen 's Contingents , with these soldiers usually being volunteers with horse @-@ riding and shooting skills , but little military experience . After Federation in 1901 , eight Australian Commonwealth Horse battalions of the newly created Australian Army were also sent to South Africa , although they saw little fighting before the war ended . Some Australians later joined local South African irregular units , instead of returning home after discharge . These soldiers were part of the British Army , and were subject to British military discipline . Such units included the Bushveldt Carbineers which gained notoriety as the unit in which Harry \" Breaker \" Morant and Peter Handcock served in before their court martial and execution for war", "max_new_tokens": 16} +{"prompt": " A selection of the most notable stelae at Tikal follows :\n\nStela 1 dates to the 5th century and depicts the king Siyaj Chan K 'awiil II in a standing position .\n\nStela 4 is dated to AD 396 , during the reign of Yax Nuun Ayiin after the intrusion of Teotihuacan in the Maya area . The stela displays a mix of Maya and Teotihuacan qualities , and deities from both cultures . It has a portrait of the king with the Underworld Jaguar God under one arm and the Mexican Tlaloc under the other . His helmet is a simplified version of the Teotihuacan War Serpent . Unusually for Maya sculpture , but typically for Teotihuacan , Yax Nuun Ayiin is depicted with a frontal face , rather than in profile .\n\nStela 5 was dedicated in 744 by Yik 'in Chan K 'awiil .\n\nStela 6 is a badly damaged monument dating to 514 and bears the name of the \" Lady of Tikal \" who celebrated the end of the 4th K 'atun in that year .\n\nStela 10 is twinned with Stela 12 but is badly damaged . It described the accession of Kaloomte ' B 'alam in the early 6th century and earlier events in his career , including the capture of a prisoner depicted on the monument .\n\nStela 11 was the last monument ever erected at Tikal ; it was dedicated in 869 by Jasaw Chan K 'awiil II .\n\nStela 12 is linked to the queen known as the \" Lady of Tikal \" and king Kaloomte ' B 'alam . The queen is described as performing the year @-@ ending rituals but the monument was dedicated in honor of the king .\n\nStela 16 was dedicated in 711 , during the reign of Jasaw Chan K 'awiil I. The sculpture , including a portrait of the king and a hieroglyphic text , are limited to the front face of the monument . It was found in Complex N , west of Temple III .\n\nStela 18 was one of two stelae erected by Yax Nuun Ayiin I to celebrate the k 'atun @-@ ending of AD 396 . It was re @-@ erected at the base of Temple 34 , his funerary shrine .\n\nStela 19 was dedicated in 790 by Yax Nuun Ayiin II .\n\nStela 20 was found in Complex P , in Group H , and was moved to the Museo Nacional de Arqueología y Etnología in Guatemala City .\n\nStela 21 was dedicated in 736 by Yik 'in Chan K 'awiil . Only the bottom of the stela is intact , the rest having been mutilated in ancient times . The surviving sculpture is of fine quality , consisting of the feet of a figure and of accompanying hieroglyphic text . The stela is associated with Altar 9 and is located in front of Temple VI .\n\nStela 22 was dedicated in 771 by Yax Nuun Ayiin II in the northern enclosure of Group Q , a twin @-@ pyramid complex . The face of the figure on the stela has been mutilated .\n\nStela 23 was broken in antiquity and was re @-@ erected in a residential complex . The defaced portrait on the monument is that of the so @-@ called \" Lady of Tikal \" , a daughter of Chak Tok Ich 'aak II who became queen at the age of six but never ruled in her own right , being paired with male co @-@ rulers . It dates to the early 6th century .\n\nStela 24 was erected at the foot of Temple 3 in 810 , accompanied by Altar 7 . Both were broken into fragments in ancient times , although the name of Dark Sun survives on three fragments .\n\nStela 26 was found in the summit shrine of Temple 34 , underneath a broken masonry altar . The monument had originally been erected at the base of the temple during the Early Classic period and was later broken , probably at the beginning of the Late Classic . Its remains were then interred within the temple shrine .\n\nStela 29 bears a Long Count ( 8 @.@ 12 @.@ 14 @.@ 8 @.@ 15 ) date equivalent to AD 292 , the earliest surviving Long Count date from the Maya lowlands . The stela is also the earliest monument to bear the Tikal emblem glyph . It bears a sculpture of the king facing to the right , holding the head of an underworld jaguar god", "max_new_tokens": 32} +{"prompt": "179 ( Bridge Street ) , where Route 165 ends and Route 29 turns west for a one @-@ block wrong @-@ way concurrency with the two @-@ lane , undivided Route 179 , lasting to the intersection of Main Street , where Route 29 turns north on Main Street .\n\nRoute 29 follows Main Street north through Lambertville , crossing into Delaware Township . It comes to an interchange with U.S. Route 202 just east of the New Hope @-@ Lambertville Toll Bridge , with access to northbound U.S. Route 202 and from southbound U.S. Route 202 provided by way of Alexauken Creek Road . Route 29 continues along the Delaware River and enters Stockton . The route intersects Bridge Street , which crosses the Delaware River on the Centre Bridge @-@ Stockton Bridge and continues into Pennsylvania as Pennsylvania Route 263 . Shortly after that intersection , Route 29 intersects the southern terminus of County Route 523 ( Stockton @-@ Flemington Road ) . Route 29 crosses back into Delaware Township , where it meets the southern terminus of County Route 519 ( Kingwood @-@ Stockton Road ) .\n\nRoute 29 makes a sharp left turn and heads west along the river as a rural road , crossing into Kingwood Township , where the name of the road changes from Main Street to Daniel Bray Highway . Here , it intersects with County Route 651 ( Byram @-@ Kingwood Road ) . The route bends to the north and continues along the Delaware River for several miles , crossing into Frenchtown , where the route becomes Trenton Road . Upon entering Frenchtown , an end shield for northbound Route 29 is posted to mark the end of state maintenance , which officially ends at the Washington Street intersection , where maintenance is transferred to the county . Despite this , Route 29 officially continues farther north along Trenton Road to its northern terminus at Route 12 ( Bridge Street / Race Street ) , a short distance east of Route 12 ’ s western terminus at the Uhlerstown @-@ Frenchtown Bridge . The southern terminus of County Route 513 is located a block north of the northern terminus of Route 29 along Route 12 .\n\nThe current route was originally legislated in 1911 as part of the Delaware River Drive , a named state highway that was proposed to run from along the Delaware River from Trenton to the New York border in Montague Township . Route 29 was originally defined in 1927 to run from Trenton to Newark . The original route ran from downtown Trenton along State Street and Sanhican Drive . From there , it followed its current alignment to Lambertville , where it followed present @-@ day Route 179 to Ringoes to present @-@ day County Route 514 , which it followed to Woods Tavern . The route turned north on U.S. Route 206 ( also designated Route 31 ) and followed that route to Somerville , where it followed U.S. Route 22 to Newark . The current alignment of Route 29 from Lambertville to Route 12 in Frenchtown was designated Route 29A in 1927 .\n\nThe alignment of Route 29 between Ringoes and Somerville was eventually shifted to follow present @-@ day U.S. Route 202 , which was also Route 30 ( now Route 31 ) between Ringoes and Flemington and Route 12 between Flemington and Somerville . In 1938 , Route 29B was planned as an extension of Route 29A from Frenchtown to Route 28 ( now Route 122 ) in Alpha . While this road was never built , much of the alignment north of Milford is served by County Route 519 . In 1948 , a spur route , Route S29 , was created , running along U.S. Route 202 ( Bridge Street ) in Lambertville to the New Hope @-@ Lambertville Bridge . In the 1953 New Jersey state highway renumbering , Route 29 was redefined to continue north from Lambertville to Frenchtown on Route 29A , and the Route 29 designation between Lambertville and Newark was removed in favor of U.S. Route 202 between Lambertville and Somerville and U.S. Route 22 between Somerville and Newark . The section of former Route 29 between Route 29A and Route S29 became Route 165 .\n\nPlans for a limited @-@ access route along the Route 29 corridor go back to 1932 , when a parkway was proposed along the Delaware River between Trenton and Lambertville ; this proposal never materialized . Plans for a freeway were resurrected in the early 1950s to construct a road , the John Fitch Parkway , that was to run from Hamilton Township to Interstate 95 in Ewing Township . This road was built between 1954 and 1957 from South Warren Street in Trenton to present @-@ day Interstate 95 in Ewing Township . Construction of this portion John Fitch Parkway took up much of Trenton ’ s available waterfront along the Delaware River . The former alignment of Route 29 in the northern part of Trenton and in Ewing Township became Route 175 .\n\nBetween 1990 and 1995 , the Route 29 freeway was built between the Interstate 195 / Interstate 295 interchange and Route 129 in Hamilton Township . A two @-@ lane street , Lamberton Road , connected the two freeway sections . Plans were then made to fill the gap between the two freeway sections in Trenton . Construction began in 1997 on the Route 29 freeway between Route 129 and the Morrisville @-@ Trenton Railroad Bridge . The road was to include two traffic lights at Cass Street and South Warren Street and a tunnel which was to be built as a covered roadway on the bank of the Delaware River . The tunnel was originally scheduled to be complete by 2001 but was delayed after the Army Corps of Engineers discovered many environmental violations that occurred with construction of the tunnel . The roof was put in place in October 2001 and the tunnel officially opened to traffic on March 2 , 2002 . A restriction to trucks over 13 tons was put in place and made permanent in November 2002 .\n\nBy the 2000s , the state gave the part of Route 29 ( South Main Street ) between Route 165 and Route 179 in Lambertville to the city , and Route 29 was rerouted to use all of Route 165 and one block of Route 179 . Prior to this , South Main Street had been turned one @-@ way southbound . Route 165 , which is only signed on overhead street signs , still exists , though it is fully concurrent with Route 29 .", "max_new_tokens": 32} +{"prompt": " ( 310 km ) northeast of Reno , Nevada .\n\nThe range and surrounding non @-@ mountainous areas cover an area of 811 square miles ( 2 @,@ 100 km2 ) . The mountains run 51 miles ( 82 km ) north to south and 36 miles ( 58 km ) east to west . More of the range is in Oregon ( 78 % ) than in Nevada ( 22 % ) . The highest point in the range is Orevada View Benchmark , which is 8 @,@ 506 feet ( 2 @,@ 593 m ) above sea level and is located in Nevada about one mile south of the Oregon border . About two miles southeast of Orevada View is Disaster Peak , \" a large , symmetrical butte that is visible throughout the region . \" At 7 @,@ 781 feet ( 2 @,@ 372 m ) , Disaster Peak anchors the southern end of the mountains in a sub @-@ range called The Granites .\n\nThe Oregon Canyon Mountains border the Trout Creek Mountains on the east along the Harney – Malheur county line ( according to the United States Geological Survey 's definitions ) , while the Pueblo Mountains are the next range west of the Trout Creek Mountains . The Bilk Creek Mountains in both Oregon and Nevada border the Trout Creek Mountains on the southwest ; the two ranges are separated by Log Cabin Creek and South Fork Cottonwood Creek . South of the Trout Creek Mountains is the Kings River Valley , which separates the Bilk Creek Mountains on the west from the Montana Mountains on the east .\n\nThe terrain in the Trout Creek Mountains varies from broad , flat basins and rolling ridges to high rock escarpments cut by deep canyons . The canyons have steep walls with loose talus slopes at the bottoms . There are meadows around springs in the mountains , although most streams in the range do not flow year @-@ round . Major streams that flow off the north slopes of the mountains include ( from west to east ) Cottonwood Creek , Trout Creek , Willow Creek , and Whitehorse Creek . These streams all flow into endorheic basins in Harney County , Oregon . Trout Creek and Whitehorse Creek are the largest of the four . The Kings River and McDermitt Creek each drain an area on the south slopes of the Trout Creek Mountains . The Kings River begins in The Granites and flows south into Nevada , where it meets the Quinn River , which evaporates in the Black Rock Desert . McDermitt Creek begins in Oregon a few miles north of The Granites and flows generally east , crossing the Oregon – Nevada border five times before disappearing into the floor of the Quinn River Valley south of McDermitt .\n\nThe mountains lie within the Basin and Range Province or Great Basin of the Western United States , which is characterized by a series of parallel fault blocks that form long north – south mountain ranges separated by wide , high @-@ desert valleys . The Trout Creek Mountains are uplifted and tilted blocks with steep escarpments along the southern and eastern sides of the range . The southern area of the range , known as The Granites , has numerous outcroppings of Cretaceous age granite . These granite outcrops are commonly found in the eroded valleys below the volcanic ridgelines .\n\nThe Trout Creek Mountains are composed mostly of basalt from a shield volcano that once stood where Steens Mountain is today . Crustal thinning and the Yellowstone hotspot , which was then beneath southeastern Oregon , induced eruptions from Steens and nearby vents about 17 million years ago , in the Miocene . The vents produced a series of lava flows that spread across the land now known as the Trout Creek Mountains . Eruptions from the Steens volcano lasted for about one million years , and at least 70 separate lava flows occurred . Under the resulting basalt rock lie much older metamorphic rocks that may be related to some of the Triassic age formations of the Blue Mountains in northeastern Oregon . Within these metamorphic rocks are diorite and granodiorite intrusive bodies which were presumably intruded during the Cretaceous Period .\n\nThe broad McDermitt Caldera is a prominent geologic feature in the Trout Creek Mountains . The oval @-@ shaped caldera is a collapsed lava dome that straddles the Oregon – Nevada border on the eastern side of the range and south of the Oregon Canyon Mountains . It is about 28 miles ( 45 km ) long and 22 miles ( 35 km ) wide . The lava dome was created by volcanic eruptions in the early Miocene . A total of five large ash flows were produced along with a large rhyolite dome structure . The caldera formed when the dome collapsed about 16 million years ago . The caldera contains significant ore deposits , and mercury and uranium have been mined at eight or more sites in and around the caldera . Other areas in the caldera were mined for ores of antimony , cesium , and lithium .\n\nThe Trout Creek Mountains are semi @-@ arid because they are in the eastern rain shadow of mountain ranges to the west . When moist air from the Pacific Ocean moves eastward over the Oregon and California coastal ranges and the Cascade Range , most precipitation falls in those mountains before reaching the Trout Creek Mountains . As a result , the average annual precipitation in the Trout Creek Mountains is only 8 to 26 inches ( 200 to 660 mm ) per year , with most areas receiving between 8 and 12 inches ( 200 and 300 mm ) annually . Much of the annual precipitation occurs between the beginning of March and the end of June . Most of the rest falls as snow during the fall and winter months . Snowpack at elevations below 6 @,@ 000 feet ( 1 @,@ 800 m ) usually melts by April ; however , at the higher elevations , snow can remain until mid @-@ June . Local flooding often occurs in the spring as the snowpack melts .\n\nThe prevailing winds are from the west @-@ southwest , and they are normally strongest in March and April . Brief , intense thunderstorms are common between April and October . Thunderstorms in the summer months tend to be more isolated and often produce dry lightning strikes .\n\nVegetation in the Trout Creek Mountains is dominated by big sagebrush and desert grasses . Other common shrubs include bitterbrush , snowberry , and Ceanothus . There are also patches of mountain mahogany in some areas . Common grass species include Idaho fescue , bluebunch wheatgrass , cheatgrass , western needlegrass , Sandberg 's bluegrass , Thurber 's needlegrass , and bottlebrush squirreltail , as well as basin wildrye in some well @-@ drained areas .\n\nLess than one percent of the range consists of meadow wetlands and riparian greenways ( vegetation along stream banks ) . However , these areas are vital to the local ecosystem . The meadows surround springs , which are mostly on gently sloping uplands or in stream bottoms , and range in size from about 1 to 5 acres ( 0 @.@ 40 to 2 @.@ 02 ha ) . Narrow riparian greenways follow the year @-@ round streams . Many greenway areas have quaking aspen and willow groves . Cottonwood and alder groves can be found at lower elevations where terrain is flatter and stream channels are wider . Sedges and rushes are also native to these stream bottoms . Years of heavy livestock grazing in parts of the range resulted in the loss of some grass species , riparian vegetation , and young aspen and willow trees . Restoration of riparian areas began in the early 1970s , and plans to reduce grazing were implemented in the 1980s and early 1990s . However , large wildfires in southeastern Oregon during the summer of 2012 burned much of the range 's vegetation , damaging riparian ecosystems and killing hundreds of grazing cattle .\n\nAnimals in the Trout Creek Mountains are adapted to the environment of the High Desert . Pronghorn are common in the open , sagebrush @-@ covered basins , while mule deer live in the cottonwood and willow groves . There are also bighorn sheep , cougars , and bobcats in the high country . Jackrabbits and coyotes are prevalent throughout the range . Mustangs sometimes pass through the mountains as they roam the Great Basin . Some other mammals include the northern pocket gopher , mountain cottontail , and Belding 's ground squirrel . North American beavers live in and along streams , as do Pacific tree frogs , western spadefoot toads , and garter snakes . Native bird species include the sage grouse , mountain chickadee , gray @-@ headed junco , black @-@ throated gray warbler , Virginia 's warbler , MacGillivray 's warbler , pine siskin , red crossbill , bushtit , hermit thrush , northern goshawk , and species of raven and eagle .\n\nSeveral streams in the Trout Creek Mountains are home to trout , including the rare Lahontan cutthroat trout subspecies . These include Willow Creek , Whitehorse Creek , Little Whitehorse Creek , Doolittle Creek , Fifteen Mile Creek , Indian Creek , Sage Canyon Creek , Line Canyon Creek , and some tributaries of McDermitt Creek . Lahontan cutthroat trout live in small , isolated populations that are often confined to individual streams , many of them in the Trout Creek Mountains . These populations have significant genetic differences due to their history of isolation . For most of the 20th century , the trout 's numbers declined considerably . It was listed under federal law as an endangered species in 1970 and was reclassified as threatened in 1975 . Reasons for the fish 's decline included habitat degradation from cattle grazing , drought", "max_new_tokens": 16} diff --git a/src/lab5/datasets/performance_small.jsonl b/src/lab5/datasets/performance_small.jsonl new file mode 100644 index 00000000..dbb897c0 --- /dev/null +++ b/src/lab5/datasets/performance_small.jsonl @@ -0,0 +1,4 @@ +{"prompt": "biggin , were former Blues , while Cambridge also saw 30 @-@ year @-@ old Tom Edwards return , having rowed in ", "max_new_tokens": 32} +{"prompt": " 24th Infantry Battalion was the first to contact the Japanese , carrying out an attack against Japanese positions around Dawe 's Creek on 17 April . Supported by a troop of Matilda tanks from the 2 / 4th Armoured Regiment , an artillery barrage which fired over 700 shells", "max_new_tokens": 16} +{"prompt": " starring Bolan that included Starr 's footage of a T. Rex concert held at Wembley on 18 March . With \" Back Off Boogaloo \" , NME critic Bob Woffinden noted Starr 's success in establishing himself in the two years since the Beatles ' break @-@ up , and wrote that the single \" confirmed that he and Harrison , dark horses both , were the ones who had managed their solo careers more purposefully and intelligently \" compared with McCartney and Lennon .\n\nThe song was a hit in the US , reaching number 9 on the Billboard Hot 100 , and achieved Starr 's best", "max_new_tokens": 8} +{"prompt": " to fresh air . He said that he was told by them that the person fell off the stage . The ambulance arrived 5 – 10 minutes later .\n\nAnother eyewitness was a fan , Alena Rozsívalová , who testified that she saw Blythe shove Nosek , saying \" [ h ] e climbed onto the stage , and when he tried to stand up , Blythe shoved him . \" According to her , the shove was strong enough for Nosek to fall behind the front row of the fans into a place where no fans were staying at the moment . She testified that Nosek fell backwards . Another attendee of the concert who took the stand that day did not recall seeing any fall by the end of the concert .\n\nA defense @-@ appointed expert witness in the field of biomechanics testified on March 5 that Nosek could not turn 180 degrees during the fall and that should he be falling forward , he could not have sustained an injury to the back of his head . He further said that if Nosek fell over the first row of the fans , he must have been not only pushed , but must have jumped himself . The State Attorney immediately attacked this testimony , claiming that the expert witness omitted the conditions at the place and time ,", "max_new_tokens": 4} diff --git a/src/lab5/datasets/quality_public.jsonl b/src/lab5/datasets/quality_public.jsonl new file mode 100644 index 00000000..8a9fa593 --- /dev/null +++ b/src/lab5/datasets/quality_public.jsonl @@ -0,0 +1,62 @@ +{"text": "31 mi ) upstream , and Dumonceau was another 7 kilometers ( 4 mi ) further behind him . Kutuzov knew the size of the French force--its division strength--and its positions , and he knew that most of the dragoons were not covering the French flank but had turned north . He also knew , or had made a good supposition , about Napoleon 's orders , so he knew what to offer Mortier and Gazan as bait .\n\nIn addition to the Russian generals , the council included Austrian commanders Lieutenant Field Marshal Johann Heinrich von Schmitt and Friedrich Karl Wilhelm , Fürst zu Hohenlohe ."} +{"text": " in The Egoist in May 1915 . Flint was at pains to emphasise the contribution of the Eiffel Tower poets , especially Edward Storer . Pound , who believed that the \" Hellenic hardness \" that he saw as the distinguishing quality of the poems of H.D. and Aldington was likely to be diluted by the \" custard \" of Storer , was to play no further direct role in the history of the Imagists . He went on to co @-@ found the Vorticists with his friend , the painter and writer Wyndham Lewis .\n\nAround this time , the American Imagist Amy Lowell moved to London"} +{"text": " of being gay . Ball removed the sequence because it did not fit the structure of the rest of the film — Col. Fitts was the only character to have a flashback — and because it removed the element of surprise from Col. Fitts ' later pass at Lester . Ball said he had to write it for his own benefit to know what happened to Col. Fitts , though all that remained in later drafts was subtext .\n\nBall remained involved throughout production ; he had signed a television show development deal , so had to get permission from his producers to take a year off to be close to American Beauty . Ball was on @-"} +{"text": " MMA , even though he was offered a deal \" ten times more \" than what he had made previously in his MMA career . He further elaborated that , while he was training for months for a return to the UFC , he felt \" physically great but something was lacking mentally \" . Lesnar added that \" [ he 's ] an older caveman now , so [ he ] makes smarter caveman decisions \" and that he chose to sign with WWE instead of returning to MMA because he could \" work part @-@ time with full @-@ time pay \" .\n\nThough Lesnar said he was \" closing the door on MMA \""} +{"text": " and ferry disruptions . According to the PAGASA in its post @-@ storm report , a total of 13 people lost their lives during the storm . On Hainan , the storm helped end one of the worst summer droughts in almost 65 years , although it also left heavy crop damage , wrecking 64 @,@ 000 ha ( 160 @,@ 000 acres ) of fields and killing 400 head of livestock . With about 800 homes destroyed , damage on Hainan amounted to $ 197 million ( 2003 USD )"} +{"text": "-@ upgraded Parma to typhoon status . The next day , the typhoon turned to the northeast due to another approaching trough . With decreasing wind shear and warmer waters , Parma re @-@ intensified significantly on October 29 , reaching a secondary peak of 165 km / h ( 105 mph ) according to JMA , and 215 km / h ( 135 mph ) according to JTWC . The storm moved very closely along the track it took several days prior . Increasing wind shear on October 30 caused rapid weakening , resulting in the eye dissipating . By the next day ,"} +{"text": " leaving the hamlet .\n\nOutside of Warren Corners , the route heads across rural areas along the Cambria – Lockport town line . It soon enters the small hamlet of Hickory Corners , where the road passes under Lower Mountain Road ( CR 902 ) . Access to the highway is made by way of Town Line Road Spur ( CR 114 ) , a connector leading to Lower Mountain Road . NY 93 continues southward along the town line , changing names to Junction Road at an intersection with Upper Mountain Road ( CR 5 ) west of the city of Lockport . From here , the route crosses over CSX Transportation"} +{"text": " rest of the conflict . The squadron continued to train as an air @-@ portable unit , and participated in a number of exercises to prepare for its new duties , including reconnaissance of enemy positions and counter @-@ attacking enemy infantry and armour .\n\nOn 13 December 1943 , the War Office decided to expand the squadron into a regiment equipped with a combination of light tanks and conventional reconnaissance vehicles such as scout cars , and on 1 April 1944 , it was re @-@ designated as the 6th Airborne Armoured Reconnaissance Regiment . The regiment consisted of a Headquarters Squadron , a"} +{"text": " Angle Slam on both Big Show and Lesnar , pinning the champion to become WWE Champion for a fourth time .\n\nLesnar continued to aggressively pursue the WWE title despite his friendship with Angle . Mr. McMahon found his way into the angle , at first berating Lesnar , who had involved himself in McMahon 's rivalry with Zach Gowen , for losing to Angle . This all turned out to be a swerve that came into focus on the August 7 , 2003 SmackDown ! in Kelowna , British Columbia . That night , Lesnar and McMahon were to face each other in a steel cage match with Angle as"} +{"text": " was unsuccessfully attacked by an American submarine at 18 : 43 . Around 19 : 00 Ozawa learned about a force of destroyers and cruisers that drove off the Japanese destroyers rescuing survivors from some of the carriers lost earlier in the day and sank Chiyoda . He ordered the Fourth Carrier Division to reverse course and engage the Americans , but the battleships were unable to find them , and Ozawa ordered them to reverse course and head for Amami Ōshima . When they arrived on 27 October , Ozawa transferred to Hyūga and hoisted his flag aboard her . While en route for"} +{"text": " for sitcom laughs , it did so with \" unexpected nuance \" . Hentzi criticized how the film made a mystery of Lester 's murder , believing it manipulative and simply a way of generating suspense . McCarthy cited the production and costume design as pluses , and said the soundtrack was good at creating \" ironic counterpoint [ s ] \" to the story . Hentzi concluded that American Beauty was \" vital but uneven \" ; he felt the film 's examination of \" the ways which teenagers and adults imagine each other 's lives \" was its best point , and that although Lester and Angela 's dynamic was familiar , its romantic irony"} +{"text": " of Gazan 's force provided any necessary rear guard action , and the following morning the remaining men were evacuated from the north shore of the Danube , while they maintained possession of only Spitz and Weissenkirchen on the north bank .\n\nThe losses were staggering : Gazan lost close to 40 percent of his division to death and wounds . Aside from losing five guns , 47 officers and 895 men under his command were captured , bringing the loss of effectives closer to 60 percent ; furthermore , he lost the eagles of the 4th Infantry Regiment ( France ) and the eagle and guidon"} +{"text": "11 ) and later in Europe ( February 2012 ) and North America ( May 2012 ) .\n\nOn release , the four reviewers of Electronic Gaming Monthly applauded Kirby 's Block Ball for modifying the Breakout formula to create a new and enjoyable game . They especially praised the unique power @-@ ups , though Crispin Boyer and Sushi X also felt the game was too short and easy . Nintendo Power said they enjoyed Block Ball and its number of stages , but wondered how its eight megabits of memory were being used . The magazine found the parts where Kirby eats the unbreakable blocks to be innovative"} +{"text": " alongside an Allied cargo ship . Another attempt failed when Plazas placed a bomb inside an ammunition store but was not able to bring the explosive . It was not until 1942 that the operations begun to succeed . In January 1942 , two Spanish agents manage to destroy two aircraft at the North Front landing strip .\n\nFinanced , trained and equipped by the Germans , the saboteurs sank the armed trawler HMT Erin , and destroyed the auxiliary minesweeper HMT Honju , which resulted in the deaths of six British seamen on 18 January 1942 . Plazas was assisted by"} +{"text": "00 houses and damaged 13 @,@ 000 homes and businesses , leaving 25 @,@ 000 people homeless . About 1 @.@ 47 million lost power , and widespread crop damage occurred , resulting in the worst rice crop in 23 years . Across South Korea , Maemi killed 117 people , and overall damage totaled ₩ 5 @.@ 52 trillion won ( $ 4 @.@ 8 billion USD ) .\n\nIn the middle of September , the monsoon trough spawned a rapidly organizing disturbance east @-@ northeast of Luzon , with weak wind"} +{"text": "0 Top 10 - # 6 Sports Film\n\nIn the decades since its release , The Hustler has cemented its reputation as a classic . Roger Ebert , echoing earlier praise for the performances , direction , and cinematography and adding laurels for editor Dede Allen , cites the film as \" one of those films where scenes have such psychic weight that they grow in our memories . \" He further cites Fast Eddie Felson as one of \" only a handful of movie characters so real that the audience refers to them as touchstones . \" TV Guide calls the film a \" dark stunner \" offering \" a grim world whose only bright spot is"} +{"text": " h ) . The shape of the central dense overcast is also considered . The eye pattern utilizes the coldness of the cloud tops within the surrounding mass of thunderstorms and contrasts it with the temperature within the eye itself . The larger the temperature difference is , the stronger the tropical cyclone . Once a pattern is identified , the storm features ( such as length and curvature of banding features ) are further analyzed to arrive at a particular T @-@ number . The CCC pattern indicates little development is occurring , despite the cold cloud tops associated with the quickly evolving feature .\n\nSeveral agencies issue Dvorak intensity numbers for tropical cyclones and their precursors , including the"} +{"text": " , \" Asylum of the Daleks \" . Coleman was cast because of her chemistry with Matt Smith , and especially because she was able to talk faster than him . She auditioned for the role of Clara , not Oswin from \" Asylum \" , as the concept of the two characters being the same only occurred to Moffat whilst casting for Clara . The production team requested that the press and fans who attended advanced screenings keep Coleman 's appearance a secret until \" Asylum \" was broadcast ; the effort was ultimately successful . Moffat stated that the introduction of a new companion made \" the show feel different \" and brought the story to \" a new beginning \""} +{"text": " the 1960s . In these stories , the Great Intelligence uses robot Yeti as its physical presence . The events of The Web of Fear are alluded to by the Doctor in \" The Snowmen \" when he presents the London Underground biscuit tin to the Great Intelligence in Dr Simeon 's laboratory ; the Intelligence states , \" I do not understand these markings \" , in reference to the 1967 London Underground map design on the tin . The Doctor remarks that the Underground is a \" key strategic weakness in metropolitan living \" , referring to ( and possibly setting in motion ) the future Yeti attack on London via the Underground ."} +{"text": " a wise and moderate emperor by ancient historians . Nerva 's greatest success was his ability to ensure a peaceful transition of power after his death , thus founding the Nerva – Antonine dynasty .\n\nMarcus Cocceius Nerva was born in the village of Narni , 50 kilometers north of Rome , to the family of Marcus Cocceius Nerva , Suffect Consul in 40 , and Sergia Plautilla . Ancient sources report the date as either 30 or 35 . He had at least one attested sister , named Cocceia , who married Lucius Salvius Titianus Otho"} +{"text": "iments were in various states of disorganization and some companies had virtually disappeared . Keiser hoped he could organize a defense along the Changnyong @-@ Yongsan road east of the Naktong River , and prevent North Korean access to the passes eastward leading to Miryang and Ch 'ongdo .\n\nAt 09 : 00 Walker requested the US Air Force to make a maximum effort along the Naktong River from Toksong @-@ dong , just above the US 2nd Division boundary , southward and to a depth of 15 miles ( 24 km ) west of the river . He"} +{"text": "30 mbar ( hPa ; 27 @.@ 46 inHg ) . Following peak intensity , Kujira would begin to track northwest and oscillate in strength , cresting an additional two times in intensity . On April 21 , the typhoon was downgraded to tropical storm intensity and began to track erratically for several days east of Taiwan . However , on April 24 , Kujira would resume a northward track and begin to weaken , and on April 24 was downgraded to tropical depression strength as it made landfall on Kyushu . Following landfall , Kujira transitioned into an extratropical cyclone on"} +{"text": "53 ( Carney Road ) and CR 42 ( Rapids Road ) . It continues on a southward track past the eastern terminus of CR 218 ( Hunts Corner – Akron Road ) to the outskirts of the village of Akron , where the highway turns east onto Lewis Road and soon enters the village limits . NY 93 runs past a line of homes before intersecting Cedar Street , a road maintained by Erie County as CR 261 north of the village .\n\nThe route turns south at Cedar Street , following the residential street into downtown Akron . Here , NY 93 intersects with CR 573 ( John"} +{"text": " \" while others maintained \" the hard rock and the heavy stuff . \" Jon Wiederhorn of MTV said that \" for every thrash riff there 's a tunefully grungy passage , for every flailing guitar line there 's a rock @-@ radio hook . \" When talking about the track \" 30 / 30 @-@ 150 \" , he said parts are \" bludgeoning , barbed and heavy , \" while others are \" soaring and triumphant , \" with the production of Raskulinecz helping balance the album 's heaviness with its radio @-@ accessibility . Come What ( ever"} +{"text": " sodomy . Eliot , in a letter to John Hayward dated 27 August 1942 , explained why he changed the wording :\n\nI think you will recognise that it was necessary to get rid of Brunetto for two reasons . The first is that the visionary figure has now become somewhat more definite and will no doubt be identified by some readers with Yeats though I do not mean anything so precise as that . However , I do not wish to take the responsibility of putting Yeats or anybody else into Hell and I do not want to impute to him the particular vice which took Brunetto there . Secondly , although the reference to"} +{"text": " the violet and white colouring of his cope , which is built up through broad , thick , brush @-@ strokes . The influence can be further seen in the gold @-@ coloured ornaments on the back of the seat that extend on both sides of the figure . Art historian Armin Zweite describes the work as a mixture of reverence and subversion that pays tribute to Velázquez , while at the same time deconstructs his painting .\n\nSylvester detects the influence of late works by Titian in other aspects , especially in the deep and rich colouring , Velázquez 's portrayals of Philip IV , and agrees with identification of past"} +{"text": " ringpost . As paramedics tended to a screaming Angle , Lesnar finished off the assault with a kneebreaker on the ringside barricade , breaking the champion 's leg in storyline .\n\nWith Angle temporarily put out of action , Lesnar 's rivalry with Heyman and the Big Show resumed , which culminated in a match at the Royal Rumble in January 2003 with the winner being placed into the Royal Rumble later in the evening . Lesnar would defeat Big Show and entered the Royal Rumble as the # 29 entry , the second to last competitor to enter the match . He eliminated Matt Hardy and The"} +{"text": "00 long tons ) to 40 @,@ 444 tonnes ( 39 @,@ 805 long tons ) . This also reduced the draught to 9 @.@ 03 metres ( 29 ft 8 in ) . The overhang of the flight deck at the stern increased the overall length to 219 @.@ 62 metres ( 720 ft 6 in ) and the beam was slightly reduced to 31 @.@ 71 metres ( 104 ft 0 in ) .\n\nUpon commissioning , the sister ships were assigned to the "} +{"text": " had no supporting artillery , and the narrow space prevented them from taking advantage of their size . It was Dokhturov 's turn to face attackers at his front and rear , until the arrival of Schmitt 's column , which had wended its way through the mountains in the west .\n\nSchmitt arrived at dusk , and the action continued well after dark ; in mid @-@ November night falls at close to 17 : 00 in the upper Danube climes . Despite the darkness , Schmitt descended out of the defiles and deployed his troops to assail Dupont 's flank . As his Russians entered the fray , they"} +{"text": " the Saurian , but to the Batrachian order of Reptiles . \" Owen recognized the animal was not a \" saurian \" reptile , yet he also referred Jaeger 's Phytosaurus to the genus . Although the two genera both have similarly sized conical teeth , Phytosaurus was later found to be a crocodile @-@ like reptile . Additional material , including skulls , firmly placed Labyrinthodon as an amphibian . Jaeger also named Salamandroides giganteus in 1828 , basing it on partial occiput , or back portion of the skull . In 1833 , he described a complete skull of S"} +{"text": " Band @-@ stop and multiple band filters are also possible .\n\nSome of the impedance terms and section terms used in this article are pictured in the diagram below . Image theory defines quantities in terms of an infinite cascade of two @-@ port sections , and in the case of the filters being discussed , an infinite ladder network of L @-@ sections . Here \" L \" should not be confused with the inductance L – in electronic filter topology , \" L \" refers to the specific filter shape which resembles inverted letter \" L \" .\n\nThe sections of the hypothetical infinite filter are made of series elements having impedance 2Z and shunt"} +{"text": " about 100 kilometres ( 62 mi ) inland from the coast . The Olmecs are regarded as the first civilization to develop in Mesoamerica and the Olmec heartland is one of six cradles of civilization worldwide , the others being the Norte Chico culture of South America , the Erlitou culture of China 's Yellow River , the Indus Valley Civilization of south Asia , the civilization of ancient Egypt and the Sumerian civilization of ancient Iraq . Of these , only the Olmec civilization developed in a lowland tropical forest setting .\n\nThe Olmecs were the first inhabitants of the Americas to construct monumental architecture and to"} +{"text": ",@ 000 yards of total offense . Rifenburg held the University of Michigan 's single season and career record for touchdown receptions ( eight in a season ; sixteen career ) until his records were broken by Anthony Carter in 1980 .\n\nIn 1948 , Rifenburg was drafted by the Philadelphia Eagles in the 15th round of the NFL draft , and he was also drafted by the New York Yankees of the All @-@ America Football Conference . He had intended to play in 1949 with the Yankees , but suffered a knee injury in a practice session for the August 1949 Chicago College All @-@ Star Game . Press accounts at the time noted that the injury \" will probably keep him out of pro football all season , if not forever . \" The incident led to a debate as to whether NFL owners should \" bar their men from playing with the college all @-@ stars . \"\n\nRifenburg landed a job at WJR radio in Detroit , but he left his sportscaster 's job to join the Detroit Lions . In the 1950 NFL season , Rifenburg came back from his injury to play for the Detroit Lions . He played in "} +{"text": "-@ handed pitcher in 2011 , the Players Choice Award for Most Outstanding National League pitcher , the Gold Glove Award as the top fielding pitcher in the NL and the Sporting News ( TSN ) National League Pitcher of the Year . He was additionally selected as the starting pitcher for the TSN NL All @-@ Star Team . On November 17 , he was honored with the National League Cy Young Award , making him the youngest Cy Young winner since Dwight Gooden of the 1985 New York Mets . He was the 8th Dodger pitcher to win the award , the first since Éric Gagné in the 2003 season .\n\nOn February 7 , 2012 , Kershaw and the Dodgers agreed on a two @-@ year , $ 19 million contract . The contract was the second highest for a player in his first year of arbitration ( after Tim Lincecum 's $ 23 million 2 @-@ year contract in 2010 ) .\n\nKershaw was the Dodgers ' Opening Day starter for the second year in a row , where he pitched three innings of shutout ball against the San Diego Padres at Petco Park before being removed from the game due to"} +{"text": " Gate when Germany invaded Belgium in 1940 .\n\nThe only memorial created by the Commission that was not in the form of a monument or cemetery was the Opththalmic Institute at Giza , Egypt — complete with library , and bacteriology and pathology departments — as its memorial to men of the Egyptian Labour Corps and Camel Transport Corps . Its erection was agreed with local political pressure .\n\nFrom the start of the Second World War in 1939 , the Commission organised grave registration units and , planning ahead based on the experience gained from the First World War , earmarked land for use as cemeteries . When the war began turning in favour of the Allies , the Commission was able to begin restoring its First World War cemeteries and memorials . It also began the task of commemorating the 600 @,@ 000 Commonwealth casualties from the Second World War . In 1949 , the Commission completed Dieppe Canadian War Cemetery , the first of 559 new cemeteries and 36 new memorials . Eventually , over 350 @,@ 000 new headstones were erected . Many were made from Hopton Wood stone . The wider scale of World War II , coupled with manpower shortages and unrest in some countries , meant that"} +{"text": " 1970s in the writings of Ivan van Sertima . Such speculation is not taken seriously by Mesoamerican scholars such as Richard Diehl and Ann Cyphers .\n\nAlthough all the colossal heads are broadly similar , there are distinct stylistic differences in their execution . One of the heads from San Lorenzo bears traces of plaster and red paint , suggesting that the heads were originally brightly decorated . Heads did not just represent individual Olmec rulers ; they also incorporated the very concept of rulership itself .\n\nThe production of each colossal head must have been carefully planned , given the effort required to ensure the necessary resources were available ; it seems likely that only the more powerful Olmec rulers were able to mobilise such resources . The workforce would have included sculptors , labourers , overseers , boatmen , woodworkers and other artesans producing the tools to make and move the monument , in addition to the support needed to feed and otherwise attend to these workers . The seasonal and agricultural cycles and river levels needed to have been taken into account to plan the production of the monument and the whole project may well have taken years from beginning to end .\n\nArchaeological investigation of Olmec basalt workshops suggest that the colossal heads were first roughly shaped using direct percussion to chip away both large and small flakes"} +{"text": "Operation Eastern Exit was the codename given to the military evacuation of the United States embassy in Mogadishu , the capital of Somalia , in January 1991 . In late December 1990 , violence quickly enveloped the city as armed militants began clashing with government soldiers . On 1 January 1991 , the US Ambassador to Somalia , James Keough Bishop , contacted the Department of State requesting an evacuation of the embassy , which was approved the following day . United States Central Command began planning and mobilizing forces that evening . The initial plan was to evacuate with a military transport plane through the Mogadishu International Airport , but this was later abandoned . A helicopter evacuation via the USS Guam and USS Trenton was the remaining option .\n\nOn the morning of 5 January , a 60 @-@ person Marine and Navy SEAL security detail was dispatched from Guam aboard two CH @-@ 53E Super Stallion helicopters to secure the embassy and prepare for the main evacuation . The two helicopters returned to Guam with the first 61 evacuees . Throughout the day , foreign diplomats and civilians sought refuge at the embassy . Four waves of five CH @-@ 46 Sea Knight helicopters each evacuated the embassy compound shortly after midnight"} +{"text": " of Hitler 's capture . Debates flare up over his impending trial , where it will be held and under whose jurisdiction . Orosso is identified as the nearest airfield to the last known location of the search party , and aircraft begin arriving at the hitherto unknown town . But when the search party loses radio contact with Lieber , they must make a decision : do they sit out the storms and deliver their captive to Lieber later , or do they try Hitler in the jungle before their prize is snatched from them by the world at large , who they know will be waiting ? Their decision is the latter , and against Lieber 's advice ( \" You must not let him speak ... his tongue is like no other \" ) they prepare for a trial with a judge , prosecution and defence attorneys selected from the members of the search party . Teku , a local Indian tracker , is asked to observe the trial as an independent witness .\n\nThe attention Hitler is receiving , however , renews his strength , and when the trial begins , he brushes aside his \" defence attorney \" and begins a long speech in four parts in his own defence :\n\nFirstly , Hitler claims he took his doctrines from the Jews and copied the notion of the master race from the Chosen people and their need to separate themselves from the"} +{"text": " Battalion , and other miscellaneous units of the division . This southern grouping was known as Task Force Bradley .\n\nAll three regiments of the NK 2nd Division @-@ the 4th , 17th , and 6th , in line from north to south @-@ crossed during the night to the east side of the Naktong River into the 23rd Regiment sector . The NK 2nd Division , concentrated in the Sinban @-@ ni area west of the river , had , in effect , attacked straight east across the river and was trying to seize the two avenues of advance into Changnyong above and below Lake U @-@ p 'o . On August 31 , 1950 , Lake U @-@ p 'o was a large body of water although in most places very shallow .\n\nAt dawn September 1 , Keiser at 2nd Division headquarters in Muan @-@ ni , 7 miles ( 11 km ) east of Yongsan on the Miryang road , felt his division was in the midst of a crisis . The massive North Korean attack had made deep penetrations everywhere in the division sector except in the north in the zone of the 38th Infantry"} +{"text": " of the class served in three navies : the German Imperial Navy , the Austro @-@ Hungarian Navy , and the Bulgarian Navy . In German service , they served primarily in the Flanders Flotilla , the Baltic Flotilla , and the Constantinople Flotilla .\n\nThe first Type UB I to enter service was UB @-@ 10 , which formed the nucleus of the Flanders Flotilla , on 27 March 1915 . By the end of April five more Type UB I boats had become operational . UB @-@ 10 was eventually joined in the Flanders Flotilla by UB @-@ 2 , UB @-@ 4 , UB @-@ 5 , UB @-@ 6 , UB @-@ 12 , UB @-@ 13 , UB @-@ 16 , and UB @-@ 17 ; of these , only UB @-@ 2 made the journey to Flanders by sea rather than rail .\n\nUB @-@ 4 departed on the first patrol from Flanders on 9 April , and was responsible for sinking the first ship sent down by the flotilla . The Type UB I boats of the Flanders Flotilla originally pat"} +{"text": " .\n\nHannah Dodd was born on 27 April 1992 , and is from Arcadia , New South Wales . She has sacral agenesis and spina bifida with upper limb dystonia , and is missing four vertebrae in her back . When she was about a year old , her kidneys started failing . Her entire renal system needed to be reconstructed . She has two older brothers . She can walk with the aide of a caliper , and also uses a wheelchair . As of 2012 , she is a horse riding teacher and student at the University of Western Sydney where she is majoring in sports and exercise science .\n\nDodd is a Grade IV equestrian competitor , coached by Peter Turner . Due to her sacral agenesis , when she rides her horse , she dislocates several bones every time , but as a result of anti @-@ doping rules , she has had to find alternative ways of coping with pain associated with riding .\n\nDodd has been around horses since she was four months old , and was able to ride on her own by the time she was two years old , before she learned to walk . The sport gave her a degree of independence . She started competing in 2005 , and first represented Australia in "} +{"text": " patrons had no such right .\n\nThe Senate tried to pass a law in which the crimes of one slave applied to all slaves within a household . Despite riots from the people , Nero supported the Senate on their measure , and deployed troops to organise the execution of 400 slaves affected by the law . However , he vetoed strong measures against the freedmen affected by the case .\n\nAfter tax collectors were accused of being too harsh to the poor , Nero transferred collection authority to lower commissioners . Nero banned any magistrate or procurator from exhibiting public entertainment for fear that the venue was being used as a method to sway the populace . Additionally , there were many impeachments and removals of government officials along with arrests for extortion and corruption .\n\nWhen further complaints arose that the poor were being overly taxed , Nero attempted to repeal all indirect taxes . The Senate convinced him this action would bankrupt the public treasury . As a compromise , taxes were cut from 4 @.@ 5 % to 2 @.@ 5 % . Additionally , secret government tax records were ordered to become public . To lower the cost of food imports , merchant ships were declared tax @-@ exempt .\n\nIn imitation of the Greeks , Nero built a number of gymnasiums and theatres . Enormous gladiatorial"} +{"text": " March . The film ended its North American theatrical run on June 4 , 2000 , having grossed $ 130 @.@ 1 million .\n\nAmerican Beauty had its European premiere at the London Film Festival on November 18 , 1999 ; in January 2000 , it began to screen in various territories outside North America . It debuted in Israel to \" potent \" returns , and limited releases in Germany , Italy , Austria , Switzerland , the Netherlands and Finland followed on January 21 . After January 28 opening weekends in Australia , the United Kingdom , Spain and Norway , American Beauty had earned $ 7 million in 12 countries for a total of $ 12 @.@ 1 million outside North America . On February 4 , American Beauty debuted in France and Belgium . Expanding to 303 theaters in the United Kingdom , the film ranked first at the box office with $ 1 @.@ 7 million . On the weekend of February 18 — following American Beauty 's eight nominations for the 72nd Academy Awards — the film grossed $ 11 @.@ 7 million from 21 territories , for a total of $ 65 @"} +{"text": " Roman War .\n\nIn 64 AD , most of Rome was destroyed in the Great Fire of Rome , which many Romans believed Nero himself had started in order to clear land for his planned palatial complex , the Domus Aurea . In 68 , the rebellion of Vindex in Gaul and later the acclamation of Galba in Hispania drove Nero from the throne . Facing a false report of being denounced as a public enemy who was to be executed , he committed suicide on 9 June 68 ( the first Roman emperor to do so ) . His death ended the Julio @-@ Claudian dynasty , sparking a brief period of civil wars known as the Year of the Four Emperors . Nero 's rule is often associated with tyranny and extravagance . He is known for many executions , including that of his mother , and the probable murder by poison of his stepbrother Britannicus .\n\nNero was rumored to have had captured Christians dipped in oil and set on fire in his garden at night as a source of light . This view is based on the writings of Tacitus , Suetonius and Cassius Dio , the main surviving sources for Nero 's reign , but a few surviving sources paint Nero in a more favourable light . Some sources , including some"} +{"text": " cartoon . He replaced the letters representing The New Yorker with The Bushie .\n\nThe cover was later satirized by Barry Blitt for the cover of The New Yorker on October 6 , 2008 . The cover featured Sarah Palin looking out of her window seeing only Alaska , with Russia in the far background .\n\nThe March 21 , 2009 The Economist included a story entitled \" How China sees the World \" that presents a parody that is also an homage to the original image , but depicting the viewpoint from Beijing 's Chang 'an Avenue instead of Manhattan . A caption above the illustration reads \" Illustration by Jon Berkeley ( with apologies to steinberg and The New Yorker ) \" . It accompanied an article that discussed the burgeoning Chinese economy at the time of the contemporary financial crisis .\n\nThe October 1 , 2012 cover of Mad Magazine satirized the problems with the September release of Apple Inc . ' s iOS 6 mobile operating system which included Apple Maps , a replacement for Google Maps . The work presents what View of the World might look like if one had relied upon the September 2012 version of Apple Maps to locate various landmarks .\n\nOn October 17 , 2005 , American"} +{"text": " Stevens had previously made US $ 395 @,@ 000 plus benefits in base salary , a relatively low figure for a successful Division I head basketball coach . His total compensation for 2009 – 10 was estimated at US $ 750 @,@ 000 . Stevens had received a raise after each of his three seasons at Butler and his contract contains a buyout clause estimated in the high six or low seven figures .\n\nBy re @-@ signing with Butler , Stevens temporarily ended speculation that he would leave the university for a higher paying job . Oregon , Clemson , and Wake Forest were all said to be interested in offering Stevens multi @-@ million dollar contracts to leave Butler . \" First and foremost , I ’ m loyal to Butler , \" Stevens said . When asked if he would ever leave Butler , Stevens replied \" I guess if they kicked me out . \"\n\nAfter the 2011 – 12 season , Stevens was pursued vigorously by Illinois to fill their coaching vacancy before he declined their offer .\n\nIn March 2013 , UCLA reportedly offered Stevens between $ 2 @.@ 5 and $ 3 million a year to leave Butler . Rumors circulated that Stevens was in contract"} +{"text": " and looking at statistical trends to find opponents ' weaknesses . \" Former player Ronald Nored agrees : \" We know everything we need to about our opponents , all their tendencies are broken down \" ahead of time .\n\nStevens is a proponent of using statistical analysis to enhance his coaching decisions , spending almost as much time looking at statistics as watching game film . \" I think it 's a unique way of looking at the game that may be able to help best communicate to your players \" , he explains . For example , when Butler was slumping in late 2010 , Stevens challenged his team : \" this [ 46 % defensive field goal percentage ] is where we are . This isn 't acceptable to get to where we want to go . But what does that really mean ? It 's not just get better defensively , it is , if we give up 3 less baskets a game , then we will be at 40 percent field goal percentage defense which will be top 20 in the country \" . The team got the message , improved throughout the season , and ultimately went on a March run fueled by defense . In 2012 , Stevens became the first college coach to hire someone solely for statistical research when he added Drew Cannon to the"} +{"text": " the proto @-@ Imagist interest in Japanese poetry , and , in 1912 , during a meeting with them in the British Museum tea room , Pound told H.D. and Aldington that they were Imagistes and even appended the signature H.D. Imagiste to some poems they were discussing .\n\nWhen Harriet Monroe started her Poetry magazine in 1911 , she had asked Pound to act as foreign editor . In October 1912 , he submitted thereto three poems each by H.D. and Aldington under the Imagiste rubric , ( published in the November 1912 second issue thereof ) with a note which described Aldington as ' one of the ' Imagistes ' . This note , along with the appendix note ( ' The Complete Works of T. S. Hulme ' ) in Pound 's book ( also published in Autumn 1912 ) entitled Ripostes are considered to be first appearances of the word Imagiste ( later anglicised to ' Imagists ' ) in print .\n\nAldington 's poems , Choricos , To a Greek Marble , and Au Vieux Jardin , were in the November issue of Poetry , and H.D. ' s , Hermes of the"} +{"text": " and homes , while high winds damaged crops . Damage was heaviest in Yap State , mostly in the small Ulithi atoll and Fais Island . On both islands , the typhoon contaminated the water supply and wrecked the crops . Rainfall reached 263 mm ( 10 @.@ 35 in ) on Ulithi , and gusts reached 158 km / h ( 98 mph ) . Throughout the FSM , damage totaled about $ 1 @.@ 7 million , although there were no deaths . The damage prompted the FSM government to declare two states as disaster areas , as well as a disaster declaration from the United States federal government . While Lupit was becoming extratropical , it became the first typhoon in December to threaten Japan in 13 years . The storm dropped rainfall that resulted in mudslides and flight cancellations .\n\nAn area of convection formed on May 16 to the southwest of Palau , located within an area of weak wind shear . The next day , the JMA and the JTWC both classified the system as a tropical depression . The convection was disorganized in association with multiple circulation centers , although it gradually organized . Moving westward initially , the depression turned more to the north into an area of increasing wind shear . On May 19 , the JTWC upgraded the system to Tropical Storm 03W , and on the same day , PAGASA classified it as Tropical Depression Batibot . Soon after , the convection decreased from the center , and by May 20 , all warning agencies had discontinued advisories .\n\nOn July 9 , PAGASA classified a system as Tropical Depression Falcon , off the west coast of the Philippines . The JMA also briefly initiated advisories before dropping them later on July 9 . Later in the month , PAGASA briefly issued advisories on Tropical Depression Ineng on July 30 off the east coast of Mindanao . The depression dissipated the next day , causing about P8 million ( PHP , $ 145 @,@ 000 USD ) in damage . The monsoon trough spawned a tropical depression north of Luzon on August 18 , with PAGASA naming it Lakay . There were several circulations in the region , with Tropical Storm Vamco to the northeast near Taiwan , and the overall system moved generally westward . PAGASA briefly classified Lakay as a tropical storm on August 19 before ending advisories the next day . The system spread rainfall across China , reaching 82 @.@ 4 mm ( 3 @"} +{"text": " than those other parts . \" Mendes offered Bening the role of Carolyn without the studio 's consent ; although executives were upset at Mendes , by September 1998 , DreamWorks had entered negotiations with Spacey and Bening .\n\nSpacey loosely based Lester 's early \" schlubby \" deportment on Walter Matthau . During the film , Lester 's physique improves from flabby to toned ; Spacey worked out during filming to improve his body , but because Mendes shot the scenes out of chronological order , Spacey varied postures to portray the stages . Before filming , Mendes and Spacey analyzed Jack Lemmon 's performance in The Apartment ( 1960 ) , because Mendes wanted Spacey to emulate \" the way [ Lemmon ] moved , the way he looked , the way he was in that office and the way he was an ordinary man and yet a special man \" . Spacey 's voiceover is a throwback to Sunset Boulevard ( 1950 ) , which is also narrated in retrospect by a dead character . Mendes felt it evoked Lester 's — and the film 's — loneliness . Bening recalled women from her youth to inform her performance : \" I used to babysit constantly . You 'd go to church and see how people present themselves on the outside , and then be inside their house and see the difference . \" Bening and a hair stylist collaborated to create a \" PTA president coif \" hairstyle , and Mendes and production designer Naomi Shohan researched mail @-@ order catalogs to better establish Carolyn 's environment of a \" spotless suburban manor \" . To help Bening get into Carolyn 's mindset , Mendes gave her music that he believed Carolyn would like . He lent Bening the Bobby Darin version of the song \" Don 't Rain on My Parade \" , which she enjoyed and persuaded the director to include it for a scene in which Carolyn sings in her car .\n\nFor the roles of Jane , Ricky , and Angela , DreamWorks gave Mendes carte blanche . By November 1998 , Thora Birch , Wes Bentley , and Mena Suvari had been cast in the parts — in Birch 's case , despite the fact she was underage for her nude scene . As Birch was 16 at the time she made the film , and thus classified as a minor in the United States , her parents had to approve her brief topless scene in the movie . Child labor representatives and they were on the set for the shooting of the scene . Bentley overcame competition"} +{"text": " a misunderstanding between Hall and Mendes . Mendes asked Hall to prepare the shot in his absence ; Hall assumed the characters would look for privacy , so he placed them in a narrow passage between a truck and the building , intending to light from the top of the truck . When Mendes returned , he explained that the characters did not care if they were seen . He removed the truck and Hall had to rethink the lighting ; he lit it from the left , with a large light crossing the actors , and with a soft light behind the camera . Hall felt the consequent wide shot \" worked perfectly for the tone of the scene \" . Hall made sure to keep rain , or the suggestion of it , in every shot near the end of the film . In one shot during Lester 's encounter with Angela at the Burnhams ' home , Hall created rain effects on the foreground cross lights ; in another , he partly lit the pair through French windows to which he had added material to make the rain run slower , intensifying the light ( although the strength of the outside light was unrealistic for a night scene , Hall felt it justified because of the strong contrasts it produced ) . For the close @-@ ups when Lester and Angela move to the couch , Hall tried to keep rain in the frame , lighting through the window onto the ceiling behind Lester . He also used rain boxes to produce rain patterns where he wanted without lighting the entire room .\n\nThomas Newman 's score was recorded in Santa Monica , California . He mainly used percussion instruments to create the mood and rhythm , the inspiration for which was provided by Mendes . Newman \" favored pulse , rhythm , and color over melody \" , making for a more minimalist score than he had previously created . He built each cue around \" small , endlessly repeating phrases \" — often , the only variety through a \" thinning of the texture for eight bars \" . The percussion instruments included tablas , bongos , cymbals , piano , xylophones , and marimbas ; also featured were guitars , flute , and world music instruments . Newman also used electronic music and on \" quirkier \" tracks employed more unorthodox methods , such as tapping metal mixing bowls with a finger and using a detuned mandolin . Newman believed the score helped move the film along without disturbing the \" moral ambiguity \" of the script : \" It was a real delicate balancing act in terms of what music worked to preserve [ that ] . \"\n\nThe soundtrack features songs by Newman , Bobby Darin , The Who , Free , Eels , The Guess Who , Bill Withers , Betty Carter ,"} +{"text": " across the river and took it up before dawn the next morning . Carrying parties of 50 civilians guarded by four North Korean soldiers crossed the river continuously at night , an estimated total of 800 @-@ 1 @,@ 000 carriers being used at this crossing site .\n\nNorth of the US 9th Infantry and the battles in the Naktong Bulge and around Yongsan , the US 23d Infantry Regiment after daylight of September 1 was in a very precarious position . Its 1st Battalion had been driven from the river positions and isolated 3 miles ( 4 @.@ 8 km ) westward . Approximately 400 North Koreans now overran the regimental command post , compelling Freeman to withdraw it about 600 yards ( 550 m ) . There , 5 miles ( 8 @.@ 0 km ) northwest of Changnyong , the US 23rd Infantry Headquarters and Headquarters Company , miscellaneous regimental units , and regimental staff officers checked the North Koreans in a 3 @-@ hour fight .\n\nThe North Koreans advanced to Changnyong itself during the afternoon of September 2 , and ROK National Police withdrew from the town . North Koreans were in Changnyong that evening . With his communications broken southward to the 2nd Infantry Division headquarters and the 9th Infantry , Haynes during the day decided to send a tank patrol down the Yongsan road in an effort to re @-@ establish communication . C Company , 72nd Tank Battalion , led its tanks southward . They had to fight their way down the road through several roadblocks . Of the three tanks that started , only the lead tank got through to Yongsan . There , it delivered an overlay of Task Force Haynes ' positions to Bradley .\n\nStill farther northward in the zone of the US 38th Infantry the North Koreans were also active . After the North Korean breakthrough during the night of August 31 , Keiser had ordered the 2nd Battalion , 38th Infantry , to move south and help the 23rd Infantry establish a defensive position west of Changnyong . In attempting to do this , the battalion found North Korean troops already on the ridges along the road . They had penetrated to Hill 284 overlooking the 38th Infantry command post . This hill and Hill 209 dominated the rear areas of the regiment . At 06 : 00 September 3 , 30"} +{"text": "4 hurricane on the Saffir @-@ Simpson scale . Interpolation suggested that the storm reached major hurricane status , or Category 3 status , on September 2 .\n\nThe hurricane initially followed the course of another hurricane that passed through the area in late August , which ultimately struck Cuba and Texas . This hurricane instead maintained a general west @-@ northwest track . After moving through the northern Bahamas , the hurricane weakened slightly before making landfall at Jupiter , Florida , at 0500 UTC on September 4 . A station there reported a pressure of 27 @.@ 98 inHg ( 948 mb ) during a 40 minute period of the eye 's passage ; this suggested a landfall strength of 125 mph ( 201 km / h ) . At the time , the radius of maximum winds was 15 mi ( 24 km ) , which was smaller than average . After landfall , the hurricane weakened rapidly while crossing the state . It briefly emerged into the Gulf of Mexico as a tropical storm early on September 5 . A few hours later while continuing to the northwest , it made another landfall near Rosewood — a ghost town in Levy County , east of Cedar Key — with winds of about 65 mph ( 105 km / h ) . Turning to the north , the storm slowly weakened as it crossed into Georgia , dissipating on September 7 near Augusta .\n\nOn September 2 , a fleet of eight aircraft evacuated all white residents from West End , Grand Bahama , to Daytona Beach , Florida . While the storm was near peak intensity on September 3 , the Weather Bureau issued hurricane warnings from Miami to Melbourne , Florida , with storm warnings extending northward to Jacksonville . Later that day , storm warnings , were issued from Key West to Cedar Key . About 2 @,@ 500 people evacuated by train from areas around Lake Okeechobee . By evening on September 3 , high tides sent sea spray over coastal seawalls in Palm Beach County as residents boarded up buildings ; structures on Clematis Street in West Palm Beach were said to be a \" solid front \" of plywood . Along the coast , observers reported very rough seas as the eye neared land .\n\nThe powerful hurricane moved over or near several islands in the Bahamas . Winds on Spanish Wells and Harbour Island were both estimated at around 140 mph ( 225 km / h ) . Winds reached 110 mph ( 177 km /"} +{"text": "Benjamin Paul \" Ben \" Amos ( born 10 April 1990 ) is an English professional footballer who plays as a goalkeeper for Bolton Wanderers . Born in Macclesfield , Cheshire , Amos began his career with Crewe Alexandra 's youth academy , but joined Manchester United at the age of 11 . He has spent time on loan at Peterborough United , Molde , and Oldham Athletic . Additionally , Amos is an England youth international , having represented his country at every level from Under @-@ 16 to Under @-@ 21 .\n\nAmos was born in Macclesfield , Cheshire and was a member of the Crewe Alexandra academy until he was released at the age of 10 . While at Crewe , he also played for another local team , Bollington United , as a centre midfielder . One year later , Amos was playing for his local team against the team at the top of the table , and they needed to win the match to win the league . Amos ' team 's goalkeeper was injured during the game , and as the tallest on the team , Amos was put in goal ; however , he had also been the team 's regular penalty taker all season , so when they were awarded a penalty , he went all the way up the pitch to take the kick . He scored , and his team went on to win the game 3 – 2 , together with the league title . After the game , his parents told him that a Manchester United scout had been watching him and that he had been invited for trials . Amos joined Manchester United at the age of 11 .\n\nIn his first season at Manchester United , Amos became a regular starter for the club 's Under @-@ 13 team , playing in 19 out of 27 matches in the 2001 – 02 season . Amos was named as an unused substitute for the Under @-@ 18 side for the first time on 8 January 2005 , for a league game against Manchester City . His first appearance for the Under @-@ 18s came exactly nine months later , on 8 October 2005 , coming on as a substitute for Danny Rose after starting goalkeeper Ron @-@ Robert Zieler was sent off in a 2 – 0 defeat to Bolton Wanderers . He was regularly named as an unused substitute during the 2005 – 06 season – including for two reserve team matches – but became"} +{"text": " to annoy Napoleon than Mortier had . In assessing the battle and its aftermath , historians have laid the blame and credit for its outcome not only on Mortier and Gazan : \" Napoleon , aware of Mortier 's danger and his own culpability for it , vented his frustration on Murat , whom he unjustly accused of abandoning Mortier for the empty glory of riding through Vienna . \"\n\nAfter the victory at Austerlitz , Napoleon dispersed the VIII Corps and reassigned Mortier . However disappointed he may have been with Mortier , Napoleon was pleased with Gazan 's performance . As recognition of his conduct in what the French called \" the immortal Battle of Dürenstein \" , Gazan received the Officer 's Grand Cross of the Legion of Honor .\n\nThe loss of Schmitt was a significant blow to the Austrian military organization . Called out of retirement for this specific task , he was one of their most experienced general staff officers , other than the Archduke Charles . From the summer of 1796 until his retirement in 1800 he had been Chief of the Quartermaster General Staff of the Army , the Lower Rhine , the Rhine and the Army of Germany . Furthermore , he was a trusted member of Archduke Charles ' staff . He had helped to design several of Charles ' more important victories at Emmendingen , Schliengen , the sieges at Kehl and Hünigen , the battles at Ostrach and Stockach , and the northern Swiss Campaign of 1799 that included battles at Winterthur and Zürich . An experienced officer and excellent tactician , he might well have made a more effective Chief of the Quartermaster General Staff of the Coalition Army at the Battle of Austerlitz than his eventual replacement , Franz von Weyrother . In Schmitt 's absence Weyrother , the architect of the Austrian catastrophe at Hohenlinden in 1800 , was chosen to develop the general battle plan of Coalition action at Austerlitz . Schmitt , undoubtedly a far better tactician than Weyrother , and possessed of superior training and mapping skills , would have developed a more realistic Coalition plan for Austerlitz . Schmitt 's presence would probably not have been enough to turn that defeat into a victory , but it would have mitigated the magnitude of the Coalition 's losses ; Austerlitz was considered one of Napoleon 's finest triumphs .\n\nIn the broader picture , despite the important major naval engagements , the outcome of the War of the Third Coalition was determined on the Continent , predominantly in"} +{"text": " floating batteries were deployed in 1855 as a supplement to the wooden steam battle fleet in the Crimean War . The role of the battery was to assist unarmored mortar and gunboats bombarding shore fortifications . The French used three of their ironclad batteries ( Lave , Tonnante and Dévastation ) in 1855 against the defenses at the Battle of Kinburn on the Black Sea , where they were effective against Russian shore defences . They would later be used again during the Italian war in the Adriatic in 1859 . The British floating batteries Glatton and Meteor arrived too late to participate to the action at Kinburn . The British planned to use theirs in the Baltic Sea against the well @-@ fortified naval base at Kronstadt .\n\nThe batteries have a claim to the title of the first ironclad warships but they were capable of only 4 knots ( 7 km / h ) under their own power : they operated under their own power at the Battle of Kinburn , but had to be towed for long range transit . They were also arguably marginal to the work of the navy . The brief success of the floating ironclad batteries convinced France to begin work on armored warships for their battlefleet .\n\nBy the end of the 1850s it was clear that France was unable to match British building of steam warships , and to regain the strategic initiative a dramatic change was required . The result was the first ocean @-@ going ironclad , the Gloire , begun in 1857 and launched in 1859 .\n\nGloire 's wooden hull was modelled on that of a steam ship of the line , reduced to one deck , sheathed in iron plates 4 @.@ 5 inches ( 110 mm ) thick . She was propelled by a steam engine , driving a single screw propeller for a speed of 13 knots ( 24 km / h ) . She was armed with thirty @-@ six 6 @.@ 4 @-@ inch ( 160 mm ) rifled guns . France proceeded to construct 16 ironclad warships , including two more sister ships to Gloire , and the only two @-@ decked broadside ironclads ever built , Magenta and Solférino .\n\nThe Royal Navy had not been keen to sacrifice its advantage in steam ships of the line , but was determined that the first British ironclad would outmatch the French ships in every respect , particularly"} +{"text": " had offered Lesnar a contract , but on August 2 , 2005 , WWE 's official website reported that Lesnar had withdrawn from any involvement with the company . The lawsuit began to enter settlement talks on September 21 , but did not get solved .\n\nOn January 14 , 2006 , Judge Christopher Droney stated that unless WWE gave him a good argument between then and the 25th , he would rule in favor of Lesnar , giving him a summary judgment . This would have enabled Lesnar to work anywhere , immediately . WWE was later granted a deadline postponement . On April 24 , WWE announced on WWE.com that both parties had reached a settlement . On June 12 , a federal judge dismissed the case at the request of both legal parties .\n\nLesnar returned to the WWE on April 2 , 2012 , on Raw , as a heel by confronting and delivering an F @-@ 5 to John Cena . The following week on Raw , general manager John Laurinaitis revealed that he signed Lesnar to bring \" legitimacy \" back to the WWE and become the \" new face of the WWE \" . Laurinaitis also announced that Lesnar would face Cena at Extreme Rules with the Extreme Rules stipulation later added to the match . Lesnar was dominant throughout the match until Cena punched Lesnar in the face with a steel chain wrapped around his fist . Cena then delivered the Attitude Adjustment to Lesnar onto steel steps and Lesnar lost the match .\n\nThe following night on Raw , WWE 's Chief Operating Officer Triple H refused to give in to Lesnar 's unreasonable contract demands ( which included being given his own personal jet and having Raw renamed to Monday Night Raw Starring Brock Lesnar ) , resulting in Lesnar attacking him and breaking his arm with a Kimura lock in storyline . The next week on Raw , Paul Heyman made his return as Lesnar 's legal representative and claimed that Lesnar was quitting WWE . He later announced a lawsuit against WWE for breach of contract . At No Way Out in June , Triple H challenged Lesnar , who was not present , to a match at SummerSlam which Lesnar refused . Stephanie McMahon would later goad Heyman into accepting the match on Lesnar 's behalf on July 23 at Raw 1000 . On August 19 , at SummerSlam , Lesnar defeated Triple H in a No Disqualification match via submission after once again breaking his arm in storyline . The following night on Raw , Lesnar declared himself the new \" King of Kings \" and said that he would depart from WWE after his victory over Triple H , stating that he had conquered everything in the company .\n\nLesnar returned on the January 28 , 2013 episode of Raw , confronting Vince McMahon who was about to fire Heyman , and Despite Heyman 's pleas , Lesnar hit McMahon with an F @-@ 5 , breaking McMahon 's pelvis in storyline . The following week , during The Miz 's MizTV talk show , Raw Managing Supervisor Vickie Guerrero revealed herself as the one who signed Lesnar to a new contract to impress McMahon . On the February 25 episode of Raw , Lesnar once again attempted to attack McMahon , only to get into a brawl with the returning Triple H , which resulted in Lesnar legitimately having his head split open and requiring eighteen stitches . The following week , Triple H issued a challenge to Lesnar , requesting a rematch with him at WrestleMania 29 , which Lesnar accepted but only after Triple signed a contract and Lesnar named the stipulation . The following week , after Triple H signed the contract and assaulted Heyman , the stipulation was revealed as No Holds Barred with Triple H 's career on the line . Lesnar ended up losing the match after Triple H hit him with a Pedigree onto the steel steps . On the April 15 episode of Raw , Lesnar attacked 3MB ( Heath Slater , Drew McIntyre , and Jinder Mahal ) before Heyman challenged Triple H to face Lesnar in a steel cage match at Extreme Rules , which Triple H accepted the following week . On May 19 at the pay @-@ per @-@ view , after interference from Heyman , he defeated Triple H and ended their feud . Lesnar returned on the June 17 episode of Raw , attacking Heyman 's fellow client CM Punk with an F @-@ 5 . Despite the accusations from Punk , Heyman claimed that he was not behind Lesnar 's attack on him . However , in July , Heyman turned on Punk , and claimed that Punk could not beat Lesnar , which led to Lesnar making his return and attacking Punk on the July 15 episode of Raw . The following week on Raw , Punk challenged Lesnar to a match at SummerSlam , where Lesnar defeated Punk in a no disqualification match .\n\nOn the December 30 episode of Raw , Lesnar returned with Hey"} +{"text": "achitomi for the clade containing all temnospondyls except edopoids and dendrerpetontids . Below is the cladogram from Schoch 's analysis :\n\nModern amphibians ( frogs , salamanders , and caecilians ) are classified in Lissamphibia . Lissamphibians appear to have arisen in the Permian . Molecular clock estimates place the first lissamphibian in the Late Carboniferous , but the first member of Batrachia ( frogs and salamanders , but not caecilians ) is estimated to have appeared in the Middle Permian using the same technique . Using fossil evidence , there are three main theories for the origin of modern amphibians .\n\nOne is that they evolved from dissorophoid temnospondyls . Another is that they evolved from lepospondyls , most likely the lysorophians . A third hypothesis is that caecilians descended from lepospondyls and frogs and salamanders evolved from dissorophoids .\n\nRecently , the theory that temnospondyls were the ancestors of all lissamphibians has gained wide support . The skull morphology of some small temnospondyls has been compared to those of modern frogs and salamanders , but the presence of bicuspid , pedicellate teeth in small , paedomorphic or immature temnospondyls has been cited as the most convincing argument in favor of the temnospondyl origin of lissamphibians . Seen in lissamphibians and many dissorophoid temnospondyls , pedicellate teeth have calcified tips and bases . During the development of most tetrapods , teeth begin to calcify at their tips . Calcification normally proceeds downward to the base of the tooth , but calcification from the tip stops abruptly in pedicellate teeth . Calcification resumes at the base , leaving an area in the center of the tooth uncalcified . This pattern is seen in living amphibians and fossils .\n\nThe dissorophoid family Amphibamidae is thought to be most closely related to Lissamphibia . In 2008 , an amphibamid called Gerobatrachus hottoni was named from Texas and was nicknamed the \" frogamander \" for its frog @-@ like head and salamander @-@ like body . It was thought to be the most closely related temnospondyl to lissamphibians and was placed as the sister taxon of the group in a phylogenetic analysis . Another species of amphibamid called Doleserpeton annectens is now thought to be even more closely related to lissamphibians . Unlike Gerobatrachus , Doleserpeton was known since 1969 , and the presence of pedicellate teeth in its jaws has led some paleontologists to conclude soon after its naming that it was a relative of modern amphibians . It was first described as a \" protolissamphibian \" , and the specific name annectens means \" connecting \" in reference to its inferred transitional position between temnospondyls and lissamphibians . The structure of its tympanum , a disk @-@ like membrane that functions like an ear drum , is similar to that of frogs and has also been used as evidence for a close relationship . Other features including the shape of the palate and the back of the skull , the short ribs , and the smooth skull surface also point to it being a closer relative of lissamphibians than is Gerobatrachus . Below is a cladogram modified from Sigurdsen and Bolt ( 2010 ) showing the relationships of Gerobatrachus , Doleserpeton , and Lissamphibia :\n\nAlthough the earliest temnospondyls were primarily semiaquatic , they had the ability to feed on land . Later , eryopoids and dissorophoids , some well adapted to terrestrial life , also fed on land . Some eryopoids became better adapted toward life in water , and shifted their diets toward aquatic organisms . The first primarily aquatic feeders were archegosaurs in the Permian . Trematosaurs and capitosaurs became independently aquatic and also returned to this type of feeding .\n\nMost aquatic stereospondyls have flattened heads . When feeding , they probably opened their mouths by lifting their skulls instead of lowering their lower jaws . The jaw mechanics of the plagiosaurid Gerrothorax is well known , and is one of the most highly adapted . Gerrothorax is thought to have lifted its skull to around 50 ° above horizontal through the flexing of the atlanto @-@ occipital joint between the occipital condyles of the skull and the atlas vertebra of the neck . As the skull is raised , the quadrate bone pushes forward and causes the lower jaw to protrude outward . Other stereospondyls probably also lifted their skulls , but they are not as well adapted for such movement . D.M.S. Watson was the first"} +{"text": " 600 BC . It appears unfinished and has suffered severe damage through weathering , making analysis difficult . It had a large headdress that reaches to the eyebrows but any details have been lost through erosion . Straps descend in front of each ear and continue to the base of the monument . The ears are wearing large flattened rings that overlap the straps ; they probably represent jade ornaments of a type that have been recovered in the Olmec region . Although most of the facial detail is lost , the crinkling of the bridge of the nose is still evident , a feature that is common to the frowning expressions of the other Olmec colossal heads .\n\nLa Venta Monument 4 measures 2 @.@ 26 metres ( 7 @.@ 4 ft ) high by 1 @.@ 98 metres ( 6 @.@ 5 ft ) wide and 1 @.@ 86 metres ( 6 @.@ 1 ft ) deep . It weighs 19 @.@ 8 tons . It was found a few metres to the west of Monument 2 and has been moved to the Parque @-@ Museo La Venta . As with the other heads in the group , its archaeological context has been radiocarbon dated to between 1000 and 600 BC . The headdress is elaborate and , although damaged , various details are still discernible . The base of the headdress is formed by three horizontal strips running over the forehead . One side is decorated with a double @-@ disc motif that may have been repeated on the other ; if so , damage to the right side has obliterated any trace of it . The top of the headdress is decorated with the clawed foot of a bird of prey . Either straps or plaits of hair descend on either side of the face , from the headdress to the base of the monument . Only one earspool survives ; it is flat , in the form of a rounded square , and is decorated with a cross motif . The ears have been completely eroded away and the lips are damaged . The surviving features display a frown and creasing around the nose and cheeks . The head displays prominent teeth .\n\nThe two heads at Tres Zapotes , with the La Cobata head , are stylistically distinct from the other known examples . Beatriz de la Fuente views them as a late regional survival of an older tradition while other scholars argue that they are merely the kind of regional variant to be expected in a frontier settlement . These heads are sculpted with relatively simple headdresses ; they have squat , wide proportions and distinctive facial features . The two Tres Zapotes heads are the earliest known stone monuments from the site . The discovery of one of the Tres Zapotes heads in the nineteenth century led to the first archaeological investigations of Olmec culture , carried out by Matthew Stirling in 1938 .\n\nTres Zapotes Monument A ( also known as Tres Zapotes Colossal Head 1 ) was the first colossal head to be found , discovered by accident in the middle of the nineteenth century , 1 kilometre ( 0 @.@ 62 mi ) to the north of the modern village of Tres Zapotes . After its discovery it remained half @-@ buried until it was excavated by Matthew Stirling in 1939 . At some point it was moved to the plaza of the modern village , probably in the early 1960s . It has since been moved to the Museo Comunitario de Tres Zapotes . Monument A stands 1 @.@ 47 metres ( 4 @.@ 8 ft ) tall ; it measures 1 @.@ 5 metres ( 4 @.@ 9 ft ) wide by 1 @.@ 45 metres ( 4 @.@ 8 ft ) deep , and is estimated to weigh 7 @.@ 8 tons . The head is sculpted with a simple headdress with a wide band that is otherwise unadorned , and wears rectangular ear ornaments that project forwards onto the cheeks . The face is carved with deep creases between the cheeks and the nose and around the mouth ; the forehead is creased into a frown . The upper lip has suffered recent damage , with the left portion flaking away .\n\nTres Zapotes Monument Q ( also known as the Nestape Head and Tres Zapotes Colossal Head 2 ) measures 1 @.@ 45 metres ( 4 @.@ 8 ft ) high by 1 @.@ 34 metres ( 4 @.@ 4 ft ) wide by 1 @.@ 26 metres ( 4 @.@ 1 ft ) deep and weighs 8 @.@ 5 tons . Its exact date of discovery is unknown but is estimated to have been some time in the 1940s , when it was struck by machinery being used to clear vegetation from Nestape hill . Monument Q was the eleventh colossal head to be discovered . It was moved to the plaza of Santiago Tuxtla in 1951 and remains there to this day . Monument Q was first"} +{"text": "inus , an 11th @-@ century monk .\n\nDio Chrysostom\n\nDio Chrysostom ( c . 40 – 120 ) , a Greek philosopher and historian , wrote the Roman people were very happy with Nero and would have allowed him to rule indefinitely . They longed for his rule once he was gone and embraced imposters when they appeared :\n\nIndeed the truth about this has not come out even yet ; for so far as the rest of his subjects were concerned , there was nothing to prevent his continuing to be Emperor for all time , seeing that even now everybody wishes he were still alive . And the great majority do believe that he still is , although in a certain sense he has died not once but often along with those who had been firmly convinced that he was still alive .\n\nEpictetus\n\nEpictetus ( c . 55 – 135 ) was the slave to Nero 's scribe Epaphroditos . He makes a few passing negative comments on Nero 's character in his work , but makes no remarks on the nature of his rule . He describes Nero as a spoiled , angry and unhappy man .\n\nJosephus\n\nThe historian Josephus ( c . 37 – 100 ) , while calling Nero a tyrant , was also the first to mention bias against Nero . Of other historians , he said :\n\nBut I omit any further discourse about these affairs ; for there have been a great many who have composed the history of Nero ; some of which have departed from the truth of facts out of favour , as having received benefits from him ; while others , out of hatred to him , and the great ill @-@ will which they bore him , have so impudently raved against him with their lies , that they justly deserve to be condemned . Nor do I wonder at such as have told lies of Nero , since they have not in their writings preserved the truth of history as to those facts that were earlier than his time , even when the actors could have no way incurred their hatred , since those writers lived a long time after them .\n\nLucan\n\nThough more of a poet than historian , Lucanus ( c . 39 – 65 ) has one of the kindest accounts of Nero 's rule . He writes of peace and prosperity under Nero in contrast to previous war and strife . Ironically , he was later involved in a conspiracy to overthrow Nero and was executed .\n\nPhilostratus\n\nPhilostratus II \" the Athenian \" ( c . 172 – 250 ) spoke of Nero in the Life of Apollonius Tyana ( Books 4 – 5 ) . Though he has a generally bad or dim view of Nero , he speaks of others ' positive reception of Nero in the East .\n\nPliny the Elder\n\nThe history of Nero by Pliny the Elder ( c . 24 – 79 ) did not survive . Still , there are several references to Nero in Pliny 's Natural Histories . Pliny has one of the worst opinions of Nero and calls him an \" enemy of mankind . \"\n\nPlutarch\n\nPlutarch ( c . 46 – 127 ) mentions Nero indirectly in his account of the Life of Galba and the Life of Otho . Nero is portrayed as a tyrant , but those that replace him are not described as better .\n\nSeneca the Younger\n\nIt is not surprising that Seneca ( c . 4 BC – 65 ) , Nero 's teacher and advisor , writes very well of Nero .\n\nSuetonius\n\nSuetonius ( c . 69 – 130 ) was a member of the equestrian order , and he was the head of the department of the imperial correspondence . While in this position , Suetonius started writing biographies of the emperors , accentuating the anecdotal and sensational aspects .\n\nTacitus\n\nThe Annals by Tacitus ( c . 56 – 117 ) is the most detailed and comprehensive history on the rule of Nero , despite being incomplete after the year 66 . Tacitus described the rule of the Julio @-@ Claudian emperors as generally unjust . He also thought that existing writing on them was unbalanced :\n\nThe histories of Tiberius , Caius , Claudius and Nero , while they were in power , were falsified through terror , and after their death were written under the irritation of a recent hatred .\n\nTacitus was the son of a procurator , who married into the elite family of Agricola . He entered his political life as a senator after Nero 's death and , by Tacitus ' own admission , owed much to Nero 's rivals . Realising that this bias may be apparent to others , Tacitus protests that his writing is true .\n\nGirolamo Cardano\n\nIn 1562 Girolamo Cardano published in Basel his Encomium Neronis , which was one of the first historical references of the Modern era to portray Nero in a"} +{"text": " Together with the rest of the Philippines , Manila lies entirely within the tropics . Its proximity to the equator means that the temperature range is very small , rarely going below 20 ° C ( 68 ° F ) or above 38 ° C ( 100 ° F ) . Temperature extremes have ranged from 14 @.@ 5 ° C ( 58 @.@ 1 ° F ) on January 11 , 1914 to 38 @.@ 6 ° C ( 101 @.@ 5 ° F ) on May 7 , 1915 .\n\nHumidity levels are usually very high all year round . Manila has a distinct dry season from December through May , and a relatively lengthy wet season that covers the remaining period with slightly cooler temperatures . In the rainy season it rarely rains all day but the rainfall is very heavy during short periods . Typhoons usually occur from June to September .\n\nDue to industrial waste and automobiles , Manila suffers from air pollution , affecting 98 % of the population . Annually , the air pollution causes more than 4 @,@ 000 deaths . Ermita is Manila 's most air polluted district due to open dump sites and industrial waste . According to a report in 2003 , The Pasig River is one of the most polluted rivers in the world with 150 tons of domestic waste and 75 tons of industrial waste dumped daily .\n\nAnnually , Manila is hit with 6 to 7 typhoons creating floods . In 2009 , Typhoon Ketsana struck Philippines . In the aftermath of Typhoon Ketsana , the lack of infrastructure led to one of the worst floodings in the Philippines and creating a significant amount of pollution . Following the aftermath of Typhoon Ketsana , the city began to dredge its rivers and improve its drainage network . The Pasig River Rehabilitation Commission is in charge of cleaning up the Pasig River and tributaries for transportation , recreation and tourism purposes . Rehabilitation efforts have resulted in the creation of parks along the riverside , along with stricter pollution controls .\n\nManila has architecturally significant buildings in a wide range of styles spanning distinct historical and cultural periods . Architectural styles reflect American , Spanish , Chinese , and Malay influences . Prominet Filipino architects such as Antonio Toledo , Felipe Roxas , Juan M. Arellano and Tomás Mapúa have designed significant buildings in Manila such as churches , government offices , theaters , mansions , schools and universities .\n\nManila is known for its distinct Art Deco theaters which are designed by National Artists such as Juan Nakpil and Pablo Antonio . The historic Escolta Street in Binondo features many buildings of neo @-@ classical and beaux @-@ arts architectural style , many of which were designed by prominent Filipino architects during the American Rule in the 1920s to the late 1930s . Many architects , artists , historians and heritage advocacy groups are pushing for the revival of Escolta Street , which was once the premier street of the Philippines .\n\nUnfortunately , much of Manila 's prewar and Spanish colonial architecture was destroyed during World War II . Reconstruction took place afterwards , replacing the destroyed historic Spanish @-@ era buildings with modern ones , erasing much of the city 's character . Some buildings destroyed by the war have been reconstructed , such as the Old Legislative Building ( National Museum ) , Ayuntamiento de Manila ( Bureau of the Treasury ) and the currently under construction San Ignacio Church ( Museo de Intramuros ) . Plans have been laid out to rehabilitate several neglected historic buildings and places such as Plaza Del Carmen , San Sebastian Church and the Manila Metropolitan Theater and soon Spanish @-@ era shops and houses in Quiapo , Binondo , and San Nicolas will be restored to its former splendor , as a part of a movement to restore Manila to its former glory .\n\nSince Manila is prone to earthquakes , the Spanish colonial architects invented the style called Earthquake Baroque which the churches and government buildings during the Spanish colonial period adopted . As a result , succeeding earthquakes of the 18th and 19th centuries barely affected Manila , although it did periodically level the surrounding area . Modern buildings in and around Manila are designed or have retrofitted to withstand an 8 @.@ 2 magnitude quake in accordance to the country 's building code .\n\nManila is composed of fourteen districts according to Republic Act No. 409 , otherwise known as the Revised Charter of the City of Manila . Two were later added , which are Santa Mesa ( partitioned off from Sampaloc ) and San Andres ( partitioned off from Santa Ana ) .\n\nThe city has 896 barangays that are known by sequential numbers instead of names . These barangays are further group into 100 zones for administrative and municipal purposes .\n\nThe headquarters of the Philippine Coast Guard is located at the South Harbor in Port Area near Intramuros and Ermita . The Philippine Navy on the other hand has its headquarters in Naval Station Jose Andrada located along Roxas Boulevard in Malate . Furthermore , the AFP Joint Task Force @-@ National Capital Region was created in 2012 to ensure peace and stability in Metro Manila , of which Manila is a part . It bears the same functions of the deactivated National Capital Regional Command , although it operates on a much smaller size than its predecessor .\n\nThere are an estimated 4 million slum dwellers living in Manila as of 2014 .\n\nAccording to the 2015 census , the population of the city was 1 @,@ 780 @,@ 148 , making it the second most populous city in the Philippines .\n\nManila is the most densely populated city in the world with 43 @,@ 079 inhabitants per km2 . District 6 is listed as being the most dense with 68 @,@ 266 inhabitants per km2 , followed by District 1 with 64 @,@ 936 and District 2 with 64 @,@ 710 , respectively . District 5 is the least densely populated area with 19 @,@ 235 .\n\nManila 's population density dwarfs that of Kolkata ( 27 @,@ 774 inhabitants per km2 ) , Mumbai ( 22 @,@ 937 inhabitants per km2 ) , Paris ( 20 @,@ 164 inhabitants per km2 ) , Dhaka ( 19 @,@ 447 inhabitants per km2 ) , Shanghai ( 16 @,@ 364 inhabitants per km2 , with its most dense district , Nanshi , having a density of 56 @,@ 785 inhabitants per km2 ) , and Tokyo ( 10 @,@ 087 inhabitants per km2 ) .\n\nThe vernacular language is Filipino , based mostly on the Tagalog of surrounding areas , and this Manila form of speaking Tagalog has essentially become the lingua franca of the Philippines , having spread throughout the archipelago through mass media and entertainment . Meanwhile , English is the language most widely used in education , business , and heavily in everyday usage throughout the Metro Manila region and the Philippines itself .\n\nA number of older residents can still speak basic Spanish , which used to be a mandatory subject in the curriculum of Philippine universities and colleges , and many children of Japanese Filipino , Indian Filipino , and other migrants or expatriates also speak their parents ' languages at home , aside from English and / or Filipino for everyday use . Minnan Chinese ( known as Lannang @-@ oe ) is spoken by the city 's Chinese @-@ Filipino community .\n\nThe city is a major center for commerce , banking and finance , retailing , transportation , tourism , real estate , new media as well as traditional media , advertising , legal services , accountancy , insurance , theater , fashion , and the arts in the Philippines .\n\nThe Cities and Municipalities Competitiveness Index , published by the National Competitiveness Council of the Philippines , ranks the cities , municipalities and provinces of the country according to their economic dynamism , government efficiency and infrastructure . Manila placed third in the Highly Urbanized City ( HUC ) category . Previously , Manila was the country 's most competitive city in 2015 , making it the best place to live in and do business .\n\nThe Port of Manila is the largest seaport in the Philippines , making it the premier international shipping gateway to the country . The Philippine Ports Authority is government agency responsible to oversee the operation and management of the ports . The International Container Terminal Services Inc. cited by the Asian Development Bank as one of the top five major maritime terminal operators in the world has its headquarters and main operations on the ports of Manila . Another port operator , the Asian Terminal Incorporated , has its corporate office and main operations in the Manila South Harbor and its container depository located in Santa Mesa .\n\nBinondo , the oldest and one of the largest Chinatown in the world , was the center of commerce and business activities in the city . Numerous residential and office skyscrapers are found within its medieval streets . Plans to make the Chinatown area into a business process outsourcing ( BPO ) hub progresses and is aggressively pursued by the city government of Manila . 30 buildings are already identified to be converted into BPO offices . These buildings are mostly located along the Escolta Street of Binondo , which are all unoccupied and can be converted into offices .\n\nDivisoria in Tondo is dubbed as the \" shopping mecca of the Philippines \" . Numerous shopping malls are located in this place , which sells products and goods at bargain price . Small vendors occupies several roads that causes pedestrian and vehicular traffic . A famous landmark in Divisoria is the Tutuban Center , a large shopping mall that is a part of the Philippine National Railways ' Main Station . It attracts 1 million people every month , but is expected to add another 400 @,@ 000 people when the LRT @-@ 2 West Extension is constructed , making it Manila 's busiest"} +{"text": " size and weight of guns also meant a movement away from the ships mounting many guns broadside , in the manner of a ship @-@ of @-@ the @-@ line , towards a handful of guns in turrets for all @-@ round fire .\n\nFrom the 1860s to the 1880s many naval designers believed that the development of the ironclad meant that the ram was again the most important weapon in naval warfare . With steam power freeing ships from the wind , and armor making them invulnerable to shellfire , the ram seemed to offer the opportunity to strike a decisive blow .\n\nThe scant damage inflicted by the guns of Monitor and Virginia at Battle of Hampton Roads and the spectacular but lucky success of the Austrian flagship SMS Erzherzog Ferdinand Max sinking the Italian Re d 'Italia at Lissa gave strength to the ramming craze . From the early 1870s to early 1880s most British naval officers thought that guns were about to be replaced as the main naval armament by the ram . Those who noted the tiny number of ships that had actually been sunk by ramming struggled to be heard .\n\nThe revival of ramming had a significant effect on naval tactics . Since the 17th century the predominant tactic of naval warfare had been the line of battle , where a fleet formed a long line to give it the best fire from its broadside guns . This tactic was totally unsuited to ramming , and the ram threw fleet tactics into disarray . The question of how an ironclad fleet should deploy in battle to make best use of the ram was never tested in battle , and if it had been , combat might have shown that rams could only be used against ships which were already stopped dead in the water .\n\nThe ram finally fell out of favour in the 1880s , as the same effect could be achieved with a torpedo , with less vulnerability to quick @-@ firing guns .\n\nThe armament of ironclads tended to become concentrated in a small number of powerful guns capable of penetrating the armor of enemy ships at range ; calibre and weight of guns increased markedly to achieve greater penetration . Throughout the ironclad era navies also grappled with the complexities of rifled versus smoothbore guns and breech @-@ loading versus muzzle @-@ loading .\n\nHMS Warrior carried a mixture of 110 @-@ pounder 7 inch ( 180 mm ) breech @-@ loading rifles and more traditional 68 @-@ pounder smoothbore guns . Warrior highlighted the challenges of picking the right armament ; the breech @-@ loaders she carried , designed by Sir William Armstrong , were intended to be the next generation of heavy armament for the Royal Navy , but were shortly withdrawn from service .\n\nBreech @-@ loading guns seemed to offer important advantages . A breech @-@ loader could be reloaded without moving the gun , a lengthy process particularly if the gun then needed to be re @-@ aimed . The Warrior 's Armstrong guns also had the virtue of being lighter than an equivalent smoothbore and , because of their rifling , more accurate . Nonetheless , the design was rejected because of problems which plagued breech @-@ loaders for decades .\n\nThe weakness of the breech @-@ loader was the obvious problem of sealing the breech . All guns are powered by the explosive conversion of gunpowder into gas . This explosion propels the shot or shell out of the front of the gun , but also imposes great stresses on the gun @-@ barrel . If the breech — which experiences some of the greatest forces in the gun — is not entirely secure , then there is a risk that either gas will discharge through the breech or that the breech will break . This in turn reduces the muzzle velocity of the weapon and can also endanger the gun crew . The Warrior 's Armstrong guns suffered from both problems ; the shells were unable to penetrate the 4 @.@ 5 in ( 118 mm ) armor of Gloire , while sometimes the screw which closed the breech flew backwards out of the gun on firing . Similar problems were experienced with the breech @-@ loading guns which became standard in the French and German navies .\n\nThese problems influenced the British to equip ships with muzzle @-@ loading weapons of increasing power until the 1880s . After a brief introduction of 100 @-@ pounder or 9 @.@ 5 @-@ inch ( 240 mm ) smoothbore Somerset Gun , which weighed 6 @.@ 5 tons ( 6 @.@ 6 t ) , the Admiralty introduced 7 @-@ inch ( 178 mm ) rifled guns , weighing 7 tons . These were followed by a series of increasingly mammoth weapons — guns weighing 12 , 25 , 25 , 38 and finally 81 tons , with calibre increasing from 8 @-@ inch ( 203 mm ) to 16 @-@ inch ( 406 mm ) .\n\nThe decision to retain muzzle @-@ loaders until the 1880s has been criticised by historians . However , at least until the late 1870s , the British muzzle @-@ loaders had superior performance in terms of both range and rate of fire than the French and Prussian breech @-@ loaders , which suffered from the same problems as had the first Armstrong guns .\n\nFrom 1875 onwards , the balance between breech- and muzzle @-@ loading changed . Captain de Bange invented a method of reliably sealing a breech , adopted by the French in 1873 . Just as compellingly , the growing size of naval guns made muzzle @-@ loading much more complicated . With guns of such size there was no prospect of hauling in the gun for re @-@ loading , or even re @-@ loading by hand , and complicated hydraulic systems were required for re @-@ loading the gun outside the turret without exposing the crew to enemy fire . In 1882 , the 81 @-@ ton , 16 @-@ inch ( 406 mm ) guns of HMS Inflexible fired only once every 11 minutes while bombarding Alexandria during the Urabi Revolt . The 100 @-@ ton , 450 mm ( 17 @.@ 72 inch ) guns of Caio Duilio could each fire a round every 15 minutes .\n\nIn the Royal Navy , the switch to breech @-@ loaders was finally made in 1879 ; as well as the significant advantages in terms of performance , opinion was swayed by an explosion on board HMS Thunderer caused by a gun being double @-@ loaded , a problem which could only happen with a muzzle @-@ loading gun .\n\nThe calibre and weight of guns could only increase so far . The larger the gun , the slower it would be to load , the greater the stresses on the ship 's hull , and the less the stability of the ship . The size of the gun peaked in the 1880s , with some of the heaviest calibres of gun ever used at sea . HMS Benbow carried two 16 @.@ 25 @-@ inch ( 413 mm ) breech @-@ loading guns , each weighing 110 tons — no British battleship would ever carry guns as large . The Italian 450 mm ( 17 @.@ 72 inch ) guns would be larger than any gun fitted to a battleship until the 18 @.@ 1 @-@ inch ( 460 mm ) armament of the Japanese Yamato class of World War II . One consideration which became more acute was that even from the original Armstrong models , following the Crimean War , range and hitting power far exceeded simple accuracy , especially at sea where the slightest roll or pitch of the vessel as ' floating weapons @-@ platform ' could negate the advantage of rifling . American ordnance experts accordingly preferred smoothbore monsters whose round shot could at least ' skip ' along the surface of the water . Actual effective combat ranges , they had learned during the Civil War , were comparable to those in the Age of Sail — though a vessel could now be smashed to pieces in only a few rounds . Smoke and the general chaos of battle only added to the problem . As a result , many naval engagements in the ' Age of the Ironclad ' were still fought at ranges within easy eyesight of their targets , and well below the maximum reach of their ships ' guns .\n\nAnother method of increasing firepower was to vary the projectile fired or the nature of the propellant . Early ironclads used black powder , which expanded rapidly after combustion ; this meant cannons had relatively short barrels , to prevent the barrel itself slowing the shell . The sharpness of the black powder explosion also meant that guns were subjected to extreme stress . One important step was to press the powder into pellets , allowing a slower , more controlled explosion and a longer barrel . A further step forward was the introduction of chemically different brown powder which combusted more slowly again . It also put less stress on the insides of the barrel , allowing guns to last longer and to be manufactured to tighter tolerances .\n\nThe development of smokeless powder , based on nitroglycerine or nitrocellulose , by the French inventor Paul Vielle in 1884 was a further step allowing smaller charges of propellant with longer barrels . The guns of the pre @-@ Dreadnought battleships of the 1890s tended to be smaller in calibre compared to the ships of the 1880s , most often 12 in ( 305 mm ) , but progressively grew in length of barrel , making use of improved propellants to gain greater muzzle velocity .\n\nThe nature of the projectiles also changed during the ironclad period . Initially , the best armor @-@ piercing projectile was a"} diff --git a/src/lab5/main.py b/src/lab5/main.py deleted file mode 100644 index a46a571b..00000000 --- a/src/lab5/main.py +++ /dev/null @@ -1,38 +0,0 @@ -import torch -import torch.nn.functional as F -from modules import Qwen3Config, Qwen3ForCausalLM -from transformers import AutoTokenizer -import time -import os -from utils.utils import generate_text - - -def main(): - MODEL_PATH = "/ocean/model/Qwen3-8B" - PROMPT = "为什么西瓜比苹果甜？" # 示例提示词，添加标记以触发思考模式 - max_length = 200 - - device = "cuda" if torch.cuda.is_available() else "cpu" - - model = Qwen3ForCausalLM.from_pretrained( - model_path=MODEL_PATH, - device=device, - torch_dtype=torch.bfloat16, - ) - tokenizer = AutoTokenizer.from_pretrained( - MODEL_PATH, trust_remote_code=True, use_fast=False - ) - model.eval() - - generate_text( - model=model, - tokenizer=tokenizer, - prompt=PROMPT, - max_length=max_length, - temperature=0, - device=device, - ) - - -if __name__ == "__main__": - main() diff --git a/src/lab5/modules/__init__.py b/src/lab5/modules/__init__.py deleted file mode 100644 index f3c61d6f..00000000 --- a/src/lab5/modules/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -Qwen3 模型模块 -""" - -from .config import Qwen3Config -from .model import Qwen3Model, Qwen3ForCausalLM -from .attention import Qwen3Attention, RMSNorm -from .mlp import Qwen3MLP -from .layer import Qwen3DecoderLayer -from .rope import RotaryEmbedding, apply_rotary_pos_emb - -__all__ = [ - "Qwen3Config", - "Qwen3Model", - "Qwen3ForCausalLM", - "Qwen3Attention", - "RMSNorm", - "Qwen3MLP", - "Qwen3DecoderLayer", - "RotaryEmbedding", - "apply_rotary_pos_emb", -] diff --git a/src/lab5/modules/attention.py b/src/lab5/modules/attention.py deleted file mode 100644 index ea76bb2a..00000000 --- a/src/lab5/modules/attention.py +++ /dev/null @@ -1,151 +0,0 @@ -""" -Multi-Head Attention Implementation -""" - -import math -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Optional, Tuple - -from .rope import RotaryEmbedding, apply_rotary_pos_emb -from .config import Qwen3Config - - -class RMSNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6, device=None, dtype=None): - """ - Qwen3RMSNorm is equivalent to T5LayerNorm - """ - super().__init__() - self.weight = nn.Parameter(torch.ones(hidden_size, device=device, dtype=dtype)) - self.variance_epsilon = eps - - def forward(self, hidden_states): - input_dtype = hidden_states.dtype - hidden_states = hidden_states.to(torch.float32) - variance = hidden_states.pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) - return self.weight * hidden_states.to(input_dtype) - - def extra_repr(self): - return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" - - -class Qwen3Attention(nn.Module): - """Qwen3 Multi-Head Attention Module""" - - def __init__( - self, - config: Qwen3Config, - layer_idx: Optional[int] = None, - device=None, - dtype: Optional[torch.dtype] = None, - ): - super().__init__() - self.config = config - self.layer_idx = layer_idx - - self.hidden_size = config.hidden_size - self.num_heads = config.num_attention_heads - self.head_dim = self.hidden_size // self.num_heads - self.num_key_value_heads = config.num_key_value_heads - self.num_key_value_groups = self.num_heads // self.num_key_value_heads - self.max_position_embeddings = config.max_position_embeddings - self.rope_theta = config.rope_theta - - if dtype is None: - dtype = torch.float32 - - if (self.head_dim * self.num_heads) != self.hidden_size: - raise ValueError( - f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" - f" and `num_heads`: {self.num_heads})." - ) - - # Linear projection layers - self.q_proj = nn.Linear( - self.hidden_size, - self.num_heads * self.head_dim, - bias=False, - device=device, - dtype=dtype, - ) - self.k_proj = nn.Linear( - self.hidden_size, - self.num_key_value_heads * self.head_dim, - bias=False, - device=device, - dtype=dtype, - ) - self.v_proj = nn.Linear( - self.hidden_size, - self.num_key_value_heads * self.head_dim, - bias=False, - device=device, - dtype=dtype, - ) - self.o_proj = nn.Linear( - self.num_heads * self.head_dim, - self.hidden_size, - bias=False, - device=device, - dtype=dtype, - ) - - # Rotary position encoding - self.rotary_emb = RotaryEmbedding( - self.head_dim, - max_position_embeddings=self.max_position_embeddings, - base=self.rope_theta, - device=device, - ) - - self.q_norm = RMSNorm( - self.head_dim, eps=config.rms_norm_eps, device=device, dtype=dtype - ) - self.k_norm = RMSNorm( - self.head_dim, eps=config.rms_norm_eps, device=device, dtype=dtype - ) - - def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): - """Reshape tensor for multi-head attention""" - return tensor.view(bsz, seq_len, -1, self.head_dim).transpose(1, 2).contiguous() - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - # 请参照实验文档 2.8 节完成 Self-Attention 模块的实现 - - # TODO: 根据 1-3 步完成 Q, K, V 的准备 - query_states = hidden_states # 这里只是一个占位符，实际代码需要替换 - key_states = hidden_states - value_states = hidden_states - - # TODO: 应用 RoPE - kv_seq_len = key_states.shape[-2] - cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) - - # TODO: 根据第 5 步完成 GQA 机制的实现 - - # TODO: 根据第 6 步完成注意力计算 - - # TODO: 根据第 7 步完成输出投影 - - # 这里的 hidden_states 只是一个占位符，需要替换为实际的注意力输出 - return hidden_states - - def _repeat_kv(self, hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: - """ - This is equivalent to torch.repeat_interleave(x, dim=1, repeats=n_rep). - The hidden_states.repeat(...).view(...) is more optimized when using torch.compile. - """ - batch, num_key_value_heads, slen, head_dim = hidden_states.shape - if n_rep == 1: - return hidden_states - hidden_states = hidden_states[:, :, None, :, :].expand( - batch, num_key_value_heads, n_rep, slen, head_dim - ) - return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) diff --git a/src/lab5/modules/config.py b/src/lab5/modules/config.py deleted file mode 100644 index 68e2c2ef..00000000 --- a/src/lab5/modules/config.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -Qwen3 模型配置类 -""" - -import json -from dataclasses import dataclass -from typing import Optional, Dict, Any - - -@dataclass -class Qwen3Config: - """Qwen3 模型配置类""" - - # 模型架构参数 - vocab_size: int = 152064 - hidden_size: int = 4096 - intermediate_size: int = 11008 - num_hidden_layers: int = 32 - num_attention_heads: int = 32 - num_key_value_heads: int = 32 - max_position_embeddings: int = 32768 - - # 激活函数和归一化 - hidden_act: str = "silu" - rms_norm_eps: float = 1e-6 - rope_theta: float = 1000000.0 - - # 注意力机制参数 - attention_dropout: float = 0.0 - - # 其他参数 - initializer_range: float = 0.02 - tie_word_embeddings: bool = False - use_cache: bool = True - - # 8B 模型特定参数 - def __post_init__(self): - """初始化后处理，确保参数一致性""" - if self.num_key_value_heads is None: - self.num_key_value_heads = self.num_attention_heads - - self.head_dim = self.hidden_size // self.num_attention_heads - - @classmethod - def from_dict(cls, config_dict: Dict[str, Any]) -> "Qwen3Config": - """从字典创建配置""" - return cls(**config_dict) - - @classmethod - def from_json(cls, json_path: str) -> "Qwen3Config": - """从JSON文件加载配置""" - with open(json_path, "r", encoding="utf-8") as f: - config_dict = json.load(f) - return cls.from_dict(config_dict) - - def to_dict(self) -> Dict[str, Any]: - """转换为字典""" - return { - "vocab_size": self.vocab_size, - "hidden_size": self.hidden_size, - "intermediate_size": self.intermediate_size, - "num_hidden_layers": self.num_hidden_layers, - "num_attention_heads": self.num_attention_heads, - "num_key_value_heads": self.num_key_value_heads, - "max_position_embeddings": self.max_position_embeddings, - "hidden_act": self.hidden_act, - "rms_norm_eps": self.rms_norm_eps, - "rope_theta": self.rope_theta, - "attention_dropout": self.attention_dropout, - "initializer_range": self.initializer_range, - "tie_word_embeddings": self.tie_word_embeddings, - "use_cache": self.use_cache, - } diff --git a/src/lab5/modules/layer.py b/src/lab5/modules/layer.py deleted file mode 100644 index 5a2a5c58..00000000 --- a/src/lab5/modules/layer.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -Qwen3 Transformer 层实现 -""" - -import torch -import torch.nn as nn -from typing import Optional, Tuple - -from .config import Qwen3Config -from .attention import Qwen3Attention, RMSNorm -from .mlp import Qwen3MLP - - -class Qwen3DecoderLayer(nn.Module): - """Qwen3 Decoder 层""" - - def __init__( - self, - config: Qwen3Config, - layer_idx: int, - device: Optional[str] = None, - dtype: Optional[torch.dtype] = None, - ): - super().__init__() - self.hidden_size = config.hidden_size - - device = "cpu" if device is None else device - dtype = torch.float32 if dtype is None else dtype - - # 注意力模块 - self.self_attn = Qwen3Attention( - config=config, layer_idx=layer_idx, device=device, dtype=dtype - ) - - # 前馈网络 - self.mlp = Qwen3MLP(config, device=device, dtype=dtype) - - # 层归一化 - self.input_layernorm = RMSNorm( - config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype - ) - self.post_attention_layernorm = RMSNorm( - config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype - ) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - """ - Args: - hidden_states: 输入隐藏状态 [batch_size, seq_len, hidden_size] - attention_mask: 注意力掩码 - position_ids: 位置编码索引 - """ - residual = hidden_states - hidden_states = self.input_layernorm(hidden_states) - - hidden_states = self.self_attn( - hidden_states=hidden_states, - attention_mask=attention_mask, - ) - - hidden_states = residual + hidden_states - - residual = hidden_states - hidden_states = self.post_attention_layernorm(hidden_states) - hidden_states = self.mlp(hidden_states) - - hidden_states = residual + hidden_states - - return hidden_states diff --git a/src/lab5/modules/mlp.py b/src/lab5/modules/mlp.py deleted file mode 100644 index 8e27ce1f..00000000 --- a/src/lab5/modules/mlp.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Feed Forward Network (FFN) 实现 -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Optional -from .config import Qwen3Config - - -class Qwen3MLP(nn.Module): - """Qwen3 前馈网络模块""" - - def __init__( - self, config: Qwen3Config, device=None, dtype: Optional[torch.dtype] = None - ): - super().__init__() - self.config = config - self.hidden_size = config.hidden_size - self.intermediate_size = config.intermediate_size - - if dtype is None: - dtype = torch.float32 - - # 门控投影和上投影 - self.gate_proj = nn.Linear( - self.hidden_size, - self.intermediate_size, - bias=False, - device=device, - dtype=dtype, - ) - self.up_proj = nn.Linear( - self.hidden_size, - self.intermediate_size, - bias=False, - device=device, - dtype=dtype, - ) - # 下投影 - self.down_proj = nn.Linear( - self.intermediate_size, - self.hidden_size, - bias=False, - device=device, - dtype=dtype, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - # TODO: 根据实验文档 2.9 节完成 FFN 模块的实现 - - return x diff --git a/src/lab5/modules/model.py b/src/lab5/modules/model.py deleted file mode 100644 index ee1e5ac4..00000000 --- a/src/lab5/modules/model.py +++ /dev/null @@ -1,562 +0,0 @@ -""" -Qwen3 模型实现 -""" - -import torch -import torch.nn as nn -from typing import Optional, Tuple, List, Union, Dict, Any -import math -import os -import json -from safetensors import safe_open -from pathlib import Path - -from .config import Qwen3Config -from .layer import Qwen3DecoderLayer -from .attention import RMSNorm - - -def _make_causal_mask( - input_ids_shape: torch.Size, - dtype: torch.dtype, - device: torch.device, - past_key_values_length: int = 0, -): - """ - 创建因果注意力掩码 - """ - bsz, tgt_len = input_ids_shape - mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) - mask_cond = torch.arange(mask.size(-1), device=device) - mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) - mask = mask.to(dtype) - - if past_key_values_length > 0: - mask = torch.cat( - [ - torch.zeros( - tgt_len, past_key_values_length, dtype=dtype, device=device - ), - mask, - ], - dim=-1, - ) - return mask[None, None, :, :].expand( - bsz, 1, tgt_len, tgt_len + past_key_values_length - ) - - -def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): - """ - 扩展注意力掩码 - """ - bsz, src_len = mask.size() - tgt_len = tgt_len if tgt_len is not None else src_len - - expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) - - inverted_mask = 1.0 - expanded_mask - - return inverted_mask.masked_fill( - inverted_mask.to(torch.bool), torch.finfo(dtype).min - ) - - -class Qwen3Model(nn.Module): - """ - Qwen3 基础模型 - """ - - def __init__( - self, - config: Qwen3Config, - device: Optional[str] = None, - dtype: Optional[torch.dtype] = None, - ): - super().__init__() - self.config = config - self.padding_idx = config.vocab_size - 1 # 通常使用最后一个token作为padding - self.vocab_size = config.vocab_size - - if device is None: - device = "cpu" - if dtype is None: - dtype = torch.float32 - - # Token 嵌入 - self.embed_tokens = nn.Embedding( - config.vocab_size, - config.hidden_size, - self.padding_idx, - device=device, - dtype=dtype, - ) - - # Transformer 层 - self.layers = nn.ModuleList( - [ - Qwen3DecoderLayer(config, layer_idx, device=device, dtype=dtype) - for layer_idx in range(config.num_hidden_layers) - ] - ) - - # 最终层归一化 - self.norm = RMSNorm( - config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype - ) - - @classmethod - def from_pretrained( - cls, - model_path: str, - device: Optional[str] = None, - torch_dtype: Optional[torch.dtype] = None, - ) -> "Qwen3Model": - """ - 从预训练模型加载权重 - - Args: - model_path: 模型路径（包含config.json和权重文件） - device: 设备 - torch_dtype: 数据类型 - """ - model_path_obj = Path(model_path) - - # 加载配置 - config_path = model_path_obj / "config.json" - if not config_path.exists(): - raise FileNotFoundError(f"配置文件未找到: {config_path}") - - with open(config_path, "r", encoding="utf-8") as f: - config_dict = json.load(f) - - # 创建Qwen3Config - qwen3_config = cls._convert_hf_config_to_qwen3(config_dict) - - # 创建模型 - model = cls(qwen3_config, device=device, dtype=torch_dtype) - - # 加载权重 - model._load_hf_weights(model_path_obj, torch_dtype) - - return model - - @staticmethod - def _convert_hf_config_to_qwen3(hf_config: Dict[str, Any]) -> Qwen3Config: - """将HuggingFace配置转换为Qwen3Config""" - # HF配置到Qwen3配置的映射 - config_mapping = { - "vocab_size": "vocab_size", - "hidden_size": "hidden_size", - "intermediate_size": "intermediate_size", - "num_hidden_layers": "num_hidden_layers", - "num_attention_heads": "num_attention_heads", - "num_key_value_heads": "num_key_value_heads", - "max_position_embeddings": "max_position_embeddings", - "hidden_act": "hidden_act", - "rms_norm_eps": "rms_norm_eps", - "rope_theta": "rope_theta", - "attention_dropout": "attention_dropout", - "initializer_range": "initializer_range", - "tie_word_embeddings": "tie_word_embeddings", - "use_cache": "use_cache", - } - - qwen3_config_dict = {} - for hf_key, qwen3_key in config_mapping.items(): - if hf_key in hf_config: - qwen3_config_dict[qwen3_key] = hf_config[hf_key] - - return Qwen3Config(**qwen3_config_dict) - - def _load_hf_weights( - self, model_path: Path, torch_dtype: Optional[torch.dtype] = None - ): - """加载HuggingFace格式的权重""" - # 查找权重文件 - safetensor_files = list(model_path.glob("*.safetensors")) - if not safetensor_files: - raise FileNotFoundError(f"在 {model_path} 中未找到 .safetensors 文件") - - # 创建权重映射 - weight_map = self._create_weight_mapping() - - # 加载所有权重 - all_weights = {} - for safetensor_file in safetensor_files: - with safe_open(safetensor_file, framework="pt", device="cpu") as f: - for key in f.keys(): - tensor = f.get_tensor(key) - if torch_dtype is not None: - tensor = tensor.to(torch_dtype) - all_weights[key] = tensor - - # 创建状态字典 - state_dict = {} - for hf_key, model_key in weight_map.items(): - if hf_key in all_weights: - state_dict[model_key] = all_weights[hf_key] - else: - print(f"警告: 权重 {hf_key} 未在模型文件中找到") - - # 加载权重到模型 - missing_keys, unexpected_keys = self.load_state_dict(state_dict, strict=False) - - if missing_keys: - print(f"缺失的权重: {missing_keys}") - if unexpected_keys: - print(f"意外的权重: {unexpected_keys}") - - def _create_weight_mapping(self) -> Dict[str, str]: - """创建HuggingFace权重名称到模型权重名称的映射""" - weight_map = {} - - # 嵌入层 - weight_map["model.embed_tokens.weight"] = "embed_tokens.weight" - - # Transformer层 - for i in range(self.config.num_hidden_layers): - layer_prefix = f"model.layers.{i}" - model_prefix = f"layers.{i}" - - # 注意力层 - weight_map[f"{layer_prefix}.self_attn.q_proj.weight"] = ( - f"{model_prefix}.self_attn.q_proj.weight" - ) - weight_map[f"{layer_prefix}.self_attn.k_proj.weight"] = ( - f"{model_prefix}.self_attn.k_proj.weight" - ) - weight_map[f"{layer_prefix}.self_attn.v_proj.weight"] = ( - f"{model_prefix}.self_attn.v_proj.weight" - ) - weight_map[f"{layer_prefix}.self_attn.o_proj.weight"] = ( - f"{model_prefix}.self_attn.o_proj.weight" - ) - - # MLP层 - weight_map[f"{layer_prefix}.mlp.gate_proj.weight"] = ( - f"{model_prefix}.mlp.gate_proj.weight" - ) - weight_map[f"{layer_prefix}.mlp.up_proj.weight"] = ( - f"{model_prefix}.mlp.up_proj.weight" - ) - weight_map[f"{layer_prefix}.mlp.down_proj.weight"] = ( - f"{model_prefix}.mlp.down_proj.weight" - ) - - # 层归一化 - weight_map[f"{layer_prefix}.input_layernorm.weight"] = ( - f"{model_prefix}.input_layernorm.weight" - ) - weight_map[f"{layer_prefix}.post_attention_layernorm.weight"] = ( - f"{model_prefix}.post_attention_layernorm.weight" - ) - - # 最终层归一化 - weight_map["model.norm.weight"] = "norm.weight" - - return weight_map - - def get_input_embeddings(self): - return self.embed_tokens - - def set_input_embeddings(self, value): - self.embed_tokens = value - - def post_init(self): - """初始化权重""" - self.apply(self._init_weights) - - def _init_weights(self, module): - """初始化权重""" - std = self.config.initializer_range - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=std) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=std) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - - def forward( - self, - input_ids: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - ) -> Union[Tuple, torch.Tensor]: - - # 获取输入形状 - batch_size, seq_length = input_ids.shape - - seq_length_with_past = seq_length - past_key_values_length = 0 - - # 嵌入 - inputs_embeds = self.embed_tokens(input_ids) - - # 注意力掩码 - if attention_mask is None: - attention_mask = torch.ones( - (batch_size, seq_length_with_past), - dtype=torch.bool, - device=inputs_embeds.device, - ) - - attention_mask = self._prepare_decoder_attention_mask( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - ) - - hidden_states = inputs_embeds - # 通过每个decoder层 - for idx, decoder_layer in enumerate(self.layers): - hidden_states = decoder_layer(hidden_states, attention_mask=attention_mask) - - hidden_states = self.norm(hidden_states) - - return hidden_states - - def _prepare_decoder_attention_mask( - self, attention_mask, input_shape, inputs_embeds, past_key_values_length - ): - """准备decoder的注意力掩码""" - # 创建因果掩码 - combined_attention_mask = None - if input_shape[-1] > 1: - combined_attention_mask = _make_causal_mask( - input_shape, - inputs_embeds.dtype, - device=inputs_embeds.device, - past_key_values_length=past_key_values_length, - ) - - if attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - expanded_attn_mask = _expand_mask( - attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] - ).to(inputs_embeds.device) - combined_attention_mask = ( - expanded_attn_mask - if combined_attention_mask is None - else expanded_attn_mask + combined_attention_mask - ) - - return combined_attention_mask - - -class Qwen3ForCausalLM(nn.Module): - """ - 用于因果语言建模的Qwen3模型 - """ - - def __init__( - self, - config: Qwen3Config, - device: Optional[str] = None, - dtype: Optional[torch.dtype] = None, - ): - super().__init__() - self.model = Qwen3Model(config, device=device, dtype=dtype) - self.vocab_size = config.vocab_size - self.lm_head = nn.Linear( - config.hidden_size, - config.vocab_size, - bias=False, - device=device, - dtype=dtype, - ) - - @classmethod - def from_pretrained( - cls, - model_path: str, - device: Optional[str] = None, - torch_dtype: Optional[torch.dtype] = None, - ) -> "Qwen3ForCausalLM": - """ - 从预训练模型加载权重 - - Args: - model_path: 模型路径（包含config.json和权重文件） - device: 设备 - torch_dtype: 数据类型 - """ - model_path_obj = Path(model_path) - - # 加载配置 - config_path = model_path_obj / "config.json" - if not config_path.exists(): - raise FileNotFoundError(f"配置文件未找到: {config_path}") - - with open(config_path, "r", encoding="utf-8") as f: - config_dict = json.load(f) - - # 创建Qwen3Config - qwen3_config = Qwen3Model._convert_hf_config_to_qwen3(config_dict) - - # 创建模型 - model = cls(qwen3_config, device=device, dtype=torch_dtype) - - # 加载权重 - model._load_hf_weights(model_path_obj, torch_dtype) - - return model - - def _load_hf_weights( - self, model_path: Path, torch_dtype: Optional[torch.dtype] = None - ): - """加载HuggingFace格式的权重（包含lm_head）""" - # 查找权重文件 - safetensor_files = list(model_path.glob("*.safetensors")) - if not safetensor_files: - raise FileNotFoundError(f"在 {model_path} 中未找到 .safetensors 文件") - - # 创建权重映射 - weight_map = self._create_weight_mapping() - - # 加载所有权重 - all_weights = {} - for safetensor_file in safetensor_files: - with safe_open(safetensor_file, framework="pt", device="cpu") as f: - for key in f.keys(): - tensor = f.get_tensor(key) - if torch_dtype is not None: - tensor = tensor.to(torch_dtype) - all_weights[key] = tensor - - # 创建状态字典 - state_dict = {} - for hf_key, model_key in weight_map.items(): - if hf_key in all_weights: - state_dict[model_key] = all_weights[hf_key] - else: - print(f"警告: 权重 {hf_key} 未在模型文件中找到") - - # 加载权重到模型 - missing_keys, unexpected_keys = self.load_state_dict(state_dict, strict=False) - - if missing_keys: - print(f"缺失的权重: {missing_keys}") - if unexpected_keys: - print(f"意外的权重: {unexpected_keys}") - - def _create_weight_mapping(self) -> Dict[str, str]: - """创建HuggingFace权重名称到模型权重名称的映射（包含lm_head）""" - weight_map = {} - - # 嵌入层 - weight_map["model.embed_tokens.weight"] = "model.embed_tokens.weight" - - # Transformer层 - for i in range(self.model.config.num_hidden_layers): - layer_prefix = f"model.layers.{i}" - model_prefix = f"model.layers.{i}" - - # 注意力层 - weight_map[f"{layer_prefix}.self_attn.q_proj.weight"] = ( - f"{model_prefix}.self_attn.q_proj.weight" - ) - weight_map[f"{layer_prefix}.self_attn.k_proj.weight"] = ( - f"{model_prefix}.self_attn.k_proj.weight" - ) - weight_map[f"{layer_prefix}.self_attn.v_proj.weight"] = ( - f"{model_prefix}.self_attn.v_proj.weight" - ) - weight_map[f"{layer_prefix}.self_attn.o_proj.weight"] = ( - f"{model_prefix}.self_attn.o_proj.weight" - ) - weight_map[f"{layer_prefix}.self_attn.q_norm.weight"] = ( - f"{model_prefix}.self_attn.q_norm.weight" - ) - weight_map[f"{layer_prefix}.self_attn.k_norm.weight"] = ( - f"{model_prefix}.self_attn.k_norm.weight" - ) - - # MLP层 - weight_map[f"{layer_prefix}.mlp.gate_proj.weight"] = ( - f"{model_prefix}.mlp.gate_proj.weight" - ) - weight_map[f"{layer_prefix}.mlp.up_proj.weight"] = ( - f"{model_prefix}.mlp.up_proj.weight" - ) - weight_map[f"{layer_prefix}.mlp.down_proj.weight"] = ( - f"{model_prefix}.mlp.down_proj.weight" - ) - - # 层归一化 - weight_map[f"{layer_prefix}.input_layernorm.weight"] = ( - f"{model_prefix}.input_layernorm.weight" - ) - weight_map[f"{layer_prefix}.post_attention_layernorm.weight"] = ( - f"{model_prefix}.post_attention_layernorm.weight" - ) - - # 最终层归一化 - weight_map["model.norm.weight"] = "model.norm.weight" - - # 语言模型头 - weight_map["lm_head.weight"] = "lm_head.weight" - - return weight_map - - def get_input_embeddings(self): - return self.model.embed_tokens - - def set_input_embeddings(self, value): - self.model.embed_tokens = value - - def get_output_embeddings(self): - return self.lm_head - - def set_output_embeddings(self, new_embeddings): - self.lm_head = new_embeddings - - def set_decoder(self, decoder): - self.model = decoder - - def get_decoder(self): - return self.model - - def post_init(self): - """初始化权重""" - self.apply(self._init_weights) - - def _init_weights(self, module): - """初始化权重""" - std = self.model.config.initializer_range - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=std) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=std) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - - def forward( - self, - input_ids: torch.LongTensor, - attention_mask: Optional[torch.Tensor] = None, - labels: Optional[torch.LongTensor] = None, - ) -> Union[Tuple, torch.Tensor]: - - # decoder输出包含(dec_features, layer_state, dec_hidden, dec_attn) - hidden_states = self.model(input_ids=input_ids, attention_mask=attention_mask) - - logits = self.lm_head(hidden_states) - logits = logits.float() - - loss = None - if labels is not None: - # 计算损失 - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - loss_fct = nn.CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.vocab_size) - shift_labels = shift_labels.view(-1) - shift_labels = shift_labels.to(shift_logits.device) - loss = loss_fct(shift_logits, shift_labels) - - return (loss, logits) if loss is not None else logits diff --git a/src/lab5/modules/rope.py b/src/lab5/modules/rope.py deleted file mode 100644 index abf51e03..00000000 --- a/src/lab5/modules/rope.py +++ /dev/null @@ -1,99 +0,0 @@ -""" -RoPE (Rotary Position Embedding) 实现 -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Tuple, Optional - - -def rotate_half(x: torch.Tensor) -> torch.Tensor: - """将张量的后半部分旋转到前半部分，并取负号""" - x1 = x[..., : x.shape[-1] // 2] - x2 = x[..., x.shape[-1] // 2 :] - return torch.cat((-x2, x1), dim=-1) - - -def apply_rotary_pos_emb( - q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor -) -> Tuple[torch.Tensor, torch.Tensor]: - """ - 应用旋转位置编码到查询和键张量 - - Args: - q: 查询张量 [batch_size, num_heads, seq_len, head_dim] - k: 键张量 [batch_size, num_heads, seq_len, head_dim] - cos: 余弦值 [1, 1, seq_len, head_dim] - sin: 正弦值 [1, 1, seq_len, head_dim] - - Returns: - 应用RoPE后的查询和键张量 - """ - q_embed = (q * cos) + (rotate_half(q) * sin) - k_embed = (k * cos) + (rotate_half(k) * sin) - return q_embed, k_embed - - -class RotaryEmbedding(nn.Module): - """旋转位置编码模块""" - - def __init__( - self, - dim: int, - max_position_embeddings: int = 2048, - base: float = 10000.0, - device=None, - ): - super().__init__() - self.dim = dim - self.max_position_embeddings = max_position_embeddings - self.base = base - - # 计算频率的倒数 - inv_freq = 1.0 / ( - self.base - ** (torch.arange(0, self.dim, 2, device=device).float() / self.dim) - ) - self.register_buffer("inv_freq", inv_freq, persistent=False) - - # 缓存 cos 和 sin 值 - self._set_cos_sin_cache(max_position_embeddings) - - def _set_cos_sin_cache(self, seq_len: int): - """设置余弦和正弦值的缓存""" - self.max_seq_len_cached = seq_len - t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device).type_as( - self.inv_freq - ) - - freqs = torch.outer(t, self.inv_freq) - emb = torch.cat((freqs, freqs), dim=-1) - - self.register_buffer("cos_cached", emb.cos(), persistent=False) - self.register_buffer("sin_cached", emb.sin(), persistent=False) - - def forward( - self, x: torch.Tensor, seq_len: Optional[int] = None - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - 前向传播 - - Args: - x: 输入张量 - seq_len: 序列长度 - - Returns: - cos 和 sin 张量 - """ - if seq_len is None: - seq_len = x.shape[-2] - - # 如果序列长度超过缓存长度，重新计算 - if seq_len > self.max_seq_len_cached: - self._set_cos_sin_cache(seq_len) - - return ( - self.cos_cached[:seq_len].to(dtype=x.dtype), - self.sin_cached[:seq_len].to(dtype=x.dtype), - ) diff --git a/src/lab5/pyproject.toml b/src/lab5/pyproject.toml new file mode 100644 index 00000000..39f89447 --- /dev/null +++ b/src/lab5/pyproject.toml @@ -0,0 +1,31 @@ +[project] +name = "lab5" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "tilelang>=0.1.12", + "torch>=2.13.0", + "torchvision>=0.28.0", + "tqdm>=4.68.4", + "transformers>=5.13.0", + "triton>=3.7.1", +] + +[project.scripts] +hpc101-difftest = "hpc101_infer.difftest:main" +hpc101-quantize = "hpc101_infer.quantization.cli:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/hpc101_infer"] + +[dependency-groups] +dev = [ + "pytest>=9.1.1", + "pytest-cov>=7.1.0", +] diff --git a/src/lab5/results/bf16-public-quality.json b/src/lab5/results/bf16-public-quality.json new file mode 100644 index 00000000..33c72589 --- /dev/null +++ b/src/lab5/results/bf16-public-quality.json @@ -0,0 +1,57 @@ +{ + "schema_version": 2, + "model": "checkpoints/gemma-4-12b", + "linear_backend": "bf16", + "dataset": "datasets/quality_public.jsonl", + "dataset_sha256": "61f9a48a4b7ee0c0bf9cb415cbcf257ee0d18666787b58dc21f25abdfc99290f", + "chunk_size": 128, + "bucket_lengths": [ + 128, + 256, + 512, + 1024, + 2048 + ], + "nll_sum": 47134.04501360642, + "token_count": 20418, + "sequence_count": 62, + "mean_nll": 2.3084555301012055, + "perplexity": 10.058877018904823, + "by_bucket": { + "128": { + "nll_sum": 11969.064922064194, + "token_count": 4064, + "sequence_count": 32, + "mean_nll": 2.945143927673276, + "perplexity": 19.013398743787207 + }, + "256": { + "nll_sum": 9916.180838369379, + "token_count": 4080, + "sequence_count": 16, + "mean_nll": 2.430436479992495, + "perplexity": 11.363841086941411 + }, + "512": { + "nll_sum": 9503.518965206691, + "token_count": 4088, + "sequence_count": 8, + "mean_nll": 2.3247355590035936, + "perplexity": 10.22397608985863 + }, + "1024": { + "nll_sum": 7809.134812243028, + "token_count": 4092, + "sequence_count": 4, + "mean_nll": 1.90839071657943, + "perplexity": 6.742229904552721 + }, + "2048": { + "nll_sum": 7936.145475723126, + "token_count": 4094, + "sequence_count": 2, + "mean_nll": 1.9384820409680328, + "perplexity": 6.948195884870821 + } + } +} diff --git a/src/lab5/scripts/evaluate_quality.py b/src/lab5/scripts/evaluate_quality.py new file mode 100644 index 00000000..ee49ffef --- /dev/null +++ b/src/lab5/scripts/evaluate_quality.py @@ -0,0 +1,344 @@ +from __future__ import annotations + +import argparse +import hashlib +import json +import math +from collections import defaultdict +from collections.abc import Mapping, Sequence +from pathlib import Path +from typing import Any + +import torch +from tqdm.auto import tqdm + +from hpc101_infer import EngineConfig, InferenceEngine +from hpc101_infer.evaluation import evaluate_nll, load_quality_token_ids + +DEFAULT_BUCKETS = (128, 256, 512, 1024, 2048) +DTYPES = { + "bfloat16": torch.bfloat16, + "float16": torch.float16, + "float32": torch.float32, +} + + +def _dataset_sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _load_reference(path: Path) -> dict[str, object]: + reference = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(reference, dict): + raise ValueError("reference must contain a JSON object") + return reference + + +def _reference_mean_nll(reference: Mapping[str, object]) -> float: + if "mean_nll" in reference: + return float(reference["mean_nll"]) + if "nll_sum" in reference and "token_count" in reference: + return float(reference["nll_sum"]) / int(reference["token_count"]) + raise ValueError("reference must contain mean_nll or nll_sum and token_count") + + +def _positive_int(value: str) -> int: + parsed = int(value) + if parsed <= 0: + raise argparse.ArgumentTypeError("bucket lengths must be positive") + return parsed + + +def _normalize_buckets(values: Sequence[int]) -> tuple[int, ...]: + if not values: + raise ValueError("at least one bucket length is required") + if len(set(values)) != len(values): + raise ValueError("bucket lengths must not contain duplicates") + return tuple(sorted(values)) + + +def _group_token_sequences( + token_sequences: Sequence[Sequence[int]], + buckets: Sequence[int], + *, + max_sequence_length: int, +) -> dict[int, list[Sequence[int]]]: + allowed = set(buckets) + grouped: dict[int, list[Sequence[int]]] = defaultdict(list) + for sequence_number, token_ids in enumerate(token_sequences, start=1): + sequence_length = len(token_ids) + if sequence_length > max_sequence_length: + raise ValueError( + f"quality sequence {sequence_number} has {sequence_length} tokens, " + f"exceeding --max-sequence-length={max_sequence_length}" + ) + if sequence_length not in allowed: + raise ValueError( + f"quality sequence {sequence_number} has unexpected length " + f"{sequence_length}; configured buckets are {tuple(buckets)}" + ) + grouped[sequence_length].append(token_ids) + if not grouped: + raise ValueError("quality dataset must contain at least one sequence") + return dict(sorted(grouped.items())) + + +def _evaluate_bucketed_nll( + engine: InferenceEngine, + grouped_sequences: Mapping[int, Sequence[Sequence[int]]], + *, + chunk_size: int, + show_progress: bool = False, +) -> tuple[dict[str, float | int], dict[str, dict[str, float | int]]]: + by_bucket: dict[str, dict[str, float | int]] = {} + nll_sum = 0.0 + token_count = 0 + sequence_count = 0 + + total_sequences = sum(len(sequences) for sequences in grouped_sequences.values()) + with tqdm( + total=total_sequences, + desc="quality evaluation", + unit="record", + disable=not show_progress, + dynamic_ncols=True, + ) as progress: + + def track_progress(sequences: Sequence[Sequence[int]]): + for token_ids in sequences: + yield token_ids + progress.update(1) + + for bucket, sequences in sorted(grouped_sequences.items()): + progress.set_postfix(bucket=bucket) + bucket_result = evaluate_nll( + engine, + track_progress(sequences), + chunk_size=chunk_size, + ) + by_bucket[str(bucket)] = bucket_result.to_dict() + nll_sum += bucket_result.nll_sum + token_count += bucket_result.token_count + sequence_count += bucket_result.sequence_count + + mean_nll = nll_sum / token_count + global_result: dict[str, float | int] = { + "nll_sum": nll_sum, + "token_count": token_count, + "sequence_count": sequence_count, + "mean_nll": mean_nll, + "perplexity": math.exp(mean_nll), + } + return global_result, by_bucket + + +def _bucket_mapping(result: Mapping[str, object], name: str) -> Mapping[str, object]: + by_bucket = result.get("by_bucket") + if not isinstance(by_bucket, Mapping): + raise ValueError(f"{name} must contain a by_bucket object") + return by_bucket + + +def _validate_reference( + reference: Mapping[str, object], + result: Mapping[str, object], +) -> None: + for key in ( + "dataset_sha256", + "chunk_size", + "token_count", + "sequence_count", + "bucket_lengths", + ): + if key in reference and reference[key] != result[key]: + raise ValueError( + f"reference {key}={reference[key]!r} does not match " + f"current result {result[key]!r}" + ) + + reference_buckets = _bucket_mapping(reference, "reference") + result_buckets = _bucket_mapping(result, "current result") + if set(reference_buckets) != set(result_buckets): + raise ValueError( + f"reference buckets {sorted(reference_buckets)} do not match " + f"current buckets {sorted(result_buckets)}" + ) + + for bucket, current_value in result_buckets.items(): + reference_value = reference_buckets[bucket] + if not isinstance(reference_value, Mapping) or not isinstance( + current_value, Mapping + ): + raise ValueError(f"bucket {bucket} must contain result objects") + for key in ("token_count", "sequence_count"): + if reference_value.get(key) != current_value.get(key): + raise ValueError( + f"reference bucket {bucket} {key}=" + f"{reference_value.get(key)!r} does not match current " + f"{current_value.get(key)!r}" + ) + + +def _add_reference_metrics( + result: dict[str, object], + reference: Mapping[str, object], + *, + reference_path: Path, +) -> None: + reference_mean_nll = _reference_mean_nll(reference) + delta_nll = float(result["mean_nll"]) - reference_mean_nll + result.update( + { + "reference": str(reference_path), + "reference_mean_nll": reference_mean_nll, + "delta_nll": delta_nll, + "perplexity_ratio": math.exp(delta_nll), + } + ) + + reference_buckets = _bucket_mapping(reference, "reference") + result_buckets = _bucket_mapping(result, "current result") + for bucket, current_value in result_buckets.items(): + reference_value = reference_buckets[bucket] + if not isinstance(reference_value, Mapping) or not isinstance( + current_value, dict + ): + raise ValueError(f"bucket {bucket} must contain result objects") + bucket_reference_mean_nll = _reference_mean_nll(reference_value) + bucket_delta_nll = float(current_value["mean_nll"]) - bucket_reference_mean_nll + current_value.update( + { + "reference_mean_nll": bucket_reference_mean_nll, + "delta_nll": bucket_delta_nll, + "perplexity_ratio": math.exp(bucket_delta_nll), + } + ) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Evaluate teacher-forcing NLL through InferenceEngine." + ) + parser.add_argument("--model", type=Path, required=True) + parser.add_argument("--dataset", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--reference", type=Path) + parser.add_argument("--max-delta-nll", type=float) + parser.add_argument("--device", default="cuda") + parser.add_argument("--dtype", choices=DTYPES, default="bfloat16") + parser.add_argument( + "--linear-backend", + choices=("bf16", "int4_reference"), + default="int4_reference", + ) + parser.add_argument("--max-sequence-length", type=int, default=2048) + parser.add_argument("--chunk-size", type=int, default=128) + parser.add_argument( + "--buckets", + nargs="+", + type=_positive_int, + default=DEFAULT_BUCKETS, + metavar="LENGTH", + help="accepted token sequence lengths", + ) + parser.add_argument("--limit", type=int) + parser.add_argument("--text-key", default="text") + parser.add_argument( + "--add-special-tokens", + action=argparse.BooleanOptionalAction, + default=True, + ) + parser.add_argument( + "--progress", + action=argparse.BooleanOptionalAction, + default=True, + help="show record-level evaluation progress", + ) + return parser + + +def main() -> None: + parser = build_parser() + args = parser.parse_args() + + if args.max_delta_nll is not None and args.reference is None: + parser.error("--max-delta-nll requires --reference") + try: + buckets = _normalize_buckets(args.buckets) + except ValueError as error: + parser.error(str(error)) + if buckets[-1] > args.max_sequence_length: + parser.error( + f"largest bucket {buckets[-1]} exceeds " + f"--max-sequence-length={args.max_sequence_length}" + ) + + engine = InferenceEngine.from_pretrained( + str(args.model), + EngineConfig( + dtype=DTYPES[args.dtype], + device=args.device, + max_batch_size=1, + scheduler_batch_size=1, + max_sequence_length=args.max_sequence_length, + linear_backend=args.linear_backend, + ), + ) + token_sequences = load_quality_token_ids( + args.dataset, + engine.tokenizer, + text_key=args.text_key, + limit=args.limit, + add_special_tokens=args.add_special_tokens, + ) + grouped_sequences = _group_token_sequences( + token_sequences, + buckets, + max_sequence_length=args.max_sequence_length, + ) + global_result, by_bucket = _evaluate_bucketed_nll( + engine, + grouped_sequences, + chunk_size=args.chunk_size, + show_progress=args.progress, + ) + + result: dict[str, Any] = { + "schema_version": 2, + "model": str(args.model), + "linear_backend": args.linear_backend, + "dataset": str(args.dataset), + "dataset_sha256": _dataset_sha256(args.dataset), + "chunk_size": args.chunk_size, + "bucket_lengths": list(buckets), + **global_result, + "by_bucket": by_bucket, + } + if args.reference is not None: + reference = _load_reference(args.reference) + _validate_reference(reference, result) + _add_reference_metrics( + result, + reference, + reference_path=args.reference, + ) + if args.max_delta_nll is not None: + result["max_delta_nll"] = args.max_delta_nll + result["passed"] = result["delta_nll"] <= args.max_delta_nll + + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text( + json.dumps(result, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8", + ) + print(json.dumps(result, ensure_ascii=False)) + if result.get("passed") is False: + raise SystemExit(1) + + +if __name__ == "__main__": + main() diff --git a/src/lab5/scripts/quantize.py b/src/lab5/scripts/quantize.py new file mode 100644 index 00000000..06a445ab --- /dev/null +++ b/src/lab5/scripts/quantize.py @@ -0,0 +1,5 @@ +from hpc101_infer.quantization.cli import main + + +if __name__ == "__main__": + main() diff --git a/src/lab5/scripts/run_generation_queue.py b/src/lab5/scripts/run_generation_queue.py new file mode 100644 index 00000000..a14c9029 --- /dev/null +++ b/src/lab5/scripts/run_generation_queue.py @@ -0,0 +1,193 @@ +import argparse +import json +import sys +from dataclasses import asdict +from pathlib import Path +from time import perf_counter +from typing import TextIO + +import torch +from tqdm.auto import tqdm + +from hpc101_infer import ( + EngineConfig, + GenerationRequest, + Runner, + InferenceEngine, + SamplingParams, +) + +DTYPES = { + "bfloat16": torch.bfloat16, + "float16": torch.float16, + "float32": torch.float32, +} + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run queued static-batch generation from a JSONL request stream." + ) + parser.add_argument("--model", required=True, help="Model checkpoint directory.") + parser.add_argument( + "--input", + required=True, + help="Input JSONL path, or - to read from stdin.", + ) + parser.add_argument( + "--output", + default="-", + help="Output JSONL path, or - to write to stdout.", + ) + parser.add_argument("--device", default="cuda") + parser.add_argument("--dtype", choices=DTYPES, default="bfloat16") + parser.add_argument("--batch-size", type=int, default=1) + parser.add_argument("--max-sequence-length", type=int, default=4096) + parser.add_argument("--max-new-tokens", type=int, default=32) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument( + "--no-synchronize-metrics", + action="store_true", + help="Do not synchronize the device around individual metric regions.", + ) + parser.add_argument( + "--progress", + action=argparse.BooleanOptionalAction, + default=True, + help="Show request completion progress.", + ) + return parser.parse_args() + + +def open_input(path: str) -> TextIO: + if path == "-": + return sys.stdin + return Path(path).open() + + +def open_output(path: str) -> TextIO: + if path == "-": + return sys.stdout + output_path = Path(path) + output_path.parent.mkdir(parents=True, exist_ok=True) + return output_path.open("w") + + +def count_requests(path: str) -> int | None: + if path == "-": + return None + with Path(path).open() as stream: + return sum(1 for line in stream if line.strip()) + + +def parse_request( + record: object, + *, + default_max_new_tokens: int, +) -> GenerationRequest: + if not isinstance(record, dict): + raise ValueError("each request must be a JSON object") + sampling_record = record.get("sampling_params", {}) + if not isinstance(sampling_record, dict): + raise ValueError("sampling_params must be a JSON object") + stop_token_ids = record.get("stop_token_ids") + if stop_token_ids is not None and not isinstance(stop_token_ids, (list, tuple)): + raise ValueError("stop_token_ids must be a JSON array") + return GenerationRequest( + prompt=record.get("prompt"), + input_ids=record.get("input_ids"), + max_new_tokens=record.get("max_new_tokens", default_max_new_tokens), + stop_token_ids=None if stop_token_ids is None else tuple(stop_token_ids), + sampling_params=SamplingParams(**sampling_record), + ) + + +def iter_requests( + stream: TextIO, + *, + default_max_new_tokens: int, +): + for line_number, line in enumerate(stream, start=1): + if not line.strip(): + continue + try: + record = json.loads(line) + yield parse_request( + record, + default_max_new_tokens=default_max_new_tokens, + ) + except (TypeError, ValueError) as error: + raise ValueError( + f"invalid request on line {line_number}: {error}" + ) from error + + +def main() -> None: + args = parse_args() + config = EngineConfig( + dtype=DTYPES[args.dtype], + device=args.device, + max_batch_size=args.batch_size, + scheduler_batch_size=args.batch_size, + max_sequence_length=args.max_sequence_length, + seed=args.seed, + synchronize_metrics=not args.no_synchronize_metrics, + ) + engine = InferenceEngine.from_pretrained(args.model, config) + runner = Runner(engine) + + input_stream = open_input(args.input) + output_stream = open_output(args.output) + try: + total_requests = count_requests(args.input) if args.progress else None + started = perf_counter() + with tqdm( + total=total_requests, + desc="generation", + unit="request", + disable=not args.progress, + dynamic_ncols=True, + ) as progress: + outputs = runner.run( + iter_requests( + input_stream, + default_max_new_tokens=args.max_new_tokens, + ), + on_completed=progress.update, + ) + elapsed = perf_counter() - started + for request_id, output in enumerate(outputs): + output_stream.write( + json.dumps( + {"request_id": request_id, **asdict(output)}, + ensure_ascii=False, + ) + + "\n" + ) + output_stream.flush() + finally: + if input_stream is not sys.stdin: + input_stream.close() + if output_stream is not sys.stdout: + output_stream.close() + + generated_tokens = sum(output.generated_tokens for output in outputs) + print( + json.dumps( + { + "requests": len(outputs), + "generated_tokens": generated_tokens, + "elapsed_s": elapsed, + "requests_per_s": len(outputs) / elapsed if elapsed else 0.0, + "generated_tokens_per_s": ( + generated_tokens / elapsed if elapsed else 0.0 + ), + "batch_size": args.batch_size, + } + ), + file=sys.stderr, + ) + + +if __name__ == "__main__": + main() diff --git a/src/lab5/simple_test.py b/src/lab5/simple_test.py deleted file mode 100644 index 1c78efb5..00000000 --- a/src/lab5/simple_test.py +++ /dev/null @@ -1,222 +0,0 @@ -import torch -import numpy as np -import gc -import unittest -from transformers import AutoTokenizer, AutoModelForCausalLM -from modules import Qwen3Config, Qwen3ForCausalLM -from utils.utils import generate_text - - -# ANSI颜色代码 -class Colors: - RED = "\033[91m" - GREEN = "\033[92m" - YELLOW = "\033[93m" - BLUE = "\033[94m" - MAGENTA = "\033[95m" - CYAN = "\033[96m" - WHITE = "\033[97m" - BOLD = "\033[1m" - UNDERLINE = "\033[4m" - END = "\033[0m" # 结束颜色 - - -def load_custom_model(): - """只加载自定义模型""" - MODEL_PATH = "/ocean/model/Qwen3-8B" - device = "cuda" if torch.cuda.is_available() else "cpu" - torch.set_default_device(device) - torch_dtype = torch.bfloat16 - - custom_model = Qwen3ForCausalLM.from_pretrained( - model_path=MODEL_PATH, - device=device, - torch_dtype=torch_dtype, - ) - custom_model.eval() - return custom_model, device, torch_dtype - - -def load_baseline_model(): - """只加载基线模型""" - MODEL_PATH = "/ocean/model/Qwen3-8B" - device = "cuda" if torch.cuda.is_available() else "cpu" - torch.set_default_device(device) - torch_dtype = torch.bfloat16 - - baseline_model = AutoModelForCausalLM.from_pretrained( - MODEL_PATH, - torch_dtype=torch_dtype, - trust_remote_code=True, - ) - baseline_model.model.to(device) - baseline_model.eval() - return baseline_model, device, torch_dtype - - -def load_tokenizer(): - """加载tokenizer""" - MODEL_PATH = "/ocean/model/Qwen3-8B" - tokenizer = AutoTokenizer.from_pretrained( - MODEL_PATH, trust_remote_code=True, use_fast=False - ) - return tokenizer - - -def cleanup_model(model): - """清理模型并释放内存""" - if model is not None: - del model - - # 清理GPU缓存 - if torch.cuda.is_available(): - torch.cuda.empty_cache() - torch.cuda.synchronize() - - # 强制垃圾回收 - gc.collect() - - -def generate_with_custom_model(model, tokenizer, prompt, max_length=20, device="cpu"): - """使用自定义模型生成文本""" - return generate_text( - model=model, - tokenizer=tokenizer, - prompt=prompt, - max_length=max_length, - temperature=0, - device=device, - ) - - -def generate_with_baseline_model(model, tokenizer, prompt, max_length=20, device="cpu"): - """使用transformers基线模型生成文本""" - return generate_text( - model=model, - tokenizer=tokenizer, - prompt=prompt, - max_length=max_length, - temperature=0, - device=device, - ) - - -def compare_model_outputs(): - """比较两个模型的输出一致性""" - # 加载tokenizer（两个模型共用） - tokenizer = load_tokenizer() - - # 测试用的提示词 - test_prompts = [ - "Once upon a time in a land far, far away, there lived a dragon who", - "The quick brown fox", - "在一个阳光明媚的早晨", - "To be or not to be", - ] - - max_length = 10 # 较短的生成长度以便快速测试 - results = [] - custom_outputs = [] - baseline_outputs = [] - - # ================ 测试自定义模型 ================ - print(f"{Colors.BLUE}正在加载目标模型...{Colors.END}") - custom_model, device, torch_dtype = load_custom_model() - print(f"{Colors.BLUE}目标模型生成中...{Colors.END}") - for i, prompt in enumerate(test_prompts): - custom_output = generate_with_custom_model( - custom_model, tokenizer, prompt, max_length, device - ) - custom_outputs.append(custom_output) - # 清理自定义模型 - del custom_model - print(f"{Colors.GREEN}目标模型测试完成{Colors.END}") - - # ================ 测试基线模型 ================ - print(f"{Colors.BLUE}正在加载基线模型...{Colors.END}") - baseline_model, device, torch_dtype = load_baseline_model() - print(f"{Colors.BLUE}基线模型生成中...{Colors.END}") - for i, prompt in enumerate(test_prompts): - baseline_output = generate_with_baseline_model( - baseline_model, tokenizer, prompt, max_length, device - ) - baseline_outputs.append(baseline_output) - # 清理基线模型 - cleanup_model(baseline_model) - print(f"{Colors.GREEN}基线模型测试完成{Colors.END}") - - # 比较输出 - print("\n" + Colors.BOLD + "=" * 60 + Colors.END) - print(Colors.BOLD + Colors.MAGENTA + " " * 20 + "测试结果对比" + Colors.END) - print(Colors.BOLD + "=" * 60 + Colors.END) - - for i, prompt in enumerate(test_prompts): - is_consistent = custom_outputs[i] == baseline_outputs[i] - results.append( - { - "prompt": prompt, - "custom_output": custom_outputs[i], - "baseline_output": baseline_outputs[i], - "is_consistent": is_consistent, - } - ) - - # 美化输出 - if is_consistent: - status_icon = "✅" - status_text = f"{Colors.GREEN}通过{Colors.END}" - else: - status_icon = "❌" - status_text = f"{Colors.RED}失败{Colors.END}" - - print(f"\n{Colors.BOLD}测试点 {i+1}: {status_icon} {status_text}{Colors.END}") - print(f"{Colors.CYAN}提示词:{Colors.END} {prompt}") - print(f"{Colors.YELLOW}目标模型:{Colors.END} {custom_outputs[i]}") - print(f"{Colors.YELLOW}基线模型:{Colors.END} {baseline_outputs[i]}") - if not is_consistent: - print(f"{Colors.RED}⚠️ 输出不一致{Colors.END}") - print(Colors.WHITE + "-" * 50 + Colors.END) - - print(f"{Colors.GREEN}所有测试完成{Colors.END}") - - return results - - -def main(): - """主函数""" - print(f"{Colors.BOLD}{Colors.CYAN}开始模型一致性测试...{Colors.END}") - - try: - results = compare_model_outputs() - - print("\n" + Colors.BOLD + "=" * 60 + Colors.END) - print(Colors.BOLD + Colors.MAGENTA + " " * 20 + "最终测试汇总" + Colors.END) - print(Colors.BOLD + "=" * 60 + Colors.END) - - consistent_count = sum(1 for r in results if r["is_consistent"]) - total_count = len(results) - - print( - f"{Colors.BOLD}一致性测试结果: {Colors.CYAN}{consistent_count}/{total_count}{Colors.END} {Colors.BOLD}通过{Colors.END}" - ) - - if consistent_count == total_count: - print( - f"{Colors.GREEN}{Colors.BOLD}🎉 恭喜！所有测试都通过了！模型实现正确！{Colors.END}" - ) - else: - print( - f"{Colors.RED}{Colors.BOLD}⚠️ 有 {total_count - consistent_count} 个测试失败，请检查模型实现{Colors.END}" - ) - - except Exception as e: - print(f"{Colors.RED}测试过程中出现错误: {e}{Colors.END}") - finally: - # 最终清理 - if torch.cuda.is_available(): - torch.cuda.empty_cache() - gc.collect() - - -if __name__ == "__main__": - main() diff --git a/src/lab5/src/hpc101_infer/__init__.py b/src/lab5/src/hpc101_infer/__init__.py new file mode 100644 index 00000000..04e71a03 --- /dev/null +++ b/src/lab5/src/hpc101_infer/__init__.py @@ -0,0 +1,17 @@ +from hpc101_infer.config import EngineConfig +from hpc101_infer.engine import InferenceEngine +from hpc101_infer.quantization.config import QuantizationConfig +from hpc101_infer.runner import CompletedRequest, Runner, RequestHandle +from hpc101_infer.types import GenerationOutput, GenerationRequest, SamplingParams + +__all__ = [ + "CompletedRequest", + "EngineConfig", + "GenerationOutput", + "GenerationRequest", + "Runner", + "InferenceEngine", + "QuantizationConfig", + "RequestHandle", + "SamplingParams", +] diff --git a/src/lab5/src/hpc101_infer/config.py b/src/lab5/src/hpc101_infer/config.py new file mode 100644 index 00000000..4c429152 --- /dev/null +++ b/src/lab5/src/hpc101_infer/config.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from dataclasses import dataclass + +import torch + + +@dataclass(frozen=True) +class EngineConfig: + dtype: torch.dtype = torch.bfloat16 + device: str = "cuda" + max_batch_size: int = 1 + scheduler_batch_size: int = 1 + max_sequence_length: int = 4096 + attention_backend: str = "eager" + linear_backend: str = "bf16" + scheduler_backend: str = "static_batch" + seed: int = 0 + synchronize_metrics: bool = True + + def __post_init__(self) -> None: + if self.max_batch_size <= 0: + raise ValueError("max_batch_size must be positive") + if self.scheduler_batch_size <= 0: + raise ValueError("scheduler_batch_size must be positive") + if self.scheduler_batch_size > self.max_batch_size: + raise ValueError("scheduler_batch_size must not exceed max_batch_size") + if self.max_sequence_length <= 0: + raise ValueError("max_sequence_length must be positive") + if self.attention_backend != "eager": + raise ValueError("only the eager attention backend is implemented") + if self.linear_backend not in {"bf16", "int4_reference"}: + raise ValueError("linear_backend must be bf16 or int4_reference") + if self.scheduler_backend != "static_batch": + raise ValueError("only the static_batch scheduler is implemented") diff --git a/src/lab5/src/hpc101_infer/data/__init__.py b/src/lab5/src/hpc101_infer/data/__init__.py new file mode 100644 index 00000000..3fce8de4 --- /dev/null +++ b/src/lab5/src/hpc101_infer/data/__init__.py @@ -0,0 +1,9 @@ +from hpc101_infer.data.jsonl import iter_jsonl_texts, load_jsonl_texts +from hpc101_infer.data.tokenization import load_token_ids, texts_to_token_ids + +__all__ = [ + "iter_jsonl_texts", + "load_jsonl_texts", + "load_token_ids", + "texts_to_token_ids", +] diff --git a/src/lab5/src/hpc101_infer/data/jsonl.py b/src/lab5/src/hpc101_infer/data/jsonl.py new file mode 100644 index 00000000..0bfae9e7 --- /dev/null +++ b/src/lab5/src/hpc101_infer/data/jsonl.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +import json +from collections.abc import Iterator +from pathlib import Path + + +def iter_jsonl_texts( + path: str | Path, + text_key: str = "text", +) -> Iterator[str]: + path = Path(path) + if not path.is_file(): + raise FileNotFoundError(f"JSONL file not found: {path}") + + with path.open(encoding="utf-8") as handle: + for line_number, line in enumerate(handle, start=1): + if not line.strip(): + continue + try: + record = json.loads(line) + except json.JSONDecodeError as error: + raise ValueError( + f"invalid JSON in {path} at line {line_number}: {error.msg}" + ) from error + if not isinstance(record, dict): + raise ValueError( + f"expected a JSON object in {path} at line {line_number}" + ) + if text_key not in record: + raise ValueError( + f"missing {text_key!r} in {path} at line {line_number}" + ) + text = record[text_key] + if not isinstance(text, str): + raise ValueError( + f"expected {text_key!r} to be a string in {path} " + f"at line {line_number}" + ) + yield text + + +def load_jsonl_texts( + path: str | Path, + text_key: str = "text", + limit: int | None = None, +) -> list[str]: + if limit is not None and limit < 0: + raise ValueError("limit must be non-negative") + if limit == 0: + return [] + + texts: list[str] = [] + for text in iter_jsonl_texts(path, text_key=text_key): + texts.append(text) + if limit is not None and len(texts) >= limit: + break + return texts diff --git a/src/lab5/src/hpc101_infer/data/tokenization.py b/src/lab5/src/hpc101_infer/data/tokenization.py new file mode 100644 index 00000000..e25d2222 --- /dev/null +++ b/src/lab5/src/hpc101_infer/data/tokenization.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from numbers import Integral +from pathlib import Path +from typing import Any, Protocol + +import torch + +from hpc101_infer.data.jsonl import load_jsonl_texts + + +class Tokenizer(Protocol): + def __call__(self, texts: str | list[str], **kwargs: Any) -> Mapping[str, Any]: ... + + +def load_token_ids( + source: str | Path, + tokenizer: Tokenizer, + *, + text_key: str = "text", + limit: int | None = None, + **tokenizer_kwargs: Any, +) -> list[list[int]]: + """Tokenize one JSON object or every record in a JSONL file. + + The outer list always corresponds to input records, including when ``source`` + is a single mapping. No padding is added, so each inner list keeps its natural + sequence length. + """ + if limit is not None and limit < 0: + raise ValueError("limit must be non-negative") + if limit == 0: + return [] + + texts = load_jsonl_texts(source, text_key=text_key, limit=limit) + + token_id_sequences: list[list[int]] = [] + for record_number, text in enumerate(texts, start=1): + encoded = tokenizer(text, **tokenizer_kwargs) + if "input_ids" not in encoded: + raise ValueError("tokenizer output does not contain input_ids") + + input_ids = encoded["input_ids"] + if isinstance(input_ids, torch.Tensor): + input_ids = input_ids.tolist() + if not isinstance(input_ids, Sequence) or isinstance(input_ids, (str, bytes)): + raise ValueError("tokenizer input_ids must be a one-dimensional sequence") + if not input_ids: + raise ValueError( + f"tokenizer returned empty input_ids for record {record_number}" + ) + if any( + not isinstance(token_id, Integral) or isinstance(token_id, bool) + for token_id in input_ids + ): + raise TypeError("tokenizer input_ids must contain only integers") + token_id_sequences.append([int(token_id) for token_id in input_ids]) + return token_id_sequences + + +def texts_to_token_ids( + texts: Sequence[str], + tokenizer: Tokenizer, +) -> torch.Tensor: + if not texts: + raise ValueError("texts must not be empty") + + encoded = tokenizer(list(texts), padding=True, return_tensors="pt") + if "input_ids" not in encoded: + raise ValueError("tokenizer output does not contain input_ids") + + input_ids = torch.as_tensor(encoded["input_ids"]) + if input_ids.ndim != 2: + raise ValueError("tokenizer input_ids must have shape [batch, seq]") + if input_ids.shape[0] != len(texts) or input_ids.shape[1] == 0: + raise ValueError("tokenizer input_ids have an invalid shape") + if input_ids.is_floating_point(): + raise TypeError("tokenizer input_ids must use an integer dtype") + return input_ids.long().contiguous() diff --git a/src/lab5/src/hpc101_infer/engine.py b/src/lab5/src/hpc101_infer/engine.py new file mode 100644 index 00000000..2bd73b24 --- /dev/null +++ b/src/lab5/src/hpc101_infer/engine.py @@ -0,0 +1,423 @@ +"""面向教学的同步推理引擎，显式展示 prefill、decode 和 KV cache 数据流。""" + +from __future__ import annotations + +import math +from time import perf_counter +from typing import Any + +import torch +from torch.nn import functional as F + +from hpc101_infer.config import EngineConfig +from hpc101_infer.models.gemma4 import Gemma4ForCausalLM +from hpc101_infer.models.loader import load_gemma4 +from hpc101_infer.runtime.batch import Batch +from hpc101_infer.runtime.kv_cache import KVCache +from hpc101_infer.runtime.metrics import measure_operation +from hpc101_infer.sampling import Sampler +from hpc101_infer.scheduler import RequestState, create_scheduler +from hpc101_infer.types import ( + DecodeOutput, + GenerationOutput, + GenerationRequest, + PrefillOutput, + RequestMetrics, + ScoreOutput, +) + + +class InferenceEngine: + def __init__( + self, + model: Gemma4ForCausalLM, + config: EngineConfig, + tokenizer: Any | None = None, + ) -> None: + self.config = config + self.tokenizer = tokenizer + self.device = torch.device(config.device) + first_parameter = next(model.parameters()) + if ( + first_parameter.device != self.device + or first_parameter.dtype != config.dtype + ): + model = model.to(device=self.device, dtype=config.dtype) + self.model = model.eval() + self.cache = KVCache.allocate( + model.config, + config.max_batch_size, + config.max_sequence_length, + config.dtype, + self.device, + ) + self.sampler = Sampler(self.device, self.model.config.vocab_size) + self._batch_size = 0 + torch.manual_seed(config.seed) + + @classmethod + def from_pretrained( + cls, model_path: str, config: EngineConfig + ) -> "InferenceEngine": + from transformers import AutoTokenizer + + model = load_gemma4( + model_path, + device=config.device, + dtype=config.dtype, + max_position_embeddings=config.max_sequence_length, + linear_backend=config.linear_backend, + ) + tokenizer = AutoTokenizer.from_pretrained(model_path) + return cls(model, config, tokenizer) + + def _validate_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: + if input_ids.ndim != 2: + raise ValueError("input_ids must have shape [batch, sequence]") + if input_ids.shape[0] > self.config.max_batch_size: + raise ValueError("batch exceeds max_batch_size") + if input_ids.shape[1] > self.config.max_sequence_length: + raise ValueError("sequence exceeds max_sequence_length") + if input_ids.shape[1] == 0: + raise ValueError("input sequence must not be empty") + return input_ids.to(device=self.device, dtype=torch.long) + + @torch.inference_mode() + def prefill( + self, input_ids: torch.Tensor, max_seq_len: int | None = None + ) -> PrefillOutput: + """ + 处理完整 prompt，并把每层 K/V 写入一个全新的 cache。 + + ``input_ids`` 的形状为 ``[batch, prompt_length]``。返回的 logits + 只保留每个请求最后一个有效 prompt token 对应的下一 token 分布。 + """ + input_ids = self._validate_input_ids(input_ids) + batch_size, query_length = input_ids.shape + pad_token_id = self.model.config.pad_token_id + sequence_lengths = (input_ids != pad_token_id).sum(dim=1, dtype=torch.long) + positions = torch.arange(query_length, device=self.device).unsqueeze(0) + positions = positions.expand(batch_size, -1) + self.cache.reset(batch_size) + self._batch_size = batch_size + model_input = Batch( + input_ids=input_ids, + positions=positions, + sequence_lengths=sequence_lengths, + curr_max_seq_len=max_seq_len or query_length, + mode="prefill", + ) + with measure_operation(self.device, self.config.synchronize_metrics) as metrics: + logits = self.model(model_input, self.cache) + batch_indices = torch.arange(batch_size, device=self.device) + next_logits = logits[batch_indices, sequence_lengths - 1] + return PrefillOutput( + next_logits, + sequence_lengths.clone(), + metrics.latency_s, + metrics.peak_allocated_bytes, + metrics.peak_reserved_bytes, + ) + + @torch.inference_mode() + def decode_step( + self, + token_ids: torch.Tensor, + max_seq_len: int, + active: torch.Tensor | None = None, + ) -> DecodeOutput: + """ + 为 batch 中仍活跃的请求各追加一个 token。 + + ``active`` 为 false 的 slot 仍保留在静态 batch 中，但其序列长度不会 + 增加，因此已经完成的请求不会继续扩展 KV cache。 + """ + if self._batch_size == 0: + raise RuntimeError("prefill() must be called before decode_step()") + if token_ids.ndim == 1: + token_ids = token_ids[:, None] + if token_ids.shape != (self._batch_size, 1): + raise ValueError("decode token_ids must have shape [batch] or [batch, 1]") + token_ids = token_ids.to(device=self.device, dtype=torch.long) + if active is None: + active = torch.ones(self._batch_size, dtype=torch.bool, device=self.device) + else: + active = active.to(device=self.device, dtype=torch.bool) + if active.shape != (self._batch_size,): + raise ValueError("active must have shape [batch]") + + current_lengths = self.cache.lengths[: self._batch_size].clone() + next_lengths = current_lengths + active.long() + positions = current_lengths[:, None] + model_input = Batch( + input_ids=token_ids, + positions=positions, + sequence_lengths=next_lengths, + curr_max_seq_len=max_seq_len, + mode="decode", + ) + with measure_operation(self.device, self.config.synchronize_metrics) as metrics: + logits = self.model(model_input, self.cache, logits_to_keep=1) + return DecodeOutput( + logits[:, -1], + next_lengths.clone(), + metrics.latency_s, + metrics.peak_allocated_bytes, + metrics.peak_reserved_bytes, + ) + + def _encode_requests( + self, requests: list[GenerationRequest] + ) -> tuple[torch.Tensor, list[list[int]]]: + encoded: list[list[int]] = [] + for request in requests: + if request.input_ids is not None: + encoded.append(list(request.input_ids)) + else: + if self.tokenizer is None: + raise RuntimeError("a tokenizer is required for text prompts") + encoded.append( + list( + self.tokenizer(request.prompt, add_special_tokens=True)[ + "input_ids" + ] + ) + ) + for request, tokens in zip(requests, encoded, strict=True): + if len(tokens) + request.max_new_tokens > self.config.max_sequence_length: + raise ValueError( + "prompt and requested output exceed max_sequence_length" + ) + max_length = max(map(len, encoded)) + if max_length > self.config.max_sequence_length: + raise ValueError("prompt exceeds max_sequence_length") + padded = torch.full( + (len(encoded), max_length), + self.model.config.pad_token_id, + dtype=torch.long, + device=self.device, + ) + for row, tokens in enumerate(encoded): + padded[row, : len(tokens)] = torch.tensor(tokens, device=self.device) + return padded, encoded + + @torch.inference_mode() + def generate(self, requests: list[GenerationRequest]) -> list[GenerationOutput]: + """对一个静态 batch 执行 prefill、采样和逐 token decode。""" + if not requests: + return [] + if len(requests) > self.config.max_batch_size: + raise ValueError("request batch exceeds max_batch_size") + + # 阶段 1：tokenize 并右侧补齐，形成固定形状的 prompt batch。 + input_ids, encoded = self._encode_requests(requests) + sampling_args = self.sampler.prepare(requests) + scheduler = create_scheduler( + self.config, + default_stop_token_ids=(self.model.config.eos_token_id,), + ) + states = [] + for request, prompt_token_ids in zip(requests, encoded, strict=True): + state = RequestState( + request=request, + prompt_token_ids=prompt_token_ids, + ) + scheduler.add_request(state) + states.append(state) + + started = perf_counter() + + # 阶段 2：一次 prefill 处理全部 prompt，并产生第一个输出 token。 + prefill_schedule = scheduler.schedule() + if prefill_schedule.mode != "prefill": + raise RuntimeError("scheduler must begin with a prefill schedule") + prefill_output = self.prefill( + input_ids, max_seq_len=prefill_schedule.max_seq_len + ) + logits = prefill_output.logits + + decode_latencies: list[list[float]] = [[] for _ in requests] + peak_allocated_bytes = prefill_output.peak_allocated_bytes + peak_reserved_bytes = prefill_output.peak_reserved_bytes + + if scheduler.has_unfinished_requests(): + next_tokens = self.sampler.sample(logits, sampling_args) + scheduler.update(next_tokens.tolist()) + + # 阶段 3：保持 batch slot 不变，每轮只为活跃请求 decode 一个 token。 + while scheduler.has_unfinished_requests(): + decode_schedule = scheduler.schedule() + if decode_schedule.mode != "decode": + raise RuntimeError("scheduler returned prefill after decoding started") + + active = torch.tensor( + [ + scheduled.num_scheduled_tokens > 0 + for scheduled in decode_schedule.requests + ], + dtype=torch.bool, + device=self.device, + ) + token_ids = torch.tensor( + [ + ( + scheduled.request.output_token_ids[-1] + if scheduled.num_scheduled_tokens > 0 + else self.model.config.pad_token_id + ) + for scheduled in decode_schedule.requests + ], + dtype=torch.long, + device=self.device, + ) + + decode = self.decode_step( + token_ids, max_seq_len=decode_schedule.max_seq_len, active=active + ) + logits = decode.logits + + peak_allocated_bytes = max( + peak_allocated_bytes, decode.peak_allocated_bytes + ) + peak_reserved_bytes = max(peak_reserved_bytes, decode.peak_reserved_bytes) + for index in active.nonzero().flatten().tolist(): + decode_latencies[index].append(decode.latency_s) + + next_tokens = self.sampler.sample(logits, sampling_args) + scheduler.update(next_tokens.tolist()) + + # 阶段 4：将共享的 batch 执行结果整理为逐请求输出和指标。 + total_latency = perf_counter() - started + outputs = [] + for index, state in enumerate(states): + if self.tokenizer is None: + text = "" + else: + text = self.tokenizer.decode( + state.output_token_ids, skip_special_tokens=True + ) + outputs.append( + GenerationOutput( + token_ids=state.output_token_ids, + text=text, + prompt_tokens=len(state.prompt_token_ids), + generated_tokens=len(state.output_token_ids), + finish_reason=state.finish_reason, + metrics=RequestMetrics( + prefill_latency_s=prefill_output.latency_s, + decode_latencies_s=tuple(decode_latencies[index]), + total_latency_s=total_latency, + peak_allocated_bytes=peak_allocated_bytes, + peak_reserved_bytes=peak_reserved_bytes, + ), + ) + ) + return outputs + + def _validate_loss_mask( + self, + loss_mask: torch.Tensor | None, + input_ids: torch.Tensor, + ) -> torch.Tensor: + if loss_mask is None: + return torch.ones_like(input_ids, dtype=torch.bool, device=self.device) + if loss_mask.shape != input_ids.shape: + raise ValueError("loss_mask must have the same shape as input_ids") + return loss_mask.to(device=self.device, dtype=torch.bool) + + @staticmethod + def _make_score_output( + nll_sum: torch.Tensor, + token_count: int, + *, + logits: torch.Tensor | None = None, + token_logprobs: torch.Tensor | None = None, + ) -> ScoreOutput: + if token_count <= 0: + raise ValueError("score requires at least one selected target token") + nll_sum_value = float(nll_sum.double().item()) + mean_nll = nll_sum_value / token_count + return ScoreOutput( + logits=logits, + token_logprobs=token_logprobs, + nll_sum=nll_sum_value, + token_count=token_count, + mean_nll=mean_nll, + mean_logprob=-mean_nll, + perplexity=math.exp(mean_nll), + ) + + @torch.inference_mode() + def score( + self, + input_ids: torch.Tensor, + loss_mask: torch.Tensor | None = None, + chunk_size: int | None = None, + ) -> ScoreOutput: + input_ids = self._validate_input_ids(input_ids) + if input_ids.shape[1] < 2: + raise ValueError("score requires at least two tokens") + loss_mask = self._validate_loss_mask(loss_mask, input_ids) + if chunk_size is not None: + return self._score_chunked(input_ids, loss_mask, chunk_size) + + logits = self.model(input_ids) + token_logprobs = ( + F.log_softmax(logits[:, :-1].float(), dim=-1) + .gather(-1, input_ids[:, 1:, None]) + .squeeze(-1) + ) + selected = token_logprobs[loss_mask[:, 1:]] + return self._make_score_output( + -selected.double().sum(), + selected.numel(), + logits=logits, + token_logprobs=token_logprobs, + ) + + def _score_chunked( + self, + input_ids: torch.Tensor, + loss_mask: torch.Tensor, + chunk_size: int, + ) -> ScoreOutput: + if chunk_size <= 0: + raise ValueError("chunk_size must be positive") + + batch_size, sequence_length = input_ids.shape + source_ids = input_ids[:, :-1] + target_ids = input_ids[:, 1:] + target_mask = loss_mask[:, 1:] + self.cache.reset(batch_size) + self._batch_size = batch_size + nll_sum = torch.zeros((), dtype=torch.float64, device=self.device) + token_count = 0 + + for start in range(0, sequence_length - 1, chunk_size): + end = min(start + chunk_size, sequence_length - 1) + positions = torch.arange(start, end, device=self.device).unsqueeze(0) + positions = positions.expand(batch_size, -1) + sequence_lengths = torch.full( + (batch_size,), + end, + dtype=torch.long, + device=self.device, + ) + model_input = Batch( + input_ids=source_ids[:, start:end], + positions=positions, + sequence_lengths=sequence_lengths, + curr_max_seq_len=end, + mode="prefill" if start == 0 else "decode", + ) + logits = self.model(model_input, self.cache) + chunk_logprobs = ( + F.log_softmax(logits.float(), dim=-1) + .gather(-1, target_ids[:, start:end, None]) + .squeeze(-1) + ) + selected = chunk_logprobs[target_mask[:, start:end]] + nll_sum += -selected.double().sum() + token_count += selected.numel() + + return self._make_score_output(nll_sum, token_count) diff --git a/src/lab5/src/hpc101_infer/evaluation/__init__.py b/src/lab5/src/hpc101_infer/evaluation/__init__.py new file mode 100644 index 00000000..8546c656 --- /dev/null +++ b/src/lab5/src/hpc101_infer/evaluation/__init__.py @@ -0,0 +1,7 @@ +from hpc101_infer.evaluation.quality import ( + DatasetNLLResult, + evaluate_nll, + load_quality_token_ids, +) + +__all__ = ["DatasetNLLResult", "evaluate_nll", "load_quality_token_ids"] diff --git a/src/lab5/src/hpc101_infer/evaluation/quality.py b/src/lab5/src/hpc101_infer/evaluation/quality.py new file mode 100644 index 00000000..b523042b --- /dev/null +++ b/src/lab5/src/hpc101_infer/evaluation/quality.py @@ -0,0 +1,126 @@ +from __future__ import annotations + +import json +import math +from collections.abc import Iterable, Mapping, Sequence +from dataclasses import asdict, dataclass +from numbers import Integral +from pathlib import Path +from typing import Any, Protocol + +import torch + +from hpc101_infer.engine import InferenceEngine + + +class Tokenizer(Protocol): + def __call__(self, text: str, **kwargs: Any) -> Mapping[str, Any]: ... + + +@dataclass(frozen=True) +class DatasetNLLResult: + nll_sum: float + token_count: int + sequence_count: int + mean_nll: float + perplexity: float + + def to_dict(self) -> dict[str, float | int]: + return asdict(self) + + +def evaluate_nll( + engine: InferenceEngine, + token_sequences: Iterable[Sequence[int]], + *, + chunk_size: int = 128, +) -> DatasetNLLResult: + if chunk_size <= 0: + raise ValueError("chunk_size must be positive") + + nll_sum = 0.0 + token_count = 0 + sequence_count = 0 + for sequence_number, token_ids in enumerate(token_sequences, start=1): + if len(token_ids) < 2: + raise ValueError( + f"quality sequence {sequence_number} must contain at least two tokens" + ) + input_ids = torch.tensor(token_ids, dtype=torch.long, device=engine.device)[None] + output = engine.score(input_ids, chunk_size=chunk_size) + nll_sum += output.nll_sum + token_count += output.token_count + sequence_count += 1 + + if token_count == 0: + raise ValueError("quality dataset must contain at least one scored token") + mean_nll = nll_sum / token_count + return DatasetNLLResult( + nll_sum=nll_sum, + token_count=token_count, + sequence_count=sequence_count, + mean_nll=mean_nll, + perplexity=math.exp(mean_nll), + ) + + +def _normalize_input_ids(input_ids: Any, line_number: int) -> list[int]: + if not isinstance(input_ids, Sequence) or isinstance(input_ids, (str, bytes)): + raise ValueError(f"input_ids must be a sequence at line {line_number}") + if any( + not isinstance(token_id, Integral) or isinstance(token_id, bool) + for token_id in input_ids + ): + raise ValueError(f"input_ids must contain integers at line {line_number}") + result = [int(token_id) for token_id in input_ids] + if len(result) < 2: + raise ValueError(f"input_ids must contain at least two tokens at line {line_number}") + return result + + +def load_quality_token_ids( + path: str | Path, + tokenizer: Tokenizer, + *, + text_key: str = "text", + limit: int | None = None, + add_special_tokens: bool = True, +) -> list[list[int]]: + if limit is not None and limit < 0: + raise ValueError("limit must be non-negative") + if limit == 0: + return [] + + path = Path(path) + sequences: list[list[int]] = [] + with path.open(encoding="utf-8") as handle: + for line_number, line in enumerate(handle, start=1): + if not line.strip(): + continue + try: + record = json.loads(line) + except json.JSONDecodeError as error: + raise ValueError( + f"invalid JSON in {path} at line {line_number}: {error.msg}" + ) from error + if not isinstance(record, dict): + raise ValueError(f"expected an object at line {line_number}") + + if "input_ids" in record: + token_ids = _normalize_input_ids(record["input_ids"], line_number) + else: + text = record.get(text_key) + if not isinstance(text, str): + raise ValueError( + f"record at line {line_number} must contain input_ids or " + f"a string {text_key!r}" + ) + encoded = tokenizer(text, add_special_tokens=add_special_tokens) + if "input_ids" not in encoded: + raise ValueError("tokenizer output does not contain input_ids") + token_ids = _normalize_input_ids(encoded["input_ids"], line_number) + + sequences.append(token_ids) + if limit is not None and len(sequences) >= limit: + break + return sequences diff --git a/src/lab5/src/hpc101_infer/layers/__init__.py b/src/lab5/src/hpc101_infer/layers/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/lab5/src/hpc101_infer/layers/attention.py b/src/lab5/src/hpc101_infer/layers/attention.py new file mode 100644 index 00000000..2bf424e1 --- /dev/null +++ b/src/lab5/src/hpc101_infer/layers/attention.py @@ -0,0 +1,274 @@ +"""Gemma 4 的全局与滑动窗口 attention 参考实现。""" + +from __future__ import annotations + +import torch +from torch import nn + +from hpc101_infer.layers.linear import BF16LinearFactory, LinearFactory +from hpc101_infer.layers.norm import RMSNorm +from hpc101_infer.layers.rotary import RotaryEmbedding +from hpc101_infer.models.config import RotaryConfig +from hpc101_infer.runtime.kv_cache import KVCache + + +def make_causal_mask( + sequence_length: int, + layer_type: str, + sliding_window: int, + device: torch.device, +) -> torch.Tensor: + """构造 ``[1, 1, query_length, key_length]`` 的加性 causal mask。""" + query = torch.arange(sequence_length, device=device).unsqueeze(1) + key = torch.arange(sequence_length, device=device).unsqueeze(0) + allowed = key <= query + if layer_type == "sliding_attention": + allowed &= key > query - sliding_window + mask = torch.zeros( + (sequence_length, sequence_length), device=device, dtype=torch.float32 + ) + return mask.masked_fill(~allowed, float("-inf")).unsqueeze(0).unsqueeze(0) + + +def make_attention_mask( + positions: torch.Tensor, + sequence_lengths: torch.Tensor, + key_length: int, + layer_type: str, + dtype: torch.dtype, + sliding_window: int = -1, +) -> tuple[torch.Tensor, torch.Tensor]: + """同时屏蔽未来 token、padding token 和滑动窗口外的历史 token。""" + key_positions = torch.arange(key_length, device=positions.device) + query_valid = positions < sequence_lengths[:, None] + allowed = key_positions[None, None, :] <= positions[:, :, None] + allowed &= key_positions[None, None, :] < sequence_lengths[:, None, None] + if layer_type == "sliding_attention": + allowed &= key_positions[None, None, :] > ( + positions[:, :, None] - sliding_window + ) + allowed &= query_valid[:, :, None] + # 全 padding 的 query 若整行都是 -inf，softmax 会产生 NaN。这里临时 + # 放行一个 key，随后再通过 query_valid 将该 query 的输出清零。 + safe_allowed = allowed.clone() + safe_allowed[..., 0] |= ~query_valid + mask = torch.zeros(allowed.shape, device=positions.device, dtype=dtype) + mask.masked_fill_(~safe_allowed, float("-inf")) + return mask.unsqueeze(1), query_valid + + +def repeat_kv(hidden_states: torch.Tensor, repeats: int) -> torch.Tensor: + """将较少的 KV heads 逻辑扩展到 query heads，实现 grouped-query attention。""" + if repeats == 1: + return hidden_states + batch, heads, seq_len, head_dim = hidden_states.shape + expanded = hidden_states[:, :, None].expand( + batch, heads, repeats, seq_len, head_dim + ) + return expanded.reshape(batch, heads * repeats, seq_len, head_dim) + + +class AttentionLayer(nn.Module): + def __init__( + self, + num_qo_heads: int, + num_kv_heads: int, + head_dim: int, + hidden_size: int, + rotary_config: RotaryConfig, + rms_norm_eps: float, + max_position_embeddings: int, + attention_k_eq_v: bool = False, + linear_factory: LinearFactory | None = None, + module_prefix: str = "self_attn", + ): + super().__init__() + factory = linear_factory or BF16LinearFactory() + self.head_dim = head_dim + self.num_kv_heads = num_kv_heads + self.num_kv_groups = num_qo_heads // self.num_kv_heads + self.q_proj = factory.create( + f"{module_prefix}.q_proj", hidden_size, num_qo_heads * self.head_dim, False + ) + self.k_proj = factory.create( + f"{module_prefix}.k_proj", + hidden_size, + self.num_kv_heads * self.head_dim, + False, + ) + self.v_proj = ( + None + if attention_k_eq_v + else factory.create( + f"{module_prefix}.v_proj", + hidden_size, + self.num_kv_heads * self.head_dim, + False, + ) + ) + self.o_proj = factory.create( + f"{module_prefix}.o_proj", + num_qo_heads * self.head_dim, + hidden_size, + False, + ) + self.q_norm = RMSNorm(self.head_dim, rms_norm_eps) + self.k_norm = RMSNorm(self.head_dim, rms_norm_eps) + self.v_norm = RMSNorm(self.head_dim, rms_norm_eps, with_scale=False) + + self.rotary = RotaryEmbedding( + rotary_config, + head_dim, + max_position_embeddings, + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_ids: torch.Tensor, + sequence_lengths: torch.Tensor, + max_seq_len: int, + layer_id: int, + kv_cache: KVCache | None = None, + ) -> torch.Tensor: + batch, seq_len, _ = hidden_states.shape + # Projection 后先保持 [batch, sequence, heads, head_dim]，应用 RoPE + # 后再转为 attention 计算使用的 [batch, heads, sequence, head_dim]。 + query = self.q_proj(hidden_states).view(batch, seq_len, -1, self.head_dim) + key = self.k_proj(hidden_states).view( + batch, seq_len, self.num_kv_heads, self.head_dim + ) + value = ( + key + if self.v_proj is None + else self.v_proj(hidden_states).view( + batch, seq_len, self.num_kv_heads, self.head_dim + ) + ) + query = self.q_norm(query) + key = self.k_norm(key) + query, key = self.rotary(position_ids, query, key) + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = self.v_norm(value).transpose(1, 2) + if kv_cache is not None: + kv_cache.write(layer_id, position_ids, key, value) + cached = kv_cache.view(layer_id, max_seq_len) + key, value = cached.key, cached.value + key = repeat_kv(key, self.num_kv_groups) + value = repeat_kv(value, self.num_kv_groups) + scores = torch.matmul(query, key.transpose(2, 3)) + mask, query_valid = make_attention_mask( + positions=position_ids, + sequence_lengths=sequence_lengths, + key_length=key.shape[2], + layer_type="full_attention", + dtype=scores.dtype, + ) + scores = scores + mask + prob = torch.softmax(scores, dim=-1, dtype=torch.float32).to(query.dtype) + output = torch.matmul(prob, value).transpose(1, 2).reshape(batch, seq_len, -1) + output = output * query_valid.unsqueeze(-1) + return self.o_proj(output) + + +class SlidingAttentionLayer(nn.Module): + def __init__( + self, + num_qo_heads: int, + num_kv_heads: int, + head_dim: int, + hidden_size: int, + rotary_config: RotaryConfig, + rms_norm_eps: float, + max_position_embeddings: int, + sliding_window: int, + linear_factory: LinearFactory | None = None, + module_prefix: str = "self_attn", + ): + super().__init__() + factory = linear_factory or BF16LinearFactory() + self.head_dim = head_dim + self.num_kv_heads = num_kv_heads + self.num_kv_groups = num_qo_heads // self.num_kv_heads + self.sliding_window = sliding_window + self.q_proj = factory.create( + f"{module_prefix}.q_proj", hidden_size, num_qo_heads * self.head_dim, False + ) + self.k_proj = factory.create( + f"{module_prefix}.k_proj", + hidden_size, + self.num_kv_heads * self.head_dim, + False, + ) + self.v_proj = factory.create( + f"{module_prefix}.v_proj", + hidden_size, + self.num_kv_heads * self.head_dim, + False, + ) + self.o_proj = factory.create( + f"{module_prefix}.o_proj", + num_qo_heads * self.head_dim, + hidden_size, + False, + ) + self.q_norm = RMSNorm(self.head_dim, rms_norm_eps) + self.k_norm = RMSNorm(self.head_dim, rms_norm_eps) + self.v_norm = RMSNorm(self.head_dim, rms_norm_eps, with_scale=False) + + self.rotary = RotaryEmbedding( + rotary_config, + head_dim, + max_position_embeddings, + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_ids: torch.Tensor, + sequence_lengths: torch.Tensor, + max_seq_len: int, + layer_id: int, + kv_cache: KVCache | None = None, + ) -> torch.Tensor: + batch, seq_len, _ = hidden_states.shape + # Projection 后先保持 [batch, sequence, heads, head_dim]，应用 RoPE + # 后再转为 attention 计算使用的 [batch, heads, sequence, head_dim]。 + query = self.q_proj(hidden_states).view(batch, seq_len, -1, self.head_dim) + key = self.k_proj(hidden_states).view( + batch, seq_len, self.num_kv_heads, self.head_dim + ) + value = ( + key + if self.v_proj is None + else self.v_proj(hidden_states).view( + batch, seq_len, self.num_kv_heads, self.head_dim + ) + ) + query = self.q_norm(query) + key = self.k_norm(key) + query, key = self.rotary(position_ids, query, key) + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = self.v_norm(value).transpose(1, 2) + if kv_cache is not None: + kv_cache.write(layer_id, position_ids, key, value) + cached = kv_cache.view(layer_id, max_seq_len) + key, value = cached.key, cached.value + key = repeat_kv(key, self.num_kv_groups) + value = repeat_kv(value, self.num_kv_groups) + scores = torch.matmul(query, key.transpose(2, 3)) + mask, query_valid = make_attention_mask( + positions=position_ids, + sequence_lengths=sequence_lengths, + key_length=key.shape[2], + layer_type="sliding_attention", + dtype=scores.dtype, + sliding_window=self.sliding_window, + ) + scores = scores + mask + prob = torch.softmax(scores, dim=-1, dtype=torch.float32).to(query.dtype) + output = torch.matmul(prob, value).transpose(1, 2).reshape(batch, seq_len, -1) + output = output * query_valid.unsqueeze(-1) + return self.o_proj(output) diff --git a/src/lab5/src/hpc101_infer/layers/linear.py b/src/lab5/src/hpc101_infer/layers/linear.py new file mode 100644 index 00000000..40a6872f --- /dev/null +++ b/src/lab5/src/hpc101_infer/layers/linear.py @@ -0,0 +1,201 @@ +"""BF16 Linear 与持久化 INT4 权重的参考 Linear 实现。""" + +from __future__ import annotations + +from typing import Mapping, Protocol + +import torch +from torch import nn +from torch.nn import functional as F + +from hpc101_infer.quantization.packing import dequantize_weight +from hpc101_infer.quantization.types import QuantizedModuleManifest, QuantizedWeight + + +class LinearFactory(Protocol): + def create( + self, + module_name: str, + in_features: int, + out_features: int, + bias: bool, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> nn.Module: ... + + +class BF16LinearFactory: + def create( + self, + module_name: str, + in_features: int, + out_features: int, + bias: bool, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> nn.Linear: + del module_name + return nn.Linear( + in_features, + out_features, + bias=bias, + device=device, + dtype=dtype, + ) + + +class QuantizedLinear(nn.Module): + """保存 packed INT4 权重，并在 forward 中临时反量化。 + + ``qweight`` 使用一个 uint8 保存两个 4-bit code；``scales`` 和可选的 + ``zeros`` 按 ``[out_features, num_groups]`` 存储。输入维度会补齐到 + group size 的整数倍，但矩阵乘前仍只接收原始 ``in_features``。 + """ + def __init__( + self, + in_features: int, + out_features: int, + group_size: int, + *, + symmetric: bool = True, + padded_in_features: int | None = None, + bias: bool = False, + device: torch.device | str | None = None, + scale_dtype: torch.dtype = torch.float16, + ) -> None: + super().__init__() + if padded_in_features is None: + padded_in_features = ( + (in_features + group_size - 1) // group_size * group_size + ) + if padded_in_features < in_features or padded_in_features % group_size: + raise ValueError("invalid padded input size") + self.in_features = in_features + self.out_features = out_features + self.group_size = group_size + self.symmetric = symmetric + self.padded_in_features = padded_in_features + self.register_buffer( + "qweight", + torch.empty( + out_features, + padded_in_features // 2, + dtype=torch.uint8, + device=device, + ), + ) + self.register_buffer( + "scales", + torch.empty( + out_features, + padded_in_features // group_size, + dtype=scale_dtype, + device=device, + ), + ) + self.register_buffer( + "zeros", + ( + None + if symmetric + else torch.empty( + out_features, + padded_in_features // group_size, + dtype=torch.uint8, + device=device, + ) + ), + ) + if bias: + self.bias = nn.Parameter(torch.empty(out_features, device=device)) + else: + self.register_parameter("bias", None) + + @classmethod + def from_quantized_weight( + cls, quantized: QuantizedWeight, bias: torch.Tensor | None = None + ) -> "QuantizedLinear": + out_features, in_features = quantized.original_shape + module = cls( + in_features, + out_features, + quantized.group_size, + symmetric=quantized.symmetric, + padded_in_features=quantized.padded_shape[1], + bias=bias is not None, + device=quantized.qweight.device, + scale_dtype=quantized.scales.dtype, + ) + module.qweight.copy_(quantized.qweight) + module.scales.copy_(quantized.scales) + if module.zeros is not None and quantized.zeros is not None: + module.zeros.copy_(quantized.zeros) + if bias is not None: + module.bias.data.copy_(bias) + return module + + def quantized_weight(self) -> QuantizedWeight: + return QuantizedWeight( + qweight=self.qweight, + scales=self.scales, + zeros=self.zeros, + original_shape=(self.out_features, self.in_features), + padded_shape=(self.out_features, self.padded_in_features), + bits=4, + group_size=self.group_size, + symmetric=self.symmetric, + packing="uint8_little_nibble", + ) + + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + # 参考实现允许在单次 forward 内生成高精度临时权重，但不会把完整 + # FP16/BF16 权重持久保存在 module 中。融合 kernel 可替换这一过程。 + weight = dequantize_weight(self.quantized_weight(), dtype=inputs.dtype) + return F.linear(inputs, weight, self.bias) + + +class QuantizedLinearFactory: + def __init__( + self, + manifest: Mapping[str, QuantizedModuleManifest], + *, + scale_dtype: torch.dtype = torch.float16, + ) -> None: + if scale_dtype not in {torch.float16, torch.bfloat16}: + raise ValueError("scale_dtype must be float16 or bfloat16") + self.manifest = dict(manifest) + self.scale_dtype = scale_dtype + self._fallback = BF16LinearFactory() + + def create( + self, + module_name: str, + in_features: int, + out_features: int, + bias: bool, + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + ) -> nn.Module: + entry = self.manifest.get(module_name) + if entry is None: + return self._fallback.create( + module_name, in_features, out_features, bias, device, dtype + ) + if entry.original_shape != (out_features, in_features): + raise RuntimeError( + f"manifest shape mismatch for {module_name}: " + f"manifest={entry.original_shape}, model={(out_features, in_features)}" + ) + scale_dtype = ( + dtype if dtype in {torch.float16, torch.bfloat16} else self.scale_dtype + ) + return QuantizedLinear( + in_features, + out_features, + entry.group_size, + symmetric=entry.symmetric, + padded_in_features=entry.padded_shape[1], + bias=bias, + device=device, + scale_dtype=scale_dtype, + ) diff --git a/src/lab5/src/hpc101_infer/layers/norm.py b/src/lab5/src/hpc101_infer/layers/norm.py new file mode 100644 index 00000000..4b5d6067 --- /dev/null +++ b/src/lab5/src/hpc101_infer/layers/norm.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +import torch +from torch import nn + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-6, with_scale: bool = True): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) if with_scale else None + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + mean_squared = hidden_states.float().pow(2).mean(-1, keepdim=True) + self.eps + output = hidden_states.float() * torch.pow(mean_squared, -0.5) + if self.weight is not None: + output = output * self.weight.float() + return output.to(hidden_states.dtype) diff --git a/src/lab5/src/hpc101_infer/layers/rotary.py b/src/lab5/src/hpc101_infer/layers/rotary.py new file mode 100644 index 00000000..f6b60098 --- /dev/null +++ b/src/lab5/src/hpc101_infer/layers/rotary.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +import torch + +from hpc101_infer.models.config import RotaryConfig + + +class RotaryEmbedding(torch.nn.Module): + def __init__( + self, + config: RotaryConfig, + head_dim: int, + max_position_embeddings: int, + ): + super().__init__() + self.config = config + self.head_dim = head_dim + self.max_position_embeddings = max_position_embeddings + + cache = self._build_cache(max_position_embeddings, torch.device("cpu")) + self.register_buffer("_cos_sin_cache", cache, persistent=False) + + def _build_cache( + self, max_position_embeddings: int, device: torch.device + ) -> torch.Tensor: + config = self.config + head_dim = self.head_dim + + base = config.rope_theta + if config.rope_type == "proportional": + rotary_pairs = int( + float(config.partial_rotary_factor or 1.0) * head_dim // 2 + ) + rotated = 1.0 / ( + base + ** ( + torch.arange( + 0, + 2 * rotary_pairs, + 2, + dtype=torch.float32, + device=device, + ) + / head_dim + ) + ) + inv_freq = torch.nn.functional.pad( + rotated, (0, head_dim // 2 - rotary_pairs) + ) / float(config.factor) + else: + inv_freq = 1.0 / ( + base + ** ( + torch.arange(0, head_dim, 2, dtype=torch.float32, device=device) + / head_dim + ) + ) + + positions = torch.arange( + max_position_embeddings, dtype=torch.float32, device=device + ) + freqs = torch.einsum("i,j->ij", positions, inv_freq) + embedding = torch.cat((freqs, freqs), dim=-1) + return torch.cat((embedding.cos(), embedding.sin()), dim=-1) + + def materialize(self, device: str | torch.device) -> None: + device = torch.device(device) + self._cos_sin_cache = self._build_cache(self.max_position_embeddings, device) + + def _rotate_half(self, x: torch.Tensor) -> torch.Tensor: + first, second = x.chunk(2, dim=-1) + return torch.cat((-second, first), dim=-1) + + def _apply_rotary( + self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor + ) -> torch.Tensor: + cos = cos.unsqueeze(2) + sin = sin.unsqueeze(2) + return (x * cos) + (self._rotate_half(x) * sin) + + def forward( + self, position_ids: torch.Tensor, query: torch.Tensor, key: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + cos_sin = self._cos_sin_cache[position_ids] + cos = cos_sin[..., : self.head_dim].to(query.dtype) + sin = cos_sin[..., self.head_dim :].to(query.dtype) + return self._apply_rotary(query, cos, sin), self._apply_rotary(key, cos, sin) diff --git a/src/lab5/src/hpc101_infer/models/__init__.py b/src/lab5/src/hpc101_infer/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/lab5/src/hpc101_infer/models/config.py b/src/lab5/src/hpc101_infer/models/config.py new file mode 100644 index 00000000..f357c64f --- /dev/null +++ b/src/lab5/src/hpc101_infer/models/config.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any + + +@dataclass(frozen=True) +class RotaryConfig: + rope_type: str + rope_theta: float + partial_rotary_factor: float | None = None + factor: float = 1.0 + + def get(self, key: str, default: float | None = None) -> float | None: + return getattr(self, key, default) + + +@dataclass(frozen=True) +class Gemma4TextConfig: + vocab_size: int + hidden_size: int + intermediate_size: int + num_hidden_layers: int + num_attention_heads: int + num_key_value_heads: int + num_global_key_value_heads: int + head_dim: int + global_head_dim: int + layer_types: tuple[str, ...] + sliding_window: int + max_position_embeddings: int + rms_norm_eps: float + hidden_activation: str + final_logit_softcapping: float | None + rope_parameters: dict[str, dict[str, Any]] + pad_token_id: int + bos_token_id: int + eos_token_id: int + attention_k_eq_v: bool = True + + @classmethod + def from_pretrained(cls, model_path: str | Path) -> "Gemma4TextConfig": + raw = json.loads((Path(model_path) / "config.json").read_text()) + text = raw.get("text_config", raw) + if text.get("model_type") != "gemma4_unified_text": + raise ValueError(f"unsupported model_type: {text.get('model_type')!r}") + + return cls( + vocab_size=text["vocab_size"], + hidden_size=text["hidden_size"], + intermediate_size=text["intermediate_size"], + num_hidden_layers=text["num_hidden_layers"], + num_attention_heads=text["num_attention_heads"], + num_key_value_heads=text["num_key_value_heads"], + num_global_key_value_heads=text["num_global_key_value_heads"], + head_dim=text["head_dim"], + global_head_dim=text["global_head_dim"], + layer_types=tuple(text["layer_types"]), + sliding_window=text["sliding_window"], + max_position_embeddings=text["max_position_embeddings"], + rms_norm_eps=text["rms_norm_eps"], + hidden_activation=text["hidden_activation"], + final_logit_softcapping=text.get("final_logit_softcapping"), + rope_parameters=text["rope_parameters"], + pad_token_id=text["pad_token_id"], + bos_token_id=text["bos_token_id"], + eos_token_id=text["eos_token_id"], + attention_k_eq_v=text.get("attention_k_eq_v", True), + ) + + def get_rope_config(self, layer_type: str) -> RotaryConfig: + params = self.rope_parameters[layer_type] + return RotaryConfig( + rope_type=params["rope_type"], + rope_theta=params["rope_theta"], + partial_rotary_factor=params.get("partial_rotary_factor"), + factor=params.get("factor", 1.0), + ) diff --git a/src/lab5/src/hpc101_infer/models/gemma4.py b/src/lab5/src/hpc101_infer/models/gemma4.py new file mode 100644 index 00000000..301bf9bc --- /dev/null +++ b/src/lab5/src/hpc101_infer/models/gemma4.py @@ -0,0 +1,194 @@ +from __future__ import annotations + +import math + +import torch +from torch import nn +from torch.nn import functional as F + +from hpc101_infer.layers.attention import AttentionLayer, SlidingAttentionLayer +from hpc101_infer.layers.linear import BF16LinearFactory, LinearFactory +from hpc101_infer.layers.norm import RMSNorm +from hpc101_infer.models.config import Gemma4TextConfig +from hpc101_infer.runtime.batch import Batch +from hpc101_infer.runtime.kv_cache import KVCache + + +class MLP(nn.Module): + def __init__( + self, + config: Gemma4TextConfig, + linear_factory: LinearFactory, + module_prefix: str, + ): + super().__init__() + self.gate_proj = linear_factory.create( + f"{module_prefix}.gate_proj", + config.hidden_size, + config.intermediate_size, + False, + ) + self.up_proj = linear_factory.create( + f"{module_prefix}.up_proj", + config.hidden_size, + config.intermediate_size, + False, + ) + self.down_proj = linear_factory.create( + f"{module_prefix}.down_proj", + config.intermediate_size, + config.hidden_size, + False, + ) + if config.hidden_activation != "gelu_pytorch_tanh": + raise ValueError(f"unsupported activation: {config.hidden_activation}") + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return self.down_proj( + F.gelu(self.gate_proj(hidden_states), approximate="tanh") + * self.up_proj(hidden_states) + ) + + +class DecoderLayer(nn.Module): + def __init__( + self, + config: Gemma4TextConfig, + layer_idx: int, + linear_factory: LinearFactory, + ): + super().__init__() + module_prefix = f"layers.{layer_idx}" + self.layer_idx = layer_idx + self.attn_type = config.layer_types[layer_idx] + match self.attn_type: + case "full_attention": + self.self_attn = AttentionLayer( + config.num_attention_heads, + config.num_global_key_value_heads, + config.global_head_dim, + config.hidden_size, + config.get_rope_config(self.attn_type), + config.rms_norm_eps, + config.max_position_embeddings, + config.attention_k_eq_v, + linear_factory, + f"{module_prefix}.self_attn", + ) + case "sliding_attention": + self.self_attn = SlidingAttentionLayer( + config.num_attention_heads, + config.num_key_value_heads, + config.head_dim, + config.hidden_size, + config.get_rope_config(self.attn_type), + config.rms_norm_eps, + config.max_position_embeddings, + config.sliding_window, + linear_factory, + f"{module_prefix}.self_attn", + ) + case _: + raise ValueError(f"Unsupported attention type: {self.attn_type}") + + self.mlp = MLP(config, linear_factory, f"{module_prefix}.mlp") + self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps) + self.pre_feedforward_layernorm = RMSNorm( + config.hidden_size, config.rms_norm_eps + ) + self.post_feedforward_layernorm = RMSNorm( + config.hidden_size, config.rms_norm_eps + ) + self.register_buffer("layer_scalar", torch.ones(1)) + + def forward( + self, + hidden_states: torch.Tensor, + position_ids: torch.Tensor, + sequence_lengths: torch.Tensor, + max_seq_len: int, + kv_cache: KVCache | None, + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.self_attn( + self.input_layernorm(hidden_states), + position_ids, + sequence_lengths, + max_seq_len, + self.layer_idx, + kv_cache, + ) + hidden_states = residual + self.post_attention_layernorm(hidden_states) + residual = hidden_states + hidden_states = self.mlp(self.pre_feedforward_layernorm(hidden_states)) + return ( + residual + self.post_feedforward_layernorm(hidden_states) + ) * self.layer_scalar + + +class Gemma4ForCausalLM(nn.Module): + def __init__( + self, + config: Gemma4TextConfig, + linear_factory: LinearFactory | None = None, + ): + super().__init__() + self.config = config + linear_factory = linear_factory or BF16LinearFactory() + self.embed_tokens = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.layers = nn.ModuleList( + [ + DecoderLayer(config, idx, linear_factory) + for idx in range(config.num_hidden_layers) + ] + ) + self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps) + self.embed_scale = math.sqrt(config.hidden_size) + + def forward( + self, + model_input: Batch | torch.Tensor, + kv_cache: KVCache | None = None, + logits_to_keep: int = 0, + ) -> torch.Tensor: + if isinstance(model_input, torch.Tensor): + input_ids = model_input + position_ids = ( + torch.arange(input_ids.shape[1], device=input_ids.device) + .unsqueeze(0) + .expand_as(input_ids) + ) + sequence_lengths = torch.full( + (input_ids.shape[0],), + input_ids.shape[1], + dtype=torch.long, + device=input_ids.device, + ) + max_seq_len = input_ids.shape[1] + else: + input_ids = model_input.input_ids + position_ids = model_input.positions + sequence_lengths = model_input.sequence_lengths + max_seq_len = model_input.curr_max_seq_len + hidden_states = self.embed_tokens(input_ids) * torch.tensor( + self.embed_scale, + device=input_ids.device, + dtype=self.embed_tokens.weight.dtype, + ) + for layer in self.layers: + hidden_states = layer( + hidden_states, position_ids, sequence_lengths, max_seq_len, kv_cache + ) + if kv_cache is not None: + kv_cache.commit(sequence_lengths) + hidden_states = self.norm(hidden_states) + if logits_to_keep: + hidden_states = hidden_states[:, -logits_to_keep:] + logits = F.linear(hidden_states, self.embed_tokens.weight) + if self.config.final_logit_softcapping is not None: + cap = self.config.final_logit_softcapping + logits = torch.tanh(logits / cap) * cap + return logits diff --git a/src/lab5/src/hpc101_infer/models/loader.py b/src/lab5/src/hpc101_infer/models/loader.py new file mode 100644 index 00000000..10682970 --- /dev/null +++ b/src/lab5/src/hpc101_infer/models/loader.py @@ -0,0 +1,165 @@ +"""从 Hugging Face safetensors checkpoint 流式 materialize Gemma 4。""" + +from __future__ import annotations + +import json +from dataclasses import replace +from pathlib import Path +from typing import Iterator + +import torch +from safetensors import safe_open +from torch import nn + +from hpc101_infer.layers.linear import QuantizedLinearFactory +from hpc101_infer.layers.rotary import RotaryEmbedding +from hpc101_infer.models.config import Gemma4TextConfig +from hpc101_infer.models.gemma4 import Gemma4ForCausalLM +from hpc101_infer.quantization.checkpoint import ( + HFSafetensorsSource, + QuantizedCheckpointSource, +) +from hpc101_infer.quantization.types import SCALE_DTYPES + + +def _resolve_tensor(model: nn.Module, name: str) -> tuple[nn.Module, str, torch.Tensor]: + module_path, separator, tensor_name = name.rpartition(".") + module = model.get_submodule(module_path) if separator else model + if tensor_name in module._parameters: + tensor = module._parameters[tensor_name] + elif tensor_name in module._buffers: + tensor = module._buffers[tensor_name] + else: + raise KeyError(name) + if tensor is None: + raise KeyError(name) + return module, tensor_name, tensor + + +def _materialize_tensor( + model: nn.Module, + name: str, + checkpoint_tensor: torch.Tensor, + device: torch.device, + dtype: torch.dtype, + *, + preserve_target_dtype: bool = False, +) -> None: + module, tensor_name, target = _resolve_tensor(model, name) + if checkpoint_tensor.shape != target.shape: + raise RuntimeError( + f"shape mismatch for {name}: checkpoint={tuple(checkpoint_tensor.shape)}, " + f"model={tuple(target.shape)}" + ) + target_dtype = ( + target.dtype + if preserve_target_dtype or not checkpoint_tensor.is_floating_point() + else dtype + ) + materialized = checkpoint_tensor.to(device=device, dtype=target_dtype) + if tensor_name in module._parameters: + module._parameters[tensor_name] = nn.Parameter( + materialized, requires_grad=target.requires_grad + ) + else: + module._buffers[tensor_name] = materialized + + +def _quantized_key_map(source: QuantizedCheckpointSource) -> dict[str, str]: + result: dict[str, str] = {} + for module_name, entry in source.manifest.items(): + result[entry.qweight_key] = f"{module_name}.qweight" + result[entry.scales_key] = f"{module_name}.scales" + if entry.zeros_key is not None: + result[entry.zeros_key] = f"{module_name}.zeros" + return result + + +def load_gemma4( + model_path: str | Path, + device: str | torch.device = "cuda", + dtype: torch.dtype = torch.bfloat16, + max_position_embeddings: int | None = None, + linear_backend: str = "bf16", +) -> Gemma4ForCausalLM: + """在 meta device 构造模型，再逐 tensor 加载到目标设备。 + + 该过程避免先在 CPU 上构造完整 ``state_dict``。量化 checkpoint 会根据 + manifest 创建 ``QuantizedLinear``，普通 checkpoint 则创建 BF16 Linear。 + """ + model_path = Path(model_path) + device = torch.device(device) + config = Gemma4TextConfig.from_pretrained(model_path) + if max_position_embeddings is not None: + if max_position_embeddings <= 0: + raise ValueError("max_position_embeddings must be positive") + if max_position_embeddings > config.max_position_embeddings: + raise ValueError("runtime max_position_embeddings exceeds model capacity") + config = replace(config, max_position_embeddings=max_position_embeddings) + + is_quantized = (model_path / "quantization_config.json").is_file() + if linear_backend not in {"bf16", "int4_reference"}: + raise ValueError(f"unsupported linear backend: {linear_backend}") + if linear_backend == "int4_reference" and not is_quantized: + raise ValueError("int4_reference requires a quantized checkpoint") + + if is_quantized: + source = QuantizedCheckpointSource(model_path) + scale_dtype = SCALE_DTYPES[source.quantization_config.scale_dtype] + linear_factory = QuantizedLinearFactory( + source.manifest, scale_dtype=scale_dtype + ) + source_to_local = _quantized_key_map(source) + prefix = "" + else: + source = HFSafetensorsSource(model_path) + linear_factory = None + source_to_local = {} + prefix = "model.language_model." + + # meta model 只记录结构和 shape，不为完整模型分配真实权重存储。 + with torch.device("meta"): + model = Gemma4ForCausalLM(config, linear_factory=linear_factory) + + expected = set(model.state_dict()) + loaded: set[str] = set() + unexpected: set[str] = set() + duplicated: set[str] = set() + + for checkpoint_key in source.keys(): + if prefix and not checkpoint_key.startswith(prefix): + continue + local_key = source_to_local.get( + checkpoint_key, + checkpoint_key[len(prefix) :] if prefix else checkpoint_key, + ) + if local_key in loaded: + duplicated.add(local_key) + continue + if local_key not in expected: + unexpected.add(local_key) + continue + checkpoint_tensor = source.load_tensor(checkpoint_key) + _materialize_tensor( + model, + local_key, + checkpoint_tensor, + device, + dtype, + preserve_target_dtype=is_quantized and local_key.endswith(".scales"), + ) + loaded.add(local_key) + del checkpoint_tensor + + missing = expected - loaded + if missing or unexpected or duplicated: + raise RuntimeError( + "checkpoint mismatch: " + f"missing={sorted(missing)}, unexpected={sorted(unexpected)}, " + f"duplicated={sorted(duplicated)}" + ) + + for module in model.modules(): + if isinstance(module, RotaryEmbedding): + module.materialize(device) + return model.eval() diff --git a/src/lab5/src/hpc101_infer/quantization/__init__.py b/src/lab5/src/hpc101_infer/quantization/__init__.py new file mode 100644 index 00000000..a0fa2829 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/__init__.py @@ -0,0 +1,23 @@ +from hpc101_infer.quantization.config import QuantizationConfig +from hpc101_infer.quantization.packing import ( + dequantize_weight, + pack_int4, + unpack_int4, +) +from hpc101_infer.quantization.types import QuantizedWeight + + +def quantize_checkpoint(*args, **kwargs): + from hpc101_infer.quantization.pipeline import quantize_checkpoint as run + + return run(*args, **kwargs) + + +__all__ = [ + "QuantizationConfig", + "QuantizedWeight", + "dequantize_weight", + "pack_int4", + "quantize_checkpoint", + "unpack_int4", +] diff --git a/src/lab5/src/hpc101_infer/quantization/calibration.py b/src/lab5/src/hpc101_infer/quantization/calibration.py new file mode 100644 index 00000000..590bdc13 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/calibration.py @@ -0,0 +1,121 @@ +"""量化校准数据的 CPU 存储、micro-batch 回放和 Linear 输入捕获。""" + +from __future__ import annotations + +from collections.abc import Iterator +from dataclasses import dataclass + +import torch + + +@dataclass(frozen=True) +class CalibrationBatch: + hidden_states: torch.Tensor + sequence_lengths: torch.Tensor + + +class CalibrationStore: + """在 CPU 保存某一层的输入 hidden states，避免校准集长期占用显存。""" + def __init__( + self, + hidden_states: torch.Tensor, + sequence_lengths: torch.Tensor, + *, + pin_memory: bool = False, + ) -> None: + self.hidden_states = hidden_states.detach().cpu().contiguous() + self.sequence_lengths = sequence_lengths.detach().cpu().long().contiguous() + if pin_memory: + self.hidden_states = self.hidden_states.pin_memory() + self.sequence_lengths = self.sequence_lengths.pin_memory() + + def micro_batches(self, batch_size: int) -> Iterator[CalibrationBatch]: + if batch_size <= 0: + raise ValueError("batch_size must be positive") + for start in range(0, self.hidden_states.shape[0], batch_size): + end = start + batch_size + yield CalibrationBatch( + hidden_states=self.hidden_states[start:end], + sequence_lengths=self.sequence_lengths[start:end], + ) + + def __len__(self) -> int: + return self.hidden_states.shape[0] + + +class ActivationCapture: + """通过 forward pre-hook 收集目标 Linear 的有效输入 token。 + + 捕获结果统一展平为 ``[valid_tokens, in_features]``。``max_tokens`` + 限制每个模块保留的 token 数，防止长校准集耗尽主机内存。 + """ + def __init__( + self, + modules: dict[str, torch.nn.Module], + *, + max_tokens: int, + pin_memory: bool = False, + ) -> None: + if max_tokens <= 0: + raise ValueError("max_tokens must be positive") + self.modules = dict(modules) + self.max_tokens = max_tokens + self.pin_memory = pin_memory + self._captured: dict[str, list[torch.Tensor]] = {name: [] for name in modules} + self._token_counts = {name: 0 for name in modules} + self._handles: list[torch.utils.hooks.RemovableHandle] = [] + self._sequence_lengths: torch.Tensor | None = None + + def set_sequence_lengths(self, sequence_lengths: torch.Tensor) -> None: + self._sequence_lengths = sequence_lengths.detach().long() + + def _hook(self, name: str): + def capture(module, args) -> None: + del module + if not args or not isinstance(args[0], torch.Tensor): + raise TypeError("captured module input must begin with a tensor") + remaining = self.max_tokens - self._token_counts[name] + if remaining <= 0: + return + + inputs = args[0].detach() + if self._sequence_lengths is None: + values = inputs.reshape(-1, inputs.shape[-1]) + else: + # 只保留真实 token；右侧 padding 不应参与激活统计。 + sequence_lengths = self._sequence_lengths.to(inputs.device) + positions = torch.arange(inputs.shape[1], device=inputs.device) + valid = positions.unsqueeze(0) < sequence_lengths.unsqueeze(1) + values = inputs[valid] + + values = values[:remaining].cpu().contiguous() + if self.pin_memory: + values = values.pin_memory() + self._captured[name].append(values) + self._token_counts[name] += values.shape[0] + + return capture + + def __enter__(self) -> "ActivationCapture": + if self._handles: + raise RuntimeError("activation capture is already active") + self._handles = [ + module.register_forward_pre_hook(self._hook(name)) + for name, module in self.modules.items() + ] + return self + + def __exit__(self, exc_type, exc_value, traceback) -> None: + del exc_type, exc_value, traceback + for handle in self._handles: + handle.remove() + self._handles = [] + self._sequence_lengths = None + + def activations(self, name: str) -> torch.Tensor: + if name not in self._captured: + raise KeyError(name) + tensors = self._captured[name] + if not tensors: + raise RuntimeError(f"no activations captured for {name}") + return torch.cat(tensors, dim=0) diff --git a/src/lab5/src/hpc101_infer/quantization/checkpoint.py b/src/lab5/src/hpc101_infer/quantization/checkpoint.py new file mode 100644 index 00000000..ca3a6ca6 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/checkpoint.py @@ -0,0 +1,195 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Iterable, Protocol + +import torch +from safetensors import safe_open +from safetensors.torch import save_file + +from hpc101_infer.quantization.config import QuantizationConfig +from hpc101_infer.quantization.types import ( + QuantizedModuleManifest, + TensorMetadata, +) + + +class CheckpointSource(Protocol): + def keys(self) -> Iterable[str]: ... + + def metadata(self, name: str) -> TensorMetadata: ... + + def load_tensor(self, name: str) -> torch.Tensor: ... + + +def checkpoint_weight_map(model_path: str | Path) -> dict[str, str]: + model_path = Path(model_path) + index_path = model_path / "model.safetensors.index.json" + if index_path.is_file(): + raw = json.loads(index_path.read_text()) + weight_map = raw.get("weight_map") + if not isinstance(weight_map, dict) or not weight_map: + raise RuntimeError(f"invalid checkpoint index: {index_path}") + missing = sorted( + { + shard + for shard in weight_map.values() + if not (model_path / shard).is_file() + } + ) + if missing: + raise RuntimeError(f"checkpoint shards not found: {missing}") + return dict(weight_map) + + checkpoint = model_path / "model.safetensors" + if checkpoint.is_file(): + with safe_open(checkpoint, framework="pt", device="cpu") as handle: + return {key: checkpoint.name for key in handle.keys()} + raise FileNotFoundError(f"no safetensors checkpoint found in {model_path}") + + +class HFSafetensorsSource: + def __init__(self, model_path: str | Path) -> None: + self.model_path = Path(model_path) + declared = checkpoint_weight_map(self.model_path) + self._weight_map: dict[str, str] = {} + for shard in dict.fromkeys(declared.values()): + with safe_open( + self.model_path / shard, framework="pt", device="cpu" + ) as handle: + for key in handle.keys(): + if key in self._weight_map: + raise RuntimeError(f"duplicated checkpoint tensor: {key}") + self._weight_map[key] = shard + declared_keys = set(declared) + actual_keys = set(self._weight_map) + if declared_keys != actual_keys: + raise RuntimeError( + "checkpoint index mismatch: " + f"missing={sorted(declared_keys - actual_keys)}, " + f"unexpected={sorted(actual_keys - declared_keys)}" + ) + + def keys(self) -> tuple[str, ...]: + return tuple(self._weight_map) + + def metadata(self, name: str) -> TensorMetadata: + try: + shard = self._weight_map[name] + except KeyError as error: + raise KeyError(name) from error + dtype_map = { + "BF16": torch.bfloat16, + "F16": torch.float16, + "F32": torch.float32, + "F64": torch.float64, + "I8": torch.int8, + "I16": torch.int16, + "I32": torch.int32, + "I64": torch.int64, + "U8": torch.uint8, + "BOOL": torch.bool, + } + with safe_open(self.model_path / shard, framework="pt", device="cpu") as handle: + tensor_slice = handle.get_slice(name) + return TensorMetadata( + tuple(tensor_slice.get_shape()), + dtype_map[tensor_slice.get_dtype()], + shard, + ) + + def load_tensor(self, name: str) -> torch.Tensor: + try: + shard = self._weight_map[name] + except KeyError as error: + raise KeyError(name) from error + with safe_open(self.model_path / shard, framework="pt", device="cpu") as handle: + return handle.get_tensor(name) + + +class QuantizedCheckpointSource(HFSafetensorsSource): + def __init__(self, model_path: str | Path) -> None: + super().__init__(model_path) + manifest_path = self.model_path / "manifest.json" + config_path = self.model_path / "quantization_config.json" + if not manifest_path.is_file() or not config_path.is_file(): + raise FileNotFoundError("quantized checkpoint metadata is incomplete") + raw_manifest = json.loads(manifest_path.read_text()) + modules = raw_manifest.get("modules") + if not isinstance(modules, dict): + raise RuntimeError("invalid quantized checkpoint manifest") + self.manifest = { + name: QuantizedModuleManifest.from_dict(entry) + for name, entry in modules.items() + } + self.quantization_config = QuantizationConfig.from_dict( + json.loads(config_path.read_text()) + ) + + +class CheckpointWriter: + def __init__( + self, + output_dir: str | Path, + *, + max_shard_size_bytes: int = 1 << 30, + ) -> None: + if max_shard_size_bytes <= 0: + raise ValueError("max_shard_size_bytes must be positive") + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.max_shard_size_bytes = max_shard_size_bytes + self._buffer: dict[str, torch.Tensor] = {} + self._buffer_bytes = 0 + self._weight_map: dict[str, str] = {} + self._temporary_shards: list[Path] = [] + + def add_tensor(self, name: str, tensor: torch.Tensor) -> None: + if name in self._weight_map or name in self._buffer: + raise RuntimeError(f"duplicated output tensor: {name}") + tensor = tensor.detach().cpu().contiguous() + size = tensor.numel() * tensor.element_size() + if self._buffer and self._buffer_bytes + size > self.max_shard_size_bytes: + self._flush() + self._buffer[name] = tensor + self._buffer_bytes += size + + def _flush(self) -> None: + if not self._buffer: + return + shard_index = len(self._temporary_shards) + 1 + path = self.output_dir / f"model-{shard_index:05d}.safetensors" + save_file(self._buffer, path) + for name in self._buffer: + self._weight_map[name] = path.name + self._temporary_shards.append(path) + self._buffer = {} + self._buffer_bytes = 0 + + def finalize(self) -> dict[str, str]: + self._flush() + if not self._temporary_shards: + raise RuntimeError("cannot finalize an empty checkpoint") + shard_count = len(self._temporary_shards) + renamed: dict[str, str] = {} + for index, old_path in enumerate(self._temporary_shards, 1): + new_name = f"model-{index:05d}-of-{shard_count:05d}.safetensors" + new_path = self.output_dir / new_name + old_path.replace(new_path) + renamed[old_path.name] = new_name + self._weight_map = { + key: renamed[shard] for key, shard in self._weight_map.items() + } + total_size = sum( + (self.output_dir / shard).stat().st_size + for shard in set(self._weight_map.values()) + ) + index = { + "metadata": {"total_size": total_size}, + "weight_map": self._weight_map, + } + (self.output_dir / "model.safetensors.index.json").write_text( + json.dumps(index, indent=2, sort_keys=True) + "\n" + ) + return dict(self._weight_map) diff --git a/src/lab5/src/hpc101_infer/quantization/cli.py b/src/lab5/src/hpc101_infer/quantization/cli.py new file mode 100644 index 00000000..f195bc87 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/cli.py @@ -0,0 +1,137 @@ +from __future__ import annotations + +import argparse +from collections.abc import Sequence + +import torch +from transformers import AutoTokenizer + +from hpc101_infer.data import load_token_ids +from hpc101_infer.quantization import quantize_checkpoint +from hpc101_infer.quantization.config import QuantizationConfig +from hpc101_infer.quantization.registry import available_quantization_methods + + +def _positive_int(value: str) -> int: + parsed = int(value) + if parsed <= 0: + raise argparse.ArgumentTypeError("must be a positive integer") + return parsed + + +def _fraction(value: str) -> float: + parsed = float(value) + if not 0.0 < parsed < 1.0: + raise argparse.ArgumentTypeError("must be in (0, 1)") + return parsed + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Create a W4A16 Gemma 4 checkpoint") + parser.add_argument("--model", required=True, help="source Hugging Face checkpoint") + parser.add_argument("--output", required=True, help="output checkpoint directory") + parser.add_argument( + "--algorithm", choices=available_quantization_methods(), default="rtn" + ) + parser.add_argument("--group-size", type=int, choices=(64, 128), default=128) + parser.add_argument("--asymmetric", action="store_true") + parser.add_argument( + "--scale-dtype", choices=("float16", "bfloat16"), default="float16" + ) + parser.add_argument("--device", default="cpu") + parser.add_argument("--max-shard-size-mib", type=int, default=1024) + parser.add_argument( + "--calibration-file", help="path to calibration JSONL text file" + ) + parser.add_argument( + "--calibration-limit", + type=_positive_int, + help="use at most this many JSONL records", + ) + parser.add_argument( + "--calibration-micro-batch-size", + type=_positive_int, + default=1, + ) + parser.add_argument( + "--max-calibration-tokens", + type=_positive_int, + default=4096, + help="maximum valid activation tokens captured per Linear module", + ) + parser.add_argument( + "--gptq-block-size", + type=_positive_int, + default=128, + ) + parser.add_argument( + "--gptq-damp-percent", + type=_fraction, + default=0.01, + ) + parser.add_argument( + "--verbose", action="store_true", help="enable verbose loss printing" + ) + return parser.parse_args(argv) + + +def main() -> None: + args = parse_args() + calibration = None + if args.algorithm == "gptq": + calibration = { + "gptq": { + "block_size": args.gptq_block_size, + "damp_percent": args.gptq_damp_percent, + } + } + + config = QuantizationConfig( + algorithm=args.algorithm, + group_size=args.group_size, + symmetric=not args.asymmetric, + scale_dtype=args.scale_dtype, + calibration=calibration, + source_checkpoint=args.model, + ) + + calibration_input_ids = None + if args.calibration_file: + tokenizer = AutoTokenizer.from_pretrained( + args.model, + local_files_only=True, + ) + calibration_token_ids = load_token_ids( + args.calibration_file, + tokenizer, + limit=args.calibration_limit, + ) + if not calibration_token_ids: + raise ValueError("calibration JSONL must contain at least one record") + if tokenizer.pad_token_id is None: + raise ValueError("tokenizer must define pad_token_id for calibration") + max_length = max(len(token_ids) for token_ids in calibration_token_ids) + calibration_input_ids = torch.full( + (len(calibration_token_ids), max_length), + tokenizer.pad_token_id, + dtype=torch.long, + ) + for row, token_ids in enumerate(calibration_token_ids): + calibration_input_ids[row, : len(token_ids)] = torch.tensor(token_ids) + + manifest = quantize_checkpoint( + args.model, + args.output, + config, + calibration_input_ids=calibration_input_ids, + calibration_micro_batch_size=args.calibration_micro_batch_size, + max_calibration_tokens=args.max_calibration_tokens, + device=args.device, + max_shard_size_bytes=args.max_shard_size_mib * 1024 * 1024, + print_layer_loss=args.verbose, + ) + print(f"quantized {len(manifest)} Linear modules into {args.output}") + + +if __name__ == "__main__": + main() diff --git a/src/lab5/src/hpc101_infer/quantization/config.py b/src/lab5/src/hpc101_infer/quantization/config.py new file mode 100644 index 00000000..31a6d984 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/config.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from dataclasses import asdict, dataclass +from typing import Any + + +@dataclass(frozen=True) +class QuantizationConfig: + algorithm: str = "rtn" + version: str = "1" + bits: int = 4 + group_size: int = 128 + symmetric: bool = True + scale_dtype: str = "float16" + packing: str = "uint8_little_nibble" + target_modules: tuple[str, ...] = ( + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + ) + calibration: dict[str, Any] | None = None + source_checkpoint: str | None = None + propagate_quantized: bool = True + + def __post_init__(self) -> None: + if self.algorithm not in {"rtn", "gptq"}: + raise ValueError(f"unsupported quantization algorithm: {self.algorithm}") + if self.bits != 4: + raise ValueError("only 4-bit weight quantization is supported") + if self.group_size not in {64, 128}: + raise ValueError("group_size must be 64 or 128") + if self.scale_dtype not in {"float16", "bfloat16"}: + raise ValueError("scale_dtype must be float16 or bfloat16") + if self.packing != "uint8_little_nibble": + raise ValueError(f"unsupported packing layout: {self.packing}") + if not self.target_modules: + raise ValueError("target_modules must not be empty") + + def to_dict(self) -> dict[str, Any]: + result = asdict(self) + result["target_modules"] = list(self.target_modules) + return result + + @classmethod + def from_dict(cls, raw: dict[str, Any]) -> "QuantizationConfig": + values = dict(raw) + if "target_modules" in values: + values["target_modules"] = tuple(values["target_modules"]) + return cls(**values) diff --git a/src/lab5/src/hpc101_infer/quantization/methods/__init__.py b/src/lab5/src/hpc101_infer/quantization/methods/__init__.py new file mode 100644 index 00000000..bbc28a56 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/methods/__init__.py @@ -0,0 +1,7 @@ +from hpc101_infer.quantization.methods.gptq import GPTQQuantizationMethod +from hpc101_infer.quantization.methods.rtn import RTNQuantizationMethod + +__all__ = [ + "GPTQQuantizationMethod", + "RTNQuantizationMethod", +] diff --git a/src/lab5/src/hpc101_infer/quantization/methods/base.py b/src/lab5/src/hpc101_infer/quantization/methods/base.py new file mode 100644 index 00000000..de84ae4e --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/methods/base.py @@ -0,0 +1,7 @@ +from hpc101_infer.quantization.types import ( + LayerContext, + LayerQuantizationResult, + QuantizationMethod, +) + +__all__ = ["LayerContext", "LayerQuantizationResult", "QuantizationMethod"] diff --git a/src/lab5/src/hpc101_infer/quantization/methods/gptq.py b/src/lab5/src/hpc101_infer/quantization/methods/gptq.py new file mode 100644 index 00000000..2b3976a0 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/methods/gptq.py @@ -0,0 +1,215 @@ +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Any, Mapping + +import torch +from torch import nn +from torch.nn import functional as F + +from hpc101_infer.quantization.packing import pack_int4 +from hpc101_infer.quantization.types import ( + LayerContext, + LayerQuantizationResult, + QuantizedWeight, + SCALE_DTYPES, +) + + +@dataclass(frozen=True) +class GPTQOptions: + block_size: int + damp_percent: float + + +@dataclass(frozen=True) +class GPTQModuleState: + activations: torch.Tensor + activation_tokens: int + + +@dataclass(frozen=True) +class GPTQLayerState: + modules: Mapping[str, GPTQModuleState] + options: GPTQOptions + + +def _calibration_option( + calibration: Mapping[str, Any], + name: str, + default: int | float, +) -> int | float: + gptq = calibration.get("gptq", calibration) + if not isinstance(gptq, Mapping): + raise TypeError("config.calibration['gptq'] must be a mapping") + return gptq.get(name, default) + + +def _parse_gptq_options(calibration: Mapping[str, Any]) -> GPTQOptions: + block_size = _calibration_option(calibration, "block_size", 128) + damp_percent = _calibration_option(calibration, "damp_percent", 0.01) + + if ( + not isinstance(block_size, int) + or isinstance(block_size, bool) + or block_size <= 0 + ): + raise ValueError("GPTQ block_size must be a positive integer") + if ( + not isinstance(damp_percent, (int, float)) + or isinstance(damp_percent, bool) + or not 0.0 < float(damp_percent) < 1.0 + ): + raise ValueError("GPTQ damp_percent must be in (0, 1)") + + gptq = calibration.get("gptq", calibration) + if isinstance(gptq, Mapping) and gptq.get("desc_act", False): + raise ValueError("GPTQ desc_act is not supported by this checkpoint format") + return GPTQOptions( + block_size=block_size, + damp_percent=float(damp_percent), + ) + + +def quantize_weight_gptq( + weight: torch.Tensor, + activations: torch.Tensor, + group_size: int, + *, + block_size: int = 128, + damp_percent: float = 0.01, + symmetric: bool = True, + scale_dtype: torch.dtype = torch.float16, +) -> tuple[QuantizedWeight, dict[str, float | int]]: + """ + 使用 GPTQ 算法将权重量化为 INT4。 + + 参数： + weight: 待量化的权重张量，形状为 (out_features, in_features)。 + activations: 校准数据集的输入激活，形状为 (calibration_tokens, in_features)。 + group_size: 量化粒度，即每个 group 中的列数。 + block_size: 分块计算时每个 block 中的列数。 + damp_percent: 阻尼比例，用于改善 Hessian 的数值稳定性。 + symmetric: 是否使用对称量化。 + scale_dtype: 缩放因子的 dtype。 + + 返回： + quantized_weight: 量化后的权重对象，具体详见 QuantizedWeight 类的定义。 + metadatas: 量化过程中的统计信息，不影响评测，用于分析和调试。 + """ + + if weight.ndim != 2 or not weight.is_floating_point(): + raise ValueError("weight must be a floating-point matrix") + if activations.ndim != 2 or not activations.is_floating_point(): + raise ValueError("activations must be a floating-point matrix") + if activations.shape[1] != weight.shape[1]: + raise ValueError("activation width does not match weight input size") + if group_size <= 0: + raise ValueError("group_size must be positive") + if block_size <= 0: + raise ValueError("block_size must be positive") + if not 0.0 < damp_percent < 1.0: + raise ValueError("damp_percent must be in (0, 1)") + if scale_dtype not in SCALE_DTYPES.values(): + raise ValueError("unsupported scale dtype") + if not torch.isfinite(weight).all(): + raise ValueError("weight must contain only finite values") + + raise NotImplementedError("TODO: Implement the GPTQ quantization algorithm here.") + + quantized = QuantizedWeight(...) + metadata: dict[str, float | int] = { + "activation_tokens": activations.shape[0], + "block_size": block_size, + "damp_percent": damp_percent, + "dead_columns": ..., + "predicted_loss": ..., + } + return quantized, metadata + + +class GPTQQuantizationMethod: + name = "gptq" + version = "1" + + def calibrate_layer(self, context: LayerContext) -> GPTQLayerState: + if context.activations is None: + raise ValueError("GPTQ requires calibration activations") + + options = _parse_gptq_options(context.config.calibration or {}) + modules = dict(context.layer.named_modules()) + states: dict[str, GPTQModuleState] = {} + for name in context.target_modules: + module = modules.get(name) + if not isinstance(module, nn.Linear): + raise TypeError(f"target is not a Linear module: {name}") + if name not in context.activations: + raise ValueError(f"missing calibration activations for {name}") + + activations = context.activations[name] + if activations.ndim != 2 or activations.shape[1] != module.in_features: + raise ValueError( + f"invalid calibration activation shape for {name}: " + f"expected [tokens, {module.in_features}], " + f"got {tuple(activations.shape)}" + ) + if activations.shape[0] == 0: + raise ValueError(f"calibration activations for {name} are empty") + if not activations.is_floating_point(): + raise TypeError(f"calibration activations for {name} must be floating") + if not torch.isfinite(activations).all(): + raise ValueError(f"calibration activations for {name} are not finite") + + states[name] = GPTQModuleState( + activations=activations.detach(), + activation_tokens=activations.shape[0], + ) + + return GPTQLayerState(modules=states, options=options) + + def quantize_layer( + self, context: LayerContext, state: GPTQLayerState + ) -> LayerQuantizationResult: + if not isinstance(state, GPTQLayerState): + raise TypeError("state must be a GPTQLayerState") + + scale_dtype = SCALE_DTYPES[context.config.scale_dtype] + modules = dict(context.layer.named_modules()) + weights: dict[str, QuantizedWeight] = {} + module_metadata: dict[str, dict[str, float | int]] = {} + + for name in context.target_modules: + module = modules.get(name) + if not isinstance(module, nn.Linear): + raise TypeError(f"target is not a Linear module: {name}") + module_state = state.modules.get(name) + if module_state is None: + raise ValueError(f"missing GPTQ calibration state for {name}") + + try: + weights[name], module_metadata[name] = quantize_weight_gptq( + module.weight, + module_state.activations, + context.config.group_size, + block_size=state.options.block_size, + damp_percent=state.options.damp_percent, + symmetric=context.config.symmetric, + scale_dtype=scale_dtype, + ) + except RuntimeError as error: + raise RuntimeError( + f"GPTQ failed for layer {context.layer_index} module {name} " + f"with {module_state.activation_tokens} calibration tokens: {error}" + ) from error + + return LayerQuantizationResult( + weights=weights, + metadata={ + "gptq": { + "block_size": state.options.block_size, + "damp_percent": state.options.damp_percent, + "modules": module_metadata, + } + }, + ) diff --git a/src/lab5/src/hpc101_infer/quantization/methods/rtn.py b/src/lab5/src/hpc101_infer/quantization/methods/rtn.py new file mode 100644 index 00000000..afefa05d --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/methods/rtn.py @@ -0,0 +1,99 @@ +"""Round-to-Nearest INT4 基线，用于验证 packing 与推理闭环。""" + +from __future__ import annotations + +import math +import torch +from torch import nn +from torch.nn import functional as F + +from hpc101_infer.quantization.types import ( + LayerContext, + LayerQuantizationResult, + QuantizedWeight, + SCALE_DTYPES, +) + +from hpc101_infer.quantization.packing import pack_int4 + + +def quantize_weight_rtn( + weight: torch.Tensor, + group_size: int, + *, + symmetric: bool = True, + scale_dtype: torch.dtype = torch.float16, +) -> QuantizedWeight: + """逐输出通道、逐 group 量化权重，不使用任何校准激活。 + + RTN 是便于调试的参考基线：它验证 group padding、scale/zero point、 + INT4 packing 和 checkpoint 加载，但不补偿量化误差。 + """ + if weight.ndim != 2 or not weight.is_floating_point(): + raise ValueError("weight must be a floating-point matrix") + if group_size <= 0: + raise ValueError("group_size must be positive") + if scale_dtype not in SCALE_DTYPES.values(): + raise ValueError("unsupported scale dtype") + + weight = weight.detach() + out_features, in_features = weight.shape + padded_in_features = math.ceil(in_features / group_size) * group_size + # 仅在输入维补零，使每个输出通道都能重排为完整的量化 groups。 + padded = F.pad(weight.float(), (0, padded_in_features - in_features)) + groups = padded.view(out_features, -1, group_size) + + if symmetric: + scales = groups.abs().amax(dim=-1) / 7.0 + scales = scales.clamp_min(torch.finfo(torch.float32).eps) + quantized = torch.round(groups / scales.unsqueeze(-1)).clamp(-8, 7) + encoded = (quantized.to(torch.int16) + 8).to(torch.uint8) + zeros = None + else: + zero = torch.zeros_like(groups[..., 0]) + minimum = torch.minimum(groups.amin(dim=-1), zero) + maximum = torch.maximum(groups.amax(dim=-1), zero) + scales = ((maximum - minimum) / 15.0).clamp_min(torch.finfo(torch.float32).eps) + zeros = torch.round(-minimum / scales).clamp(0, 15).to(torch.uint8) + encoded = torch.round(groups / scales.unsqueeze(-1)) + encoded = (encoded + zeros.unsqueeze(-1)).clamp(0, 15).to(torch.uint8) + + return QuantizedWeight( + qweight=pack_int4(encoded.reshape(out_features, padded_in_features)), + scales=scales.to(scale_dtype), + zeros=zeros, + original_shape=(out_features, in_features), + padded_shape=(out_features, padded_in_features), + bits=4, + group_size=group_size, + symmetric=symmetric, + packing="uint8_little_nibble", + ) + + +class RTNQuantizationMethod: + name = "rtn" + version = "1" + + def calibrate_layer(self, context: LayerContext) -> None: + del context + return None + + def quantize_layer( + self, context: LayerContext, state: None + ) -> LayerQuantizationResult: + del state + scale_dtype = SCALE_DTYPES[context.config.scale_dtype] + weights = {} + modules = dict(context.layer.named_modules()) + for name in context.target_modules: + module = modules[name] + if not isinstance(module, nn.Linear): + raise TypeError(f"target is not a Linear module: {name}") + weights[name] = quantize_weight_rtn( + module.weight, + context.config.group_size, + symmetric=context.config.symmetric, + scale_dtype=scale_dtype, + ) + return LayerQuantizationResult(weights=weights, metadata={}) diff --git a/src/lab5/src/hpc101_infer/quantization/packing.py b/src/lab5/src/hpc101_infer/quantization/packing.py new file mode 100644 index 00000000..656503c0 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/packing.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import math + +import torch +from torch.nn import functional as F + +from hpc101_infer.quantization.types import QuantizedWeight + + +def pack_int4(values: torch.Tensor) -> torch.Tensor: + if values.dtype not in { + torch.uint8, + torch.int8, + torch.int16, + torch.int32, + torch.int64, + }: + raise TypeError("int4 values must use an integer dtype") + if values.shape[-1] % 2: + values = F.pad(values, (0, 1)) + values = values.to(torch.uint8) & 0x0F + return values[..., 0::2] | (values[..., 1::2] << 4) + + +def unpack_int4(packed: torch.Tensor, length: int | None = None) -> torch.Tensor: + if packed.dtype != torch.uint8: + raise TypeError("packed int4 tensor must use torch.uint8") + unpacked = torch.empty( + (*packed.shape[:-1], packed.shape[-1] * 2), + dtype=torch.uint8, + device=packed.device, + ) + unpacked[..., 0::2] = packed & 0x0F + unpacked[..., 1::2] = packed >> 4 + if length is not None: + if length < 0 or length > unpacked.shape[-1]: + raise ValueError("invalid unpacked length") + unpacked = unpacked[..., :length] + return unpacked + + +def dequantize_weight( + quantized: QuantizedWeight, + *, + dtype: torch.dtype = torch.bfloat16, +) -> torch.Tensor: + if quantized.bits != 4: + raise ValueError("only int4 weights are supported") + out_features, padded_in_features = quantized.padded_shape + encoded = unpack_int4(quantized.qweight, padded_in_features) + encoded = encoded.view(out_features, -1, quantized.group_size).to(dtype) + scales = quantized.scales.to(dtype).unsqueeze(-1) + if quantized.symmetric: + values = encoded - 8.0 + else: + if quantized.zeros is None: + raise ValueError("asymmetric quantization requires zero points") + values = encoded - quantized.zeros.to(dtype).unsqueeze(-1) + weight = (values * scales).reshape(out_features, padded_in_features) + return weight[:, : quantized.original_shape[1]].to(dtype) diff --git a/src/lab5/src/hpc101_infer/quantization/pipeline.py b/src/lab5/src/hpc101_infer/quantization/pipeline.py new file mode 100644 index 00000000..d2b85c80 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/pipeline.py @@ -0,0 +1,599 @@ +from __future__ import annotations + +import json +import math +import shutil +from collections import defaultdict +from pathlib import Path +from typing import Any + +import torch +from torch import nn +from torch.nn import functional as F + +from hpc101_infer.layers.linear import BF16LinearFactory +from hpc101_infer.layers.rotary import RotaryEmbedding +from hpc101_infer.models.config import Gemma4TextConfig +from hpc101_infer.models.gemma4 import DecoderLayer +from hpc101_infer.quantization.calibration import ActivationCapture, CalibrationStore +from hpc101_infer.quantization.checkpoint import ( + CheckpointWriter, + HFSafetensorsSource, +) +from hpc101_infer.quantization.config import QuantizationConfig +from hpc101_infer.quantization.packing import dequantize_weight +from hpc101_infer.quantization.registry import create_quantization_method +from hpc101_infer.quantization.types import ( + LayerContext, + LayerQuantizationResult, + QuantizationMethod, + QuantizedModuleManifest, + QuantizedWeight, +) + +__all__ = ["quantize_checkpoint"] + +_CHECKPOINT_PREFIX = "model.language_model." +_EXCLUDED_AUXILIARY_FILES = { + "config.json", + "model.safetensors", + "model.safetensors.index.json", + "manifest.json", + "quantization_config.json", +} + + +def _parse_target_weight( + local_key: str, + target_modules: tuple[str, ...], +) -> tuple[int, str] | None: + if not local_key.startswith("layers.") or not local_key.endswith(".weight"): + return None + + parts = local_key.split(".") + if len(parts) < 4 or parts[-2] not in target_modules: + return None + + try: + layer_index = int(parts[1]) + except ValueError: + return None + module_name = ".".join(parts[2:]).removesuffix(".weight") + return layer_index, module_name + + +def _resolve_tensor( + module: nn.Module, + name: str, +) -> tuple[nn.Module, str, torch.Tensor]: + module_path, separator, tensor_name = name.rpartition(".") + parent = module.get_submodule(module_path) if separator else module + if tensor_name in parent._parameters: + tensor = parent._parameters[tensor_name] + elif tensor_name in parent._buffers: + tensor = parent._buffers[tensor_name] + else: + raise KeyError(name) + if tensor is None: + raise KeyError(name) + return parent, tensor_name, tensor + + +def _materialize_tensor( + module: nn.Module, + name: str, + checkpoint_tensor: torch.Tensor, + device: torch.device, +) -> None: + parent, tensor_name, target = _resolve_tensor(module, name) + if checkpoint_tensor.shape != target.shape: + raise RuntimeError( + f"shape mismatch for {name}: checkpoint={tuple(checkpoint_tensor.shape)}, " + f"model={tuple(target.shape)}" + ) + materialized = checkpoint_tensor.to(device=device) + if tensor_name in parent._parameters: + parent._parameters[tensor_name] = nn.Parameter( + materialized, + requires_grad=target.requires_grad, + ) + else: + parent._buffers[tensor_name] = materialized + + +class QuantizationPipeline: + def __init__( + self, + source_model_path: str | Path, + output_dir: str | Path, + config: QuantizationConfig, + *, + method: QuantizationMethod | None, + calibration_input_ids: torch.Tensor | None, + calibration_micro_batch_size: int, + max_calibration_tokens: int, + print_layer_loss: bool, + device: str | torch.device, + max_shard_size_bytes: int, + ) -> None: + if calibration_micro_batch_size <= 0: + raise ValueError("calibration_micro_batch_size must be positive") + if max_calibration_tokens <= 0: + raise ValueError("max_calibration_tokens must be positive") + if calibration_input_ids is not None: + if calibration_input_ids.ndim != 2: + raise ValueError("calibration_input_ids must have shape [batch, seq]") + if ( + calibration_input_ids.shape[0] == 0 + or calibration_input_ids.shape[1] == 0 + ): + raise ValueError("calibration_input_ids must not be empty") + if calibration_input_ids.is_floating_point(): + raise TypeError("calibration_input_ids must use an integer dtype") + calibration_input_ids = ( + calibration_input_ids.detach().cpu().long().contiguous() + ) + if print_layer_loss and calibration_input_ids is None: + raise ValueError("print_layer_loss requires calibration_input_ids") + + self.source_model_path = Path(source_model_path) + self.output_dir = Path(output_dir) + self.config = config + self.method = QuantizationPipeline._resolve_method(config, method) + self.calibration_input_ids = calibration_input_ids + self.calibration_micro_batch_size = calibration_micro_batch_size + self.max_calibration_tokens = max_calibration_tokens + self.print_layer_loss = print_layer_loss + self.device = torch.device(device) + self.max_shard_size_bytes = max_shard_size_bytes + self.source = HFSafetensorsSource(self.source_model_path) + self.source_keys = frozenset(self.source.keys()) + self.model_config = Gemma4TextConfig.from_pretrained(self.source_model_path) + self.manifest: dict[str, QuantizedModuleManifest] = {} + self.layer_metadata: dict[str, dict[str, Any]] = {} + + def run(self) -> dict[str, QuantizedModuleManifest]: + self.output_dir.mkdir(parents=True, exist_ok=True) + writer = CheckpointWriter( + self.output_dir, + max_shard_size_bytes=self.max_shard_size_bytes, + ) + targets = self._copy_unquantized_tensors(writer) + if not targets: + raise RuntimeError("source checkpoint contains no target Linear weights") + + unexpected_layers = sorted( + set(targets) - set(range(self.model_config.num_hidden_layers)) + ) + if unexpected_layers: + raise RuntimeError( + f"target weights reference invalid layers: {unexpected_layers}" + ) + + calibration_store = self._build_initial_calibration_store() + for layer_index in range(self.model_config.num_hidden_layers): + target_modules = tuple(sorted(targets.get(layer_index, ()))) + if not target_modules: + raise RuntimeError( + f"layer {layer_index} contains no target Linear weights" + ) + calibration_store = self._quantize_layer( + writer, + layer_index, + target_modules, + calibration_store, + ) + + writer.finalize() + self._write_metadata(self._read_model_config()) + self._copy_auxiliary_files() + return self.manifest + + def _copy_unquantized_tensors( + self, + writer: CheckpointWriter, + ) -> dict[int, list[str]]: + targets: dict[int, list[str]] = defaultdict(list) + for checkpoint_key in self.source.keys(): + if not checkpoint_key.startswith(_CHECKPOINT_PREFIX): + continue + + local_key = checkpoint_key.removeprefix(_CHECKPOINT_PREFIX) + target = _parse_target_weight(local_key, self.config.target_modules) + if target is None: + writer.add_tensor(local_key, self.source.load_tensor(checkpoint_key)) + continue + + layer_index, module_name = target + targets[layer_index].append(module_name) + return targets + + def _build_initial_calibration_store(self) -> CalibrationStore | None: + if self.calibration_input_ids is None: + return None + if ( + self.calibration_input_ids.shape[1] + > self.model_config.max_position_embeddings + ): + raise ValueError( + "calibration sequence length exceeds model max_position_embeddings" + ) + + embedding_key = f"{_CHECKPOINT_PREFIX}embed_tokens.weight" + if embedding_key not in self.source_keys: + raise RuntimeError(f"source checkpoint is missing {embedding_key}") + is_padding = self.calibration_input_ids == self.model_config.pad_token_id + sequence_lengths = (~is_padding).sum(dim=1, dtype=torch.long) + + embedding = self.source.load_tensor(embedding_key).to(self.device) + hidden_states = [] + with torch.inference_mode(): + for start in range( + 0, + self.calibration_input_ids.shape[0], + self.calibration_micro_batch_size, + ): + input_ids = self.calibration_input_ids[ + start : start + self.calibration_micro_batch_size + ].to(self.device) + batch_hidden_states = F.embedding(input_ids, embedding) + batch_hidden_states = batch_hidden_states * math.sqrt( + self.model_config.hidden_size + ) + hidden_states.append(batch_hidden_states.cpu()) + return CalibrationStore(torch.cat(hidden_states, dim=0), sequence_lengths) + + def _load_layer(self, layer_index: int) -> DecoderLayer: + with torch.device("meta"): + layer = DecoderLayer( + self.model_config, layer_index, linear_factory=BF16LinearFactory() + ) + + prefix = f"{_CHECKPOINT_PREFIX}layers.{layer_index}." + for local_name in layer.state_dict(): + checkpoint_key = f"{prefix}{local_name}" + if checkpoint_key not in self.source_keys: + raise RuntimeError(f"source checkpoint is missing {checkpoint_key}") + checkpoint_tensor = self.source.load_tensor(checkpoint_key) + _materialize_tensor(layer, local_name, checkpoint_tensor, self.device) + + for module in layer.modules(): + if isinstance(module, RotaryEmbedding): + module.materialize(self.device) + return layer.eval() + + def _target_linears( + self, + layer: DecoderLayer, + target_modules: tuple[str, ...], + ) -> dict[str, nn.Linear]: + modules = dict(layer.named_modules()) + targets: dict[str, nn.Linear] = {} + for name in target_modules: + module = modules.get(name) + if not isinstance(module, nn.Linear): + raise TypeError(f"target is not a Linear module: {name}") + targets[name] = module + return targets + + def _replay_layer( + self, + layer: DecoderLayer, + store: CalibrationStore, + *, + collect_outputs: bool, + capture: ActivationCapture | None = None, + ) -> CalibrationStore | None: + outputs = [] + output_sequence_lengths = [] + with torch.inference_mode(): + for batch in store.micro_batches(self.calibration_micro_batch_size): + hidden_states = batch.hidden_states.to(self.device) + sequence_lengths = batch.sequence_lengths.to(self.device) + batch_size, sequence_length = hidden_states.shape[:2] + position_ids = ( + torch.arange(sequence_length, device=self.device) + .unsqueeze(0) + .expand(batch_size, -1) + ) + if capture is not None: + capture.set_sequence_lengths(sequence_lengths) + output = layer( + hidden_states, + position_ids, + sequence_lengths, + max_seq_len=sequence_length, + kv_cache=None, + ) + if collect_outputs: + outputs.append(output.cpu()) + output_sequence_lengths.append(batch.sequence_lengths) + if not collect_outputs: + return None + return CalibrationStore( + torch.cat(outputs, dim=0), + torch.cat(output_sequence_lengths, dim=0), + ) + + def _apply_fake_quantized_weights( + self, + layer: DecoderLayer, + result: LayerQuantizationResult, + ) -> None: + for name, quantized in result.weights.items(): + module = layer.get_submodule(name) + if not isinstance(module, nn.Linear): + raise TypeError(f"target is not a Linear module: {name}") + fake_quantized = dequantize_weight( + quantized, + dtype=module.weight.dtype, + ).to(self.device) + module.weight = nn.Parameter(fake_quantized, requires_grad=False) + + @staticmethod + def _module_quantization_loss( + module: nn.Linear, + quantized: QuantizedWeight, + activations: torch.Tensor, + *, + max_chunk_output_elements: int = 1 << 20, + ) -> float: + if activations.ndim != 2 or activations.shape[1] != module.in_features: + raise ValueError("module activations have an invalid shape") + if max_chunk_output_elements <= 0: + raise ValueError("max_chunk_output_elements must be positive") + + device = module.weight.device + dtype = module.weight.dtype + fake_quantized = dequantize_weight(quantized, dtype=dtype).to(device) + chunk_tokens = max(1, max_chunk_output_elements // module.out_features) + squared_error = 0.0 + output_elements = 0 + + with torch.inference_mode(): + for start in range(0, activations.shape[0], chunk_tokens): + inputs = activations[start : start + chunk_tokens].to( + device=device, + dtype=dtype, + ) + reference = F.linear(inputs, module.weight, module.bias) + reconstructed = F.linear(inputs, fake_quantized, module.bias) + difference = reference.float() - reconstructed.float() + squared_error += difference.square().sum().item() + output_elements += difference.numel() + + if output_elements == 0: + raise ValueError("module activations must not be empty") + return squared_error / output_elements + + @staticmethod + def _layer_quantization_loss( + reference: CalibrationStore, + quantized: CalibrationStore, + ) -> float: + if not torch.equal(reference.sequence_lengths, quantized.sequence_lengths): + raise ValueError("calibration stores use different sequence lengths") + positions = torch.arange(reference.hidden_states.shape[1]) + valid = positions.unsqueeze(0) < reference.sequence_lengths.unsqueeze(1) + return F.mse_loss( + quantized.hidden_states[valid].float(), + reference.hidden_states[valid].float(), + ).item() + + def _quantize_layer( + self, + writer: CheckpointWriter, + layer_index: int, + target_modules: tuple[str, ...], + calibration_store: CalibrationStore | None, + ) -> CalibrationStore | None: + layer = self._load_layer(layer_index) + targets = self._target_linears(layer, target_modules) + activations = None + next_store = None + reference_store = None + has_next_layer = layer_index + 1 < self.model_config.num_hidden_layers + + if calibration_store is not None: + with ActivationCapture( + targets, + max_tokens=self.max_calibration_tokens, + ) as capture: + next_store = self._replay_layer( + layer, + calibration_store, + collect_outputs=self.print_layer_loss + or (has_next_layer and not self.config.propagate_quantized), + capture=capture, + ) + activations = {name: capture.activations(name) for name in target_modules} + if self.print_layer_loss: + reference_store = next_store + + context = LayerContext( + layer_index=layer_index, + layer_type=self.model_config.layer_types[layer_index], + layer=layer, + activations=activations, + config=self.config, + target_modules=target_modules, + ) + result = self._run_method(context) + if self.print_layer_loss: + if activations is None: + raise RuntimeError("module loss printing requires calibration activations") + for module_name in target_modules: + quantized = result.weights[module_name] + loss = self._module_quantization_loss( + targets[module_name], + quantized, + activations[module_name], + ) + print( + f"layer {layer_index} module {module_name} " + f"quantization loss (MSE): {loss:.6e}" + ) + for module_name, quantized in result.weights.items(): + self._write_quantized_weight( + writer, + f"layers.{layer_index}.{module_name}", + quantized, + ) + if result.metadata: + self.layer_metadata[str(layer_index)] = result.metadata + + needs_quantized_outputs = calibration_store is not None and ( + self.print_layer_loss + or (has_next_layer and self.config.propagate_quantized) + ) + if needs_quantized_outputs: + self._apply_fake_quantized_weights(layer, result) + quantized_store = self._replay_layer( + layer, + calibration_store, + collect_outputs=True, + ) + if quantized_store is None: + raise RuntimeError("quantized layer replay produced no outputs") + if self.print_layer_loss: + if reference_store is None: + raise RuntimeError("reference layer replay produced no outputs") + loss = self._layer_quantization_loss( + reference_store, + quantized_store, + ) + print(f"layer {layer_index} quantization loss (MSE): {loss:.6e}") + if has_next_layer and self.config.propagate_quantized: + next_store = quantized_store + return next_store + + def _write_quantized_weight( + self, + writer: CheckpointWriter, + module_name: str, + quantized: QuantizedWeight, + ) -> None: + qweight_key = f"{module_name}.qweight" + scales_key = f"{module_name}.scales" + zeros_key = None if quantized.zeros is None else f"{module_name}.zeros" + + writer.add_tensor(qweight_key, quantized.qweight) + writer.add_tensor(scales_key, quantized.scales) + if zeros_key is not None and quantized.zeros is not None: + writer.add_tensor(zeros_key, quantized.zeros) + + self.manifest[module_name] = QuantizedModuleManifest( + original_shape=quantized.original_shape, + padded_shape=quantized.padded_shape, + qweight_key=qweight_key, + scales_key=scales_key, + zeros_key=zeros_key, + bits=quantized.bits, + group_size=quantized.group_size, + symmetric=quantized.symmetric, + packing=quantized.packing, + ) + + @staticmethod + def _resolve_method( + config: QuantizationConfig, + method: QuantizationMethod | None, + ) -> QuantizationMethod: + resolved = method or create_quantization_method(config.algorithm) + if resolved.name != config.algorithm: + raise ValueError( + f"quantization method {resolved.name!r} does not match " + f"config algorithm {config.algorithm!r}" + ) + if resolved.version != config.version: + raise ValueError( + f"quantization method version {resolved.version!r} does not match " + f"config version {config.version!r}" + ) + return resolved + + def _run_method( + self, + context: LayerContext, + ) -> LayerQuantizationResult: + state = self.method.calibrate_layer(context) + result = self.method.quantize_layer(context, state) + + expected = set(context.target_modules) + actual = set(result.weights) + if actual != expected: + missing = sorted(expected - actual) + unexpected = sorted(actual - expected) + raise RuntimeError( + "quantization method returned an invalid target set: " + f"missing={missing}, unexpected={unexpected}" + ) + return result + + def _read_model_config(self) -> dict[str, Any]: + return json.loads((self.source_model_path / "config.json").read_text()) + + def _write_metadata(self, model_config: dict[str, Any]) -> None: + (self.output_dir / "config.json").write_text( + json.dumps(model_config, indent=2, sort_keys=True) + "\n" + ) + (self.output_dir / "quantization_config.json").write_text( + json.dumps(self.config.to_dict(), indent=2, sort_keys=True) + "\n" + ) + + raw_manifest: dict[str, Any] = { + "format_version": 1, + "method": { + "name": self.method.name, + "version": self.method.version, + }, + "modules": { + name: entry.to_dict() for name, entry in sorted(self.manifest.items()) + }, + } + if self.layer_metadata: + raw_manifest["layer_metadata"] = self.layer_metadata + (self.output_dir / "manifest.json").write_text( + json.dumps(raw_manifest, indent=2, sort_keys=True) + "\n" + ) + + def _copy_auxiliary_files(self) -> None: + for path in self.source_model_path.iterdir(): + if ( + path.is_file() + and path.name not in _EXCLUDED_AUXILIARY_FILES + and path.suffix != ".safetensors" + ): + shutil.copy2(path, self.output_dir / path.name) + + +def quantize_checkpoint( + source_model_path: str | Path, + output_dir: str | Path, + config: QuantizationConfig, + *, + method: QuantizationMethod | None = None, + calibration_input_ids: torch.Tensor | None = None, + calibration_micro_batch_size: int = 1, + max_calibration_tokens: int = 4096, + print_layer_loss: bool = False, + device: str | torch.device = "cpu", + max_shard_size_bytes: int = 1 << 30, +) -> dict[str, QuantizedModuleManifest]: + """Quantize a checkpoint, optionally printing per-layer output MSE. + + ``print_layer_loss`` compares each layer's original and fake-quantized + outputs, so it requires ``calibration_input_ids``. + """ + return QuantizationPipeline( + source_model_path, + output_dir, + config, + method=method, + calibration_input_ids=calibration_input_ids, + calibration_micro_batch_size=calibration_micro_batch_size, + max_calibration_tokens=max_calibration_tokens, + print_layer_loss=print_layer_loss, + device=device, + max_shard_size_bytes=max_shard_size_bytes, + ).run() diff --git a/src/lab5/src/hpc101_infer/quantization/recipes/__init__.py b/src/lab5/src/hpc101_infer/quantization/recipes/__init__.py new file mode 100644 index 00000000..db8dd945 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/recipes/__init__.py @@ -0,0 +1,3 @@ +from hpc101_infer.quantization.recipes.gemma4 import gemma4_target_linears + +__all__ = ["gemma4_target_linears"] diff --git a/src/lab5/src/hpc101_infer/quantization/recipes/gemma4.py b/src/lab5/src/hpc101_infer/quantization/recipes/gemma4.py new file mode 100644 index 00000000..11de2f0f --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/recipes/gemma4.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +from torch import nn + +TARGET_LINEAR_NAMES = frozenset( + {"q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"} +) + + +def gemma4_target_linears(model: nn.Module) -> dict[str, nn.Linear]: + targets = {} + for name, module in model.named_modules(): + if ( + isinstance(module, nn.Linear) + and name.startswith("layers.") + and name.rsplit(".", 1)[-1] in TARGET_LINEAR_NAMES + ): + targets[name] = module + return targets diff --git a/src/lab5/src/hpc101_infer/quantization/registry.py b/src/lab5/src/hpc101_infer/quantization/registry.py new file mode 100644 index 00000000..9cc2bdd3 --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/registry.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from hpc101_infer.quantization.methods import ( + GPTQQuantizationMethod, + RTNQuantizationMethod, +) +from hpc101_infer.quantization.types import QuantizationMethod + +_METHODS = { + "gptq": GPTQQuantizationMethod, + "rtn": RTNQuantizationMethod, +} + + +def available_quantization_methods() -> tuple[str, ...]: + return tuple(sorted(_METHODS)) + + +def create_quantization_method(name: str) -> QuantizationMethod: + try: + method = _METHODS[name] + except KeyError as error: + raise ValueError(f"unknown quantization method: {name}") from error + return method() diff --git a/src/lab5/src/hpc101_infer/quantization/types.py b/src/lab5/src/hpc101_infer/quantization/types.py new file mode 100644 index 00000000..9404f21f --- /dev/null +++ b/src/lab5/src/hpc101_infer/quantization/types.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Mapping, Protocol + +import torch +from torch import nn + +from hpc101_infer.quantization.config import QuantizationConfig + +SCALE_DTYPES: Mapping[str, torch.dtype] = { + "float16": torch.float16, + "bfloat16": torch.bfloat16, +} + + +@dataclass(frozen=True) +class QuantizedWeight: + qweight: torch.Tensor + scales: torch.Tensor + zeros: torch.Tensor | None + original_shape: tuple[int, int] + padded_shape: tuple[int, int] + bits: int + group_size: int + symmetric: bool + packing: str + + +@dataclass(frozen=True) +class TensorMetadata: + shape: tuple[int, ...] + dtype: torch.dtype + shard: str + + +@dataclass(frozen=True) +class QuantizedModuleManifest: + original_shape: tuple[int, int] + padded_shape: tuple[int, int] + qweight_key: str + scales_key: str + zeros_key: str | None + bits: int + group_size: int + symmetric: bool + packing: str + + def to_dict(self) -> dict[str, Any]: + return { + "original_shape": list(self.original_shape), + "padded_shape": list(self.padded_shape), + "qweight_key": self.qweight_key, + "scales_key": self.scales_key, + "zeros_key": self.zeros_key, + "bits": self.bits, + "group_size": self.group_size, + "symmetric": self.symmetric, + "packing": self.packing, + } + + @classmethod + def from_dict(cls, raw: dict[str, Any]) -> "QuantizedModuleManifest": + return cls( + original_shape=tuple(raw["original_shape"]), + padded_shape=tuple(raw["padded_shape"]), + qweight_key=raw["qweight_key"], + scales_key=raw["scales_key"], + zeros_key=raw.get("zeros_key"), + bits=raw["bits"], + group_size=raw["group_size"], + symmetric=raw["symmetric"], + packing=raw["packing"], + ) + + +@dataclass +class LayerContext: + layer_index: int + layer_type: str + layer: nn.Module + activations: Mapping[str, torch.Tensor] | None + config: QuantizationConfig + target_modules: tuple[str, ...] + + +@dataclass +class LayerQuantizationResult: + weights: dict[str, QuantizedWeight] + metadata: dict[str, Any] + + +class QuantizationMethod(Protocol): + name: str + version: str + + def calibrate_layer(self, context: LayerContext) -> Any: ... + + def quantize_layer( + self, context: LayerContext, state: Any + ) -> LayerQuantizationResult: ... diff --git a/src/lab5/src/hpc101_infer/runner.py b/src/lab5/src/hpc101_infer/runner.py new file mode 100644 index 00000000..7bac9707 --- /dev/null +++ b/src/lab5/src/hpc101_infer/runner.py @@ -0,0 +1,119 @@ +from __future__ import annotations + +from concurrent.futures import Future +from dataclasses import dataclass, field +from itertools import count +from queue import Empty, Queue +from threading import Lock +from typing import Callable, Iterable + +from hpc101_infer.engine import InferenceEngine +from hpc101_infer.types import GenerationOutput, GenerationRequest + + +@dataclass(frozen=True) +class RequestHandle: + request_id: int + _future: Future[GenerationOutput] = field(repr=False, compare=False) + + def done(self) -> bool: + return self._future.done() + + def result(self, timeout: float | None = None) -> GenerationOutput: + return self._future.result(timeout=timeout) + + +@dataclass(frozen=True) +class CompletedRequest: + request_id: int + output: GenerationOutput + + +@dataclass(frozen=True) +class PendingRequest: + handle: RequestHandle + request: GenerationRequest + + +class Runner: + def __init__(self, engine: InferenceEngine) -> None: + self.engine = engine + self._pending: Queue[PendingRequest] = Queue() + self._request_ids = count() + self._submit_lock = Lock() + self._step_lock = Lock() + + @property + def pending_count(self) -> int: + return self._pending.qsize() + + def submit(self, request: GenerationRequest) -> RequestHandle: + with self._submit_lock: + request_id = next(self._request_ids) + handle = RequestHandle(request_id, Future()) + self._pending.put_nowait(PendingRequest(handle, request)) + return handle + + def has_pending_requests(self) -> bool: + return not self._pending.empty() + + def step(self) -> list[CompletedRequest]: + with self._step_lock: + try: + batch = [self._pending.get_nowait()] + except Empty: + return [] + + # Fill the batch with pending requests up to the scheduler batch size + for _ in range(self.engine.config.scheduler_batch_size - 1): + try: + batch.append(self._pending.get_nowait()) + except Empty: + break + + try: + # Run the inference engine on the batch of requests + outputs = self.engine.generate([item.request for item in batch]) + if len(outputs) != len(batch): + raise RuntimeError("engine output count does not match batch size") + completed = [] + for item, output in zip(batch, outputs, strict=True): + item.handle._future.set_result(output) + completed.append(CompletedRequest(item.handle.request_id, output)) + return completed + except BaseException as error: + for item in batch: + item.handle._future.set_exception(error) + raise + finally: + for _ in batch: + self._pending.task_done() + + def drain( + self, + *, + on_completed: Callable[[int], None] | None = None, + ) -> list[CompletedRequest]: + completed = [] + while self.has_pending_requests(): + completed_batch = self.step() + completed.extend(completed_batch) + if completed_batch and on_completed is not None: + on_completed(len(completed_batch)) + return completed + + def run( + self, + requests: Iterable[GenerationRequest], + *, + on_completed: Callable[[int], None] | None = None, + ) -> list[GenerationOutput]: + handles = [] + for request in requests: + handles.append(self.submit(request)) + if self.pending_count >= self.engine.config.scheduler_batch_size: + completed = self.step() + if completed and on_completed is not None: + on_completed(len(completed)) + self.drain(on_completed=on_completed) + return [handle.result() for handle in handles] diff --git a/src/lab5/src/hpc101_infer/runtime/__init__.py b/src/lab5/src/hpc101_infer/runtime/__init__.py new file mode 100644 index 00000000..88562b3c --- /dev/null +++ b/src/lab5/src/hpc101_infer/runtime/__init__.py @@ -0,0 +1,11 @@ +from hpc101_infer.runtime.batch import Batch +from hpc101_infer.runtime.kv_cache import KVCache, LayerKVCache, LayerKVView +from hpc101_infer.runtime.metrics import OperationMetrics + +__all__ = [ + "KVCache", + "LayerKVCache", + "LayerKVView", + "Batch", + "OperationMetrics", +] diff --git a/src/lab5/src/hpc101_infer/runtime/batch.py b/src/lab5/src/hpc101_infer/runtime/batch.py new file mode 100644 index 00000000..1bd4752d --- /dev/null +++ b/src/lab5/src/hpc101_infer/runtime/batch.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal + +import torch + + +@dataclass(frozen=True) +class Batch: + input_ids: torch.Tensor + positions: torch.Tensor + sequence_lengths: torch.Tensor + mode: Literal["prefill", "decode"] + curr_max_seq_len: int = 0 + + @property + def batch_size(self) -> int: + return self.input_ids.shape[0] + + @property + def query_length(self) -> int: + return self.input_ids.shape[1] diff --git a/src/lab5/src/hpc101_infer/runtime/device.py b/src/lab5/src/hpc101_infer/runtime/device.py new file mode 100644 index 00000000..e420bb52 --- /dev/null +++ b/src/lab5/src/hpc101_infer/runtime/device.py @@ -0,0 +1,8 @@ +from __future__ import annotations + +import torch + + +def synchronize(device: torch.device, enabled: bool = True) -> None: + if enabled and device.type == "cuda": + torch.cuda.synchronize(device) diff --git a/src/lab5/src/hpc101_infer/runtime/kv_cache.py b/src/lab5/src/hpc101_infer/runtime/kv_cache.py new file mode 100644 index 00000000..f641f61b --- /dev/null +++ b/src/lab5/src/hpc101_infer/runtime/kv_cache.py @@ -0,0 +1,158 @@ +"""连续预分配 KV cache，用于展示 prefill 与 decode 共享状态的方式。""" + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + +from hpc101_infer.models.config import Gemma4TextConfig + + +@dataclass +class LayerKVCache: + """单层 attention 的 K/V 存储。 + + ``key`` 和 ``value`` 的布局均为 + ``[max_batch, kv_heads, max_sequence_length, head_dim]``。 + """ + key: torch.Tensor + value: torch.Tensor + lengths: torch.Tensor + max_batch_size: int + max_sequence_length: int + batch_size: int = 0 + + def reset(self, batch_size: int) -> None: + """开始新的静态 batch；旧 tensor 不清零，只重置有效长度。""" + if not 0 < batch_size <= self.max_batch_size: + raise ValueError( + f"batch_size must be in [1, {self.max_batch_size}], " + f"got {batch_size}" + ) + self.batch_size = batch_size + self.lengths.zero_() + + def write( + self, + positions: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + ) -> None: + """按绝对 token 位置写入当前层新计算出的 K/V。""" + batch_size, query_length = positions.shape + expected_prefix = (batch_size, self.key.shape[1]) + expected_suffix = (query_length, self.key.shape[3]) + if key.shape != expected_prefix + expected_suffix: + raise ValueError( + f"invalid key shape {tuple(key.shape)}, expected " + f"{expected_prefix + expected_suffix}" + ) + for batch_idx in range(batch_size): + target = positions[batch_idx] + self.key[batch_idx].index_copy_(1, target, key[batch_idx]) + self.value[batch_idx].index_copy_(1, target, value[batch_idx]) + + def view(self, max_length: int) -> "LayerKVView": + """只暴露当前 batch 在 ``max_length`` 以内的连续 cache 前缀。""" + return LayerKVView( + key=self.key[: self.batch_size, :, :max_length, :], + value=self.value[: self.batch_size, :, :max_length, :], + ) + + def commit(self, sequence_lengths: torch.Tensor) -> None: + """在一次模型 forward 完成后提交新的有效序列长度。""" + self.lengths[: self.batch_size].copy_(sequence_lengths) + + +@dataclass(frozen=True) +class LayerKVView: + key: torch.Tensor + value: torch.Tensor + + +class KVCache: + """管理所有 decoder layer 的预分配 cache，并统一提交序列长度。""" + def __init__( + self, + layers: list[LayerKVCache], + max_batch_size: int, + max_sequence_length: int, + ) -> None: + if not layers: + raise ValueError("KV cache must contain at least one layer") + self.layers = layers + self.max_batch_size = max_batch_size + self.max_sequence_length = max_sequence_length + + @classmethod + def allocate( + cls, + config: Gemma4TextConfig, + max_batch_size: int, + max_sequence_length: int, + dtype: torch.dtype, + device: str | torch.device, + ) -> "KVCache": + if max_batch_size <= 0 or max_sequence_length <= 0: + raise ValueError("cache capacities must be positive") + device = torch.device(device) + layers = [] + for layer_type in config.layer_types: + if layer_type == "full_attention": + kv_heads = config.num_global_key_value_heads + head_dim = config.global_head_dim + elif layer_type == "sliding_attention": + kv_heads = config.num_key_value_heads + head_dim = config.head_dim + else: + raise ValueError(f"unsupported attention type: {layer_type!r}") + shape = (max_batch_size, kv_heads, max_sequence_length, head_dim) + layers.append( + LayerKVCache( + key=torch.empty(shape, dtype=dtype, device=device), + value=torch.empty(shape, dtype=dtype, device=device), + lengths=torch.zeros( + max_batch_size, dtype=torch.long, device=device + ), + max_batch_size=max_batch_size, + max_sequence_length=max_sequence_length, + ) + ) + return cls(layers, max_batch_size, max_sequence_length) + + @property + def lengths(self) -> torch.Tensor: + return self.layers[0].lengths + + @property + def batch_size(self) -> int: + return self.layers[0].batch_size + + @property + def device(self) -> torch.device: + return self.lengths.device + + @property + def dtype(self) -> torch.dtype: + return self.layers[0].key.dtype + + def reset(self, batch_size: int) -> None: + for layer in self.layers: + layer.reset(batch_size) + + def write( + self, + layer_id: int, + positions: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + ) -> None: + self.layers[layer_id].write(positions, key, value) + + def view(self, layer_id: int, max_length: int) -> LayerKVView: + return self.layers[layer_id].view(max_length) + + def commit(self, sequence_lengths: torch.Tensor) -> None: + for layer in self.layers: + layer.commit(sequence_lengths) diff --git a/src/lab5/src/hpc101_infer/runtime/metrics.py b/src/lab5/src/hpc101_infer/runtime/metrics.py new file mode 100644 index 00000000..619531eb --- /dev/null +++ b/src/lab5/src/hpc101_infer/runtime/metrics.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +from dataclasses import dataclass +from time import perf_counter +from typing import Callable, TypeVar +from contextlib import contextmanager + +import torch + +from hpc101_infer.runtime.device import synchronize + +T = TypeVar("T") + + +@dataclass +class OperationMetrics: + latency_s: float = 0.0 + peak_allocated_bytes: int = 0 + peak_reserved_bytes: int = 0 + + +@contextmanager +def measure_operation(device: torch.device, synchronize_cuda: bool): + synchronize(device, synchronize_cuda) + if device.type == "cuda": + torch.cuda.reset_peak_memory_stats(device) + start = perf_counter() + metrics = OperationMetrics() + try: + yield metrics + finally: + metrics.latency_s = perf_counter() - start + if device.type == "cuda": + metrics.peak_allocated_bytes = torch.cuda.max_memory_allocated(device) + metrics.peak_reserved_bytes = torch.cuda.max_memory_reserved(device) + else: + metrics.peak_allocated_bytes = 0 + metrics.peak_reserved_bytes = 0 + return metrics diff --git a/src/lab5/src/hpc101_infer/sampling.py b/src/lab5/src/hpc101_infer/sampling.py new file mode 100644 index 00000000..01c0a116 --- /dev/null +++ b/src/lab5/src/hpc101_infer/sampling.py @@ -0,0 +1,100 @@ +from dataclasses import dataclass + +import torch + +from hpc101_infer.types import GenerationRequest + + +def _make_device_tensor( + data: list[int] | list[float], dtype: torch.dtype, device: torch.device +) -> torch.Tensor: + return torch.tensor( + data, + dtype=dtype, + pin_memory=device.type == "cuda", + ).to(device, non_blocking=device.type == "cuda") + + +@dataclass +class BatchSamplingArgs: + temperatures: torch.Tensor | None + top_k: torch.Tensor | None = None + top_p: torch.Tensor | None = None + + +class Sampler: + def __init__(self, device: torch.device, vocab_size: int): + if vocab_size <= 0: + raise ValueError("vocab_size must be positive") + self.device = device + self.vocab_size = vocab_size + + def prepare(self, requests: list[GenerationRequest]) -> BatchSamplingArgs: + if not requests: + raise ValueError("requests must not be empty") + params = [request.sampling_params for request in requests] + if all(param.is_greedy for param in params): + return BatchSamplingArgs(temperatures=None) + + temperatures = _make_device_tensor( + [1.0 if param.is_greedy else param.temperature for param in params], + torch.float32, + self.device, + ) + effective_top_k = [ + ( + 1 + if param.is_greedy + else ( + min(param.top_k, self.vocab_size) + if param.top_k > 0 + else self.vocab_size + ) + ) + for param in params + ] + effective_top_p = [1.0 if param.is_greedy else param.top_p for param in params] + + top_k = None + if any(value < self.vocab_size for value in effective_top_k): + top_k = _make_device_tensor(effective_top_k, torch.int32, self.device) + + top_p = None + if any(value < 1.0 for value in effective_top_p): + top_p = _make_device_tensor(effective_top_p, torch.float32, self.device) + + return BatchSamplingArgs(temperatures=temperatures, top_k=top_k, top_p=top_p) + + def sample(self, logits: torch.Tensor, args: BatchSamplingArgs) -> torch.Tensor: + if args.temperatures is None: + return torch.argmax(logits, dim=-1) + + filtered_logits = logits.float() / args.temperatures.unsqueeze(-1) + + if args.top_k is None and args.top_p is None: + probabilities = torch.softmax(filtered_logits, dim=-1) + return torch.multinomial(probabilities, num_samples=1).squeeze(-1) + + sorted_logits, sorted_indices = torch.sort( + filtered_logits, + dim=-1, + descending=True, + ) + token_ranks = torch.arange(self.vocab_size, device=self.device).unsqueeze(0) + + if args.top_k is not None: + top_k_mask = token_ranks >= args.top_k.unsqueeze(-1) + sorted_logits = sorted_logits.masked_fill(top_k_mask, float("-inf")) + + if args.top_p is not None: + sorted_probabilities = torch.softmax(sorted_logits, dim=-1) + cumulative_probabilities = sorted_probabilities.cumsum(dim=-1) + top_p_mask = ( + cumulative_probabilities - sorted_probabilities + ) >= args.top_p.unsqueeze(-1) + sorted_logits = sorted_logits.masked_fill(top_p_mask, float("-inf")) + + sorted_probabilities = torch.softmax(sorted_logits, dim=-1) + sampled_sorted_indices = torch.multinomial(sorted_probabilities, num_samples=1) + next_tokens = sorted_indices.gather(dim=-1, index=sampled_sorted_indices) + return next_tokens.squeeze(-1) diff --git a/src/lab5/src/hpc101_infer/scheduler/__init__.py b/src/lab5/src/hpc101_infer/scheduler/__init__.py new file mode 100644 index 00000000..4e0aa378 --- /dev/null +++ b/src/lab5/src/hpc101_infer/scheduler/__init__.py @@ -0,0 +1,32 @@ +from hpc101_infer.config import EngineConfig +from hpc101_infer.scheduler.base import ( + RequestState, + RequestStatus, + ScheduledOutput, + ScheduledRequest, + Scheduler, +) +from hpc101_infer.scheduler.static_batch import StaticBatchScheduler + + +def create_scheduler( + config: EngineConfig, + *, + default_stop_token_ids: tuple[int, ...], +) -> Scheduler: + if config.scheduler_backend == "static_batch": + return StaticBatchScheduler( + default_stop_token_ids=default_stop_token_ids, + ) + raise ValueError(f"unsupported scheduler backend: {config.scheduler_backend}") + + +__all__ = [ + "RequestState", + "RequestStatus", + "ScheduledOutput", + "ScheduledRequest", + "Scheduler", + "StaticBatchScheduler", + "create_scheduler", +] diff --git a/src/lab5/src/hpc101_infer/scheduler/base.py b/src/lab5/src/hpc101_infer/scheduler/base.py new file mode 100644 index 00000000..667e5cd1 --- /dev/null +++ b/src/lab5/src/hpc101_infer/scheduler/base.py @@ -0,0 +1,49 @@ +from dataclasses import dataclass, field +from enum import Enum +from typing import Literal, Protocol + +from hpc101_infer.types import GenerationRequest + + +class RequestStatus(Enum): + PENDING = "pending" + PREFILLING = "prefilling" + DECODING = "decoding" + COMPLETED = "completed" + FAILED = "failed" + + +@dataclass +class RequestState: + request: GenerationRequest + prompt_token_ids: list[int] + output_token_ids: list[int] = field(default_factory=list) + num_computed_tokens: int = 0 + status: RequestStatus = RequestStatus.PENDING + finish_reason: str = "" + + +@dataclass +class ScheduledRequest: + request: RequestState + num_scheduled_tokens: int = 0 + + +@dataclass +class ScheduledOutput: + mode: Literal["prefill", "decode"] + requests: list[ScheduledRequest] + + @property + def max_seq_len(self) -> int: + return max(scheduled.request.num_computed_tokens for scheduled in self.requests) + + +class Scheduler(Protocol): + def add_request(self, request: RequestState) -> None: ... + + def schedule(self) -> ScheduledOutput: ... + + def update(self, token_ids: list[int]) -> None: ... + + def has_unfinished_requests(self) -> bool: ... diff --git a/src/lab5/src/hpc101_infer/scheduler/static_batch.py b/src/lab5/src/hpc101_infer/scheduler/static_batch.py new file mode 100644 index 00000000..bf649c74 --- /dev/null +++ b/src/lab5/src/hpc101_infer/scheduler/static_batch.py @@ -0,0 +1,91 @@ +from hpc101_infer.scheduler.base import ( + RequestState, + RequestStatus, + ScheduledOutput, + ScheduledRequest, +) + + +class StaticBatchScheduler: + def __init__(self, default_stop_token_ids: tuple[int, ...]) -> None: + self.default_stop_token_ids = default_stop_token_ids + self.requests: list[RequestState] = [] + self._prefill_scheduled = False + + def add_request(self, request: RequestState) -> None: + if self._prefill_scheduled: + raise RuntimeError( + "cannot add requests after static batch scheduling starts" + ) + if request.status is not RequestStatus.PENDING: + raise ValueError("new requests must be pending") + self.requests.append(request) + + def schedule(self) -> ScheduledOutput: + if not self.requests: + raise RuntimeError("cannot schedule an empty batch") + + if not self._prefill_scheduled: + self._prefill_scheduled = True + scheduled = [] + for state in self.requests: + state.num_computed_tokens = len(state.prompt_token_ids) + if state.request.max_new_tokens == 0: + state.status = RequestStatus.COMPLETED + state.finish_reason = "length" + else: + state.status = RequestStatus.PREFILLING + scheduled.append( + ScheduledRequest( + request=state, + num_scheduled_tokens=len(state.prompt_token_ids), + ) + ) + return ScheduledOutput(mode="prefill", requests=scheduled) + + if not self.has_unfinished_requests(): + raise RuntimeError("cannot schedule a completed batch") + + scheduled = [] + for state in self.requests: + num_scheduled_tokens = int(state.status is RequestStatus.DECODING) + state.num_computed_tokens += num_scheduled_tokens + scheduled.append( + ScheduledRequest( + request=state, + num_scheduled_tokens=num_scheduled_tokens, + ) + ) + return ScheduledOutput(mode="decode", requests=scheduled) + + def update(self, token_ids: list[int]) -> None: + if len(token_ids) != len(self.requests): + raise ValueError("token_ids must match the static batch size") + + for state, token_id in zip(self.requests, token_ids, strict=True): + if state.status not in ( + RequestStatus.PREFILLING, + RequestStatus.DECODING, + ): + continue + + state.output_token_ids.append(token_id) + stop_token_ids = ( + self.default_stop_token_ids + if state.request.stop_token_ids is None + else state.request.stop_token_ids + ) + if token_id in stop_token_ids: + state.status = RequestStatus.COMPLETED + state.finish_reason = "stop" + elif len(state.output_token_ids) >= state.request.max_new_tokens: + state.status = RequestStatus.COMPLETED + state.finish_reason = "length" + else: + state.status = RequestStatus.DECODING + + def has_unfinished_requests(self) -> bool: + return any( + state.status not in (RequestStatus.COMPLETED, RequestStatus.FAILED) + for state in self.requests + ) diff --git a/src/lab5/src/hpc101_infer/types.py b/src/lab5/src/hpc101_infer/types.py new file mode 100644 index 00000000..438c0035 --- /dev/null +++ b/src/lab5/src/hpc101_infer/types.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +from dataclasses import dataclass, field + +import torch + + +@dataclass(frozen=True) +class SamplingParams: + temperature: float = 0.0 + top_k: int = -1 + top_p: float = 1.0 + + def __post_init__(self) -> None: + if self.temperature < 0.0: + raise ValueError("temperature must be non-negative") + if self.top_k == 0 or self.top_k < -1: + raise ValueError("top_k must be -1 or a positive integer") + if not 0.0 < self.top_p <= 1.0: + raise ValueError("top_p must be in (0, 1]") + + @property + def is_greedy(self) -> bool: + return self.temperature == 0.0 or self.top_k == 1 + + +@dataclass(frozen=True) +class GenerationRequest: + prompt: str | None = None + input_ids: list[int] | None = None + max_new_tokens: int = 1 + stop_token_ids: tuple[int, ...] | None = None + sampling_params: SamplingParams = field(default_factory=SamplingParams) + + def __post_init__(self) -> None: + if (self.prompt is None) == (self.input_ids is None): + raise ValueError("exactly one of prompt and input_ids must be provided") + if self.input_ids is not None and not self.input_ids: + raise ValueError("input_ids must not be empty") + if self.max_new_tokens < 0: + raise ValueError("max_new_tokens must be non-negative") + + +@dataclass(frozen=True) +class RequestMetrics: + prefill_latency_s: float + decode_latencies_s: tuple[float, ...] = () + total_latency_s: float = 0.0 + peak_allocated_bytes: int = 0 + peak_reserved_bytes: int = 0 + + @property + def ttft_s(self) -> float: + return self.prefill_latency_s + + @property + def mean_tpot_s(self) -> float: + if not self.decode_latencies_s: + return 0.0 + return sum(self.decode_latencies_s) / len(self.decode_latencies_s) + + def pretty(self) -> str: + return f""" +TTFT (s): {self.ttft_s:.4f} +Mean TPOT (s): {self.mean_tpot_s:.4f} +Total latency (s): {self.total_latency_s:.4f} +Peak allocated bytes: {self.peak_allocated_bytes/1048576:.2f} MB +Peak reserved bytes: {self.peak_reserved_bytes/1048576:.2f} MB + """ + + +@dataclass(frozen=True) +class PrefillOutput: + logits: torch.Tensor + sequence_lengths: torch.Tensor + latency_s: float + peak_allocated_bytes: int = 0 + peak_reserved_bytes: int = 0 + + +@dataclass(frozen=True) +class DecodeOutput: + logits: torch.Tensor + sequence_lengths: torch.Tensor + latency_s: float + peak_allocated_bytes: int = 0 + peak_reserved_bytes: int = 0 + + +@dataclass(frozen=True) +class GenerationOutput: + token_ids: list[int] + text: str + prompt_tokens: int + generated_tokens: int + finish_reason: str + metrics: RequestMetrics + + +@dataclass(frozen=True) +class ScoreOutput: + logits: torch.Tensor | None + token_logprobs: torch.Tensor | None + nll_sum: float + token_count: int + mean_nll: float + mean_logprob: float + perplexity: float diff --git a/src/lab5/utils/utils.py b/src/lab5/utils/utils.py deleted file mode 100644 index 19b9fd5b..00000000 --- a/src/lab5/utils/utils.py +++ /dev/null @@ -1,90 +0,0 @@ -import time -import torch -import torch.nn.functional as F - - -def generate_text( - model, tokenizer, prompt, max_length=100, temperature=0.8, top_k=50, device="cpu" -): - """生成文本""" - print(f"\n输入提示: {prompt}") - print("=" * 50) - - # 编码输入 - inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True) - input_ids = inputs["input_ids"].to(device) - attention_mask = inputs["attention_mask"].to(device) - - print(f"输入 token 数量: {input_ids.shape[1]}") - - # 生成设置 - generated_ids = input_ids.clone() - - start_time = time.time() - - with torch.no_grad(): - for step in range(max_length): - - # 前向传播 - results = model(input_ids=generated_ids, attention_mask=attention_mask) - if hasattr(results, "logits"): - logits = results.logits - else: - logits = results - - if temperature == 0: - # 直接取最大值作为下一个 token - next_token = torch.argmax(logits[:, -1, :], dim=-1, keepdim=True) - else: - # 获取下一个 token 的概率分布 - next_token_logits = logits[:, -1, :] / temperature - - # Top-k 采样 - if top_k > 0: - # 获取 top-k 个最高的分数 - top_k_logits, top_k_indices = torch.topk(next_token_logits, top_k) - # 创建掩码，只保留 top-k - next_token_logits = torch.full_like( - next_token_logits, float("-inf") - ) - next_token_logits.scatter_(1, top_k_indices, top_k_logits) - - # 应用 softmax 并采样 - probs = F.softmax(next_token_logits, dim=-1) - next_token = torch.multinomial(probs, num_samples=1) - - # 添加到生成的序列中 - generated_ids = torch.cat([generated_ids, next_token], dim=-1) - - # 更新注意力掩码 - attention_mask = torch.cat( - [ - attention_mask, - torch.ones( - (attention_mask.shape[0], 1), - device=device, - dtype=attention_mask.dtype, - ), - ], - dim=-1, - ) - - # 检查是否生成了结束 token - if next_token.item() == tokenizer.eos_token_id: - break - - end_time = time.time() - - # 解码生成的文本 - generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) - - print(f"\n生成完成!") - print(f"生成时间: {end_time - start_time:.2f} 秒") - print(f"生成 token 数量: {generated_ids.shape[1] - input_ids.shape[1]}") - print( - f"生成速度: {(generated_ids.shape[1] - input_ids.shape[1]) / (end_time - start_time):.2f} tokens/秒" - ) - print("=" * 50) - print(f"完整输出: {generated_text}") - - return generated_text From ae7eb37b01d3c24f54d5b6e5dc875da86e50dc0a Mon Sep 17 00:00:00 2001 From: silvermilight Date: Thu, 23 Jul 2026 00:14:52 +0800 Subject: [PATCH 2/4] docs(lab5): finish experiment documentation --- docs/guide/env.md | 121 --- docs/guide/index.md | 276 ++----- docs/guide/job.md | 313 +++----- docs/guide/oj.md | 181 +---- docs/guide/overview.md | 52 +- 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mode 100644 docs/lab/Lab5-TinyLLM/image/gqa.png delete mode 100644 docs/lab/Lab5-TinyLLM/image/mha.png delete mode 100644 docs/lab/Lab5-TinyLLM/image/qwen3_arch.png delete mode 100644 docs/lab/Lab5-TinyLLM/image/silu.png delete mode 100644 docs/lab/Lab5-TinyLLM/index.md diff --git a/docs/guide/env.md b/docs/guide/env.md deleted file mode 100644 index 7df1013f..00000000 --- a/docs/guide/env.md +++ /dev/null @@ -1,121 +0,0 @@ -# 软件环境 - -!!! todo - - - [ ] Spack 使用说明 - - [ ] HPC 中常用软件平台说明 - - [ ] 接入可观测性平台，动态监测软件环境 - -!!! bug - - 已知且未修复的问题： - - - VTune modulefile 的 PATH 路径错误。 - - 原因：oneAPI 自带的 modulefile 对 lmod 有兼容性问题。 - - 解决办法：自行修改 PATH。或使用 `/opt/intel/oneapi/setvars.sh` 加载 oneAPI 环境。 - -## 环境、包管理器 - -### Spack - -!!! note "Tips" - Spack 安装或解析依赖过程较慢属于正常现象。若出现长时间无响应的情况，可添加 `--debug` 参数以查看详细调试信息。 - -集群上目前在 `/pxe/opt/spack` 目录下安装了 `Spack`（下称集群 Spack），提供了常用的编译环境。 你可以通过如下命令，将集群 Spack 设置为本地 Spack 的上游 Spack 实例，从而复用集群 Spack 已安装的软件包，节省安装时间。 - -```shell -# Load Cluster Level spack -source /pxe/opt/spack/share/spack/setup-env.sh -``` - -你可以通过 `spack find` 来查看已经安装的软件包。 -```shell -$ spack find - -- linux-debian12-haswell / gcc@12.2.0 -------------------------- - aocc@5.0.0 curl@7.88.1 gettext@0.21 intel-oneapi-mpi@2021.14.1 libfabric@1.22.0 libxml2@2.13.5 mpich@4.2.3 openmpi@5.0.6 pmix@5.0.5 rdma-core@52.0 xz@5.4.6 - autoconf@2.71 diffutils@3.8 glibc@2.36 intel-oneapi-vtune@2025.0.1 libffi@3.4.6 llvm@19.1.7 mvapich2@2.3.7-1 openssh@9.2p1 py-docutils@0.20.1 readline@8.2 yaksa@0.3 -``` - -并通过 `spack load ` 来加载相应软件包。 -```shell -$ spack load cuda@12.8.0 -$ nvcc --version - nvcc: NVIDIA (R) Cuda compiler driver - Copyright (c) 2005-2025 NVIDIA Corporation - Built on Wed_Jan_15_19:20:09_PST_2025 - Cuda compilation tools, release 12.8, V12.8.61 - Build cuda_12.8.r12.8/compiler.35404655_0 -``` - ---- - -需要使用多个软件包时，逐个执行 `spack load` 比较繁琐，我们可以通过环境来管理一组软件包。集群 Spack 预装了几个环境，你可以通过 `spack env list` 来查看已经构建好的环境： -```shell -$ spack env list - ==> 4 environments - hpc101-cuda hpc101-gnu hpc101-intel hpc101-llvm -``` - -并通过 `spack env activate ` 来加载相应环境中的软件包。 - -加载环境后，你可以通过 `spack find` 来查看该环境内的软件包。 - -```shell -$ spack find - ==> In environment hpc101-intel - ==> 5 root specs - [+] intel-oneapi-compilers [+] intel-oneapi-mpi [+] intel-oneapi-vtune - [+] intel-oneapi-mkl [+] intel-oneapi-tbb - - -- linux-debian12-icelake / gcc@12.2.0 -------------------------- - gcc-runtime@12.2.0 intel-oneapi-mpi@2021.14.0 - glibc@2.36 intel-oneapi-tbb@2022.0.0 - intel-oneapi-compilers@2025.0.0 intel-oneapi-vtune@2025.0.0 - intel-oneapi-mkl@2024.2.2 - ==> 7 installed packages -``` - -如有需要，你也可以构建自己的环境。 - -更多 Spack 操作，请参考 [该文档](https://docs.zjusct.io/operation/software/spack/). - -### Conda - -集群预装了 Conda，用户不需要自行安装。你可以直接使用 `conda activate` 激活默认 Conda 环境： - -```text -user@machine:~$ conda activate -(base) user@machine:~$ -``` - -在 `base` 环境下，我们预装了 numpy、pytorch 等常用的包。你没有权限直接修改 `base` 环境。如果这些包不能满足你的需求，可以联系我们添加。 - -当然，如果 `base` 环境不符合你的需求，也可以直接创建自己的环境。新创建的环境将保存在你的家目录下： - -```bash hl_lines="10" -(base) user@machine:~$ conda create -n myenv python=3.8 -Channels: - - defaults -Platform: linux-64 -Collecting package metadata (repodata.json): done -Solving environment: done - -## Package Plan ## - - environment location: /home/user/.conda/envs/myenv - - added / updated specs: - - python=3.8 - -Proceed ([y]/n)? -``` - -Conda 支持多级配置文件。你可以在 `~/.condarc` 中配置个人的 Conda 环境，包括添加 channels、设置 proxy 等。你可以使用 `conda info` 查看当前 Conda 实例的配置： - -```bash -(base) user@machine~$ conda info - active environment : base - active env location : /opt/conda - user config file : /home/user/.condarc - populated config files : /opt/conda/.condarc -``` diff --git a/docs/guide/index.md b/docs/guide/index.md index b9407a12..bd269949 100644 --- a/docs/guide/index.md +++ b/docs/guide/index.md @@ -1,15 +1,19 @@ -# 登陆节点 +# 登录平台 -我们需要通过登录节点进入集群，集群的计算节点不能直接访问，只能通过登录节点进行连接。同时，登录节点也不适合运行任何计算任务。 +课程集群通过统一的 Web 平台使用，有两个入口： -目前开放的登陆节点是一台虚拟机，配置如下： +- 校网：**** +- 公网（校外可访问）：**** -- 节点名: `hpc101` -- CPU: Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz 128 线程 -- 内存: 256GB -- 每位同学家目录配额 50GB +!!! warning "注意" -登陆节点限制了每个用户最多使用8个CPU核心(800% CPU Time) 和 12GB 内存。 + 由于平台鉴权使用的 ZJU Git 依赖校网环境，故若未给账户设置密码，仍需使用VPN/校网环境登录平台并设置密码。 + +在平台上你可以： + +- 创建和管理 **DevPod**（开发容器，通过 SSH 连接） +- 向各分区**提交计算任务**（类 Slurm 的 `hpc` 命令行） +- **提交实验报告**（平台从你的家目录收取文件树） !!! warning "重要使用须知" @@ -21,241 +25,109 @@ - 重要数据及时本地备份。 - - hpc101 节点是登陆节点，请不要在上面运行计算程序，计算任务请到计算节点运行。 - -# 登录集群 - -## 配置 SSH 密钥登录 +## 登录 -集群仅会支持 SSH 密钥登录。请按照以下步骤配置 SSH 密钥登录： +打开平台，点击 **Sign in with GitLab**，用你的 [ZJU Git](https://git.zju.edu.cn/) 账号授权即可。 -1. 在 [ZJU Git](https://git.zju.edu.cn/) 中添加你的 SSH 公钥。集群 SSH 验证将从这里动态获取你的公钥。你可以通过 `https://git.zju.edu.cn/学号.keys` 查看你添加的公钥。如果没有配置过 SSH 密钥对，请参考 [Lab 0 Linux Crash Course](../lab/Lab0-LinuxCrashCourse/index.md) 中的配置方法。 -1. 登录集群时，使用 `ssh 账户名+节点名@clusters.zju.edu.cn` 登录。如果你的 SSH 密钥配置正确，你将登录到集群登录节点。**账户名为 h+学号**。 +平台账户名为 **h + 学号**。 - !!! example +!!! example - 学号 3260100000 的用户名为 `h3260100000` + 学号 3260100000 的同学，平台账户名为 `h3260100000`。 - 用户 `h3260100000` 登录到 `hpc101` 节点，应当执行的命令为： +## 创建 DevPod - ```shell - ssh h3260100000+hpc101@clusters.zju.edu.cn - ``` - - !!! warning "警告" - 如果这一步你看到了输入密码的提示，说明 SSH 密钥登录配置有误，需要重新检查。 - - **请不要在登录集群时输入自己的密码**，这是因为登录节点配置了蜜罐，输入的密码会被明文记录在日志中。 - -1. `clusters.zju.edu.cn` 支持 SSH 的端口有：22、80、443。如果默认端口（22）无法连接，请尝试使用另外两个端口： - - ```shell - ssh username+nodename@clusters.zju.edu.cn -p 443 - ``` +DevPod 是你在集群上的开发环境：一个常驻的容器，带有课程实验所需的完整工具链。 - 受限于学校网络安全策略，目前已知下列情况下 22 端口无法使用，请使用 80 或 443 端口： +1. 登录平台后，点击 **创建第一个**（或右上角 **New +**）。 +1. 任取一个名称（如 `mypod`），选择实验对应的环境模板（如做 x86 任务选 x86 模板，做 RISC-V 任务选 RISC-V 模板，具体以实验文档指引为准）。 +1. 等待状态从 `Pending` 变为 `Running`。 - - **校外访问** (注意放假后校外访问时需要更改端口) - - RVPN（zju-connect）访问 +!!! tip -1. 在集群内，要通过 SSH 连接到其他节点，不需要输入用户名，直接使用 `ssh nodename` 即可。 + 每人可同时运行的 DevPod 数量有限。可按需暂停/删除不使用的 DevPod。 - !!! example +## 通过 SSH 连接 DevPod - 在集群内的 `hpc101` 节点上 SSH 到 `hpc102` 节点： +### 配置 SSH 公钥 - ```shell - ssh hpc102 - ``` +集群仅支持 SSH 密钥登录。请在 [ZJU Git](https://git.zju.edu.cn/) 中添加你的 SSH 公钥，集群会从这里动态获取。你可以通过 `https://git.zju.edu.cn/学号.keys` 查看已添加的公钥。如果没有配置过 SSH 密钥对，请参考 [Lab 0 Linux Crash Course](../lab/Lab0-LinuxCrashCourse/index.md) 中的配置方法。 -!!! tip +### 连接 - 如果使用终端命令行或者 Visual Studio Code 连接集群，可以通过配置 `ssh config` 来获得更加优雅的登陆方式。 +DevPod 运行后，可使用下面的命令连接： - 对于 Linux 和 macOS, 这一配置位于 `~/.ssh/config`，对于 Windows 则位于 `C:\Users\<用户名>\.ssh\config` - - 例如： +```shell +ssh h学号+DevPod名+hpc101@clusters.zju.edu.cn -p 443 +``` - ```text title="~/.ssh/config" - Host hpc101 - User h<学号>+hpc101 - HostName clusters.zju.edu.cn - ForwardX11 yes - Port 443 - ``` +!!! example - 此后可以使用 + 用户 `h3260100000` 连接自己名为 `mypod` 的 DevPod： ```shell - ssh hpc101 + ssh h3260100000+mypod+hpc101@clusters.zju.edu.cn -p 443 ``` - 或在 Visual Studio Code 建立远程 SSH 连接时选择 `hpc101` 连接到集群 - - -!!! warning - - 登陆节点限制了每个用户最多使用8个CPU核心(800% CPU Time),和 12GB 内存。请不要在登陆节点上运行任何计算任务。 - - - - -## 下一步：[提交作业](./job.md) - -## Optional: 开启 X11 Forwarding - - - -在完成课程实验任务时，我们可能会需要使用一些图形化界面的程序，比如 Intel VTune Profiler。但这些软件安装在远程的计算节点上，我们如何在自己的电脑上使用这些图形化程序呢？这就需要通过 X11 Forwarding 来实现。 - -### 在系统上安装 X Server - -我们可以使用 SSH 的 X11 Forwarding 功能，将远程服务器的图形界面显示到本地。 +!!! warning "警告" -当然，这需要本地有 X Server (如果你对这背后的原因比较好奇，请阅读 “X11 Forwarding 原理”)，请阅读接下来的指导，在你的电脑上安装 X Server。 + 如果这一步你看到了输入密码的提示，说明 SSH 密钥登录配置有误，需要重新检查。 -??? info "X11 Forwarding 原理" + **请不要在登录集群时输入自己的密码**，这是因为登录入口配置了蜜罐，输入的密码会被明文记录在日志中。 - !!! info inline "" +`clusters.zju.edu.cn` 支持 SSH 的端口有：22、80、443。如果默认端口（22）无法连接，请尝试使用另外两个端口。受限于学校网络安全策略，目前已知下列情况下 22 端口无法使用，请使用 80 或 443 端口： -
-
- ![x11_arch](index.assets/x11_arch.webp) -
- X 视窗系统架构 -
- [MakeTechEasier](https://www.maketecheasier.com/the-x-window-system/) -
-
+- **校外访问**（注意放假后校外访问时需要更改端口） +- RVPN（zju-connect）访问 - X 视窗系统（X Window System）是一种在 UNIX 和类 UNIX 操作系统上运行的图形用户界面系统。为了使多个程序共享一套硬件（如显示器、键盘和鼠标），它设计为服务器/客户端架构，如上图所示： +!!! tip "推荐：配置 ssh config" - - X Server：控制硬件，向客户端提供访问硬件的服务。 - - X Client：每当你启动一个图形界面（Graphical User Interface, GUI）应用程序时，它会有一个 X Client，负责与 X Server 交互，使画面显示在屏幕上。 + 如果使用终端命令行或者 Visual Studio Code 连接集群，可以通过配置 `ssh config` 来获得更加优雅的登录方式。 - X 视窗系统的服务器/客户端通信协议可以通过网络传输，这就是 X11 Forwarding 的原理。 + 对于 Linux 和 macOS，这一配置位于 `~/.ssh/config`；对于 Windows 则位于 `C:\Users\<用户名>\.ssh\config`。例如： -
-
- ![x11_forwarding](index.assets/x11_forwarding.webp){ width=80% align=center } -
- X11 Forwarding 原理 -
- [Teleport](https://goteleport.com/blog/x11-forwarding/) -
-
- - -!!! note "安装 X Server" - === "Windows" - - 如果你在 Lab 0 时安装并配置了 WSL，[它自带 X Server](https://learn.microsoft.com/en-us/windows/wsl/tutorials/gui-apps)，使用 WSL 连接时无需安装额外工具。 - - 此外，推荐使用 MobaXTerm 来进行连接，具体配置如下: - - 1. 创建新的 SSH Session，在设置里按照如下步骤配置: - - ![MobaXTerm New SSH Session](index.assets/moba-1.webp) - - 1. 在 Expert SSH Settings 中，选择协议版本为 SSHv2. - - ![MobaXTerm SSH Settings](index.assets/moba-2.webp) - - 1. 点击确认，随后便可在左侧侧边栏中找到 HPC101 的配置，双击进行连接。 - - 你也可以尝试下面的工具: - - - [Xming](http://www.straightrunning.com/XmingNotes/) - - [vcxsrv](https://sourceforge.net/projects/vcxsrv/) - - === "macOS" - - 推荐使用 [xQuartz](https://www.xquartz.org/)。 - - ```bash - brew install xquartz - ``` - - === "Linux" - - 如果你的桌面环境使用的是 X11，那么无需进行任何额外配置。 - - 如果使用 Wayland 桌面环境，则可能需要安装并配置 XWayland, 请根据你的发行版进行配置。 - -### 使用 SSH X11 Forwarding - - -默认情况下，SSH 不会转发 X11 会话, 你只需要在连接到集群时使用的 SSH 命令上添加 `-X`，或在 SSH Config 中添加 `ForwardX11 yes` 即可启用 X11 转发。 - - -### 测试 X11 Forwarding - -为了测试 X11 Forwarding，也为了熟悉集群操作，推荐同学们在集群上编译 Lab 1 中的 Angband 游戏。 + ```text title="~/.ssh/config" + Host mypod + User h3260100000+mypod+hpc101 + HostName clusters.zju.edu.cn + Port 443 + ``` -打开你编译好的 Angband，如果一切顺利，你会看到如下的界面: + 此后可以使用 -```bash -user@local:~$ ssh -X user@remote -user@remote:~$ ./Angband-4.2.5/src/angband -``` + ```shell + ssh mypod + ``` -
- ![x11_success](index.assets/x11_success.webp){ width=80% align=center } -
- X11 Forwarding 成功 -
-
+ 或在 Visual Studio Code 建立远程 SSH 连接时选择 `mypod` 连接到你的 DevPod。 -但如果你遇到了类似如下的错误: +## 家目录 -```bash -user@local:~$ ssh user@remote -user@remote:~$ ./Angband-4.2.5/src/angband -./Angband-4.2.5/src/angband: Angband needs at least an 80x24 'curses' screen -``` +你的家目录 `~` 在**所有 DevPod 和所有计算任务之间共享**，且路径相同：在 DevPod 里准备的代码和脚本，计算任务可以直接使用；换一个 DevPod，家目录里的东西还在。 -这说明 X11 Forwarding 配置出现了问题，可能需要配置环境变量，如 `echo "export DISPLAY=:0" >> ~/.zprofile`，具体请根据产生的错误信息进行调整。 +与之相对，容器内家目录**之外**的位置（如 `/`，`/tmp`）是每个容器私有的临时空间，容器回收后即消失，且有较小的容量限额。请把所有需要保留的文件放在家目录下。 -### 常见问题 +同时，由于家目录使用的 `nfs` 要求较低的网络延迟，不同物理地域机器的 DevPod、计算容器的家目录不同。 -请根据你的情况检查以下问题： +## 常见问题 !!! question "是否能看到如下的 SSH Banner？" ```shell - $ ssh username+nodename@clusters.zju.edu.cn - * * * * * * * * * * * * * * * * * * * * * * * * * * * - * ███████╗ ██╗██╗ ██╗███████╗ ██████╗████████╗* - * ╚══███╔╝ ██║██║ ██║██╔════╝██╔════╝╚══██╔══╝* - * ███╔╝ ██║██║ ██║███████╗██║ ██║ * - * ███╔╝ ██ ██║██║ ██║╚════██║██║ ██║ * - * ███████╗╚█████╔╝╚██████╔╝███████║╚██████╗ ██║ * - * ╚══════╝ ╚════╝ ╚═════╝ ╚══════╝ ╚═════╝ ╚═╝ * - * * * * * * * * * * * * * * * * * * * * * * * * * * * - Mon, 01 Jul 2024 12:47:19 +0000 - Your IP: ***.***.***.*** - User: username - %%% 我靠，桌子上怎么有水啊，显卡沾水了😱😱😱 + $ ssh h学号+DevPod名+hpc101@clusters.zju.edu.cn -p 443 + Wed, 22 Jul 2026 13:39:59 +0000 + h学号+DevPod名+hpc101 + ***.***.***.***:**** ``` ??? note "不能看见 SSH Banner" 未能连接到 `clusters.zju.edu.cn`。 - - 校网环境：检查 `clusters.zju.edu.cn` 是否解析到 `10.78.18.247`，检查 DNS 设置是否正确。 + - 校网环境：检查 `clusters.zju.edu.cn` 的 DNS 解析是否正常。 - 校外环境：尝试 80 或 443 端口，检查防火墙设置。 - 如果使用的是 MobaXTerm、XShell 等软件，需要在其选项里找到关于协商 ssh 版本的选项，并指定为 SSHv2 - - - MobaXTerm: 在 SSH Session 设置中, `Expert SSH Settings` -> `SSH version` 中选择 `SSHv2` - - ![MobaXTerm SSH Settings](index.assets/moba-2.webp) + 如果使用的是 MobaXTerm、XShell 等软件，需要在其选项里找到关于协商 SSH 版本的选项，并指定为 SSHv2。 ??? note "能看见 SSH Banner" @@ -263,18 +135,16 @@ user@remote:~$ ./Angband-4.2.5/src/angband | 错误信息 | 原因 | | --- | --- | - | `username@clusters.zju.edu.cn: Permission denied (publickey).` | 用户名或 SSH 密钥未配置正确 | + | `Permission denied (publickey).` | 用户名或 SSH 公钥未配置正确，检查 ZJU Git 上的公钥 | + | `Unknown host "..."` | 缺少 `+hpc101` 后缀 | | `Connection closed by ***.***.***.*** port ***` | 内部错误，请联系管理员 | - | `* Unknown host "nodename"` | 目标节点不存在，请检查节点名称
若未添加节点名称，则默认节点不在线，请联系管理员 | - | `Failed to connect to remote host`
`no route to host`
`i/o timeout` | 目标节点不在线，请联系管理员 | + | `* Failed to connect to remote host: ssh: handshake failed: ssh: unable to authenticate, attempted methods [none publickey], no supported methods remain` | DevPod 不存在或不处于 Running 状态 | ### 集群 SSH 代理原理 -集群目前使用 [OpenNG](https://github.com/mrhaoxx/OpenNG) 提供的 SSH 代理功能，原理如下图所示： +集群使用 [OpenNG](https://github.com/mrhaoxx/OpenNG) 提供的 SSH 代理功能：代理通过 TCP 头识别 SSH 连接并按用户名中的路由信息（`用户+目标`）转发，详见 [:simple-github: 源码](https://github.com/mrhaoxx/OpenNG/blob/f59461d12c48a9410967c7f4dd5a5ae1df251eef/tcp/detect.go#L116)。 -
-![ssh](index.assets/ssh.webp){ align=center } -
集群 SSH 代理登录原理
-
+## 下一步 -OpenNG 通过 TCP 头识别 SSH 连接，进行接管，详见 [:simple-github: 源码](https://github.com/mrhaoxx/OpenNG/blob/f59461d12c48a9410967c7f4dd5a5ae1df251eef/tcp/detect.go#L116)。 \ No newline at end of file +- [提交计算任务](./job.md) +- [提交实验报告](./submit.md) diff --git a/docs/guide/job.md b/docs/guide/job.md index 48e916d5..7e1fbe28 100644 --- a/docs/guide/job.md +++ b/docs/guide/job.md @@ -1,265 +1,164 @@ -# 提交作业 +# 提交计算任务 -## Slurm 简介 +DevPod 适合编辑、编译和小规模调试，但它的资源是受限的。需要完整算力（整机核数、独占绑核、GPU）时，需要把程序作为**计算任务**提交到分区队列中运行。 -!!! quote +在 DevPod 内使用 `hpc` 命令提交和管理任务。它的用法是 Slurm 风格的，如果你用过 Slurm 会觉得很熟悉： - - [Quick Start User Guide](https://slurm.schedmd.com/quickstart.html) +!!! tip "Tips for agent" + `hpc skills` 显示 `hpc` 的用法。 - Slurm 提供三大功能： +| Slurm | hpc | 说明 | +| --- | --- | --- | +| `sinfo` | `hpc partitions`（别名 `hpc sinfo`） | 查看分区容量、限制、可用镜像 | +| `srun cmd` | `hpc submit cmd` | 运行一条命令并实时输出 | +| `sbatch job.sh` | `hpc submit job.sh` | 提交脚本，`#HPC` 指令设置参数 | +| `srun --pty bash` | `hpc submit --interactive bash` + `hpc attach` | 交互式任务 | +| `squeue` | `hpc squeue` | 查看排队/运行中的任务 | +| `scancel` | `hpc cancel` | 取消任务 | - - 控制用户对节点的访问 - - 提供并行任务执行框架 - - 提供任务调度和队列 +在同一个物理区域内，任务和 DevPod **共享同一个家目录**，故在 DevPod 里准备好代码，直接 `hpc submit` 即可在计算节点上运行。 - Slurm 中的几个实体： +## 分区 - - 节点（node）组成分区（partition），可以视为任务队列 - - 任务（job）由一个或多个步骤（step）组成 - - -## 分区简介 - -通过 `sinfo` 命令可以查看分区信息： +任务提交到**分区**（partition）。用下面的命令查看你能用的分区、它们的资源范围和排队情况： ```shell -sinfo -M6* up 1:00:00 4 mix M[600-603] -M6* up 1:00:00 1 idle M604 -M7 up 1:00:00 2 idle M[700-701] -V100 up 1:00:00 3 mix v[00-01,13] -V100 up 1:00:00 29 idle v[02-03,10-12,20-23,30-33,40-43,50-53,60-63,70-73] -kunpeng up 30:00 1 idle k11 -riscv up 30:00 1 idle* rv01 -riscv up 30:00 1 idle rv00 +hpc partitions # 分区容量、CPU/GPU/内存/时长限制、允许的镜像 +hpc limits # 每个分区对「你」的限制（可申请的资源范围、同时任务数上限） ``` -- M6, M7 分区是超算队自有节点。 +各实验开放的分区以实验文档和平台页面为准。不指定 `-p` 时任务会进入默认分区。 -- V100 分区是信息中心提供的 GPU 节点，共 8 台物理机，64 个 V100 GPU。我们把它切成了 32 个节点，每个节点有 2 个 GPU。 +## 提交任务 -- kunpeng 分区由信息中心提供的鲲鹏 920 节点构成。 +### 直接运行命令 -- riscv 分区由[**进迭时空**](https://www.spacemit.com/)提供的给超算队的 RISC-V 开发板构成。 +```shell +hpc submit -p intels4 -c 4 -m 8Gi ./build/myprog +``` -**具体参见 [集群概况](./overview.md)** +任务会排队、启动，然后把输出实时流回你的终端，结束后返回。加 `-d`（detach）则只打印任务号立即返回。 -!!! tip +!!! warning "hpc 的参数必须写在命令前面" - 对于 M6 集群，`m60[0-1]` CPU 一样, `m60[2-4]` CPU 一样，跑任务的时候尽量避免混用。提交任务时可以使用 `-w` 参数指定使用哪些节点。 + 从第一个非选项参数（你的命令）开始，后面的内容会**原样传给你的程序**，不再被 `hpc` 解析： -## 提交任务 + ```shell + hpc submit uname -a # -a 传给 uname + hpc submit python3 -m http.server -p 80 # -m、-p 80 都传给 python3 + hpc submit -c 4 -p intels4 uname -a # -c/-p 是 hpc 的，-a 是 uname 的 + ``` -Slurm 有三种提交任务的方式： + 要给任务传管道、`&&` 等 shell 语法，把整条命令作为一个带引号的参数： -- `srun` -- `sbatch` -- `salloc` + ```shell + hpc submit 'make -j && ./run.sh 2>&1 | tee run.log' + ``` -### 批处理任务 +常用参数： -使用 `sbatch` 提交任务时，脚本文件模板如下： +| 参数 | 说明 | +| --- | --- | +| `-p, --partition` | 目标分区（省略则用默认分区） | +| `-c, --cpu N` | CPU 核数 | +| `-g, --gpu N` | GPU 数（仅支持 GPU 的分区） | +| `-m, --mem SIZE` | 内存，如 `8Gi` | +| `-t, --time DUR` | 最长运行时长，如 `30m`、`1h30m` | +| `-i, --image` | 容器镜像（须在分区允许列表内，默认取第一个） | +| `-e, --env K=V,…` | 环境变量 | +| `-o, --output PATH` | 输出文件（见下） | +| `-n, --name` | 任务显示名 | +| `-d, --detach` | 提交后立即返回任务号，不流式输出 | +| `--interactive` | 交互式任务（见下） | + +### 提交脚本（sbatch 风格） + +把参数写进脚本的 `#HPC` 指令（也兼容 `#SBATCH` 写法），直接提交脚本文件： ```shell title="job.sh" #!/bin/bash -#SBATCH --job-name=hpl -#SBATCH --partition=M6 -#SBATCH --nodes=4 # Number of nodes -#SBATCH --ntasks=8 # Number of MPI ranks -#SBATCH --ntasks-per-node=2 # Number of MPI ranks per node -#SBATCH --cpus-per-task=8 # Number of OpenMP threads for each MPI rank -#SBATCH --time=00:00:30 -#SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) -#SBATCH --mail-user=mail@zju.edu # Email address -#SBATCH --output=%x_%j.log # Standard output and error log -## Command(s) to run (example): -module load openmpi hpl -export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK -mpirun xhpl +#HPC --partition=intels4 +#HPC --cpu=8 +#HPC --mem=16Gi +#HPC --time=30m +#HPC --output=result_%j.log + +make -j +./build/myprog ``` -sbatch提交任务的方法可以参考: [https://hpc.pku.edu.cn/_book/guide/slurm/sbatch.html](https://hpc.pku.edu.cn/_book/guide/slurm/sbatch.html) - -在运行 Intel MPI 时，需要设置环境变量 `I_MPI_PMI_LIBRARY`。例如，使用 `srun` 运行 `xhpl` 时: - -```shell hl_lines="3 10 12" -spack load intel-oneapi-mpi - -export I_MPI_PMI_LIBRARY=/slurm/libpmi2.so.0.0.0 -export I_MPI_DEBUG=+5 - -# 使用 srun 命令运行 xhpl 程序 -# -N 2: 使用 2 个节点 -# -p M7: 在 M7 分区运行 -# -w M700,M701: 指定使用 M700 和 M701 两个节点 -# --mpi=pmi2: 使用 PMI2 接口进行 MPI 通信 -# --ntasks-per-node=1: 每个节点运行 1 个 MPI 进程 -# --cpus-per-task=96: 每个任务使用 96 个 CPU 核心 -srun -N 2 -p M7 -w M700,M701 --mpi=pmi2 --ntasks-per-node=1 --cpus-per-task=96 xhpl +```shell +hpc submit job.sh ``` -!!! tip - - 我们推荐使用 Intel MPI。 - - -!!! warning - - 请注意，slurm 会强制绑定任务（包括ssh会话里的进程）运行在CPU的某些核心上，在提交作业时如果不指定 `--cpus-per-task`，则默认使用 1 个线程。 - 这时所有进程都会运行在同一个核心上。 - - - -输出文件名有一些特殊的格式： - -| 格式 | 描述 | -| --- | --- | -| `%j` | 作业 ID | -| `%n` | 节点名
使用它可以将不同节点的输出分开 | -| `%x` | 任务名 | -| `%u` | 用户名 | - -举例： - -```text -job%2j-%2t.out - job128-00.out, job128-01.out, ... -``` +命令行上显式给出的参数优先于脚本内的指令。 ### 交互式任务 -使用 `salloc` 可以申请节点进行交互式任务： - -1. **申请节点**： - - ```bash - salloc -p V100 - ``` - -2. **连接到分配的节点**： - - ```bash - ssh nodename - ``` - - 你也可以使用 `-w` 参数指定申请的节点名： - - ```bash - salloc -p V100 -w v63 - ``` - -!!! tip - - 使用 `salloc` 申请到节点后，关闭终端会自动释放资源。 - -### 提交GPU任务 - -对于V100分区，使用GPU时需要指定GPU资源申请参数。Slurm提供了多种方式来申请GPU资源： - -#### 使用 `--gres` 参数 - -`--gres=gpu:数量` 用于指定申请的GPU数量： +需要一个「在计算节点上的 shell」时（类似 `srun --pty bash` / `salloc`）： ```shell -# 申请1个GPU -salloc --gres=gpu:1 -p V100 - -# 申请2个GPU -salloc --gres=gpu:2 -p V100 - -# 申请特定类型的GPU -salloc --gres=gpu:v100:1 -p V100 +hpc submit -p intels4 -c 8 --interactive bash ``` -#### 使用 `--gpus` 参数 - -`--gpus` 是更现代的GPU资源申请方式： +任务启动后自动进入终端；断开后可以随时用任务号重新接入： ```shell -# 申请1个GPU -salloc --gpus=1 -p V100 +hpc attach <任务号> +``` -# 申请2个GPU -salloc --gpus=2 -p V100 +!!! tip -# 指定每个节点的GPU数量 -salloc --gpus-per-node=2 -p V100 + 交互式任务同样占用你的任务配额并受时长限制，用完请 `exit` 或 `hpc cancel`，不要让它空挂。 -# 指定每个任务的GPU数量（用于MPI任务） -salloc --gpus-per-task=1 --ntasks=2 -p V100 -``` +### 工作目录（sbatch 语义） -#### 批处理GPU任务示例 +任务在**你提交时所在的目录**中运行（相对家目录）。在 `~/lab2` 下执行 `hpc submit`，任务的工作目录就是 `~/lab2`。 -```shell title="gpu_job.sh" -#!/bin/bash -#SBATCH --job-name=gpu_test -#SBATCH --partition=V100 -#SBATCH --nodes=1 -#SBATCH --ntasks=1 -#SBATCH --gpus=1 # 申请1个GPU -#SBATCH --cpus-per-task=8 -#SBATCH --time=01:00:00 -#SBATCH --output=%x_%j.log - -# 设置CUDA环境 -module load cuda - -# 运行GPU程序 -./your_gpu_program -``` +注意：只有家目录是共享存储。如果你在家目录之外提交，任务看不到那里的文件，会回退到 `~` 运行（提交时会有警告）。 +## 输出与日志 -## 查看任务 +批处理任务的 stdout/stderr 写入**工作目录下的持久文件**，默认 `j<任务号>.out`，可用 `-o` 自定义。文件名支持占位符： -可以使用 `squeue` 命令查看当前任务： +| 占位符 | 含义 | +| --- | --- | +| `%j` | 任务号 | +| `%u` | 用户名 | +| `%x` | 任务名 | +| `%Y %m %d %H %M %S` | 提交时间 | ```shell -squeue +hpc submit -o result_%j.log ... ``` +`hpc logs <任务号>`（`-f` 跟随）看的是容器的**实时日志**——它在任务结束、容器被回收后就没有了。要留档请以输出文件为准。 +任务的最终状态反映你的命令的退出码。 -要使用 `sacct` 命令查看历史任务，你可以按照以下步骤操作： - -1. **基本使用**：直接使用 `sacct` 命令来查看当前或者过去任务的概况。你可以在终端中输入以下命令： - - ```bash - sacct - ``` - - 这将显示最近的任务信息，包括任务 ID、任务名称、状态等。 - -2. **指定时间范围**：如果你只想查看特定时间范围内的任务，可以使用 `--start` 和 `--end` 参数。例如，要查看从 2024 年 8 月 1 日到 8 月 31 日之间的任务，你可以使用： - - ```bash - sacct --start=2024-08-01 --end=2024-08-31 - ``` +## 查看与管理 -3. **指定字段**：默认情况下，`sacct` 可能会显示许多你不关心的字段。你可以通过 `--format` 参数指定显示哪些字段。例如，如果你只想查看任务 ID、任务名称、状态和运行时间，可以使用： - - ```bash - sacct --format=JobID,JobName,State,Elapsed - ``` - -4. **查看某个特定任务**：如果你知道某个任务的 ID，并且想查看该任务的详细信息，可以直接使用任务 ID： - - ```bash - sacct -j - ``` - - 例如： +```shell +hpc ls # 我的任务列表（-s STATE 过滤状态） +hpc squeue # 全体用户的活跃任务 +hpc info <任务号> # 任务详情（状态、节点、资源） +hpc logs -f <任务号> # 实时日志 +hpc cancel <任务号> # 取消（别名 hpc rm） +``` - ```bash - sacct -j 12345 - ``` +这些信息在平台的「任务」「队列」页面上也都能看到，网页上同样可以提交任务和查看实时输出。 -5. **过滤状态**：你还可以使用 `-s` 参数来过滤任务状态。例如，查看所有失败的任务： +## 完整示例 - ```bash - sacct -s F - ``` +```shell +# 编译（在 DevPod 里） +cd ~/lab2 && make -j -6. **更多帮助**：`sacct` 命令有许多选项，你可以使用 `man sacct` 或 `sacct --help` 查看所有可用选项及其详细说明。 +# 批处理提交，流式看输出 +hpc submit -p intels4 -c 8 -m 16Gi -t 30m ./build/benchmark -通过这些步骤，你可以灵活地使用 `sacct` 命令查看并管理你的历史任务记录。 +# 或者后台提交，稍后看结果 +hpc submit -d -c 8 ./build/benchmark +hpc ls +cat ~/lab2/j123.out +``` diff --git a/docs/guide/oj.md b/docs/guide/oj.md index 83e9f0e0..35778b86 100644 --- a/docs/guide/oj.md +++ b/docs/guide/oj.md @@ -1,182 +1,3 @@ # 使用在线测评 -我们使用基于 SSH 协议的在线测评系统。登陆方式与登陆集群的方式相同。 - -!!! warning - - 在继续之前，请确认你已经可以成功[登陆集群](./index.md)。 - -!!! warning "警告" - 不要使用密码登录集群，密码登陆是蜜罐，输入的密码会被明文记录在日志中。 - -## 通过 SSH 连接到评测系统 - -在线评测系统的节点名为 `oj`。例如，可以使用 - -```shell -ssh student+oj@clusters.zju.edu.cn -p 443 -``` - -登录到系统 - -!!! tip - - 推荐修改 SSH 配置文件以获得更加流畅的使用体验。如： - - ```text title="~/.ssh/config" - Host oj - User student+oj - HostName clusters.zju.edu.cn - Port 443 - ``` - - 以下所有例子都假设你已经修改了 SSH 配置文件。 - -此时应该可以看到提示信息 - -```shell -$ ssh oj -************************************************************ -* * -* ███████╗ ██╗██╗ ██╗███████╗ ██████╗████████╗ * -* ╚══███╔╝ ██║██║ ██║██╔════╝██╔════╝╚══██╔══╝ * -* ███╔╝ ██║██║ ██║███████╗██║ ██║ * -* ███╔╝ ██ ██║██║ ██║╚════██║██║ ██║ * -* ███████╗╚█████╔╝╚██████╔╝███████║╚██████╗ ██║ * -* ╚══════╝ ╚════╝ ╚═════╝ ╚══════╝ ╚═════╝ ╚═╝ * -* * -************************************************************ -Mon, 05 Aug 2024 12:00:00 +0800 - Your IP: ***.***.***.*** - User: username+oj -%%% 我们仍未理解OpenCAEPoro为什么跑不起来多机 -Welcome to SOJ Secure Online Judge , username -2024-08-26 19:39:20 CST -Use 'submit (sub) ' to submit a problem -Use 'list (ls) [page]' to list your submissions -Use 'status (st) ' to show a submission (fuzzy match) -Use 'rank (rk) ' to show ranklist -Use 'my' to show your submission summary -``` - -!!! note - - 在线测评系统目前并没有提供可交互的用户界面，当输出完信息后，会立刻断开与你连接，这是正常现象。 - -## 上传文件 - -在测评过程中，当需要提交文件时，我们使用`sftp`进行。路径格式为 - -```text -/ -``` - -例如，如果对于题目`hello`，需要提交 `world.cpp`，可以通过 - -```shell -scp /path/to/local/file oj:hello/world.cpp -``` - -来进行上传。 - -!!! note "如果你无法正常使用 scp 进行上传" - - OpenSSH 版本 `<8.7` 时 `scp` 不支持 sftp 协议，无法使用 `scp` 进行上传，请使用 `sftp` 命令，具体用法请自行查询。 - - OpenSSH 版本 `>=8.7 && <9.0` 时 `scp` 支持 sftp 协议，但没有默认启用，请添加选项 `-s`，如: - - ```shell - scp -s /path/to/local/file oj:hello/world.cpp - ``` - - OpenSSH 版本 `>9.0` 时可以直接使用 `scp`。 - -!!! tip - - 相信聪明的你一定能举一反三。如果你没有修改 `ssh config`, 这里的命令等价于 - - ```shell - scp -P 443 /path/to/local/file student+oj@clusters.zju.edu.cn:hello/world.cpp - ``` - -!!! note - - 我们支持标准的 `sftp` 协议，你可以通过你喜欢的任意 `sftp` 客户端进行连接。 - -## 查看我的状态和题目列表 - -通过命令 - -```shell -ssh oj my -``` - -你可以查询当前各个题目的有效提交状态和总分。 - -## 提交 - -当你准备完所有文件后，你就可以进行提交了，提交不会清空已上传的文件。提交命令为 - -```shell -ssh oj submit -``` - -这会创建一个提供流式日志的 SSH 会话。关闭该 SSH 连接不会影响评测的进行。可以通过 [获取提交状态](#获取提交状态) 来获得评测日志，与流式日志完全相同。 - -## 查看提交列表 - -可以通过 - -```shell -ssh oj list -``` - -来获取你的历史提交，每页最多 10 个提交。例如，可以通过 - -```shell -ssh oj list 2 -``` - -来获得第二页。 - -!!! tip - - `list=ls` - -## 获取提交状态 - -在提交后的日志中和提交列表中，你都可以找到某个提交的 `ID`，你可以通过 - -```shell -ssh oj status -``` - -来获取这个提交的详细信息。 - -!!! tip - - `status=st` - -!!! tip - - `status`命令会对`ID`进行模糊查询，你可以通过其任意字串进行查询，仅会返回匹配的最新提交。 - - - -!!! note - - 0 分的提交不会进入统计 - -## 查看排行榜 - -通过命令 - -```shell -ssh oj rank -``` - -你可以查询当前排行榜。 - -!!! tip - - `rank=rk` +!!! danger "OJ 还在建设中" \ No newline at end of file diff --git a/docs/guide/overview.md b/docs/guide/overview.md index 18a85b37..ceeff471 100644 --- a/docs/guide/overview.md +++ b/docs/guide/overview.md @@ -1,40 +1,38 @@ # 集群概况 -目前集群硬件资源如下： +课程集群是一个跨三种 CPU 架构的异构集群，通过[统一平台](https://platform.s.zjusct.io)使用。面向课程开放的计算资源按**分区**组织： -- M6 集群 共 5 个节点 - - CPU (M600,M601)：2 x Intel Silver 4314 16Core @ 2.4GHz (32 核心 64 线程) - - CPU (M602,M603,M604)：2 x Intel Gold 5320 26Core @ 2.2GHz (52 核心 104 线程) - - 内存：DDR4 256 GB 以上 - - Ethernet：10 Gbps - - Infiniband：HDR 200 Gbps +## 计算分区 -- M7 集群 共 2 个节点 - - CPU：2 x Intel Gold 5418Y 24Core @ 2.0GHz (48 核心 96 线程) - - 内存 DDR5-4800 - - Ethernet：1 Gbps +### Intel x86 (Sapphire Rapids) -- RISC-V 集群 ([**进迭时空**](https://www.spacemit.com/)提供) 共 8 个节点 - - CPU：Spacemit X60 @ 1.6Ghz - - 内存：8GB - - Ethernet：100Mbps +- 节点：`m700`、`m701`，共 2 台 +- CPU：2 × Intel Xeon Gold 5418Y 24 核 @ 2.0GHz（每台 48 核 96 线程） +- 内存：DDR5-4800 +- 支持 AVX-512、Intel AMX 等指令集扩展 +- GPU：`m701` 附带 NVIDIA H800，以 MIG 实例（`1g.10gb`，10GB 显存）形式提供给 GPU 实验使用 +### armv8 — 鲲鹏 920 -- V100 集群 共 32 个节点，每个节点有 2 个 GPU - - GPU：NVIDIA V100 32GB * 2 - - Ethernet：10Gbps - - Infiniband：HDR 200Gbps +- 节点：`920b-1/2/3`，共 3 台 +- CPU：Kunpeng 920B，每台 256 核（ARMv8，无 SMT） +- 内存：每台 512 GB -- 鲲鹏 920 集群 共 1 个节点 - - CPU：Kunpeng 920 128Core @ 2.6GHz - - 内存：DDR4 512 GB - - Ethernet：10Gbps +### riscv — RISC-V ([**进迭时空**](https://www.spacemit.com/)提供) + +- 节点：`rv00`–`rv03`，共 4 台开发板 +- CPU：SpacemiT K1（X60 核心，RVV 1.0 向量扩展 + IME 矩阵扩展） +- 内存：每台 8 GB + +!!! note + + 各分区是否开放、具体的资源与时长限制随实验安排调整，以 `hpc partitions` 和平台页面显示为准。 ## 存储 -集群中有两个位置用于存储文件： +- **家目录 `~`**：位于专用存储节点的 SSD 阵列上，通过网络文件系统挂载到你的所有 DevPod 和计算任务中，路径一致、内容实时同步。这是**唯一的持久共享存储**，代码、数据和实验结果都放这里。不同物理地域的分区有不同的家目录。 +- **容器临时空间**：家目录之外的路径（如 `/tmp`）是每个容器私有的，有较小的容量限额，容器回收后即消失。 -- `~`：6.4T SSD 阵列，你的家目录，权限仅个人，每人限额50G -- `/river`：和家目录同一个阵列，用于共享文件 +!!! warning -这些位置均跨节点挂载，你可以在任意节点访问这些存储池。 + 请勿在集群存放与课程无关的大文件，重要数据及时本地备份。 diff --git a/docs/guide/submit.md b/docs/guide/submit.md new file mode 100644 index 00000000..7ea79f19 --- /dev/null +++ b/docs/guide/submit.md @@ -0,0 +1,37 @@ +# 提交实验报告 + +实验报告和代码通过平台的**「我的提交」**页面提交。你**不需要上传任何文件**：平台直接从你集群家目录的实验目录里收取文件并存档。在 DevPod 里把报告和代码放好，到网页上点提交即可。 + +!!! warning + + 请摆放到 `x86-5418Y` 预设的 DevPod 的家目录中，其他分区的家目录可能在不同的物理地域。 + +## 截止时间 + +!!! warning "分组错峰截止" + + 为了避免所有人挤在同一时刻提交前的算力高峰，部分实验的截止时间**按分组错开**：不同组的同学截止日期不同。你的分组和截止时间以页面上「本组截止」的显示为准。 + +## 提交流程 + +1. 在「我的提交」页面打开对应实验。 +1. 确认**目录路径**：默认为实验名（如 `lab2`，指 `~/lab2`），如果你的文件放在别处，改成对应路径（相对家目录）。 +1. 点击**预览**：页面会展示该目录的文件树，**高亮**将被收取的文件，其余文件灰显。每个实验只收取规则指定的文件（如 `report.pdf`、`src/*` 等）。 +1. 点击高亮的文件可以在线预览（PDF、图片、文本），确认收取的正是你想交的版本。 +1. 点击**提交**。 + +!!! tip "多次提交与版本" + + 截止前可以多次提交，我们以最后提交的一次为准进行评分，我们会限制提交次数，以页面上显示的为准。 + +## 常见问题 + +!!! question "预览显示某个文件读取失败 / 匹配不到？" + + - 平台以只读方式访问你的家目录，文件需要保持默认权限（其他用户可读，`0644`）。在 DevPod 里正常创建的文件都满足；如果你 `chmod 600` 过，请改回来。 + - 指向家目录**之外**的符号链接不会被收取。 + - 检查路径拼写：目录路径是相对家目录的，`lab2` 指 `~/lab2`。 + +!!! question "提交后又改了文件，存档会变吗？" + + 不会。每个版本是提交那一刻的快照，之后再改家目录里的文件不影响已提交的版本。改完再提交一次即可。 diff --git a/docs/lab/Lab5-Gemma4/image/attn_score.png b/docs/lab/Lab5-Gemma4/image/attn_score.png new file mode 100644 index 0000000000000000000000000000000000000000..845ae0e673f1084f5d4d816f24944178df25c05c GIT binary patch literal 98823 zcmeFZ5y)a25Cv@O{#P^NOzaSrZ(}cjlc80 z&xhxn`}_g-2L!f@wbop7%rUNUUDsS;%8JtHXvAo6aB%3dGH+Gk;E*xk;NX9tJOw^! z-g-v`e1msZl@^Dq7$)5XetB#9Ue-)O0gegy83hhL)DjL6b_noA41B@CJ;{NGdjfoi zhkcfV@UQjc{ub<(6zz+ObRPPT5CkiL~R!rR;{vZ=MheooleMBMMSu`F8kw!)e z3kwlmQWP5%hXxe^jY=$58Vdq)yoX!$=;_ z!+-&P`tOGNe_u%2j~fI0{J%L87|E0WW+X5EyW{+*{Yq8Yp8QYK0=w5P%9(#ZZ~y;b 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    + + +在基础的 Decoder-only 架构之上，Gemma4-12B 进行了多项改进： + +- **滑动窗口注意力（Sliding Window Attention）**：在计算注意力分数时仅考虑固定长度的上下文窗口内的 token + +- **混合注意力**：大部分层采用滑动窗口注意力，小部分层采用**全局注意力（Regular Attention）**捕捉长程依赖 + +- **分组查询注意力（Grouped Query Attention）**：在计算注意力分数时，将查询向量分为若干组，每组共享一个键值向量 + +- **权重共享**：全局注意力层内 $W_K$ 和 $W_V$ 共享权重 + +- **p-RoPE**：全局注意力层采用比例旋转位置编码（proportional RoPE）捕捉词序信息，降低对词义信息的干扰 + +- **SwiGLU 激活函数**：使用 SwiGLU 作为前馈网络的激活函数，增强模型的表达能力 + +下表简单列出 Gemma4-12B 的主要参数： + +| 参数 | 值 | 参数含义 | 简写 | +|------|----|----------|------| +| `num_hidden_layers` | 48 | Decoder Layer 数量 | $N$ | +| `hidden_size` | 3840 | 每层的隐藏层维度 | $D$ | +| `num_attention_heads` | 16 | 注意力头数量 | $H_q$ | +| `num_key_value_heads` | 8 | 滑动窗口注意力层 Key/Value 头数量 | $H_{l,kv}$ | +| `num_global_key_value_heads` | 1 | 全局注意力层 Key/Value 头数量 | $H_{g,kv}$ | +| `head_dim` | 256 | 滑动窗口注意力层每个注意力头的维度 | $D_{l,h}$ | +| `global_head_dim` | 512 | 全局注意力层每个注意力头的维度 | $D_{g,h}$ | +| `sliding_window` | 1024 | 滑动窗口大小 | $w$ | +| `intermediate_size` | 15360 | 前馈网络中间层维度 | $F$ | +| `vocab_size` | 262144 | 词汇表大小 | $V$ | + +更多参数可参考模型权重目录下的 `config.json` 文件。 + +下面我们会对 Gemma4-12B 的部分组件进行讲解，以帮助你理解模型的结构和推理过程。 + +#### 滑动窗口注意力（Sliding Window Attention） + +我们知道，标准的自注意力机制公式为： + +$$ +\begin{align*} + O &= \operatorname{softmax} \left(\frac{A + M}{\sqrt{d_k}}\right)V, \\ + A &= Q K^{\mathrm{T}}. +\end{align*} +$$ + +其中 $M$ 是掩码矩阵，用于屏蔽不需要参与计算的 token，$A$ 被称作注意力分数。在全局注意力层中，$M$ 是一个下三角矩阵，表明计算自注意力时，每个 token 只能看到它之前的 token。然而在实践中，我们发现在诸多情况下，**注意力分数的分布具有局部性**，即在位置 $i$ 的 token 对区间 $[i-w, i]$ 内的 token 的注意力分数大于其他 token 的注意力分数，其中 $w$ 是一个固定的窗口大小： + +
    + Attention Score Distribution +
    注意力分数分布示例。图源。
    +
    + +因此，通过掩码矩阵屏蔽掉窗口外的 token 的注意力分数，对模型的性能影响不会太大。具体地，我们可以使用类似下图右侧的掩码矩阵 $M$ 限制每个 token 的注意力范围： + +
    + Sliding Window Attention +
    全局注意力和滑动窗口注意力示例。图源。
    +
    + +然而，在实际情况下应用滑动窗口注意力仍需注意以下几点： + +- “注意力分数的分布具有局部性”这一假设并不总是成立的，尤其是处理长文本时，完全忽略窗口外的 token 可能会对模型性能产生较大负面影响。因此 Gemma4 采用了混合注意力的策略，在部分层保留 Regular Attention，以捕捉长程依赖。 + +- 在数学上我们可以通过设置掩码矩阵 $M$ 为条带状矩阵来实现 Sliding Window Attention，然而在实际实现中，先计算再加掩码的方式会导致大量无效计算与访存，无法发挥其优势。 + +#### 分组查询注意力（Grouped Query Attention, GQA） + +[GQA](https://arxiv.org/abs/2305.13245) 是一种用于减少 Key Value 数量和 Attention 计算量的优化方法。在 MHA 中，一个 Query Head 对应一个 Key/Value Head，但在 GQA 中，多个 Query Heads 可以共享同一个 Key/Value Head，这样可以显著减少 KV 的数量。 + +
    + Grouped Query Attention +
    Gemma4 中的分组查询注意力。图源。
    +
    + +在 Gemma4-12B 中，$H_q=16$，$H_{l,kv}=8$，$H_{g,kv}=1$，因此滑动窗口注意力层中每 2 个 Query Heads 共享 1 个 Key/Value Head，全局注意力层中所有 16 个 Query Heads 共享 1 个 Key/Value Head。 + +在进行注意力计算时，Query Heads 的数量仍然是 $H_q$，而 Key/Value Heads 的数量是 $H_{l,kv}$ 或 $H_{g,kv}$。因此我们需要通过重复 Key/Value Heads 来匹配 Query Heads 的数量。 + +#### SwiGLU 激活函数与 FFN + +Gemma4 使用了 SwiGLU 激活函数，它结合了 SiLU 和 GLU 的优点，能够更好地捕捉非线性关系。 + +**SiLU (Sigmoid Linear Unit)** 是 Sigmoid 和 ReLU 的改进版，其公式为： + +$$\text{SiLU}(x) = x \cdot \text{Sigmoid}(x)$$ + +**GLU (Gated Linear Unit)** 是一个门控机制，他的核心思想是通过一个范围在 `[0, 1]` 的门控函数来控制信息的流动。GLU 的公式为： + +$$\text{GLU}(x) = \sigma(W_g x) \odot (W_x x)$$ + +其中 $\sigma$ 是范围为 `[0,1]` 的激活函数，$W_g$ 和 $W_x$ 是可学习的权重矩阵，$\odot$ 表示逐元素乘法。 + +**SwiGLU** 是 $\sigma = \text{SiLU}$ 的 GLU 变体，其公式为： + +$$\text{SwiGLU}(x) = \text{SiLU}(W_g x) \odot (W_x x)$$ + +标准的前馈神经网络 (FFN) 由两个线性层和一个激活函数组成，可以表示为： + +$$ +\text{FFN}(x) = W_2 \cdot \sigma(W_1 \cdot x + b_1) + b_2 +$$ + +其中 $W_1$ 和 $W_2$ 是可学习的权重矩阵，$b_1$ 和 $b_2$ 是偏置项，$\sigma$ 是激活函数。 + +在 Gemma4 中，FFN 采用 SwiGLU 作为激活函数，并使用 $F$ 作为中间层的维度，包含三个线性层： + +1. **门控投影 (Gate Projection)**：计算门控值，形状为 $(D, F)$。 +2. **上投影 (Up Projection)**：计算上投影值，形状为 $(D, F)$。 +3. **下投影 (Down Projection)**：将结果投影回原维度，形状为 $(F, D)$。 + + + +### LLM 推理时的显存占用 + +LLM 推理时占用的显存主要由**模型权重**、**KV Cache**和**激活值**三部分组成： + +| 类型 | 说明 | （通常情况下）生命周期 | +| ---- | ---- | ---- | +| **模型权重** | 模型的参数权重 | 推理服务初始化时分配，服务结束时释放 | +| **KV Cache** | 各个 token 对应的 Key/Value 向量 | 在请求处理过程中分配，请求处理完毕后释放 | +| **激活值** | 请求序列在模型各层的中间计算结果 | 临时分配 | + +通常情况下，激活值在参与的计算完成之后就会被立即释放，因此其对整体显存占用的影响较小。相比之下，模型权重和 KV Cache 会持久性地占用显存，前者占用的显存大小固定，而后者占用的显存大小随请求序列长度和 batch size 线性增长。下面这个例子展示了根据模型结构和请求估算模型权重和 KV Cache 占用的显存大小的方法： + +!!! example + 假设一个 Decoder-only 的模型由 $N$ 个 GQA 全局注意力层组成，隐藏层维度为 $D$，注意力头数量为 $H_q$，Key/Value 头数量为 $H_{kv}$，每个注意力头的维度为 $D_h$，FFN 采用 SwiGLU 激活函数，中间层维度为 $F$，词表大小为 $V$，我们可以先根据各线性层形状估计参数量： + + | 参数 | 形状 | 参数 | 形状 | + | ---- | ---- | ---- | ---- | + | $W_q$ | $(H_q, D, D_h)$ | $W_{up}, W_{gate}$ | $(D, F)$ | + | $W_k, W_v$ | $(H_{kv}, D, D_h)$ | $W_{down}$ | $(F, D)$ | + | $W_o$ | $(H_q, D_h, D)$ | $W_{embed}$ | $(V, D)$ | + + 因此在参数量化精度为 x-bit 时，整个模型的参数占用的显存大约为： + + $$ + M_w = [2 V D + N D(2H_q D_h + 2 H_{kv} D_h + 3 F)] \cdot x / 8 \text{ bytes} + $$ + + 需要注意的是上述估算方式忽略了 RMSNorm 等参数规模较小的层，因此实际测量的显存占用可能会略大于估算值。 + + --- + + 下面估算静态分配策略下的 KV Cache 的显存占用，即为每个请求都按照最大的序列长度分配 KV Cache。假设目前请求的批次数（batch size）为 $B$，请求序列的最大长度为 $S$，激活值量化精度为 y-bit。那么 KV Cache 的显存占用大约为： + + $$ + M_{kv} = 2 B S N H_{kv} D_h \cdot y / 8 \text{ bytes} + $$ + + +## 任务一：GPTQ 权重量化 + +根据上文中的思路计算一下 bfloat16 精度下 Gemma4-12B 模型参数的显存占用，我们会发现参数占用的显存已经远超我们的可用显存（10 GiB），因此在全量加载模型原始权重的情况下，我们无法完成端到端的推理。 + +模型参数量化是在显存受限环境下进行 LLM 推理的常用方法。其原理是将模型权重从高精度（如 FP32 或 BF16）根据一定的量化策略映射到低精度（如 FP8 或 INT4），从而降低模型权重占用的显存。由于高精度到低精度的映射不可逆，量化会导致精度损失，但是大多数情况下由于模型参数中包含大量冗余信息，量化后的模型的性能下降幅度是可以接受的。 + +目前主流的量化策略均采用线性映射，通过零点 $z$ 和缩放因子 $s$ 即可确定高精度权重 $r$ 与低精度权重 $q$ 的映射关系： + +$$ + q = \operatorname{round}\left(\frac{r}{s} + z\right), \enspace r = s \cdot (q - z). +$$ + +![quant](image/quant.png) + +$z$ 和 $s$ 都需要用较高的精度表示，通常为原始权重的精度，并且在量化后需要存储到模型权重文件中，以便推理时对权重进行反量化，因此我们需要让多参数共享同一组 $z$ 和 $s$，共享的策略被称为**量化粒度**。常见的量化粒度有 per-tensor、per-channel、per-group 等，一般粒度越小量化损失越低，但存储量化参数的开销越大。 + +### 知识讲解：训练后量化与 GPTQ + +训练后量化（Post-Training Quantization，PTQ）是指在模型训练完成后，使用少量校准数据确定量化参数，而不再对模型进行训练或微调。在线性映射的归纳偏置下，量化策略的核心问题变为如何选择零点 $z$ 和缩放因子 $s$，以最小化量化误差： + +$$ + \underset{z, s}{\operatorname{argmin}} \mathbb{E}_{X} \left[ L(f(W, X), f(Q, X)) \right] +$$ + +其中 $W$ 是原始权重，$Q$ 是量化后的权重，$X$ 是输入数据，$f(\cdot)$ 是前向函数，$L(\cdot, \cdot)$ 是损失函数。 + +一种朴素的想法是 **RTN（Round to Nearest）**策略：对于同一个 group 中的权重，统计其最大值 $r_{max}$ 和最小值 $r_{min}$，然后将 $r_{max}$ 映射到量化范围的最大值 $q_{max}$，$r_{min}$ 映射到量化范围的最小值 $q_{min}$，从而确定缩放因子和零点： + +$$ + s = \frac{r_{max} - r_{min}}{q_{max} - q_{min}}, \enspace z = q_{min} - \frac{r_{min}}{s}. +$$ + +随后，每个权重都被独立地舍入到最近的量化值。RTN 的计算开销很低，也不需要校准数据，因此适合作为量化流程的调试基线。但是，RTN 只关心权重本身的数值误差 $\|W-Q\|$，没有考虑不同输入通道在真实推理过程中的重要性，也没有考虑多个权重的量化误差可能在层输出中相互叠加。在 INT8 等较高位宽下，这一问题通常并不明显；当权重被压缩到 INT4 时，舍入误差增大，并可能在经过后续层时进一步累积，导致模型精度明显下降。 + +实际任务中的输入分布无法被精确求得，因此 PTQ 通常选择一小部分具有代表性的样本作为**校准数据集**，用校准样本上的平均误差近似上述期望。对于一个输入维度为 $d_{in}$、输出维度为 $d_{out}$ 的线性层，设收集到的输入激活为 $X \in \mathbb{R}^{N \times d_{in}}$，权重为 $W \in \mathbb{R}^{d_{out} \times d_{in}}$，则可以使用层输出的重构误差作为优化目标： + +$$ + \min_Q \left\|XW^T-XQ^T\right\|_F^2 + = \min_Q \left\|X(W-Q)^T\right\|_F^2. +$$ + +与直接比较 $W$ 和 $Q$ 相比，这个目标会根据输入激活自动区分不同方向的重要性：如果某个输入通道在校准数据中经常出现较大的激活，那么该通道上的权重误差通常会对层输出产生更大的影响。校准数据不参与参数更新，其作用只是估计模型在真实输入分布下对量化误差的敏感程度。 + +#### GPTQ 的基本思想 + +GPTQ 是一种面向大模型的二阶训练后权重量化方法。它仍然使用普通的线性量化器产生 INT4 权重，但不再像 RTN 一样独立地量化所有权重，而是对每个线性层执行**逐列量化与误差补偿**：量化当前列后，立即调整尚未量化的列，使后续列尽可能抵消当前列引入的输出误差。 + +例如，线性层的输出可以看作各输入通道贡献之和：$w_1x_1+w_2x_2+\cdots$。如果校准数据中的 $x_1$ 和 $x_2$ 经常同时增大或减小，那么它们具有较强的相关性。此时，$w_1$ 被量化后产生的部分误差，可以通过调整尚未量化的 $w_2$ 来补偿。 + +上述层重构目标关于权重的 Hessian 矩阵只与输入激活有关。忽略不影响算法的常数后，可以写为： + +$$ + H = \frac{2}{N}X^TX. +$$ + +其中 $N$ 是校准数据的 token 数。$H$ 描述了各输入通道在校准数据中的相关性：对角元素反映单个通道的活跃程度，非对角元素则反映不同通道之间的相关性。如果两个输入通道高度相关，那么一个通道上的量化误差就有可能通过修改另一个通道对应的权重得到补偿。因此，GPTQ 使用 $H^{-1}$ 中包含的二阶信息决定误差应当如何传播。 + +设 $H^{-1}=U^TU$，其中 $U$ 是通过 Cholesky 分解得到的上三角矩阵。按照输入维度从左到右处理权重列，量化第 $i$ 列时，先使用当前 group 的 $s$ 和 $z$ 将修正后的权重列 $w_i$ 映射为可表示的 INT4 值，并反量化得到 $q_i$。随后计算归一化量化误差： + +$$ + e_i = \frac{w_i-q_i}{U_{ii}}. +$$ + +再利用 $U$ 的第 $i$ 行更新尚未量化的权重列： + +$$ + W_{:,\,i:} \leftarrow W_{:,\,i:} - e_i U_{i,\,i:}. +$$ + +这样，当前列被舍入后产生的误差会沿着输入通道之间的相关方向传播到后续列，后续列在补偿后的权重基础上进行量化。重复这一过程直到所有列都被量化，就可以在不进行反向传播和模型训练的情况下，显著降低整个线性层的输出重构误差。 + +GPTQ 的思路来源于 **OBQ（Optimal Brain Quantization）**。OBQ 会反复寻找当前最适合量化的权重，并在每次量化后更新逆 Hessian，计算开销很高。GPTQ 使用固定的列顺序，让同一输入通道在所有输出通道中同时被处理，并将多次更新组织成分块矩阵运算，从而使这一方法能够应用到大模型。 + +!!! note "阅读材料" + 想要进一步了解 GPTQ 算法原理的同学可以参考： + + - [原论文](https://arxiv.org/abs/2210.17323) + - [GPTQ 原理以及完整证明](https://declk.github.io/blog/LLMs/GPTQ%20%E5%8E%9F%E7%90%86%E4%BB%A5%E5%8F%8A%E5%AE%8C%E6%95%B4%E8%AF%81%E6%98%8E.html) + +#### 数值稳定性与分块计算 + +实际校准数据中，部分输入通道可能始终为零，或者不同通道高度相关，使 $H$ 不满秩或接近奇异矩阵，无法稳定地求逆。常见做法是先处理对角元素为零的 **dead column**，再向 Hessian 的对角线加入阻尼项： + +$$ + H \leftarrow H + \lambda I, \enspace + \lambda = \alpha \cdot \operatorname{mean}(\operatorname{diag}(H)), +$$ + +其中 $\alpha$ 是阻尼比例。阻尼可以改善 Cholesky 分解的数值稳定性，但过大的阻尼会削弱校准数据所提供的通道相关性信息，因此需要在稳定性和量化精度之间进行权衡。 + +对于大模型中的宽矩阵，直接保存逐列更新产生的所有中间结果会带来较高的访存开销。GPTQ 通常以若干连续列为一个 block，先在 block 内逐列量化并传播误差，再将整个 block 累积的误差一次性更新到后续列。 + +
    + GPTQ Procedure +
    GPTQ 分块计算示例。图源。
    +
    + +下面是 GPTQ 量化的伪代码，需要注意的是这里的 blocksize $B$ 并不是量化粒度，而是本节所述的分块计算的列数： + +
    + GPTQ Algorithm +
    GPTQ 算法流程。图源。
    +
    + +#### 本实验中的量化流程 + +本实验采用逐层量化的方式处理 Gemma4-12B： + +1. 框架首先使用校准样本运行当前 Transformer 层，并通过 hook 收集各个目标 Linear 的输入激活； +2. 根据输入激活构造 Hessian，并通过 dead-column 处理、阻尼和 Cholesky 分解得到稳定的逆 Hessian 因子； +3. 按 group 计算量化参数，按 block 逐列量化权重； +4. 每量化一列，就使用逆 Hessian 信息修正剩余列； +5. 打包 INT4 权重、scale 和 zero point，存储到新的模型权重文件中； +6. 当前层量化完成后，框架会使用反量化后的权重重新计算该层输出，并将结果作为下一层的校准输入，从而让后续层看到前面各层已经产生的量化误差。 + + +与 RTN 相比，GPTQ 需要额外的校准时间和临时显存，但只执行一次离线量化，不需要原始训练数据，也不需要重新训练模型。它将优化目标从“权重数值尽可能接近”转化为“校准数据上的层输出尽可能接近”，因此更适合本实验中的 W4A16 低位宽量化任务。 + + +### 实验任务 + +#### 任务要求 + +你的任务是实现 GPTQ 的核心算法，将 Gemma4-12B 的权重从 BF16 量化到 INT4。 + +- 我们提供的量化框架已经实现了分层量化、校准数据集激活值捕捉、INT4 权重打包等功能，请你阅读框架代码并理解整体工作流程后，基于 `src/quantization/methods/gptq.py` 中的函数接口，完成 GPTQ 的核心算法实现。 + +- 我们提供了 RTN（Round to Nearest）量化方法的参考实现，你可以阅读相关代码并理解接口设计。 + +- 我们提供了一份校准数据集 `calibration-256.jsonl`，包含 256 条英文文本，你可以直接使用该数据集，也可以自行准备其他数据集。我们欢迎你尝试不同规模、不同长度、不同语义的校准数据集，并在报告中分析其对量化精度的影响。 + +- 在实现 GPTQ 算法后，你需要使用量化框架将 Gemma4-12B 的参数量化到 INT4，并将量化后的权重保存到新的模型权重文件中，量化框架入口脚本为 `scripts/quantize.py`。 + +- 量化后的权重需要在测试集上通过精度评测，确保量化精度损失 $\Delta \mathrm{NLL} < 0.2$。我们提供了公开测试集 `quality_public.jsonl`，你可以使用该数据集自行测试。 + +!!! note "量化精度损失的评估" + 我们使用 **NLL（Negative Log-Likelihood，负对数似然）**衡量量化前后模型对文本的建模能力。对于 token 序列 $x_1,x_2,\ldots,x_L$，评测器在每个位置向模型提供真实的历史 token，并计算模型为下一个真实 token 分配的概率。序列的平均 NLL 为： + + $$ + \mathrm{NLL} + = -\frac{1}{L-1}\sum_{t=1}^{L-1} + \log p(x_{t+1}\mid x_1,\ldots,x_t). + $$ + + NLL 越小，表示模型为真实文本分配的概率越高。我们主要关注测试集上的全局平均 $\mathrm{NLL}$，即 + + $$ + \mathrm{NLL}_{\mathrm{avg}} + = \frac{\sum_{i=1}^{M} S_i \cdot \mathrm{NLL}_i}{\sum_{i=1}^{M} S_i}, + $$ + + 评测时，我们分别在原始 BF16 模型和量化后的 INT4 模型上运行完全相同的数据集，并计算： + + $$ + \Delta\mathrm{NLL} + = \mathrm{NLL}_{\mathrm{INT4}}-\mathrm{NLL}_{\mathrm{BF16}}. + $$ + + 在框架中，你可以使用以下命令进行量化精度评测： + + ```bash + python3 scripts/evaluate_quality.py \ + --model \ + --dataset \ + --output \ + --chunk-size 512 \ + --max-sequence-length 2048 \ + --linear-backend int4_reference + ``` + + 输出文件中的 `mean_nll` 字段即为全局平均 $\mathrm{NLL}_{\mathrm{INT4}}$。该脚本也会统计不同长度区间的请求的平均 $\mathrm{NLL}$，仅供参考。由于 10GiB 显存下无法直接运行 BF16 精度的推理，我们额外提供公开测试集的精度评测数据 `results/bf16-public-quality.json`，你可以从中获取 $\mathrm{NLL}_{\mathrm{BF16}}$。 + + +#### 输入输出接口 + +你需要实现的 GPTQ 算法核心函数如下： + +```python +def quantize_weight_gptq( + weight: torch.Tensor, + activations: torch.Tensor, + group_size: int, + *, + block_size: int = 128, + damp_percent: float = 0.01, + symmetric: bool = True, + scale_dtype: torch.dtype = torch.float16, +) -> tuple[QuantizedWeight, dict[str, float | int]]: + """ + 使用 GPTQ 算法将权重量化为 INT4。 + + 参数： + weight: 待量化的权重张量，形状为 (out_features, in_features)。 + activations: 校准数据集的输入激活，形状为 (calibration_tokens, in_features)。 + group_size: 量化粒度，即每个 group 中的列数。 + block_size: 分块计算时每个 block 中的列数。 + damp_percent: 阻尼比例，用于改善 Hessian 的数值稳定性。 + symmetric: 是否使用对称量化。 + scale_dtype: 缩放因子的 dtype。 + + 返回： + quantized_weight: 量化后的权重对象，具体详见 QuantizedWeight 类的定义。 + metadatas: 量化过程中的统计信息，不影响评测，用于分析和调试。 + """ + + quantized = QuantizedWeight(...) + metadata: dict[str, float | int] = { + "activation_tokens": activations.shape[0], + "block_size": block_size, + "damp_percent": damp_percent, + "dead_columns": ..., + "predicted_loss": ..., + } + return quantized, metadata +``` + +## 任务二：端到端推理性能优化 + +将 Gemma4-12B 模型量化到 INT4 后，我们终于可以在 10 GiB 显存限制下进行完整的推理了。然而，在使用推理框架加载量化后的模型进行推理时，你可能会发现模型的推理效率不如预期，下面我们将提供一些优化方向供你参考。 + +### 性能分析方法 + +在进行性能优化前，我们需要先对推理过程进行性能分析，找出性能瓶颈。在分析像 LLM 推理系统这样的复杂异构应用时，我们一般会采用自顶向下的分析方法，先定位 CPU 端各函数的执行时间和 GPU 端各算子的执行时间，确定性能瓶颈在 CPU 端还是 GPU 端，再进一步分析瓶颈函数或算子内部的性能问题。 + +你可以继续使用 Nvidia Nsight Systems 对推理系统进行分析，但是对于基于 pytorch 编写的应用，我们更推荐使用 Pytorch Profiler 来进行整体性能分析。Pytorch Profiler 的输出格式是 JSONL 格式的 Chrome Trace 文件，可以用 [perfetto](https://ui.perfetto.dev/) 或 [chrome://tracing](chrome://tracing) 打开进行可视化分析，还支持用 SQL 查询性能分析数据。 + +使用 Pytorch Profiler 进行性能分析的工作流程如下： + +- 在代码中将需要分析的区域用 `with torch.profiler.profile(...)` 包裹起来，指定需要收集的事件类型和输出文件路径； + +- 运行程序，生成性能分析文件； + +- 使用可视化工具打开性能分析文件，查看 CPU 端函数和 GPU 端算子的执行时间，找出性能瓶颈； + +??? example + ![pytorch_profiler_example](image/profiler_example.png) + + 例如在上面这张 timeline 中，我们可以看到 CPU 大部分时间都在同步等待 GPU 端某个算子执行完成，这通常是由于 CPU 端的控制流或数据依赖于该 GPU 算子的执行结果，导致 CPU 端无法继续发射后续算子。我们可以检查 CPU-GPU 同步触发的原因，并判断该同步是否可以被消除。 + +### 方向一：显存占用优化 + +在 LLM 推理中，提高 batch size 可以均摊算子发射的开销和数据搬运的延迟，从而提高吞吐量。然而，提高 batch size 意味着 KV Cache 和激活值占用的显存同比增长。为了让固定的显存资源能支持更大的 batch size，我们需要着重关注以下方向： + +- 降低模型权重占用的峰值显存 + +- 相同请求数下，降低 KV Cache 占用的峰值显存 + +#### 权重 Offloading + +权重 Offloading 的基本思想是只让当前计算所需的模型权重常驻 GPU，其余权重保存在 CPU 内存中，并在执行到对应层之前搬运到 GPU。Gemma4 的 Transformer 层按顺序执行，因此在计算第 $i$ 层时，第 $i+1$ 层之后的权重暂时不会参与计算。我们可以利用这一特点，将若干层的权重放在 CPU 内存中，并使用一块可复用的 GPU 缓冲区逐层加载权重，从而降低模型权重占用的峰值显存。 + +最简单的实现方式是**同步 Offloading**：在每层计算开始前将该层权重从 CPU 搬运到 GPU，等待搬运完成后执行计算，再复用这部分显存加载下一层权重。这种方法容易保证正确性，但 Host-to-Device 数据搬运与 GPU 计算完全串行，可能使 PCIe 传输成为新的性能瓶颈。 + +为了隐藏数据搬运开销，可以进一步实现**异步 Offloading**。你可能需要准备两组 GPU 权重缓冲区，并使用独立的 CUDA Stream，在第 $i$ 层执行计算时预取第 $i+1$ 层的权重；计算流在使用下一组权重前，通过 CUDA Event 等待搬运完成。对于 INT4 模型，需要将打包后的权重、scale 和 zero point 作为一个整体进行管理，避免遗漏量化参数或在前向过程中反复创建临时张量。 + +在实现时你可以控制 Offloading 的粒度，例如根据显存预算选择常驻层与卸载层，或是以多个连续的 DecoderLayer 为粒度进行卸载，并结合 profiler 分析权重搬运是否与计算充分重叠。 + +!!! tip + 建议先实现同步版本，确认输出与全量加载版本一致，并记录峰值显存和 Host-to-Device 搬运时间；再引入 pinned memory、独立 CUDA Stream、预取和双缓冲，逐步验证异步优化是否真正提高了端到端吞吐量。 + +#### Ring KV Cache + +Gemma4 同时包含全局注意力层和滑动窗口注意力层。对于窗口大小为 $W$ 的滑动窗口注意力层，位置 $t$ 的 token 最多只会访问最近 $W$ 个 token 的 Key 和 Value。更早的 KV 一旦移出窗口，之后就不会再次参与该层的计算，因此没有必要像全局注意力层一样保存完整的历史序列。 + +Ring KV Cache 使用固定大小为 $W$ 的环形缓冲区保存滑动窗口层的 KV。绝对位置为 $t$ 的 KV 被写入物理槽位 $t \bmod W$；当序列长度超过 $W$ 时，新 KV 会覆盖已经移出注意力窗口的旧 KV。这样，滑动窗口层中每个请求的 KV Cache 占用由 $O(S)$ 降低为 $O(\min(S, W))$，其中 $S$ 为当前序列长度。 + +若模型包含 $N_g$ 个全局注意力层和 $N_l$ 个滑动窗口注意力层，在忽略不同层 KV 头数差异的情况下，KV Cache 的序列长度因子可以从 $(N_g + N_l)S$ 降低为： + +$$ + N_g S + N_l \min(S, W). +$$ + +在当前框架中，你可以分别为全局注意力层和滑动窗口注意力层设计不同的缓存布局，并在滑动窗口注意力的 KV 写入与读取路径中加入环形索引。该方法不会改变模型的注意力范围，因此在索引和掩码实现正确时，不会引入额外的精度损失。 + +#### Paged Attention + +当前框架会按照 `max_batch_size` 和 `max_sequence_length` 为每个注意力层预先分配连续的 KV Cache。即使实际 batch 较小或请求提前结束，这部分显存也无法被其他请求使用。当不同请求的长度差异较大时，静态连续分配会产生大量未使用的预留空间，并限制系统能够同时处理的请求数。 + +Paged Attention 将每个请求的 KV Cache 划分为固定大小的 block，并从全局 KV block 池中按需分配物理块。每个请求维护一张 block table，用于记录逻辑 KV block 到物理 KV block 的映射。当请求长度增长到新的 block 时，系统再从空闲列表中分配物理块；当请求结束时，其占用的物理块可以立即回收到空闲列表，供后续请求复用。 + +假设 block size 为 $P$，Paged Attention 不再为每个请求预留完整的最大序列长度，而是只为已经产生的 token 分配 KV 空间。每个请求在每层最多浪费最后一个 block 中的 $P-1$ 个槽位，因此 block 越小，内部碎片越少；但 block table 更大，分配和寻址开销也更高。你需要根据请求长度分布和注意力算子的实现选择合适的 block size。 + +在当前基于 PyTorch 的框架中，可以先实现一个功能正确的版本：用 block pool 和 block table 管理 KV，在执行注意力前按照逻辑顺序收集当前请求对应的 K/V block，再复用现有注意力计算。该实现会引入额外的数据整理开销，但能够验证分页分配、扩容和回收逻辑。进一步优化时，可以改写注意力算子，使其根据 block table 直接读取离散的物理块，避免将 KV Cache 重新拼接为连续张量。 + +!!! note "阅读材料" + [Paged Attention from First Principles: A View Inside vLLM](https://hamzaelshafie.bearblog.dev/paged-attention-from-first-principles-a-view-inside-vllm/) + +### 方向二：算子优化 + +观察 profiler timeline 时，可以将空闲时间粗略分为两类： + +- **算子之间的空隙（外部 bubble）**：通常来自 Python 与算子发射开销、过多的小算子、GPU-CPU 同步或频繁的动态内存分配。它在 timeline 中表现为相邻 GPU kernel 之间存在明显空白。 +- **算子内部的停顿（内部 bubble）**：kernel 已经开始执行，但线程块可能在等待 global memory 数据、同步或其他执行单元。对于访存密集型算子，计算单元会因为数据供给不足而空闲。 + +减少外部 bubble 的常见方法包括消除不必要的同步、复用临时缓冲区、合并小算子，以及在执行形状稳定后使用 CUDA Graph。减少内部 bubble 则需要从 kernel 内部的数据访问和并行划分入手，例如采用分块计算、合并访存、提高数据复用率和减少中间结果的读写。 + +在修改算子前，应当分别分析 **prefill** 和 **decode**。Prefill 一次处理多个 token，矩阵规模较大，通常更容易利用 GPU 的并行计算能力；decode 每次只处理一个 token，许多矩阵乘法退化为矩阵-向量乘法，更容易受到权重读取和算子发射开销限制。因此，同一个 kernel 在两种阶段的最优分块策略可能不同，也不应只根据单个算子的加速比判断优化效果。 + +#### 算子融合 + +算子融合将多个前后相邻的操作放入同一个 kernel，使中间结果尽可能保留在寄存器或片上存储中，避免写回 global memory 后又被下一个算子读入。融合还可以减少 kernel 数量，从而同时缓解外部 bubble 和内部 bubble。 + +本方向要求你使用 **Triton 或 TileLang 自行编写 kernel**，不得调用 FlashAttention、xFormers、bitsandbytes、cuBLASLt 封装、PyTorch SDPA 等现成算子库。下面给出两个适合在当前框架中实现的融合算子。 + +##### 反量化-GEMM 融合 + +当前 `QuantizedLinear.forward` 会先将整块 INT4 权重反量化为 BF16/FP16 张量，再调用 `F.linear`： + +$$ + Q_4 \longrightarrow W_{16} \longrightarrow Y. +$$ + +这种实现逻辑清晰，但每次前向都会创建完整的高精度权重临时张量。反量化结果需要写入 global memory，随后又被 GEMM 读回；在 decode 阶段，同一层每次只处理少量 token，这部分数据搬运可能比实际计算更昂贵。 + +融合 kernel 不再物化完整的 $W_{\text{fp16}}$。对于输出矩阵的一个 tile，kernel 依次完成： + +1. 从 `qweight` 中读取打包的字节，并用位运算提取两个 INT4 权重； +2. 根据输入维度位置计算 group 编号，加载对应的 `scales` 和可选的 `zeros`； +3. 在寄存器或片上存储中将当前权重 tile 反量化为计算精度； +4. 立即与输入 tile 相乘并累加到 FP32 accumulator； +5. 在归约结束后转换到输出精度，并写回结果。 + +对于非对称量化，反量化关系为 + +$$ + w_{m,k}=s_{m,g(k)}\left(q_{m,k}-z_{m,g(k)}\right), + \qquad g(k)=\left\lfloor\frac{k}{G}\right\rfloor, +$$ + +其中 $G$ 是 group size。实现时需要与框架中的 `uint8_little_nibble` packing 格式保持一致，并正确处理 `padded_in_features`、末尾越界、对称/非对称量化和可选 bias。 + +你可以分别为两类工作负载选择调度策略：prefill 使用较大的 $M$ 维 tile，提高 Tensor Core 利用率；decode 的 $M$ 很小，应优先减少权重重复读取、线程块数量和发射开销。除了 `BLOCK_M`、`BLOCK_N` 和 `BLOCK_K`，还可以比较 warp 数量、pipeline stage 数量，以及 group size 与 `BLOCK_K` 对齐方式对性能的影响。 + +!!! warning + 融合 kernel 的持久参数必须仍然是打包 INT4 权重、scale、zero point 和必要的元数据。不得在模型加载时将全部权重永久展开为 BF16/FP16，也不得通过缓存完整反量化权重绕过显存限制。 + +##### Flash Attention + +当前注意力实现会依次构造 `scores`、`mask` 和 `prob`： + +$$ + A = QK^T,\qquad P=\operatorname{softmax}(A+M), \qquad O=PV. +$$ + +对于序列长度 $L$，中间矩阵 $A$ 和 $P$ 的大小均为 $O(L^2)$。它们需要多次写入和读取 global memory，长序列 prefill 时会产生较大的显存占用和带宽开销。Flash Attention 的核心思想是按块读取 $Q$、$K$、$V$，在片上完成分数计算、mask、softmax 更新和对 $V$ 的加权累加，从而不在显存中物化完整的注意力矩阵。 + +由于一个 query block 会依次遍历多个 key/value block，softmax 需要使用在线归一化算法。设处理完前若干 block 后，某一行已知最大值 $m$、指数和 $l$ 与未归一化输出累加值 $o$；读入新分数块 $A_j$ 后，可按以下方式更新： + +$$ +\begin{aligned} + m' &= \max\left(m, \operatorname{rowmax}(A_j)\right), \\ + \alpha &= e^{m-m'}, \\ + P_j &= e^{A_j-m'}, \\ + l' &= \alpha l + \operatorname{rowsum}(P_j), \\ + o' &= \alpha o + P_jV_j. +\end{aligned} +$$ + +遍历完成后输出 $O=o/l$。实际实现中应使用 FP32 保存 $m$、$l$ 和累加器，避免长序列下的溢出和精度损失。 + +为适配当前 Gemma4 推理框架，你的 kernel 至少需要正确处理： + +- 因果注意力，以及 padding token 对应的无效 query/key； +- 全局注意力层和滑动窗口注意力层； +- GQA 中 query head 与 KV head 的映射，避免先调用 `repeat_kv` 物化重复的 K/V； +- prefill 中 $q_{len}>1$ 与 decode 中 $q_{len}=1$ 的不同形状； +- KV Cache 中已经存在的历史 token，以及最后一个不完整 tile 的越界掩码。 + +全局注意力中，一个 query block 只遍历满足因果条件的 KV block；滑动窗口注意力还应跳过窗口之外的 block，而不是先计算完整分数再填充 $-\infty$。这样不仅移除了显式 mask 和注意力矩阵，还能避免执行理论上不需要的乘加运算。对于 decode，可以设计单独的窄 query kernel，以减少只有一个 query token 时的空线程和调度开销。 + +#### 优化与验证流程 + +推荐按照以下顺序完成算子优化： + +1. **建立基线**：固定输入形状、随机种子和量化 checkpoint，记录 prefill、decode、端到端吞吐量以及峰值显存； +2. **验证正确性**：在小尺寸张量上与框架原有 PyTorch 实现比较，并覆盖非整除维度、不同 batch size、不同序列长度和滑动窗口边界； +3. **进行 microbenchmark**：预热后使用 CUDA Event 测量 kernel 时间，分别测试实验评测中具有代表性的 prefill 和 decode 形状； +4. **结合 profiler 调优**：检查 kernel 数量、global memory 吞吐、Tensor Core 利用率和时间占比，再调整 tile、warp 和 pipeline 配置； +5. **回到端到端评测**：确认优化没有被其他开销抵消，并报告 TTFT、TPOT、吞吐量和峰值显存的变化。 + +算子优化必须保持模型语义不变，你需要保证你编写的 kernel 与参考的 pytorch 实现在相同输入下的输出一致。由于 softmax 的归约顺序和低精度计算会带来合理的浮点误差，可以使用适当的绝对误差和相对误差阈值。 + +实验报告中应给出“瓶颈现象—优化假设—kernel 设计—性能结果”的完整证据链。除端到端结果外，还应包含关键算子的形状、基线与优化版延迟、加速比、显存变化和正确性误差，并解释为何某些优化只对 prefill 或 decode 有效。 + +### 方向三：请求调度优化 + +当前框架下请求处理采用静态调度策略：所有请求被放到队列中，当 InferenceEngine 空闲时，框架会从队列中取出最多 batch size 个请求进行处理，待所有请求都计算完毕后，才会处理队列中的下一个 batch。 + +#### Continuous Batching + +当请求的生成长度差异较大时，较短的请求会提前结束，但它占用的 batch 槽位无法被新请求使用。GPU 只能继续等待同一 batch 中最长的请求完成，导致实际参与计算的请求数不断减少。这个现象通常被称为拖尾效应。 + +**Continuous Batching（连续批处理）**不再将一个 batch 视为不可拆分的整体，而是在每次推理迭代后重新调整活跃请求集合。一个基本的调度循环可以包含以下步骤： + +1. 检查本轮生成的 token，将达到停止条件的请求移出活跃队列，并释放其 KV Cache； +2. 根据最大 batch size 和剩余 KV Cache 容量，从等待队列中接纳新请求； +3. 对新请求执行 prefill，将其加入活跃请求集合； +4. 对处于 decoding 状态的请求各生成一个 token，然后进入下一轮调度。 + +通过及时补充新请求，Continuous Batching 可以让 decode batch size 长时间保持在较高水平，从而提高 GPU 利用率和系统吞吐量。实现时，每个活跃请求需要拥有稳定的请求 ID、状态和 KV Cache 映射；请求离开后，其 batch 槽位或分页 KV block 必须能够被安全复用。当前框架已经使用 `RequestState` 记录请求的 prefill、decoding 和 completed 状态，你可以在此基础上扩展调度器，并将一次性执行完整 batch 的流程改为可重复调用的调度步。 + +Prefill 通常处理多个 token，计算量明显大于单步 decode。如果每次有新请求到达都立即执行完整 prefill，可能阻塞正在生成的请求并增大 TPOT。一个易于实现的基础策略是分别执行新请求的 prefill batch 和活跃请求的 decode batch，并限制每轮接纳的新请求数；进一步可以为每轮设置 token budget，将长 prompt 切分为多个 chunk，使 chunked prefill 与 decode 交替执行。 + +!!! note "阅读材料" + [Continuous batching](https://huggingface.co/blog/continuous_batching) + +### 其他优化手段 + +我们欢迎你根据 profiler 结果尝试不同的优化策略，例如减少张量布局转换与临时内存分配、尝试 `torch.compile`、调整 batch 组织方式，或针对 prefill 和 decode 设计不同的执行路径。 + +你需要使用 profiler 自行定位瓶颈，并通过优化前后的数据验证判断。即使你的尝试没有带来加速，甚至造成负优化，也是有价值的实验结果：你可以在报告中记录原始现象、优化假设、实现方法、测试条件和性能变化，并分析负优化来自额外的算子发射、数据搬运、同步、访存模式恶化，还是工作负载与优化策略不匹配。 + +### 任务要求 + +你的任务是尽可能降低 Gemma4-12B 在 10 GiB 显存限制下的端到端推理延迟，具体地： + +- 你需要使用优化后的推理框架在 1/7 张 H800 上进行推理，推理框架启动入口为 `scripts/run_generation_queue.py`。 + +- 我们会给出一份公开的性能测试数据集 `performance_public.jsonl`，在测试时我们采用无限并发模式，即在开始推理之间将所有请求压入等待队列，推理过程中不再有新的请求到达。 + +- 允许修改代码框架的推理逻辑、算子实现、KV Cache 管理和请求调度策略，但不得修改模型结构，修改计时区间或跳过必要步骤。 + +!!! note "推理框架运行示例" + + 你可以使用以下命令运行推理框架，加载指定的模型，并在指定的性能测试数据集上进行推理测试。你还可以额外指定推理引擎的运行时参数，包括最大 batch size，最大序列长度等。 + + ```bash + uv run python scripts/run_generation_queue.py \ + --model \ + --input requests.jsonl \ + --output outputs.jsonl \ + --batch-size 4 \ + --max-sequence-length 2048 + ``` + +## 代码框架 + +实验代码位于仓库根目录下的 `src/lab5/` 目录，文件结构如下： + +``` +. +├── datasets +│   ├── calibration-256.jsonl # 参考校准数据集 +│   ├── performance_public.jsonl # 公开性能评测数据集 +│   ├── performance_small.jsonl # 小规模性能测试数据集 +│   └── quality_public.jsonl # 公开精度评测数据集 +├── pyproject.toml +├── README.md +├── results +│   └── bf16-public-quality.json # 公开精度评测数据集的 BF16 精度评测结果 +├── scripts +│   ├── evaluate_quality.py # 量化精度评测脚本 +│   ├── quantize.py # 量化框架入口 +│   └── run_generation_queue.py # 推理框架入口 +└── src + └── hpc101_infer + ├── config.py + ├── data + ├── engine.py + ├── evaluation + ├── __init__.py + ├── layers + ├── models + ├── quantization + ├── runner.py + ├── runtime + ├── sampling.py + ├── scheduler + └── types.py +``` + +我们提供以下工具脚本辅助你完成实验任务： + +- `scripts/quantize.py`：量化框架的入口脚本，你可以通过该脚本启动量化流程，并指定量化方法、校准数据集、量化粒度等参数。 +- `scripts/eval_quality.py`：精度评测脚本，你可以在量化完成后使用该脚本，对量化后的模型进行精度评测。 +- `scripts/run_generation_queue.py`：推理框架的入口脚本，你可以通过该脚本启动端到端推理，并按需指定请求数据集、最大 batch size、最大序列长度等参数。 + +## 评测方式 + +!!! danger "提交方式" + + 提交入口和测评方式仍在完善，完成后会更新本文档并在群内通知。 + +!!! warning "TODO：评分公式待定" + + 关于任务一和任务二对于总分的占比，以及对应任务的性能评分曲线还在商讨中，请等待😭 + +## 提交要求 + +需要提交的内容包括： + +- 实验代码压缩包 +- 实验报告 PDF +- 公开数据集上的精度评测结果和性能评测结果 JSON 文件 + +请勿提交下列内容： + +- 量化前或量化后的模型权重 +- profiler 产物 +- 任何中间结果或临时文件 + +## 思考题 + +1. 根据 Gemma4-12B 的结构，推导在 W4A16 量化下，全量加载模型权重占用的显存大小，以及 KV Cache 占用的显存大小与 batch_size、max_seq_len 的关系式，与实际测量的显存占用进行对比，分析差异原因。 +2. 滑动窗口注意力实质上是一种静态稀疏注意力机制，根据经验设置了注意力掩码的形状。请你思考如果我们不事先设定注意力掩码的形状，可以如何实现稀疏注意力？你的实现相比滑动窗口注意力会对模型的性能和推理效率产生什么影响？ +3. 我们的量化框架已经实现了分层量化，请分析分层量化时对权重的管理策略与推理时的 Offloading 策略的异同点。 + +!!! warning + 思考题可能还会继续补充，请大家保持关注... + + +## 参考资料 + +- [A Visual Guide to Gemma 4](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4) + +- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://arxiv.org/abs/2210.17323) + +- [GPTQ 原理以及完整证明](https://declk.github.io/blog/LLMs/GPTQ%20%E5%8E%9F%E7%90%86%E4%BB%A5%E5%8F%8A%E5%AE%8C%E6%95%B4%E8%AF%81%E6%98%8E.html) + +- [Paged Attention from First Principles: A View Inside vLLM](https://hamzaelshafie.bearblog.dev/paged-attention-from-first-principles-a-view-inside-vllm/) + +- [Continuous batching](https://huggingface.co/blog/continuous_batching) diff --git a/docs/lab/Lab5-TinyLLM/image/attention.png b/docs/lab/Lab5-TinyLLM/image/attention.png deleted file mode 100644 index fdab0b9982d927267c5c7839c9864a99a4a80be0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 91369 zcmd43WkXd>8z>C8RY1DCkp=uHb>uYms{!DDGq|2%zFllJPL-(Pcq zC%!|oq=kUshma5!P;!AhNPe57+;!Uuxof$LANxL!NJPY$kyV(nysLqgwr?chE)HGh zV?y587h33UdMD{YjSsO{$RAWN!h$nqztA^#ju$?(+UPl5EmqVfEeyLJ1&*AhaM&Af zd2Agl9eW(R8y6hUmS^HHK=VUDA$dc9h#(-L`SlQeaNOaZ0{{N=uP06@{3P~-|DBeG z0!d~^j)(W(F^Hf+9`F+XtmY#L`q3L>WAooJNHV7D|6^%tWPTFWj&{hucK_?uAH9pO z{sjma$`ghN(t>&W_P=8QK#2eD1Z?_$9iTiIC@|^*?>`vLNP#4!r0@}_p_!1p?seql zSJ>6m)ON5s0}-!l2VCAlLX(N6-G$?Gp)^Jnf~mei54pSYDO1$+0eWpQS-4bGM@y?H zXS84EGz%a4#t#Bzj_~|DCXzRQ5eUyVlqoz$^~atB4QaIB$JHBHpDzHJ$98NGppZ_m 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z;35}VqK@mbTyJ#$nV1>V<7lK%o3X?oj+*!5Wt@&{-@_VKy}@|b@tMcPy_{N&Zl_3c z#v$&X(yH|uT%1+JI~vExH^i}0s~6yf3#8!Qwt0*iS9zxi67T)(0{EH7E{}_I<8b6c zOF5$F_rLbctm&^5;mR87(%hh{^BvSrw7#HpzD(BD)rn9xP;ahTQMPl**#D7pKXnOA zO<16i6Q8Q236}p_nO_@c!-ecZ`wDQGgb)^i&=Pv)Af7bku{{_m8HDCY$ literal 0 HcmV?d00001 diff --git a/docs/lab/Lab5-Gemma4/index.md b/docs/lab/Lab5-Gemma4/index.md index bb1d61f0..65edebc7 100644 --- a/docs/lab/Lab5-Gemma4/index.md +++ b/docs/lab/Lab5-Gemma4/index.md @@ -325,7 +325,7 @@ $$ - 在实现 GPTQ 算法后，你需要使用量化框架将 Gemma4-12B 的参数量化到 INT4，并将量化后的权重保存到新的模型权重文件中，量化框架入口脚本为 `scripts/quantize.py`。 -- 量化后的权重需要在测试集上通过精度评测，确保量化精度损失 $\Delta \mathrm{NLL} < 0.2$。我们提供了公开测试集 `quality_public.jsonl`，你可以使用该数据集自行测试。 +- 量化后的权重需要在测试集上通过精度评测，具体评测标准详见 [评测方式](#任务一评测)。我们提供了公开测试集 `quality_public.jsonl`，你可以使用该数据集自行测试。 !!! note "量化精度损失的评估" 我们使用 **NLL（Negative Log-Likelihood，负对数似然）**衡量量化前后模型对文本的建模能力。对于 token 序列 $x_1,x_2,\ldots,x_L$，评测器在每个位置向模型提供真实的历史 token，并计算模型为下一个真实 token 分配的概率。序列的平均 NLL 为： @@ -350,20 +350,6 @@ $$ = \mathrm{NLL}_{\mathrm{INT4}}-\mathrm{NLL}_{\mathrm{BF16}}. $$ - 在框架中，你可以使用以下命令进行量化精度评测： - - ```bash - python3 scripts/evaluate_quality.py \ - --model \ - --dataset \ - --output \ - --chunk-size 512 \ - --max-sequence-length 2048 \ - --linear-backend int4_reference - ``` - - 输出文件中的 `mean_nll` 字段即为全局平均 $\mathrm{NLL}_{\mathrm{INT4}}$。该脚本也会统计不同长度区间的请求的平均 $\mathrm{NLL}$，仅供参考。由于 10GiB 显存下无法直接运行 BF16 精度的推理，我们额外提供公开测试集的精度评测数据 `results/bf16-public-quality.json`，你可以从中获取 $\mathrm{NLL}_{\mathrm{BF16}}$。 - #### 输入输出接口 @@ -614,18 +600,6 @@ Prefill 通常处理多个 token，计算量明显大于单步 decode。如果 - 允许修改代码框架的推理逻辑、算子实现、KV Cache 管理和请求调度策略，但不得修改模型结构，修改计时区间或跳过必要步骤。 -!!! note "推理框架运行示例" - - 你可以使用以下命令运行推理框架，加载指定的模型，并在指定的性能测试数据集上进行推理测试。你还可以额外指定推理引擎的运行时参数，包括最大 batch size，最大序列长度等。 - - ```bash - uv run python scripts/run_generation_queue.py \ - --model \ - --input requests.jsonl \ - --output outputs.jsonl \ - --batch-size 4 \ - --max-sequence-length 2048 - ``` ## 代码框架 @@ -633,13 +607,14 @@ Prefill 通常处理多个 token，计算量明显大于单步 decode。如果 ``` . +├── config.yaml # 配置文件 ├── datasets │   ├── calibration-256.jsonl # 参考校准数据集 │   ├── performance_public.jsonl # 公开性能评测数据集 │   ├── performance_small.jsonl # 小规模性能测试数据集 │   └── quality_public.jsonl # 公开精度评测数据集 ├── pyproject.toml -├── README.md +├── README.md # 实验框架和工具脚本说明 ├── results │   └── bf16-public-quality.json # 公开精度评测数据集的 BF16 精度评测结果 ├── scripts @@ -647,52 +622,205 @@ Prefill 通常处理多个 token，计算量明显大于单步 decode。如果 │   ├── quantize.py # 量化框架入口 │   └── run_generation_queue.py # 推理框架入口 └── src - └── hpc101_infer - ├── config.py - ├── data - ├── engine.py - ├── evaluation - ├── __init__.py - ├── layers - ├── models - ├── quantization - ├── runner.py - ├── runtime - ├── sampling.py - ├── scheduler - └── types.py + └── hpc101_infer # 实验框架 ``` 我们提供以下工具脚本辅助你完成实验任务： - `scripts/quantize.py`：量化框架的入口脚本，你可以通过该脚本启动量化流程，并指定量化方法、校准数据集、量化粒度等参数。 -- `scripts/eval_quality.py`：精度评测脚本，你可以在量化完成后使用该脚本，对量化后的模型进行精度评测。 +- `scripts/evaluate_quality.py`：精度评测脚本，你可以在量化完成后使用该脚本，对量化后的模型进行精度评测。 - `scripts/run_generation_queue.py`：推理框架的入口脚本，你可以通过该脚本启动端到端推理，并按需指定请求数据集、最大 batch size、最大序列长度等参数。 -## 评测方式 +!!! example "脚本运行示例" + + === "`quantize.py`" + + 在完成 `src/hpc101_infer/quantization/methods/gptq.py` 后，你可以运行以下命令： + + ```bash + python3 scripts/quantize.py \ + --config config.yaml \ + --model "$MODEL_DIR" \ + --output "$QUANT_DIR" \ + --device cuda \ + --verbose + ``` -!!! danger "提交方式" + 该命令会将 `$MODEL_DIR` 下的 Gemma4-12B 权重量化为 INT4，并保存到 `$QUANT_DIR`。你可以通过配置文件或命令行参数指定量化选项，包括量化方法、量化粒度、校准数据集等。 + + === "`evaluate_quality.py`" + + 在框架中，你可以参考以下命令进行量化精度评测： + + ```bash + python3 scripts/evaluate_quality.py \ + --model \ + --dataset \ + --output \ + --chunk-size 512 \ + --max-sequence-length 2048 \ + --linear-backend int4_reference + ``` + + 输出文件中的 `mean_nll` 字段即为全局平均 $\mathrm{NLL}_{\mathrm{INT4}}$。该脚本也会统计不同长度区间的请求的平均 $\mathrm{NLL}$，仅供参考。由于 10GiB 显存下无法直接运行 BF16 精度的推理，我们额外提供公开测试集的精度评测数据 `results/bf16-public-quality.json`，你可以从中获取 $\mathrm{NLL}_{\mathrm{BF16}}$。 + + === "`run_generation_queue.py`" - 提交入口和测评方式仍在完善，完成后会更新本文档并在群内通知。 + 你可以使用以下命令运行推理框架，加载指定的模型，并在指定的性能测试数据集上进行推理测试。你还可以通过配置文件或命令行参数额外指定推理引擎的运行时参数，包括最大 batch size，最大序列长度等。 + + ```bash + python3 scripts/run_generation_queue.py \ + --config config.yaml \ + --model \ + --input requests.jsonl \ + --output outputs.jsonl \ + --batch-size 1 \ + --max-sequence-length 2048 + ``` -!!! warning "TODO：评分公式待定" +**关于实验环境配置和工具脚本使用的更多细节，请参考 `README.md` 中的说明。**简单来说，两个框架入口脚本遵循以下参数处理逻辑： - 关于任务一和任务二对于总分的占比，以及对应任务的性能评分曲线还在商讨中，请等待😭 +- 命令行参数的优先级高于配置文件参数； -## 提交要求 +- 入口脚本负责解析命令行参数，而配置文件参数则会被交给具体的配置文件类进行解析； + + - 这意味着在优化过程中，你可以扩展配置文件类，以支持更多的参数选项； + - 但是模型路径、推理数据集路径、输出文件路径等参数只能通过命令行参数指定； + + + +## 评测方式 + +!!! danger "施工中" + + 目前 OJ 自动评测系统仍在建设中😭，我们会尽快发布。 + + +### 提交要求 需要提交的内容包括： -- 实验代码压缩包 +- `src` 目录下的实验代码 +- 配置文件 `config.yaml` - 实验报告 PDF - 公开数据集上的精度评测结果和性能评测结果 JSON 文件 +- （可选）自定义的校准数据集 + +提交目录结构如下： + +``` +. +├── src/ +│ └── ... +├── config.yaml +├── datasets/ # optimal +│ └── calibration.jsonl +├── report.pdf +└── results/ + ├── gptq-public-quality.json + └── gptq-public-performance.json +``` 请勿提交下列内容： +- `scripts` 目录下的工具脚本 - 量化前或量化后的模型权重 - profiler 产物 - 任何中间结果或临时文件 +### 评测流程 + +OJ 评测时会经过三个阶段： + +#### 模型量化 + +OJ 会根据你提交的量化框架代码和配置文件中的量化选项，使用 `scripts/quantize.py` 对 Gemma4-12B 进行 4-bit 量化，并将量化后的权重保存到指定目录。如果你提交了自定义的校准数据集，OJ 会使用该数据集进行量化；否则会使用默认的 `datasets/calibration-256.jsonl`。 + +如果在量化过程中出现 OOM 或者任何其他运行时错误，OJ 会直接判定该提交 0 分。 + +!!! note + 你无需在配置文件中指定量化前后模型的路径，OJ 会从命令行参数传入。 + +#### 任务一评测 + +任务一的主指标为 `scripts/evaluate_quality.py` 输出 JSON 文件中的 `delta_nll` 字段，即量化前后模型在精度测试集 `quality_public.jsonl` 上的全局平均 $\mathrm{NLL}$ 差值。你需要确保 $\Delta\mathrm{NLL} < 0.16$，否则任务一不计分，并且任务二的评测也会被跳过。 + +在 $\Delta\mathrm{NLL} < 0.16$ 的情况下，任务一的得分曲线如下图所示： + +![任务一得分曲线](image/task1_score.png) + +具体定义如下： + +$$ +S_1(x) = \begin{cases} + 100, & x < 0.1 \\[0.9em] + 100 \cdot \dfrac{e^{20x} - e^{3.6}}{e^{2} - e^{3.6}}, & 0.1 \le x < 0.16 \\[0.9em] + 0, & x \ge 0.16 +\end{cases} +$$ + +其中 $x = \Delta\mathrm{NLL}$。 + +#### 任务二评测 + +任务二的主指标为 `scripts/run_generation_queue.py` 输出 summary JSON 文件中的 `elapsed_s` 字段，即处理全部请求的总时间。性能测试集有两份： + +- `performance_public.jsonl`：主测试集，包含 10 个请求 + +

    + + - __输入分布__ + + --- + + | 长度区间 | 请求数量 | + | --------- |----------| + | 0-512 | 4 | + | 512-1024 | 2 | + | 1024-2000 | 4 | + + - __输出分布__ + + --- + + | 长度区间 | 请求数量 | + | --------- |----------| + | 0-16 | 3 | + | 16-32 | 4 | + | 32-48 | 3 | + +
    + + 主测试集的评分曲线如下图所示： + + ![任务二得分曲线](image/task2_score.png) + + 具体定义如下： + + $$ + S_2(x) = \begin{cases} + 100, & x < 36 \\[0.9em] + -\dfrac{5}{6} x + 30, & 36 \le x < 60 \\[0.9em] + \dfrac{5}{6} u \ln \dfrac{u}{x + u - 60} + 80, & 60 \le x < 150 \\[0.9em] + -\dfrac{1}{3} x + 80, & 150 \le x < 240 \\[0.9em] + 0, & x \ge 240 + \end{cases} + $$ + + 其中 $x$ 是 `elapsed_s`，$u \approx -65.56$ 是方程 $u \ln \left( 1 + \dfrac{90}{u} \right) = 60$ 的正根。 + +- `performance_long.jsonl`：Bonus 测试集，考查长上下文压力下的性能表现 + + !!! danger "Bonus 测评" + Bonus 数据集设计和测评方式尚未发布，敬请期待... + + +除报告外，你的最终得分公式如下： + +$$ +S = 0.4 S_1 + 0.6 S_2 + 0.1 S_{Bonus} +$$ + ## 思考题 1. 根据 Gemma4-12B 的结构，推导在 W4A16 量化下，全量加载模型权重占用的显存大小，以及 KV Cache 占用的显存大小与 batch_size、max_seq_len 的关系式，与实际测量的显存占用进行对比，分析差异原因。 diff --git a/src/lab5/README.md b/src/lab5/README.md index 9aad2270..4ca84e6f 100644 --- a/src/lab5/README.md +++ b/src/lab5/README.md @@ -11,48 +11,56 @@ ```text . -├── datasets/ +├── config.yaml # 配置文件 +├── datasets │   ├── calibration-256.jsonl # 参考校准数据集 │   ├── performance_public.jsonl # 公开性能评测数据集 │   ├── performance_small.jsonl # 小规模性能测试数据集 │   └── quality_public.jsonl # 公开精度评测数据集 -├── results/ -│ └── bf16-public-quality.json # BF16 公开精度基准 -├── scripts/ -│ ├── quantize.py # W4A16 量化入口 -│ ├── evaluate_quality.py # NLL 精度评测入口 -│ └── run_generation_queue.py # 端到端生成与性能测试入口 -└── src/hpc101_infer/ # 推理与量化框架 +├── pyproject.toml +├── README.md # 实验框架和工具脚本说明 +├── results +│   └── bf16-public-quality.json # 公开精度评测数据集的 BF16 精度评测结果 +├── scripts +│   ├── evaluate_quality.py # 量化精度评测脚本 +│   ├── quantize.py # 量化框架入口 +│   └── run_generation_queue.py # 推理框架入口 +└── src + └── hpc101_infer # 实验框架 ``` 下文命令均假设当前目录为仓库中的 `src/lab5/`。 ## 2. 准备环境 -项目使用 `uv` 管理 Python 环境和依赖： +在 DevPod 中，使用以下命令申请 Lab5 的实验容器并进入交互式环境： ```bash -uv sync +hpc submit -p lab5 -g 1 -t 20m --interactive bash ``` -确认三个入口脚本可以正常启动： +容器中已经预装了包管理器 `uv` 和实验所需的 Python 依赖，进入容器后，你需要将你实现的 `hpc101_infer` 安装到虚拟环境中： ```bash -uv run python scripts/quantize.py --help -uv run python scripts/evaluate_quality.py --help -uv run python scripts/run_generation_queue.py --help +uv pip install -e . --no-deps ``` -准备原始 Gemma4-12B Hugging Face checkpoint，并记住其目录。下文使用环境变量简化命令： +安装完毕后，请确认三个入口脚本可以正常启动： ```bash -export MODEL_DIR=/path/to/gemma-4-12b -export QUANT_DIR=/path/to/gemma-4-12b-gptq-w4a16 +python3 scripts/quantize.py --help +python3 scripts/evaluate_quality.py --help +python3 scripts/run_generation_queue.py --help ``` -`MODEL_DIR` 中应包含模型配置、tokenizer 和 safetensors 权重。脚本按本地 checkpoint 加载模型，不会自动下载缺失文件。 +Gemma4-12B 的原始权重（BF16）已经挂载到容器中，下文使用环境变量简化命令： -在 MIG 环境中，可通过 `CUDA_VISIBLE_DEVICES` 指定分配到的 GPU： +```bash +export MODEL_DIR=/checkpoints/gemma-4-12b +export QUANT_DIR=/path/to/quantized-gemma-4 # 你可以自行指定输出目录 +``` + +你可以通过 `nvidia-smi` 查看当前可用的 GPU 设备和显存使用情况，正常情况下你应当能看到一个 10 GiB 显存的 MIG 实例（1/7 张 H800）。如果需要指定具体的 GPU，可通过 `CUDA_VISIBLE_DEVICES` 指定分配到的 GPU，MIG 实例的 UUID 可以通过 `nvidia-smi -L` 查询： ```bash export CUDA_VISIBLE_DEVICES= @@ -60,12 +68,26 @@ export CUDA_VISIBLE_DEVICES= ## 3. 运行量化实验 +量化脚本可以从共享 YAML 文件的 `quantization` 段读取 `QuantizationConfig`： + +```bash +python3 scripts/quantize.py \ + --config config.yaml \ + --model "$MODEL_DIR" \ + --output "$QUANT_DIR" +``` + +配置文件的 `quantization` 段提供量化算法参数，外层 `calibration` 段可以指定校准数据路径、样本上限、micro batch size 和最大 token 数。模型路径、输出目录和设备仍必须通过命令行指定。显式命令行参数优先于配置文件。 + +脚本保留 `QuantizationConfig.from_yaml()` 返回的完整对象，并只覆盖命令行中显式指定的量化字段。学生可以在 `QuantizationConfig` 中新增字段、在 YAML 的 `quantization` 段设置并在量化实现中使用，无需修改 CLI。 + ### 3.1 使用 RTN 检查量化流程 RTN 不需要校准数据，适合先检查 checkpoint 的读取、打包和保存流程： ```bash -uv run python scripts/quantize.py \ +python3 scripts/quantize.py \ + --config config.yaml \ --model "$MODEL_DIR" \ --output /path/to/gemma-4-12b-rtn-w4a16 \ --algorithm rtn \ @@ -79,35 +101,29 @@ uv run python scripts/quantize.py \ 完成 `src/hpc101_infer/quantization/methods/gptq.py` 后，使用公开校准集运行 GPTQ： ```bash -uv run python scripts/quantize.py \ +python3 scripts/quantize.py \ + --config config.yaml \ --model "$MODEL_DIR" \ --output "$QUANT_DIR" \ - --algorithm gptq \ - --calibration-file datasets/calibration-256.jsonl \ - --calibration-limit 256 \ - --calibration-micro-batch-size 1 \ - --max-calibration-tokens 16384 \ - --group-size 128 \ - --gptq-block-size 128 \ - --gptq-damp-percent 0.01 \ - --scale-dtype float16 \ --device cuda \ --verbose ``` 常用量化参数： -| 参数 | 作用 | 默认值 | -| --- | --- | --- | -| `--algorithm` | 量化方法，可选 `rtn`、`gptq` | `rtn` | -| `--group-size` | 每组包含的输入列数，可选 `64` 或 `128` | `128` | -| `--asymmetric` | 使用非对称量化；不指定时使用对称量化 | 关闭 | -| `--scale-dtype` | scale 的数据类型 | `float16` | -| `--calibration-limit` | 最多读取的校准样本数 | 不限制 | -| `--max-calibration-tokens` | 每个 Linear 最多收集的有效 token 数 | `4096` | -| `--gptq-block-size` | GPTQ 分块处理的列数 | `128` | -| `--gptq-damp-percent` | Hessian 阻尼比例 | `0.01` | -| `--max-shard-size-mib` | 输出权重分片的大小上限 | `1024` | +| 参数 | 作用 | 默认值 | +| -------------------------- | -------------------------------------- | --------- | +| `--config` | 共享 YAML 配置文件 | 无 | +| `--algorithm` | 量化方法，可选 `rtn`、`gptq` | `rtn` | +| `--group-size` | 每组包含的输入列数，可选 `64` 或 `128` | `128` | +| `--symmetric` | 使用对称量化 | 开启 | +| `--asymmetric` | `--no-symmetric` 的兼容别名 | 关闭 | +| `--scale-dtype` | scale 的数据类型 | `float16` | +| `--calibration-limit` | 最多读取的校准样本数 | 不限制 | +| `--max-calibration-tokens` | 每个 Linear 最多收集的有效 token 数 | `4096` | +| `--gptq-block-size` | GPTQ 分块处理的列数 | `128` | +| `--gptq-damp-percent` | Hessian 阻尼比例 | `0.01` | +| `--max-shard-size-mib` | 输出权重分片的大小上限 | `1024` | 量化完成后，`QUANT_DIR` 中会保存 packed INT4 权重、量化配置和 manifest。不要覆盖原始 BF16 checkpoint。 @@ -124,12 +140,12 @@ results/bf16-public-quality.json 直接评测量化模型并与 BF16 基准比较： ```bash -uv run python scripts/evaluate_quality.py \ +python3 scripts/evaluate_quality.py \ --model "$QUANT_DIR" \ --dataset datasets/quality_public.jsonl \ --output results/gptq-public-quality.json \ --reference results/bf16-public-quality.json \ - --max-delta-nll 0.2 \ + --max-delta-nll 0.16 \ --linear-backend int4_reference \ --device cuda \ --dtype bfloat16 \ @@ -142,9 +158,9 @@ uv run python scripts/evaluate_quality.py \ - `mean_nll`：量化模型在公开数据集上的平均 NLL； - `delta_nll`：量化模型与 BF16 基准的 NLL 差值； - `perplexity_ratio`：量化模型与 BF16 基准的困惑度比值； -- `passed`：`delta_nll` 是否不超过 `--max-delta-nll`。 +- `passed`：`delta_nll` 是否不超过 `--max-delta-nll`，当实测 `delta_nll > max_delta_nll` 时，`passed` 为 `false`，脚本会以非零状态退出。 -实验要求公开测试集上的 `delta_nll < 0.2`。当 `passed` 为 `false` 时，脚本会以非零状态退出。 +你可以将测试结果中的 `delta_nll` 与实验手册中任务一的得分曲线进行对照，确认量化精度是否满足要求。 调试时可以使用 `--limit N` 只评测前 `N` 条记录；正式记录结果时不要限制样本数量。`--chunk-size` 只控制每次产生 logits 的 token 数，可在显存不足时适当减小。 @@ -157,10 +173,25 @@ uv run python scripts/evaluate_quality.py \ ## 5. 运行端到端性能实验 -使用量化 checkpoint 和公开性能请求运行生成脚本： +生成脚本支持从共享 YAML 文件的 `engine` 段读取 `EngineConfig`。模型、输入输出文件和生成参数必须通过命令行指定；配置文件中的其他顶层配置段不会被生成脚本使用： ```bash -uv run python scripts/run_generation_queue.py \ +python3 scripts/run_generation_queue.py \ + --config config.yaml \ + --model "$QUANT_DIR" \ + --input datasets/performance_public.jsonl \ + --output results/gptq-public-generation.jsonl \ + --summary-output results/gptq-public-summary.json +``` + +命令行中显式提供的引擎参数优先于配置文件，例如 `--batch-size 4` 会覆盖 `engine.scheduler_batch_size`。 + +脚本会保留 `EngineConfig.from_yaml()` 返回的完整配置对象，并只覆盖命令行中显式指定的字段。因此，学生在 `EngineConfig` 中增加新的 dataclass 字段、在 YAML 的 `engine` 段设置该字段并在引擎实现中使用后，不需要修改运行脚本。 + +也可以完全使用命令行运行生成脚本： + +```bash +python3 scripts/run_generation_queue.py \ --model "$QUANT_DIR" \ --input datasets/performance_public.jsonl \ --output results/gptq-public-generation.jsonl \ @@ -180,53 +211,64 @@ uv run python scripts/run_generation_queue.py \ 每条请求必须提供 `prompt` 或 `input_ids`。如果请求没有 `max_new_tokens`，脚本使用命令行参数 `--max-new-tokens`，默认值为 `32`。 -常用生成参数： - -| 参数 | 作用 | 默认值 | -| --- | --- | --- | -| `--input` | 输入 JSONL；使用 `-` 时从标准输入读取 | 必填 | -| `--output` | 输出 JSONL；使用 `-` 时写入标准输出 | `-` | -| `--batch-size` | 调度 batch size，同时决定 KV cache 的 batch 容量 | `1` | -| `--max-sequence-length` | prompt 与生成 token 的最大总长度 | `4096` | -| `--max-new-tokens` | 请求未指定时的默认生成长度 | `32` | -| `--seed` | 采样随机种子 | `0` | -| `--no-progress` | 关闭进度条 | 关闭 | -| `--no-synchronize-metrics` | 关闭各指标区间前后的设备同步 | 关闭 | - -输出请求结果写入 `--output` 指定的 JSONL 文件。整体性能摘要写入标准错误，包括： - +生成脚本参数： + +| 参数 | 作用 | 默认值或配置字段 | +| ---------------------------------------------------- | ----------------------------------------------------------------------------------- | ------------------------------------------------- | +| `--config PATH` | 共享 YAML 配置文件；脚本只读取其中的 `engine` 段，显式命令行参数优先 | 无 | +| `--model PATH` | 模型或量化 checkpoint 目录；不能从配置文件读取 | 必填 | +| `--input PATH` | 输入请求 JSONL；使用 `-` 时从标准输入读取 | 必填 | +| `--output PATH` | 逐请求生成结果 JSONL；使用 `-` 时写入标准输出 | `-` | +| `--summary-output PATH` | 将标准错误中的整体性能摘要额外写入 JSON 文件 | 无 | +| `--device DEVICE` | 推理设备，例如 `cuda` 或 `cpu` | `engine.device`，缺省为 `cuda` | +| `--dtype DTYPE` | 推理 dtype，可选 `bfloat16`、`float16`、`float32` | `engine.dtype`，缺省为 `bfloat16` | +| `--batch-size N` | 调度器每批处理的最大请求数 | `engine.scheduler_batch_size`，缺省为 `1` | +| `--max-batch-size N` | KV cache 可容纳的最大 batch；显式设置 `--batch-size` 且未设置本参数时，两者取相同值 | `engine.max_batch_size`，缺省为 `1` | +| `--max-sequence-length N` | 单条请求的 prompt 与生成 token 的最大总长度 | `engine.max_sequence_length`，缺省为 `4096` | +| `--attention-backend BACKEND` | 注意力实现；当前仅支持 `eager` | `engine.attention_backend`，缺省为 `eager` | +| `--linear-backend BACKEND` | Linear 实现，可选 `bf16`、`int4_reference` | `engine.linear_backend`，缺省为 `bf16` | +| `--scheduler-backend BACKEND` | 调度器实现；当前仅支持 `static_batch` | `engine.scheduler_backend`，缺省为 `static_batch` | +| `--max-new-tokens N` | 请求记录未提供 `max_new_tokens` 时使用的默认生成长度；不能从配置文件读取 | `32` | +| `--seed N` | 采样随机种子 | `engine.seed`，缺省为 `0` | +| `--synchronize-metrics` / `--no-synchronize-metrics` | 开启或关闭各指标计时区间前后的设备同步 | `engine.synchronize_metrics`，缺省为开启 | +| `--progress` / `--no-progress` | 开启或关闭请求完成进度条；不能从配置文件读取 | 开启 | + +输出请求结果写入 `--output` 指定的 JSONL 文件，每行对应一条请求。整体性能摘要始终写入标准错误；指定 `--summary-output` 后，同一份摘要还会写入独立的 JSON 文件。摘要包括： + +- `requests`：完成的请求数； +- `generated_tokens`：全部请求生成的 token 总数； - `elapsed_s`：处理全部请求的总时间； - `requests_per_s`：每秒完成的请求数； - `generated_tokens_per_s`：每秒生成的 token 数； - `batch_size`：本次运行使用的 batch size。 -测试不同 batch size 时，建议保留完整日志： +实验二的性能指标为 `elapsed_s`，你可以对照实验手册中的得分曲线确认优化效果。 + +测试不同 batch size 时，建议分别保存请求结果和性能摘要： ```bash -uv run python scripts/run_generation_queue.py \ +python3 scripts/run_generation_queue.py \ --model "$QUANT_DIR" \ --input datasets/performance_public.jsonl \ --output results/generation-bs4.jsonl \ + --summary-output results/generation-bs4-summary.json \ --batch-size 4 \ - --max-sequence-length 2048 \ - 2> results/generation-bs4.log + --max-sequence-length 2048 ``` -`--no-synchronize-metrics` 会改变细分指标的计时语义。进行正式对比时，应保持所有实验的该选项一致，且不得通过修改计时区间跳过必要工作。 - 如果在 `performance_public.jsonl` 数据集上测试时显存不足，可以先使用 `performance_small.jsonl` 进行测试。 ## 6. 推荐实验流程 1. 先用 RTN 跑通量化、加载和精度评测闭环； 2. 实现 GPTQ，并生成 W4A16 checkpoint； -3. 在 `quality_public.jsonl` 上确认 `delta_nll < 0.2`； +3. 在 `quality_public.jsonl` 上确认量化精度； 4. 用 `performance_public.jsonl` 记录未优化版本的性能基线； 5. 逐项实现显存、算子或调度优化； 6. 每次修改后重新检查精度，并使用相同参数重复性能测试； 7. 保存最终精度 JSON、生成结果 JSONL 和性能日志，用于实验报告与提交。 -为了让性能结果可比较，每组实验应固定模型 checkpoint、数据集、GPU、`batch-size`、`max-sequence-length`、随机种子和计时选项。建议先预热一次，再重复运行多次并报告稳定结果。 +为了让性能结果可比较，每组实验应固定模型 checkpoint、数据集、GPU、`batch-size`、`max-sequence-length`、随机种子和计时选项。有条件的情况下，建议先预热一次，再重复运行多次并报告稳定结果。 ## 7. 常见问题 diff --git a/src/lab5/config.yaml b/src/lab5/config.yaml new file mode 100644 index 00000000..eebcb647 --- /dev/null +++ b/src/lab5/config.yaml @@ -0,0 +1,38 @@ +engine: + dtype: bfloat16 + device: cuda + max_batch_size: 1 + scheduler_batch_size: 1 + max_sequence_length: 2048 + linear_backend: bf16 + scheduler_backend: static_batch + seed: 42 + synchronize_metrics: true + +quantization: + algorithm: gptq + version: 1 + bits: 4 + group_size: 128 + symmetric: true + scale_dtype: float16 + packing: uint8_little_nibble + target_modules: + - q_proj + - k_proj + - v_proj + - o_proj + - gate_proj + - up_proj + - down_proj + calibration: + gptq: + block_size: 128 + damp_percent: 0.01 + propagate_quantized: true + +calibration: + path: datasets/calibration-256.jsonl + limit: 256 + micro_batch_size: 1 + max_tokens: 4096 diff --git a/src/lab5/datasets/performance_public.jsonl b/src/lab5/datasets/performance_public.jsonl index 4b2a4d30..f1732d1b 100644 --- a/src/lab5/datasets/performance_public.jsonl +++ b/src/lab5/datasets/performance_public.jsonl @@ -1,5 +1,10 @@ {"prompt": " scenes depicting deities and people , finely carved and rubbed with vermilion , as well as jade and shell ornaments and ceramic vessels filled with offerings of food and drink . The shrine at the summit of the pyramid has three chambers , each behind the next , with the doorways spanned by wooden lintels fashioned from multiple beams . The outermost lintel is plain but the two inner lintels were carved , some of the beams were removed in the 19th century and their location is unknown , while others were taken to museums in Europe .\n\nTemple II ( also known as the Temple of the Mask ) it was built around AD 700 and stands 38 metres ( 125 ft ) high . Like other major temples at Tikal , the summit shrine had three consecutive chambers with the doorways spanned by wooden lintels , only the middle of which was carved . The temple was dedicated to the wife of Jasaw Chan K 'awil , although no tomb was found . The queen 's portrait was carved into the lintel spanning the doorway of the summit shrine . One of the beams from this lintel is now in the American Museum of Natural History in New York .\n\nTemple III ( also known as the Temple of the Jaguar Priest", "max_new_tokens": 48} +{"prompt": " excavated a massive trench across the complex , thoroughly investigating its construction history . It is a complex group with construction beginning in the Preclassic Period , around 350 BC . It developed into a funerary complex for the ruling dynasty of the Classic Period , with each additional royal burial adding new temples on top of the older structures . After AD 400 a row of tall pyramids was added to the earlier Northern Platform , which measured 100 by 80 metres ( 330 by 260 ft ) , gradually hiding it from view . Eight temple pyramids were built in the 6th century AD , each of them had an elaborate roofcomb and a stairway flanked by masks of the gods . By the 9th century AD , 43 stelae and 30 altars had been erected in the North Acropolis ; 18 of these monuments were carved with hieroglyphic texts and royal portraits . The North Acropolis continued to receive burials into the Postclassic Period .\n\nThe South Acropolis is found next to Temple V. It was built upon a large basal platform that covers an area of more than 20 @,@ 000 square metres ( 220 @", "max_new_tokens": 32} {"prompt": "ries . Regardless , the Boers were surprised and during two hours of heavy fighting , more than 50 were killed and another 40 taken prisoner . Five hundred Queenslanders and the New South Wales Lancers subsequently took part in the Siege of Kimberley in February 1900 .\n\nDespite serious set @-@ backs at Colenso , Stormberg , Magersfontein , and Spion Kop in January — and with Ladysmith still under siege — the British mounted a five division counter @-@ invasion of the Orange Free State in February . The attacking force included a division of cavalry commanded by Lieutenant General John French with the New South Wales Lancers , Queensland Mounted Infantry and New South Wales Army Medical Corps attached . First , Kimberley was relieved following the battles of Modder River and Magersfontein , and the retreating Boers defeated at Paardeberg , with the New South Wales Mounted Rifles locating the Boer general , Piet Cronjé . The British entered Bloemfontein on 13 March 1900 , while Ladysmith was relieved . Disease began to take its toll and scores of men died . Still the advance continued , with the drive to Pretoria in May including more than 3 @,@ 000 Australians . Johannesburg fell on 30 May , and the Boers withdrew from Pretoria on 3 June . The New South Wales Mounted Rifles and Western Australians saw action again at Diamond Hill on 12 June . Mafeking was relieved on 17 May .\n\nFollowing the defeat of the Afrikaner republics still the Boers held out , forming small commando units and conducting a campaign of guerrilla warfare to disrupt British troop movements and lines of supply . This new phase of resistance led to further recruiting in the Australian colonies and the raising of the Bushmen 's Contingents , with these soldiers usually being volunteers with horse @-@ riding and shooting skills , but little military experience . After Federation in 1901 , eight Australian Commonwealth Horse battalions of the newly created Australian Army were also sent to South Africa , although they saw little fighting before the war ended . Some Australians later joined local South African irregular units , instead of returning home after discharge . These soldiers were part of the British Army , and were subject to British military discipline . Such units included the Bushveldt Carbineers which gained notoriety as the unit in which Harry \" Breaker \" Morant and Peter Handcock served in before their court martial and execution for war", "max_new_tokens": 16} +{"prompt": " to provide an avenue of retreat for the fort 's garrison , but this failed because there were not enough boats to bridge the roughly one mile ( 1 @.@ 6 km ) channel separating the island from Charleston ; the unwillingness of Moultrie and Rutledge to support the effort may also have played a role . The Americans also constructed an entrenchment at the northern end of Sullivan 's Island , which was manned by more than 750 men and three small cannons , and began to fortify a guard post at Haddrell 's Point on the mainland opposite Fort Sullivan .\n\nGeneral Clinton encountered the first major problem of the attack plan on June 17 . An attempt to wade the channel between the two islands established that part of the channel was at least shoulder @-@ deep , too deep for troops to cross even without the prospect of enemy opposition . He considered using boats to ferry the troops across , but the Americans , with timely advice from General Lee , adopted a strong defensive position that was virtually impossible to bombard from ships or the Long Island position . As a result , the British and American forces faced each other across the channel , engaging in occasional and largely inconsequential cannon fire at long range . Clinton reported that this meant that Admiral Parker would have \" the glory of being defeated alone . \" The attack was originally planned for June 24 , but bad weather and contrary wind conditions prompted Parker to call it off for several days .\n\nOn the morning of June 28 , Fort Sullivan was defended by Colonel Moultrie , commanding the 2nd South Carolina Regiment and a company of the 4th South Carolina Artillery , numbering 435 men . At around 9 : 00 am that morning , a British ship fired a signal gun indicating all was ready for the attack . Less than an hour later , nine warships had sailed into positions facing the fort . Thunder and Friendship anchored about 1 @.@ 5 miles ( 2 @.@ 4 km ) from the fort while Parker took Active , Bristol , Experiment and Solebay to a closer position about 400 yards ( 370 m ) from Sullivan 's Island , where they anchored facing broadside to the fort . Each of these ships began to fire upon the fort when it reached its position , and the defenders returned the fire . Although many of Thunder 's shots landed in or near the fort , they had little effect ; according to Moultrie , \"", "max_new_tokens": 48} {"prompt": " A selection of the most notable stelae at Tikal follows :\n\nStela 1 dates to the 5th century and depicts the king Siyaj Chan K 'awiil II in a standing position .\n\nStela 4 is dated to AD 396 , during the reign of Yax Nuun Ayiin after the intrusion of Teotihuacan in the Maya area . The stela displays a mix of Maya and Teotihuacan qualities , and deities from both cultures . It has a portrait of the king with the Underworld Jaguar God under one arm and the Mexican Tlaloc under the other . His helmet is a simplified version of the Teotihuacan War Serpent . Unusually for Maya sculpture , but typically for Teotihuacan , Yax Nuun Ayiin is depicted with a frontal face , rather than in profile .\n\nStela 5 was dedicated in 744 by Yik 'in Chan K 'awiil .\n\nStela 6 is a badly damaged monument dating to 514 and bears the name of the \" Lady of Tikal \" who celebrated the end of the 4th K 'atun in that year .\n\nStela 10 is twinned with Stela 12 but is badly damaged . It described the accession of Kaloomte ' B 'alam in the early 6th century and earlier events in his career , including the capture of a prisoner depicted on the monument .\n\nStela 11 was the last monument ever erected at Tikal ; it was dedicated in 869 by Jasaw Chan K 'awiil II .\n\nStela 12 is linked to the queen known as the \" Lady of Tikal \" and king Kaloomte ' B 'alam . The queen is described as performing the year @-@ ending rituals but the monument was dedicated in honor of the king .\n\nStela 16 was dedicated in 711 , during the reign of Jasaw Chan K 'awiil I. The sculpture , including a portrait of the king and a hieroglyphic text , are limited to the front face of the monument . It was found in Complex N , west of Temple III .\n\nStela 18 was one of two stelae erected by Yax Nuun Ayiin I to celebrate the k 'atun @-@ ending of AD 396 . It was re @-@ erected at the base of Temple 34 , his funerary shrine .\n\nStela 19 was dedicated in 790 by Yax Nuun Ayiin II .\n\nStela 20 was found in Complex P , in Group H , and was moved to the Museo Nacional de Arqueología y Etnología in Guatemala City .\n\nStela 21 was dedicated in 736 by Yik 'in Chan K 'awiil . Only the bottom of the stela is intact , the rest having been mutilated in ancient times . The surviving sculpture is of fine quality , consisting of the feet of a figure and of accompanying hieroglyphic text . The stela is associated with Altar 9 and is located in front of Temple VI .\n\nStela 22 was dedicated in 771 by Yax Nuun Ayiin II in the northern enclosure of Group Q , a twin @-@ pyramid complex . The face of the figure on the stela has been mutilated .\n\nStela 23 was broken in antiquity and was re @-@ erected in a residential complex . The defaced portrait on the monument is that of the so @-@ called \" Lady of Tikal \" , a daughter of Chak Tok Ich 'aak II who became queen at the age of six but never ruled in her own right , being paired with male co @-@ rulers . It dates to the early 6th century .\n\nStela 24 was erected at the foot of Temple 3 in 810 , accompanied by Altar 7 . Both were broken into fragments in ancient times , although the name of Dark Sun survives on three fragments .\n\nStela 26 was found in the summit shrine of Temple 34 , underneath a broken masonry altar . The monument had originally been erected at the base of the temple during the Early Classic period and was later broken , probably at the beginning of the Late Classic . Its remains were then interred within the temple shrine .\n\nStela 29 bears a Long Count ( 8 @.@ 12 @.@ 14 @.@ 8 @.@ 15 ) date equivalent to AD 292 , the earliest surviving Long Count date from the Maya lowlands . The stela is also the earliest monument to bear the Tikal emblem glyph . It bears a sculpture of the king facing to the right , holding the head of an underworld jaguar god", "max_new_tokens": 32} -{"prompt": "179 ( Bridge Street ) , where Route 165 ends and Route 29 turns west for a one @-@ block wrong @-@ way concurrency with the two @-@ lane , undivided Route 179 , lasting to the intersection of Main Street , where Route 29 turns north on Main Street .\n\nRoute 29 follows Main Street north through Lambertville , crossing into Delaware Township . It comes to an interchange with U.S. Route 202 just east of the New Hope @-@ Lambertville Toll Bridge , with access to northbound U.S. Route 202 and from southbound U.S. Route 202 provided by way of Alexauken Creek Road . Route 29 continues along the Delaware River and enters Stockton . The route intersects Bridge Street , which crosses the Delaware River on the Centre Bridge @-@ Stockton Bridge and continues into Pennsylvania as Pennsylvania Route 263 . Shortly after that intersection , Route 29 intersects the southern terminus of County Route 523 ( Stockton @-@ Flemington Road ) . Route 29 crosses back into Delaware Township , where it meets the southern terminus of County Route 519 ( Kingwood @-@ Stockton Road ) .\n\nRoute 29 makes a sharp left turn and heads west along the river as a rural road , crossing into Kingwood Township , where the name of the road changes from Main Street to Daniel Bray Highway . Here , it intersects with County Route 651 ( Byram @-@ Kingwood Road ) . The route bends to the north and continues along the Delaware River for several miles , crossing into Frenchtown , where the route becomes Trenton Road . Upon entering Frenchtown , an end shield for northbound Route 29 is posted to mark the end of state maintenance , which officially ends at the Washington Street intersection , where maintenance is transferred to the county . Despite this , Route 29 officially continues farther north along Trenton Road to its northern terminus at Route 12 ( Bridge Street / Race Street ) , a short distance east of Route 12 ’ s western terminus at the Uhlerstown @-@ Frenchtown Bridge . The southern terminus of County Route 513 is located a block north of the northern terminus of Route 29 along Route 12 .\n\nThe current route was originally legislated in 1911 as part of the Delaware River Drive , a named state highway that was proposed to run from along the Delaware River from Trenton to the New York border in Montague Township . Route 29 was originally defined in 1927 to run from Trenton to Newark . The original route ran from downtown Trenton along State Street and Sanhican Drive . From there , it followed its current alignment to Lambertville , where it followed present @-@ day Route 179 to Ringoes to present @-@ day County Route 514 , which it followed to Woods Tavern . The route turned north on U.S. Route 206 ( also designated Route 31 ) and followed that route to Somerville , where it followed U.S. Route 22 to Newark . The current alignment of Route 29 from Lambertville to Route 12 in Frenchtown was designated Route 29A in 1927 .\n\nThe alignment of Route 29 between Ringoes and Somerville was eventually shifted to follow present @-@ day U.S. Route 202 , which was also Route 30 ( now Route 31 ) between Ringoes and Flemington and Route 12 between Flemington and Somerville . In 1938 , Route 29B was planned as an extension of Route 29A from Frenchtown to Route 28 ( now Route 122 ) in Alpha . While this road was never built , much of the alignment north of Milford is served by County Route 519 . In 1948 , a spur route , Route S29 , was created , running along U.S. Route 202 ( Bridge Street ) in Lambertville to the New Hope @-@ Lambertville Bridge . In the 1953 New Jersey state highway renumbering , Route 29 was redefined to continue north from Lambertville to Frenchtown on Route 29A , and the Route 29 designation between Lambertville and Newark was removed in favor of U.S. Route 202 between Lambertville and Somerville and U.S. Route 22 between Somerville and Newark . The section of former Route 29 between Route 29A and Route S29 became Route 165 .\n\nPlans for a limited @-@ access route along the Route 29 corridor go back to 1932 , when a parkway was proposed along the Delaware River between Trenton and Lambertville ; this proposal never materialized . Plans for a freeway were resurrected in the early 1950s to construct a road , the John Fitch Parkway , that was to run from Hamilton Township to Interstate 95 in Ewing Township . This road was built between 1954 and 1957 from South Warren Street in Trenton to present @-@ day Interstate 95 in Ewing Township . Construction of this portion John Fitch Parkway took up much of Trenton ’ s available waterfront along the Delaware River . The former alignment of Route 29 in the northern part of Trenton and in Ewing Township became Route 175 .\n\nBetween 1990 and 1995 , the Route 29 freeway was built between the Interstate 195 / Interstate 295 interchange and Route 129 in Hamilton Township . A two @-@ lane street , Lamberton Road , connected the two freeway sections . Plans were then made to fill the gap between the two freeway sections in Trenton . Construction began in 1997 on the Route 29 freeway between Route 129 and the Morrisville @-@ Trenton Railroad Bridge . The road was to include two traffic lights at Cass Street and South Warren Street and a tunnel which was to be built as a covered roadway on the bank of the Delaware River . The tunnel was originally scheduled to be complete by 2001 but was delayed after the Army Corps of Engineers discovered many environmental violations that occurred with construction of the tunnel . The roof was put in place in October 2001 and the tunnel officially opened to traffic on March 2 , 2002 . A restriction to trucks over 13 tons was put in place and made permanent in November 2002 .\n\nBy the 2000s , the state gave the part of Route 29 ( South Main Street ) between Route 165 and Route 179 in Lambertville to the city , and Route 29 was rerouted to use all of Route 165 and one block of Route 179 . Prior to this , South Main Street had been turned one @-@ way southbound . Route 165 , which is only signed on overhead street signs , still exists , though it is fully concurrent with Route 29 .", "max_new_tokens": 32} -{"prompt": " ( 310 km ) northeast of Reno , Nevada .\n\nThe range and surrounding non @-@ mountainous areas cover an area of 811 square miles ( 2 @,@ 100 km2 ) . The mountains run 51 miles ( 82 km ) north to south and 36 miles ( 58 km ) east to west . More of the range is in Oregon ( 78 % ) than in Nevada ( 22 % ) . The highest point in the range is Orevada View Benchmark , which is 8 @,@ 506 feet ( 2 @,@ 593 m ) above sea level and is located in Nevada about one mile south of the Oregon border . About two miles southeast of Orevada View is Disaster Peak , \" a large , symmetrical butte that is visible throughout the region . \" At 7 @,@ 781 feet ( 2 @,@ 372 m ) , Disaster Peak anchors the southern end of the mountains in a sub @-@ range called The Granites .\n\nThe Oregon Canyon Mountains border the Trout Creek Mountains on the east along the Harney – Malheur county line ( according to the United States Geological Survey 's definitions ) , while the Pueblo Mountains are the next range west of the Trout Creek Mountains . The Bilk Creek Mountains in both Oregon and Nevada border the Trout Creek Mountains on the southwest ; the two ranges are separated by Log Cabin Creek and South Fork Cottonwood Creek . South of the Trout Creek Mountains is the Kings River Valley , which separates the Bilk Creek Mountains on the west from the Montana Mountains on the east .\n\nThe terrain in the Trout Creek Mountains varies from broad , flat basins and rolling ridges to high rock escarpments cut by deep canyons . The canyons have steep walls with loose talus slopes at the bottoms . There are meadows around springs in the mountains , although most streams in the range do not flow year @-@ round . Major streams that flow off the north slopes of the mountains include ( from west to east ) Cottonwood Creek , Trout Creek , Willow Creek , and Whitehorse Creek . These streams all flow into endorheic basins in Harney County , Oregon . Trout Creek and Whitehorse Creek are the largest of the four . The Kings River and McDermitt Creek each drain an area on the south slopes of the Trout Creek Mountains . The Kings River begins in The Granites and flows south into Nevada , where it meets the Quinn River , which evaporates in the Black Rock Desert . McDermitt Creek begins in Oregon a few miles north of The Granites and flows generally east , crossing the Oregon – Nevada border five times before disappearing into the floor of the Quinn River Valley south of McDermitt .\n\nThe mountains lie within the Basin and Range Province or Great Basin of the Western United States , which is characterized by a series of parallel fault blocks that form long north – south mountain ranges separated by wide , high @-@ desert valleys . The Trout Creek Mountains are uplifted and tilted blocks with steep escarpments along the southern and eastern sides of the range . The southern area of the range , known as The Granites , has numerous outcroppings of Cretaceous age granite . These granite outcrops are commonly found in the eroded valleys below the volcanic ridgelines .\n\nThe Trout Creek Mountains are composed mostly of basalt from a shield volcano that once stood where Steens Mountain is today . Crustal thinning and the Yellowstone hotspot , which was then beneath southeastern Oregon , induced eruptions from Steens and nearby vents about 17 million years ago , in the Miocene . The vents produced a series of lava flows that spread across the land now known as the Trout Creek Mountains . Eruptions from the Steens volcano lasted for about one million years , and at least 70 separate lava flows occurred . Under the resulting basalt rock lie much older metamorphic rocks that may be related to some of the Triassic age formations of the Blue Mountains in northeastern Oregon . Within these metamorphic rocks are diorite and granodiorite intrusive bodies which were presumably intruded during the Cretaceous Period .\n\nThe broad McDermitt Caldera is a prominent geologic feature in the Trout Creek Mountains . The oval @-@ shaped caldera is a collapsed lava dome that straddles the Oregon – Nevada border on the eastern side of the range and south of the Oregon Canyon Mountains . It is about 28 miles ( 45 km ) long and 22 miles ( 35 km ) wide . The lava dome was created by volcanic eruptions in the early Miocene . A total of five large ash flows were produced along with a large rhyolite dome structure . The caldera formed when the dome collapsed about 16 million years ago . The caldera contains significant ore deposits , and mercury and uranium have been mined at eight or more sites in and around the caldera . Other areas in the caldera were mined for ores of antimony , cesium , and lithium .\n\nThe Trout Creek Mountains are semi @-@ arid because they are in the eastern rain shadow of mountain ranges to the west . When moist air from the Pacific Ocean moves eastward over the Oregon and California coastal ranges and the Cascade Range , most precipitation falls in those mountains before reaching the Trout Creek Mountains . As a result , the average annual precipitation in the Trout Creek Mountains is only 8 to 26 inches ( 200 to 660 mm ) per year , with most areas receiving between 8 and 12 inches ( 200 and 300 mm ) annually . Much of the annual precipitation occurs between the beginning of March and the end of June . Most of the rest falls as snow during the fall and winter months . Snowpack at elevations below 6 @,@ 000 feet ( 1 @,@ 800 m ) usually melts by April ; however , at the higher elevations , snow can remain until mid @-@ June . Local flooding often occurs in the spring as the snowpack melts .\n\nThe prevailing winds are from the west @-@ southwest , and they are normally strongest in March and April . Brief , intense thunderstorms are common between April and October . Thunderstorms in the summer months tend to be more isolated and often produce dry lightning strikes .\n\nVegetation in the Trout Creek Mountains is dominated by big sagebrush and desert grasses . Other common shrubs include bitterbrush , snowberry , and Ceanothus . There are also patches of mountain mahogany in some areas . Common grass species include Idaho fescue , bluebunch wheatgrass , cheatgrass , western needlegrass , Sandberg 's bluegrass , Thurber 's needlegrass , and bottlebrush squirreltail , as well as basin wildrye in some well @-@ drained areas .\n\nLess than one percent of the range consists of meadow wetlands and riparian greenways ( vegetation along stream banks ) . However , these areas are vital to the local ecosystem . The meadows surround springs , which are mostly on gently sloping uplands or in stream bottoms , and range in size from about 1 to 5 acres ( 0 @.@ 40 to 2 @.@ 02 ha ) . Narrow riparian greenways follow the year @-@ round streams . Many greenway areas have quaking aspen and willow groves . Cottonwood and alder groves can be found at lower elevations where terrain is flatter and stream channels are wider . Sedges and rushes are also native to these stream bottoms . Years of heavy livestock grazing in parts of the range resulted in the loss of some grass species , riparian vegetation , and young aspen and willow trees . Restoration of riparian areas began in the early 1970s , and plans to reduce grazing were implemented in the 1980s and early 1990s . However , large wildfires in southeastern Oregon during the summer of 2012 burned much of the range 's vegetation , damaging riparian ecosystems and killing hundreds of grazing cattle .\n\nAnimals in the Trout Creek Mountains are adapted to the environment of the High Desert . Pronghorn are common in the open , sagebrush @-@ covered basins , while mule deer live in the cottonwood and willow groves . There are also bighorn sheep , cougars , and bobcats in the high country . Jackrabbits and coyotes are prevalent throughout the range . Mustangs sometimes pass through the mountains as they roam the Great Basin . Some other mammals include the northern pocket gopher , mountain cottontail , and Belding 's ground squirrel . North American beavers live in and along streams , as do Pacific tree frogs , western spadefoot toads , and garter snakes . Native bird species include the sage grouse , mountain chickadee , gray @-@ headed junco , black @-@ throated gray warbler , Virginia 's warbler , MacGillivray 's warbler , pine siskin , red crossbill , bushtit , hermit thrush , northern goshawk , and species of raven and eagle .\n\nSeveral streams in the Trout Creek Mountains are home to trout , including the rare Lahontan cutthroat trout subspecies . These include Willow Creek , Whitehorse Creek , Little Whitehorse Creek , Doolittle Creek , Fifteen Mile Creek , Indian Creek , Sage Canyon Creek , Line Canyon Creek , and some tributaries of McDermitt Creek . Lahontan cutthroat trout live in small , isolated populations that are often confined to individual streams , many of them in the Trout Creek Mountains . These populations have significant genetic differences due to their history of isolation . For most of the 20th century , the trout 's numbers declined considerably . It was listed under federal law as an endangered species in 1970 and was reclassified as threatened in 1975 . Reasons for the fish 's decline included habitat degradation from cattle grazing , drought", "max_new_tokens": 16} +{"prompt": " first saw action in January 1941 , when the 6th Division formed part of the Commonwealth forces during Operation Compass . The division assaulted and captured Bardia on 5 January and Tobruk on 22 January , with tens of thousands of Italian troops surrendering at both towns . The 6th Division took part in the pursuit of the Italian Army and captured Benghazi on 4 February . In late February it was withdrawn for service in Greece , and was replaced by the 9th Division .\n\nThe Australian forces in the Mediterranean endured a number of campaigns during 1941 . During April , the 6th Division , other elements of I Corps and several Australian warships formed part of the Allied force which unsuccessfully attempted to defend Greece from German invasion during the Battle of Greece . At the end of this campaign , the 6th Division was evacuated to Egypt and Crete . The force at Crete subsequently fought in the Battle of Crete during May , which also ended in defeat for the Allies . Over 5 @,@ 000 Australians were captured in these campaigns , and the 6th Division required a long period of rebuilding before it was again ready for combat . The Germans and Italians also went on the offensive in North Africa at the end of March and drove the Commonwealth force there back to near the border with Egypt . The 9th Division and a brigade of the 7th Division were besieged at Tobruk ; successfully defending the key port town until they were replaced by British units in October . During June , the main body of the 7th Division , a brigade of the 6th Division and the I Corps headquarters took part in the Syria @-@ Lebanon Campaign against the Vichy French . Resistance was stronger than expected ; Australians were involved in most of the fighting and sustained most of the casualties before the French capitulated in early July .\n\nThe majority of Australian units in the Mediterranean returned to Australia in early 1942 , after the outbreak of the Pacific War . The 9th Division was the largest unit to remain in the Middle East , and played a key role in the First Battle of El Alamein during June and the Second Battle of El Alamein in October . The division returned to Australia in early 1943 , but several RAAF squadrons and RAN warships took part in the subsequent Tunisia Campaign and the Italian Campaign from 1943 until the end of the war .\n\nThe RAAF 's role in the strategic air offensive in Europe formed Australia 's main contribution to the defeat of Germany . Approximately 13 @,@ 000 Australian airmen served in dozens of British and five Australian squadrons in RAF Bomber Command between 1940 and the end of the war . Australians took part in all of Bomber Command 's major offensives and suffered heavy losses during raids on German cities and targets in France . Australian aircrew in Bomber Command had one of the highest casualty rates of any part of the Australian military during the Second World War and sustained almost 20 percent of all Australian deaths in combat ; 3 @,@ 486 were killed and hundreds more were taken prisoner . Australian airmen in light bomber and fighter squadrons also participated in the liberation of Western Europe during 1944 and 1945 and two RAAF maritime patrol squadrons served in the Battle of the Atlantic .\n\nFrom the 1920s Australia 's defence thinking was dominated by British Imperial defence policy , which was embodied by the \" Singapore strategy \" . This strategy involved the construction and defence of a major naval base at Singapore from which a large British fleet would respond to Japanese aggression in the region . To this end , a high proportion of Australian forces in Asia were concentrated in Malaya during 1940 and 1941 as the threat from Japan increased . However , as a result of the emphasis on co @-@ operation with Britain , relatively few Australian military units had been retained in Australia and the Asia @-@ Pacific region . Measures were taken to improve Australia 's defences as war with Japan loomed in 1941 , but these proved inadequate . In December 1941 , the Australian Army in the Pacific comprised the 8th Division , most of which was stationed in Malaya , and eight partially trained and equipped divisions in Australia . The RAAF was equipped with 373 aircraft , most of which were obsolete trainers , and the RAN had three cruisers and two destroyers in Australian waters .\n\nThe Australian military suffered a series of defeats during the early months of the Pacific War . The 8th Division and RAAF squadrons in Malaya formed a part of the British Commonwealth forces which were unable to stop a smaller Japanese invasion force which landed on 7 December . The British Commonwealth force withdrew to Singapore at the end of January , but was forced to surrender on 15 February", "max_new_tokens": 32} +{"prompt": "179 ( Bridge Street ) , where Route 165 ends and Route 29 turns west for a one @-@ block wrong @-@ way concurrency with the two @-@ lane , undivided Route 179 , lasting to the intersection of Main Street , where Route 29 turns north on Main Street .\n\nRoute 29 follows Main Street north through Lambertville , crossing into Delaware Township . It comes to an interchange with U.S. Route 202 just east of the New Hope @-@ Lambertville Toll Bridge , with access to northbound U.S. Route 202 and from southbound U.S. Route 202 provided by way of Alexauken Creek Road . Route 29 continues along the Delaware River and enters Stockton . The route intersects Bridge Street , which crosses the Delaware River on the Centre Bridge @-@ Stockton Bridge and continues into Pennsylvania as Pennsylvania Route 263 . Shortly after that intersection , Route 29 intersects the southern terminus of County Route 523 ( Stockton @-@ Flemington Road ) . Route 29 crosses back into Delaware Township , where it meets the southern terminus of County Route 519 ( Kingwood @-@ Stockton Road ) .\n\nRoute 29 makes a sharp left turn and heads west along the river as a rural road , crossing into Kingwood Township , where the name of the road changes from Main Street to Daniel Bray Highway . Here , it intersects with County Route 651 ( Byram @-@ Kingwood Road ) . The route bends to the north and continues along the Delaware River for several miles , crossing into Frenchtown , where the route becomes Trenton Road . Upon entering Frenchtown , an end shield for northbound Route 29 is posted to mark the end of state maintenance , which officially ends at the Washington Street intersection , where maintenance is transferred to the county . Despite this , Route 29 officially continues farther north along Trenton Road to its northern terminus at Route 12 ( Bridge Street / Race Street ) , a short distance east of Route 12 ’ s western terminus at the Uhlerstown @-@ Frenchtown Bridge . The southern terminus of County Route 513 is located a block north of the northern terminus of Route 29 along Route 12 .\n\nThe current route was originally legislated in 1911 as part of the Delaware River Drive , a named state highway that was proposed to run from along the Delaware River from Trenton to the New York border in Montague Township . Route 29 was originally defined in 1927 to run from Trenton to Newark . The original route ran from downtown Trenton along State Street and Sanhican Drive . From there , it followed its current alignment to Lambertville , where it followed present @-@ day Route 179 to Ringoes to present @-@ day County Route 514 , which it followed to Woods Tavern . The route turned north on U.S. Route 206 ( also designated Route 31 ) and followed that route to Somerville , where it followed U.S. Route 22 to Newark . The current alignment of Route 29 from Lambertville to Route 12 in Frenchtown was designated Route 29A in 1927 .\n\nThe alignment of Route 29 between Ringoes and Somerville was eventually shifted to follow present @-@ day U.S. Route 202 , which was also Route 30 ( now Route 31 ) between Ringoes and Flemington and Route 12 between Flemington and Somerville . In 1938 , Route 29B was planned as an extension of Route 29A from Frenchtown to Route 28 ( now Route 122 ) in Alpha . While this road was never built , much of the alignment north of Milford is served by County Route 519 . In 1948 , a spur route , Route S29 , was created , running along U.S. Route 202 ( Bridge Street ) in Lambertville to the New Hope @-@ Lambertville Bridge . In the 1953 New Jersey state highway renumbering , Route 29 was redefined to continue north from Lambertville to Frenchtown on Route 29A , and the Route 29 designation between Lambertville and Newark was removed in favor of U.S. Route 202 between Lambertville and Somerville and U.S. Route 22 between Somerville and Newark . The section of former Route 29 between Route 29A and Route S29 became Route 165 .\n\nPlans for a limited @-@ access route along the Route 29 corridor go back to 1932 , when a parkway was proposed along the Delaware River between Trenton and Lambertville ; this proposal never materialized . Plans for a freeway were resurrected in the early 1950s to construct a road , the John Fitch Parkway , that was to run from Hamilton Township to Interstate 95 in Ewing Township . This road was built between 1954 and 1957 from South Warren Street in Trenton to present @-@ day Interstate 95 in Ewing Township . Construction of this portion John Fitch Parkway took up much of Trenton ’ s available waterfront along the Delaware River . The former alignment of Route 29 in the northern part of Trenton and in Ewing Township became Route 175 .\n\nBetween 1990 and 1995 , the Route 29 freeway was built between the Interstate 195 / Interstate 295 interchange and Route 129 in Hamilton Township . A two @-@ lane street , Lamberton Road , connected the two freeway sections . Plans were then made to fill the gap between the two freeway sections in Trenton . Construction began in 1997 on the Route 29 freeway between Route 129 and the Morrisville @-@ Trenton Railroad Bridge . The road was to include two traffic lights at Cass Street and South Warren Street and a tunnel which was to be built as a covered roadway on the bank of the Delaware River . The tunnel was originally scheduled to be complete by 2001 but was delayed after the Army Corps of Engineers discovered many environmental violations that occurred with construction of the tunnel . The roof was put in place in October 2001 and the tunnel officially opened to traffic on March 2 , 2002 . A restriction to trucks over 13 tons was put in place and made permanent in November 2002 .\n\nBy the 2000s , the state gave the part of Route 29 ( South Main Street ) between Route 165 and Route 179 in Lambertville to the city , and Route 29 was rerouted to use all of Route 165 and one block of Route 179 . Prior to this , South Main Street had been turned one @-@ way southbound . Route 165 , which is only signed on overhead street signs , still exists , though it is fully concurrent with Route 29 .", "max_new_tokens": 48} +{"prompt": " had struck these bases extensively in November 1977 during Operation Dingo , destroying much of the ZANLA presence there , but the insurgents had since built a complex called \" New Chimoio \" , slightly to the east ; the new camps were distributed across a far larger area than the originals . In a combined airborne @-@ ground assault called Operation Snoopy , the Rhodesian Air Force , Rhodesian Light Infantry and Special Air Service wiped out much of New Chimoio on 20 September 1978 . Mozambique sent armour to ZANLA 's aid in the form of nine Soviet @-@ made T @-@ 54 tanks and four Russian BTR @-@ 152 armoured personnel carriers , but the former were routed and one of the latter destroyed by the Rhodesian security forces . According to Rhodesian figures , there were \" several hundred \" guerrillas killed , while the security forces lost only two soldiers , one of whom was accidentally killed by a friendly air strike .\n\nRhodesia then attacked ZIPRA 's bases in Zambia , in what Group Captain Peter Petter @-@ Bowyer later described as \" payback time \" for Flight 825 . Operation Gatling , launched on 19 October 1978 , was another joint @-@ force operation between the Air Force and the Army , which contributed Special Air Service and Rhodesian Light Infantry paratroopers . Gatling 's primary target , just 16 kilometres ( 10 miles ) north @-@ east of central Lusaka , was the formerly white @-@ owned Westlands Farm , which had been transformed into ZIPRA 's main headquarters and training base under the name \" Freedom Camp \" . ZIPRA presumed that Rhodesia would never dare to attack a site so close to Lusaka . About 4 @,@ 000 guerrillas underwent training at Freedom Camp , with senior ZIPRA staff also on site . The Rhodesian operation 's other targets were Chikumbi , 19 kilometres ( 12 miles ) north of Lusaka , and Mkushi Camp ; all three were to be attacked more or less simultaneously in a coordinated sweep across Zambia . Assaulting targets deep inside Zambia was a first for the Rhodesian forces ; previously only guerrillas near the border had been attacked .\n\nLed by Squadron Leader Chris Dixon , who identified himself to Lusaka Airport tower as \" Green Leader \" , a Rhodesian Air Force group flew into Zambia at very low altitudes ( thereby avoiding Zambian radar ) and took control of the country 's airspace for about a quarter of an hour during the initial assault on Westlands Farm , informing Lusaka tower that the attack was against \" Rhodesian dissidents , and not against Zambia \" , and that Rhodesian Hawker Hunters were circling the Zambian airfields under orders to shoot down any fighter that attempted to take off . The Zambians obeyed all of Green Leader 's instructions , made no attempt to resist and temporarily halted civil air traffic . Using Rufunsa airstrip in eastern Zambia as a forward base , the Rhodesian military suffered only minor casualties during the three @-@ day operation , and afterwards claimed to have killed over 1 @,@ 500 ZIPRA personnel , as well as some Cuban instructors .\n\nHistorians Paul Moorcraft and Peter McLaughlin write that this exaggerated considerably the actual number of guerrillas killed , as most of Nkomo 's army , then numbering about 10 @,@ 000 fighters , had not been touched . On the other hand , unarmed refugees often camped in or around insurgent positions , and hundreds of these had been killed in the Rhodesian raid . Moorcraft and McLaughlin comment that for the Rhodesian airmen , it would have been \" impossible to distinguish innocent refugees from young ZIPRA recruits . \" Sibanda describes Freedom Camp as \" a refugee camp for boys \" , and says \" 351 boys and girls \" were killed . He claims that the Red Cross and the UN Refugee Agency \" confirmed ZAPU 's claim that Smith 's forces struck at defenseless , civilian trainees \" .\n\nThe Rhodesian attacks on ZANLA and ZIPRA bases did much to restore white morale following the Viscount incident , though they had not actually made much impact on the respective guerrilla campaigns . Nkomo and the Zambian President Kenneth Kaunda all the same requested further military aid and better weapons from the Soviets and the British respectively . Martial law was quickly extended across Rhodesia 's rural areas , and covered three @-@ quarters of the country by the end of 1978 . Air Rhodesia , meanwhile , began developing anti @-@ Strela shielding for its Viscounts . Before this work was completed , ZIPRA shot down a second Viscount , Air Rhodesia Flight 827 , on 12 February 1979 . This time there were no survivors .\n\nFollowing the second shootdown , Air Rhodesia created a system whereby the underside of the Viscounts would be coated with low @-@ radiation paint , with the exhaust pipes concurrently shrouded . According to tests conducted by the Air Force , a Viscount so treated could not be detected by the Strela 's targeting system once it was over 2 @,@ 000 feet ( 610 m ) . There were no further Viscount shootdowns in Rhodesia .\n\nIn the elections held the following year under the Internal Settlement terms , boycotted by ZANU and ZAPU , Muzorewa won a majority , and became the first Prime Minister of the reconstituted , majority @-@ ruled state of Zimbabwe Rhodesia on 1 June 1979 . This new order failed to win international acceptance , however , and in December 1979 the Lancaster House Agreement was agreed in London by Zimbabwe Rhodesia , the UK government and the Patriotic Front , returning the country to its former colonial status . The UK government suspended the constitution and took direct control for an interim period . Fresh elections were won by Mugabe , who took power in April 1980 , concurrently with the country 's recognised independence as Zimbabwe .\n\nIn modern Zimbabwe , it is not the Viscount shootdowns but rather the retributory Rhodesian strikes against nationalist guerrilla camps that endure predominantly in the cultural memory . State media in Zimbabwe , prominently the Herald newspaper , often claim that Rhodesian forces indiscriminately and deliberately slaughtered thousands of helpless refugees during such operations . ZIPRA 's downing of civilian planes is meanwhile portrayed as a legitimate act of war on the grounds that the guerrillas might have believed them to have military personnel or equipment on board . The massacre of surviving Flight 825 passengers at the crash site is often omitted or attributed to forces other than ZIPRA ; in his 1984 memoirs Nkomo repeated his claim that ZIPRA fighters had helped the survivors , and wrote simply that \" I truly have no idea how the ten died \" .\n\nA monument to those killed in the Rhodesian attack on Chikumbi was built in Zambia in 1998 , and dedicated jointly by the Zambian and Zimbabwean governments . A memorial to the victims of the two Rhodesian Viscount incidents , dubbed the Viscount Memorial , was erected on the grounds of the Voortrekker Monument in Pretoria , South Africa in 2012 , and inaugurated on 1 September that year . The names of the", "max_new_tokens": 16} +{"prompt": " ( 310 km ) northeast of Reno , Nevada .\n\nThe range and surrounding non @-@ mountainous areas cover an area of 811 square miles ( 2 @,@ 100 km2 ) . The mountains run 51 miles ( 82 km ) north to south and 36 miles ( 58 km ) east to west . More of the range is in Oregon ( 78 % ) than in Nevada ( 22 % ) . The highest point in the range is Orevada View Benchmark , which is 8 @,@ 506 feet ( 2 @,@ 593 m ) above sea level and is located in Nevada about one mile south of the Oregon border . About two miles southeast of Orevada View is Disaster Peak , \" a large , symmetrical butte that is visible throughout the region . \" At 7 @,@ 781 feet ( 2 @,@ 372 m ) , Disaster Peak anchors the southern end of the mountains in a sub @-@ range called The Granites .\n\nThe Oregon Canyon Mountains border the Trout Creek Mountains on the east along the Harney – Malheur county line ( according to the United States Geological Survey 's definitions ) , while the Pueblo Mountains are the next range west of the Trout Creek Mountains . The Bilk Creek Mountains in both Oregon and Nevada border the Trout Creek Mountains on the southwest ; the two ranges are separated by Log Cabin Creek and South Fork Cottonwood Creek . South of the Trout Creek Mountains is the Kings River Valley , which separates the Bilk Creek Mountains on the west from the Montana Mountains on the east .\n\nThe terrain in the Trout Creek Mountains varies from broad , flat basins and rolling ridges to high rock escarpments cut by deep canyons . The canyons have steep walls with loose talus slopes at the bottoms . There are meadows around springs in the mountains , although most streams in the range do not flow year @-@ round . Major streams that flow off the north slopes of the mountains include ( from west to east ) Cottonwood Creek , Trout Creek , Willow Creek , and Whitehorse Creek . These streams all flow into endorheic basins in Harney County , Oregon . Trout Creek and Whitehorse Creek are the largest of the four . The Kings River and McDermitt Creek each drain an area on the south slopes of the Trout Creek Mountains . The Kings River begins in The Granites and flows south into Nevada , where it meets the Quinn River , which evaporates in the Black Rock Desert . McDermitt Creek begins in Oregon a few miles north of The Granites and flows generally east , crossing the Oregon – Nevada border five times before disappearing into the floor of the Quinn River Valley south of McDermitt .\n\nThe mountains lie within the Basin and Range Province or Great Basin of the Western United States , which is characterized by a series of parallel fault blocks that form long north – south mountain ranges separated by wide , high @-@ desert valleys . The Trout Creek Mountains are uplifted and tilted blocks with steep escarpments along the southern and eastern sides of the range . The southern area of the range , known as The Granites , has numerous outcroppings of Cretaceous age granite . These granite outcrops are commonly found in the eroded valleys below the volcanic ridgelines .\n\nThe Trout Creek Mountains are composed mostly of basalt from a shield volcano that once stood where Steens Mountain is today . Crustal thinning and the Yellowstone hotspot , which was then beneath southeastern Oregon , induced eruptions from Steens and nearby vents about 17 million years ago , in the Miocene . The vents produced a series of lava flows that spread across the land now known as the Trout Creek Mountains . Eruptions from the Steens volcano lasted for about one million years , and at least 70 separate lava flows occurred . Under the resulting basalt rock lie much older metamorphic rocks that may be related to some of the Triassic age formations of the Blue Mountains in northeastern Oregon . Within these metamorphic rocks are diorite and granodiorite intrusive bodies which were presumably intruded during the Cretaceous Period .\n\nThe broad McDermitt Caldera is a prominent geologic feature in the Trout Creek Mountains . The oval @-@ shaped caldera is a collapsed lava dome that straddles the Oregon – Nevada border on the eastern side of the range and south of the Oregon Canyon Mountains . It is about 28 miles ( 45 km ) long and 22 miles ( 35 km ) wide . The lava dome was created by volcanic eruptions in the early Miocene . A total of five large ash flows were produced along with a large rhyolite dome structure . The caldera formed when the dome collapsed about 16 million years ago . The caldera contains significant ore deposits , and mercury and uranium have been mined at eight or more sites in and around the caldera . Other areas in the caldera were mined for ores of antimony , cesium , and lithium .\n\nThe Trout Creek Mountains are semi @-@ arid because they are in the eastern rain shadow of mountain ranges to the west . When moist air from the Pacific Ocean moves eastward over the Oregon and California coastal ranges and the Cascade Range , most precipitation falls in those mountains before reaching the Trout Creek Mountains . As a result , the average annual precipitation in the Trout Creek Mountains is only 8 to 26 inches ( 200 to 660 mm ) per year , with most areas receiving between 8 and 12 inches ( 200 and 300 mm ) annually . Much of the annual precipitation occurs between the beginning of March and the end of June . Most of the rest falls as snow during the fall and winter months . Snowpack at elevations below 6 @,@ 000 feet ( 1 @,@ 800 m ) usually melts by April ; however , at the higher elevations , snow can remain until mid @-@ June . Local flooding often occurs in the spring as the snowpack melts .\n\nThe prevailing winds are from the west @-@ southwest , and they are normally strongest in March and April . Brief , intense thunderstorms are common between April and October . Thunderstorms in the summer months tend to be more isolated and often produce dry lightning strikes .\n\nVegetation in the Trout Creek Mountains is dominated by big sagebrush and desert grasses . Other common shrubs include bitterbrush , snowberry , and Ceanothus . There are also patches of mountain mahogany in some areas . Common grass species include Idaho fescue , bluebunch wheatgrass , cheatgrass , western needlegrass , Sandberg 's bluegrass , Thurber 's needlegrass , and bottlebrush squirreltail , as well as basin wildrye in some well @-@ drained areas .\n\nLess than one percent of the range consists of meadow wetlands and riparian greenways ( vegetation along stream banks ) . However , these areas are vital to the local ecosystem . The meadows surround springs , which are mostly on gently sloping uplands or in stream bottoms , and range in size from about 1 to 5 acres ( 0 @.@ 40 to 2 @.@ 02 ha ) . Narrow riparian greenways follow the year @-@ round streams . Many greenway areas have quaking aspen and willow groves . Cottonwood and alder groves can be found at lower elevations where terrain is flatter and stream channels are wider . Sedges and rushes are also native to these stream bottoms . Years of heavy livestock grazing in parts of the range resulted in the loss of some grass species , riparian vegetation , and young aspen and willow trees . Restoration of riparian areas began in the early 1970s , and plans to reduce grazing were implemented in the 1980s and early 1990s . However , large wildfires in southeastern Oregon during the summer of 2012 burned much of the range 's vegetation , damaging riparian ecosystems and killing hundreds of grazing cattle .\n\nAnimals in the Trout Creek Mountains are adapted to the environment of the High Desert . Pronghorn are common in the open , sagebrush @-@ covered basins , while mule deer live in the cottonwood and willow groves . There are also bighorn sheep , cougars , and bobcats in the high country . Jackrabbits and coyotes are prevalent throughout the range . Mustangs sometimes pass through the mountains as they roam the Great Basin . Some other mammals include the northern pocket gopher , mountain cottontail , and Belding 's ground squirrel . North American beavers live in and along streams , as do Pacific tree frogs , western spadefoot toads , and garter snakes . Native bird species include the sage grouse , mountain chickadee , gray @-@ headed junco , black @-@ throated gray warbler , Virginia 's warbler , MacGillivray 's warbler , pine siskin , red crossbill , bushtit , hermit thrush , northern goshawk , and species of raven and eagle .\n\nSeveral streams in the Trout Creek Mountains are home to trout , including the rare Lahontan cutthroat trout subspecies . These include Willow Creek , Whitehorse Creek , Little Whitehorse Creek , Doolittle Creek , Fifteen Mile Creek , Indian Creek , Sage Canyon Creek , Line Canyon Creek , and some tributaries of McDermitt Creek . Lahontan cutthroat trout live in small , isolated populations that are often confined to individual streams , many of them in the Trout Creek Mountains . These populations have significant genetic differences due to their history of isolation . For most of the 20th century , the trout 's numbers declined considerably . It was listed under federal law as an endangered species in 1970 and was reclassified as threatened in 1975 . Reasons for the fish 's decline included habitat degradation from cattle grazing , drought", "max_new_tokens": 32} +{"prompt": "jay Dutt , and Tomko all appeared on camera , either in backstage or in ringside segments . The wedding of Lethal and Val took place after the second $ 25 @,@ 000 Fan Challenge . The segment ended when Dutt proclaimed his love for Val in the storyline and attacked Lethal . Young , Steele , Roberts , Kamala , and Ware came to Lethal 's defense before Roberts placed a snake on top of an unconscious Dutt to end the segment .\n\nThe TNA X Division Championship was defended by then @-@ champion Petey Williams against Kaz in the opening contest of the telecast . Williams was accompanied by Rhaka Khan and Scott Steiner . The duration of the bout was 15 minutes and 19 seconds . Williams wore a protective face mask due to a broken orbital bone . Khan and Steiner interfered in the bout several times . Kaz was legitimately busted open during the match after Williams bashed him in the face with a steel pipe . Williams won the encounter after performing his signature Canadian Destroyer maneuver by slamming Kaz head @-@ first into the mat to retain the TNA X Division Championship . Abyss returned after this match by aiding Kaz who was being attacked by Khan , Steiner , and Williams .\n\nThe team of The Beautiful People and Mickie Knuckles — renamed Moose — fought the team of Gail Kim , ODB , and Roxxi in a Six Woman Tag Team match next . It lasted 10 minutes and 14 seconds . ODB won the bout for her team after running and slamming Moose against the mat back @-@ first and following with the pin .\n\nThe TNA World Tag Team Championship was defended in the third match of the event by LAX against Team 3D . Héctor Guerrero and Salinas accompanied LAX to the ring . Salinas , Guerrero , and Team 3D 's associate Johnny Devine all interfered in the encounter . Near the end when Team 3D were waiting to perform their signature 3D tag team maneuver on Hernandez , Homicide came up behind Devon and pinned him with a schoolboy pin at 15 minutes to retain the championship .\n\nTNA held two $ 25 @,@ 000 Fan Challenge segments next with then @-@ TNA Women 's Knockout Champion Awesome Kong and her manager Raisha Saeed offering fans in the crowd the chance to face Kong . Kong and Saeed then chose a willing participant and if they defeated Kong they won $ 25 @,@ 000 . The participants chosen for these segments were predetermined , with each planted in the crowd . The first encounter pitted Serena D against Kong , which Kong won after slamming Serena face @-@ first into the mat with her signature Implant Buster maneuver at 2 minutes and 26 seconds . The second was between Josie Robinson and Kong , lasting 1 minute and 42 seconds . Kong won the bout after slamming Josie back @-@ first into the mat with her signature Awesome Bomb maneuver .\n\nA.J. Styles fought Kurt Angle in the sixth encounter of the show next . The match lasted 22 minutes and 44 seconds . Angle was accompanied by Tomko to the ring . The referee forced Tomko to head to the backstage area due to the referee believing he would interfere . Styles attempted to perform his signature Styles Clash maneuver on Angle , which Angle countered into his signature Ankle Lock submission hold . Styles escaped the hold , causing Angle to crash into the referee , knocking him out in the storyline . Karen Angle then ran down to the ringside area and attempted to hand Angle a steel chair . Once Angle grabbed the chair she refused to release , allowing Styles to grab Angle and perform the Styles Clash by slamming Angle face @-@ first into the mat . Styles then followed with the pin attempt to win the contest . After the encounter , Angle and Tomko assaulted Styles .\n\nThe main event was a King of the Mountain match for the TNA World Heavyweight Championship with Kevin Nash as Special Guest Ringside Enforcer between then @-@ champion Samoa Joe and the challengers Booker T , Christian Cage , Rhino , and Robert Roode . In a King of the Mountain match , the objective is to climb a ladder and hang the championship belt on a hook above the ring . In order to do so , a wrestler must first qualify by either pinning or making another wrestler submit in the match . A wrestler who is pinned or made to submit is this placed in a penalty box for two minutes . The match is fought under no disqualification rules . Booker T was the first to qualify by pinning Rhino after slamming him back @-@ first into the mat with his signature Book End maneuver . Rhino was this placed in the penalty box . Roode was the next to qualify after bashing a ladder Cage held with a chair into him and following with a pin . Immediately afterwards , Rhino pinned Roode with a roll @-@ up pin to qualify . Later , Joe held Booker T in his signature Coquina Clutch submission hold when Cage ascended a padded turnbuckle and jumped off onto Booker T and Joe . He followed by pinning Booker T to qualify and send Booker T to the penalty box . When Booker T was released from the box , he attacked Nash with the title belt and then attempted to hang it . Nash stopped him and then dropped Booker T back @-@ first against the mat with his signature Jackknife Powerbomb maneuver . Joe was the last to qualify by pinning Roode after slamming him back and neck @-@ first into the mat with his signature Muscle Buster maneuver . Joe then hung the title belt at 19 minutes and 49 seconds to win the competition .\n\nA total of 2 @,@ 000 people attended Slammiversary , while the The Wrestling Observer Newsletter reported that 20 @,@ 000 people bought the event . Canadian Online Explorer writer Jon Waldman rated the entire event a 7 out of 10 , which was lower than the 8 out of 10 given to the 2007 edition by Jason Clevett . The 7 out of 10 was the same rating given to the 2009 edition by Chris and Bryan Sokol . The previous PPV event Sacrifice and TNA 's next PPV event Victory Road both received a 7 out of 10 by Chris Sokol and Bob Kapur , respectively . Compared to rival World Wrestling Entertainment 's ( WWE ) One Night Stand PPV event on June 1 , both performed equally , as One Night Stand received the same rating from Matt Mackinder .\n\nWaldman felt that \" TNA put on a strong PPV \" that was in \" large part to little gimmickery . \" Regarding the main event , Waldman gave it a 6 out of 10 , while the World Tag Team Championship match received a 4 out of 10 . He gave the Six Woman Tag Team match a 4 @.@ 5 out of 10 and the A.J. Styles versus Kurt Angle bout a 9 out of 10 . The X Division Championship contest was given a 7 out of 10 .\n\nWade Keller of the Pro Wrestling Torch Newsletter reviewed the show . He felt the main event had \" lots of action and drama , \" but that having to hang the belt on a hook does not make for a \" particularly dramatic end to a match as it seems like someone hanging a plant in the corner \" than \" beating someone to win a fight . \" Keller said the World Tag Team Championship contest was a \" solid tag match \" with some \" elaborate sequences \" that were \" well executed . \" Keller stated that the X Division Championship match was a \" very good opener . \" Regarding the Styles and Angle bout , Keller commented that it was a \" good match , but short of a classic must @-@ see match in part because there were too many chinlocks mid @-@ match from Angle that took the crowd out of it . \"\n\nJames Caldwell , also of the Pro Wrestling Torch Newsletter , posted a review of the show in which he felt the main event was a \" slow , plodding match \" with an \" anticlimactic finish . \" Caldwell stated that the Styles versus Angle contest was a \" fine spotlight singles match , \" but was a \" bit underwhelming when the expectations were reasonably high for two of TNA 's best wrestlers in a featured singles match . \" He went on to say that he felt it was \" missing something \" and that it \" seemed like the match just never moved out of second gear . Caldwell felt the X Division Championship bout was a \" very good opening match , \" which \" could have been a featured match to sell a few additional PPV buys , but TNA didn 't give the X Division any focus until the final show before the PPV . \" Caldwell stated the World Tag Team Championship was \" just a slow , plodding tag match . \" Regarding the marriage segment during the show , Caldwell said \" it was just a bad , bad , bad segment that died slowly and painfully in front of the live audience . \" Overall , Caldwell felt that \" TNA showcased the X Division in the opening match , \" but needed more \" athleticism on the show to balance the slow , plodding former WWE heavyweight wrestlers in the main event slots . \"\n\nAfter Slammiversary ended , crews began to disassemble the set used for the event . During this time , a man named Kevin \" Angus \" Sinex was removing a light array when the scaffolding he was positioned on collapsed , causing him to slam against the concrete floor below . He was rushed to the Baptist DeSoto Hospital in Southaven , Mississippi where he was pronounced deceased . Another worker named Paul Martin was also taken to the hospital due to his thumb being cut off in the incident . TNA released a statement regarding the incident the next day through their", "max_new_tokens": 16} diff --git a/src/lab5/pyproject.toml b/src/lab5/pyproject.toml index 39f89447..0fc2b7a3 100644 --- a/src/lab5/pyproject.toml +++ b/src/lab5/pyproject.toml @@ -21,6 +21,14 @@ hpc101-quantize = "hpc101_infer.quantization.cli:main" requires = ["hatchling"] build-backend = "hatchling.build" +[tool.uv.sources] +torch = { index = "pytorch" } + +[[tool.uv.index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cu132" +explicit = true + [tool.hatch.build.targets.wheel] packages = ["src/hpc101_infer"] diff --git a/src/lab5/scripts/run_generation_queue.py b/src/lab5/scripts/run_generation_queue.py index a14c9029..2b9baa41 100644 --- a/src/lab5/scripts/run_generation_queue.py +++ b/src/lab5/scripts/run_generation_queue.py @@ -1,33 +1,31 @@ import argparse import json import sys -from dataclasses import asdict +from dataclasses import asdict, replace from pathlib import Path from time import perf_counter -from typing import TextIO +from typing import Sequence, TextIO -import torch from tqdm.auto import tqdm from hpc101_infer import ( EngineConfig, GenerationRequest, - Runner, InferenceEngine, + Runner, SamplingParams, ) - -DTYPES = { - "bfloat16": torch.bfloat16, - "float16": torch.float16, - "float32": torch.float32, -} +from hpc101_infer.config import DTYPES -def parse_args() -> argparse.Namespace: +def build_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser( description="Run queued static-batch generation from a JSONL request stream." ) + parser.add_argument( + "--config", + help="YAML configuration path. Explicit command-line options take precedence.", + ) parser.add_argument("--model", required=True, help="Model checkpoint directory.") parser.add_argument( "--input", @@ -39,16 +37,37 @@ def parse_args() -> argparse.Namespace: default="-", help="Output JSONL path, or - to write to stdout.", ) + parser.add_argument( + "--summary-output", + help="Optional path for the aggregate summary JSON.", + ) parser.add_argument("--device", default="cuda") parser.add_argument("--dtype", choices=DTYPES, default="bfloat16") parser.add_argument("--batch-size", type=int, default=1) + parser.add_argument( + "--max-batch-size", + type=int, + help="KV cache batch capacity. Defaults to --batch-size.", + ) parser.add_argument("--max-sequence-length", type=int, default=4096) + parser.add_argument("--attention-backend", choices=("eager",), default="eager") + parser.add_argument( + "--linear-backend", + choices=("bf16", "int4_reference"), + default="bf16", + ) + parser.add_argument( + "--scheduler-backend", + choices=("static_batch",), + default="static_batch", + ) parser.add_argument("--max-new-tokens", type=int, default=32) parser.add_argument("--seed", type=int, default=0) parser.add_argument( - "--no-synchronize-metrics", - action="store_true", - help="Do not synchronize the device around individual metric regions.", + "--synchronize-metrics", + action=argparse.BooleanOptionalAction, + default=True, + help="Synchronize the device around individual metric regions.", ) parser.add_argument( "--progress", @@ -56,7 +75,113 @@ def parse_args() -> argparse.Namespace: default=True, help="Show request completion progress.", ) - return parser.parse_args() + return parser + + +def _option_is_present(arguments: Sequence[str], option: str) -> bool: + return any( + argument == option or argument.startswith(f"{option}=") + for argument in arguments + ) + + +def _dtype_name(config: EngineConfig) -> str: + return next(name for name, dtype in DTYPES.items() if dtype == config.dtype) + + +def _boolean_option_is_present(arguments: Sequence[str], option: str) -> bool: + negative_option = f"--no-{option.removeprefix('--')}" + return _option_is_present(arguments, option) or _option_is_present( + arguments, negative_option + ) + + +def _engine_overrides( + args: argparse.Namespace, + command_line_arguments: Sequence[str], +) -> dict[str, object]: + overrides: dict[str, object] = {} + if _option_is_present(command_line_arguments, "--device"): + overrides["device"] = args.device + if _option_is_present(command_line_arguments, "--dtype"): + overrides["dtype"] = DTYPES[args.dtype] + if _option_is_present(command_line_arguments, "--batch-size"): + overrides["scheduler_batch_size"] = args.batch_size + if not _option_is_present(command_line_arguments, "--max-batch-size"): + overrides["max_batch_size"] = args.batch_size + if _option_is_present(command_line_arguments, "--max-batch-size"): + overrides["max_batch_size"] = args.max_batch_size + if _option_is_present(command_line_arguments, "--max-sequence-length"): + overrides["max_sequence_length"] = args.max_sequence_length + if _option_is_present(command_line_arguments, "--attention-backend"): + overrides["attention_backend"] = args.attention_backend + if _option_is_present(command_line_arguments, "--linear-backend"): + overrides["linear_backend"] = args.linear_backend + if _option_is_present(command_line_arguments, "--scheduler-backend"): + overrides["scheduler_backend"] = args.scheduler_backend + if _option_is_present(command_line_arguments, "--seed"): + overrides["seed"] = args.seed + if _boolean_option_is_present(command_line_arguments, "--synchronize-metrics"): + overrides["synchronize_metrics"] = args.synchronize_metrics + return overrides + + +def _set_engine_argument_values( + args: argparse.Namespace, + config: EngineConfig, +) -> None: + args.device = config.device + args.dtype = _dtype_name(config) + args.batch_size = config.scheduler_batch_size + args.max_batch_size = config.max_batch_size + args.max_sequence_length = config.max_sequence_length + args.attention_backend = config.attention_backend + args.linear_backend = config.linear_backend + args.scheduler_backend = config.scheduler_backend + args.seed = config.seed + args.synchronize_metrics = config.synchronize_metrics + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + command_line_arguments = list(sys.argv[1:] if argv is None else argv) + config_parser = argparse.ArgumentParser(add_help=False) + config_parser.add_argument("--config") + config_args, _ = config_parser.parse_known_args(command_line_arguments) + + engine_config = EngineConfig() + parser = build_parser() + if config_args.config is not None: + try: + engine_config = EngineConfig.from_yaml(config_args.config) + except (OSError, ValueError) as error: + parser.error(f"cannot load --config {config_args.config!r}: {error}") + + parser.set_defaults( + device=engine_config.device, + dtype=_dtype_name(engine_config), + batch_size=engine_config.scheduler_batch_size, + max_batch_size=engine_config.max_batch_size, + max_sequence_length=engine_config.max_sequence_length, + attention_backend=engine_config.attention_backend, + linear_backend=engine_config.linear_backend, + scheduler_backend=engine_config.scheduler_backend, + seed=engine_config.seed, + synchronize_metrics=engine_config.synchronize_metrics, + ) + args = parser.parse_args(command_line_arguments) + try: + engine_config = replace( + engine_config, + **_engine_overrides(args, command_line_arguments), + ) + except ValueError as error: + parser.error(str(error)) + args.engine_config = engine_config + _set_engine_argument_values(args, engine_config) + + if args.max_new_tokens < 0: + parser.error("--max-new-tokens must be non-negative") + return args def open_input(path: str) -> TextIO: @@ -73,6 +198,15 @@ def open_output(path: str) -> TextIO: return output_path.open("w") +def write_summary(path: str, summary: dict[str, object]) -> None: + summary_path = Path(path) + summary_path.parent.mkdir(parents=True, exist_ok=True) + summary_path.write_text( + json.dumps(summary, ensure_ascii=False) + "\n", + encoding="utf-8", + ) + + def count_requests(path: str) -> int | None: if path == "-": return None @@ -124,15 +258,7 @@ def iter_requests( def main() -> None: args = parse_args() - config = EngineConfig( - dtype=DTYPES[args.dtype], - device=args.device, - max_batch_size=args.batch_size, - scheduler_batch_size=args.batch_size, - max_sequence_length=args.max_sequence_length, - seed=args.seed, - synchronize_metrics=not args.no_synchronize_metrics, - ) + config = args.engine_config engine = InferenceEngine.from_pretrained(args.model, config) runner = Runner(engine) @@ -172,21 +298,17 @@ def main() -> None: output_stream.close() generated_tokens = sum(output.generated_tokens for output in outputs) - print( - json.dumps( - { - "requests": len(outputs), - "generated_tokens": generated_tokens, - "elapsed_s": elapsed, - "requests_per_s": len(outputs) / elapsed if elapsed else 0.0, - "generated_tokens_per_s": ( - generated_tokens / elapsed if elapsed else 0.0 - ), - "batch_size": args.batch_size, - } - ), - file=sys.stderr, - ) + summary = { + "requests": len(outputs), + "generated_tokens": generated_tokens, + "elapsed_s": elapsed, + "requests_per_s": len(outputs) / elapsed if elapsed else 0.0, + "generated_tokens_per_s": generated_tokens / elapsed if elapsed else 0.0, + "batch_size": args.batch_size, + } + print(json.dumps(summary), file=sys.stderr) + if args.summary_output is not None: + write_summary(args.summary_output, summary) if __name__ == "__main__": diff --git a/src/lab5/src/hpc101_infer/config.py b/src/lab5/src/hpc101_infer/config.py index 4c429152..502ccf87 100644 --- a/src/lab5/src/hpc101_infer/config.py +++ b/src/lab5/src/hpc101_infer/config.py @@ -1,8 +1,73 @@ from __future__ import annotations -from dataclasses import dataclass +from collections.abc import Mapping +from dataclasses import dataclass, fields +from pathlib import Path import torch +import yaml + +DTYPES = { + "bfloat16": torch.bfloat16, + "float16": torch.float16, + "float32": torch.float32, +} + + +def _load_yaml_mapping(path: str | Path) -> dict[str, object]: + try: + with Path(path).open() as stream: + raw = yaml.safe_load(stream) + except yaml.YAMLError as error: + raise ValueError(f"invalid YAML: {error}") from error + if raw is None: + return {} + if not isinstance(raw, dict): + raise ValueError("configuration root must be a YAML mapping") + return raw + + +def _require_mapping(value: object, location: str) -> Mapping[str, object]: + if value is None: + return {} + if not isinstance(value, dict): + raise ValueError(f"{location} must be a YAML mapping") + return value + + +def _reject_unknown_options( + values: Mapping[str, object], + allowed: set[str], + location: str, +) -> None: + unknown = set(values) - allowed + if unknown: + names = ", ".join(sorted(map(str, unknown))) + raise ValueError(f"unknown {location} option(s): {names}") + + +def _optional_string(value: object, location: str) -> str | None: + if value is None: + return None + if not isinstance(value, str): + raise ValueError(f"{location} must be a string") + return value + + +def _optional_integer(value: object, location: str) -> int | None: + if value is None: + return None + if not isinstance(value, int) or isinstance(value, bool): + raise ValueError(f"{location} must be an integer") + return value + + +def _optional_boolean(value: object, location: str) -> bool | None: + if value is None: + return None + if not isinstance(value, bool): + raise ValueError(f"{location} must be true or false") + return value @dataclass(frozen=True) @@ -18,6 +83,60 @@ class EngineConfig: seed: int = 0 synchronize_metrics: bool = True + @classmethod + def from_mapping(cls, raw: Mapping[str, object]) -> EngineConfig: + allowed_fields = { + config_field.name for config_field in fields(cls) if config_field.init + } + _reject_unknown_options(raw, allowed_fields, "config.engine") + values: dict[str, object] = { + key: value for key, value in raw.items() if value is not None + } + + dtype = raw.get("dtype") + if dtype is not None: + if not isinstance(dtype, str) or dtype not in DTYPES: + choices = ", ".join(DTYPES) + raise ValueError(f"config.engine.dtype must be one of: {choices}") + values["dtype"] = DTYPES[dtype] + + for key in ( + "device", + "attention_backend", + "linear_backend", + "scheduler_backend", + ): + value = _optional_string(raw.get(key), f"config.engine.{key}") + if value is not None: + values[key] = value + + for key in ( + "max_batch_size", + "scheduler_batch_size", + "max_sequence_length", + "seed", + ): + value = _optional_integer(raw.get(key), f"config.engine.{key}") + if value is not None: + values[key] = value + + synchronize_metrics = _optional_boolean( + raw.get("synchronize_metrics"), + "config.engine.synchronize_metrics", + ) + if synchronize_metrics is not None: + values["synchronize_metrics"] = synchronize_metrics + + if "scheduler_batch_size" in values and "max_batch_size" not in values: + values["max_batch_size"] = values["scheduler_batch_size"] + return cls(**values) + + @classmethod + def from_yaml(cls, path: str | Path) -> EngineConfig: + raw = _load_yaml_mapping(path) + engine = _require_mapping(raw.get("engine"), "config.engine") + return cls.from_mapping(engine) + def __post_init__(self) -> None: if self.max_batch_size <= 0: raise ValueError("max_batch_size must be positive") diff --git a/src/lab5/src/hpc101_infer/engine.py b/src/lab5/src/hpc101_infer/engine.py index 2bd73b24..cd16f16c 100644 --- a/src/lab5/src/hpc101_infer/engine.py +++ b/src/lab5/src/hpc101_infer/engine.py @@ -84,7 +84,7 @@ def _validate_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: @torch.inference_mode() def prefill( - self, input_ids: torch.Tensor, max_seq_len: int | None = None + self, input_ids: torch.Tensor, batch_max_length: int | None = None ) -> PrefillOutput: """ 处理完整 prompt，并把每层 K/V 写入一个全新的 cache。 @@ -104,7 +104,7 @@ def prefill( input_ids=input_ids, positions=positions, sequence_lengths=sequence_lengths, - curr_max_seq_len=max_seq_len or query_length, + curr_max_seq_len=batch_max_length or query_length, mode="prefill", ) with measure_operation(self.device, self.config.synchronize_metrics) as metrics: @@ -123,7 +123,7 @@ def prefill( def decode_step( self, token_ids: torch.Tensor, - max_seq_len: int, + batch_max_length: int, active: torch.Tensor | None = None, ) -> DecodeOutput: """ @@ -153,7 +153,7 @@ def decode_step( input_ids=token_ids, positions=positions, sequence_lengths=next_lengths, - curr_max_seq_len=max_seq_len, + curr_max_seq_len=batch_max_length, mode="decode", ) with measure_operation(self.device, self.config.synchronize_metrics) as metrics: @@ -232,7 +232,7 @@ def generate(self, requests: list[GenerationRequest]) -> list[GenerationOutput]: if prefill_schedule.mode != "prefill": raise RuntimeError("scheduler must begin with a prefill schedule") prefill_output = self.prefill( - input_ids, max_seq_len=prefill_schedule.max_seq_len + input_ids, batch_max_length=prefill_schedule.batch_max_length ) logits = prefill_output.logits @@ -272,7 +272,9 @@ def generate(self, requests: list[GenerationRequest]) -> list[GenerationOutput]: ) decode = self.decode_step( - token_ids, max_seq_len=decode_schedule.max_seq_len, active=active + token_ids, + batch_max_length=decode_schedule.batch_max_length, + active=active, ) logits = decode.logits diff --git a/src/lab5/src/hpc101_infer/quantization/cli.py b/src/lab5/src/hpc101_infer/quantization/cli.py index f195bc87..2baf995a 100644 --- a/src/lab5/src/hpc101_infer/quantization/cli.py +++ b/src/lab5/src/hpc101_infer/quantization/cli.py @@ -1,9 +1,13 @@ from __future__ import annotations import argparse +import sys from collections.abc import Sequence +from dataclasses import dataclass, replace +from pathlib import Path import torch +import yaml from transformers import AutoTokenizer from hpc101_infer.data import load_token_ids @@ -26,19 +30,116 @@ def _fraction(value: str) -> float: return parsed -def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: +@dataclass(frozen=True) +class CalibrationInputConfig: + path: str | None = None + limit: int | None = None + micro_batch_size: int = 1 + max_tokens: int = 4096 + + @classmethod + def from_yaml(cls, path: str | Path) -> CalibrationInputConfig: + try: + with Path(path).open() as stream: + raw = yaml.safe_load(stream) + except yaml.YAMLError as error: + raise ValueError(f"invalid YAML: {error}") from error + if raw is None: + return cls() + if not isinstance(raw, dict): + raise ValueError("configuration root must be a YAML mapping") + calibration = raw.get("calibration", {}) + if calibration is None: + return cls() + if not isinstance(calibration, dict): + raise ValueError("config.calibration must be a YAML mapping") + + allowed = {"path", "limit", "micro_batch_size", "max_tokens"} + unknown = set(calibration) - allowed + if unknown: + names = ", ".join(sorted(map(str, unknown))) + raise ValueError(f"unknown config.calibration option(s): {names}") + + values: dict[str, object] = {} + calibration_path = calibration.get("path") + if calibration_path is not None: + if not isinstance(calibration_path, str): + raise ValueError("config.calibration.path must be a string") + values["path"] = calibration_path + for key in ("limit", "micro_batch_size", "max_tokens"): + value = calibration.get(key) + if value is None: + continue + if not isinstance(value, int) or isinstance(value, bool) or value <= 0: + raise ValueError(f"config.calibration.{key} must be a positive integer") + values[key] = value + return cls(**values) + + +def _option_is_present(arguments: Sequence[str], option: str) -> bool: + return any( + argument == option or argument.startswith(f"{option}=") + for argument in arguments + ) + + +def _symmetric_option_is_present(arguments: Sequence[str]) -> bool: + return any( + _option_is_present(arguments, option) + for option in ("--symmetric", "--no-symmetric", "--asymmetric") + ) + + +def _gptq_defaults(config: QuantizationConfig) -> tuple[int, float]: + calibration = config.calibration or {} + gptq = calibration.get("gptq", {}) + if not isinstance(gptq, dict): + raise ValueError("config.quantization.calibration.gptq must be a mapping") + block_size = gptq.get("block_size", 128) + damp_percent = gptq.get("damp_percent", 0.01) + if ( + not isinstance(block_size, int) + or isinstance(block_size, bool) + or block_size <= 0 + ): + raise ValueError("GPTQ block_size must be a positive integer") + if ( + not isinstance(damp_percent, (int, float)) + or isinstance(damp_percent, bool) + or not 0.0 < float(damp_percent) < 1.0 + ): + raise ValueError("GPTQ damp_percent must be in (0, 1)") + return block_size, float(damp_percent) + + +def build_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser(description="Create a W4A16 Gemma 4 checkpoint") + parser.add_argument( + "--config", + help="YAML configuration path. Explicit command-line options take precedence.", + ) parser.add_argument("--model", required=True, help="source Hugging Face checkpoint") parser.add_argument("--output", required=True, help="output checkpoint directory") parser.add_argument( "--algorithm", choices=available_quantization_methods(), default="rtn" ) parser.add_argument("--group-size", type=int, choices=(64, 128), default=128) - parser.add_argument("--asymmetric", action="store_true") + parser.add_argument( + "--symmetric", + action=argparse.BooleanOptionalAction, + default=True, + help="Use symmetric weight quantization.", + ) + parser.add_argument( + "--asymmetric", + dest="symmetric", + action="store_false", + help="Alias for --no-symmetric.", + ) parser.add_argument( "--scale-dtype", choices=("float16", "bfloat16"), default="float16" ) - parser.add_argument("--device", default="cpu") + parser.add_argument("--device", default="cuda") parser.add_argument("--max-shard-size-mib", type=int, default=1024) parser.add_argument( "--calibration-file", help="path to calibration JSONL text file" @@ -72,28 +173,107 @@ def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: parser.add_argument( "--verbose", action="store_true", help="enable verbose loss printing" ) - return parser.parse_args(argv) + return parser + + +def _quantization_overrides( + args: argparse.Namespace, + command_line_arguments: Sequence[str], +) -> dict[str, object]: + overrides: dict[str, object] = {"source_checkpoint": args.model} + if _option_is_present(command_line_arguments, "--algorithm"): + overrides["algorithm"] = args.algorithm + if _option_is_present(command_line_arguments, "--group-size"): + overrides["group_size"] = args.group_size + if _symmetric_option_is_present(command_line_arguments): + overrides["symmetric"] = args.symmetric + if _option_is_present(command_line_arguments, "--scale-dtype"): + overrides["scale_dtype"] = args.scale_dtype + return overrides + + +def _merge_quantization_config( + base_config: QuantizationConfig, + args: argparse.Namespace, + command_line_arguments: Sequence[str], +) -> QuantizationConfig: + config = replace( + base_config, + **_quantization_overrides(args, command_line_arguments), + ) + if config.algorithm != "gptq": + return config + + calibration = dict(config.calibration or {}) + gptq = dict(calibration.get("gptq", {})) + gptq["block_size"] = args.gptq_block_size + gptq["damp_percent"] = args.gptq_damp_percent + calibration["gptq"] = gptq + return replace(config, calibration=calibration) + + +def _set_quantization_argument_values( + args: argparse.Namespace, + config: QuantizationConfig, +) -> None: + args.algorithm = config.algorithm + args.group_size = config.group_size + args.symmetric = config.symmetric + args.asymmetric = not config.symmetric + args.scale_dtype = config.scale_dtype + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + command_line_arguments = list(sys.argv[1:] if argv is None else argv) + config_arguments = command_line_arguments + + config_parser = argparse.ArgumentParser(add_help=False) + config_parser.add_argument("--config") + config_args, _ = config_parser.parse_known_args(config_arguments) + + quantization_config = QuantizationConfig() + calibration_config = CalibrationInputConfig() + parser = build_parser() + if config_args.config is not None: + try: + quantization_config = QuantizationConfig.from_yaml(config_args.config) + calibration_config = CalibrationInputConfig.from_yaml(config_args.config) + except (OSError, ValueError) as error: + parser.error(f"cannot load --config {config_args.config!r}: {error}") + + try: + gptq_block_size, gptq_damp_percent = _gptq_defaults(quantization_config) + except ValueError as error: + parser.error(str(error)) + parser.set_defaults( + algorithm=quantization_config.algorithm, + group_size=quantization_config.group_size, + symmetric=quantization_config.symmetric, + scale_dtype=quantization_config.scale_dtype, + gptq_block_size=gptq_block_size, + gptq_damp_percent=gptq_damp_percent, + calibration_file=calibration_config.path, + calibration_limit=calibration_config.limit, + calibration_micro_batch_size=calibration_config.micro_batch_size, + max_calibration_tokens=calibration_config.max_tokens, + ) + args = parser.parse_args(command_line_arguments) + try: + quantization_config = _merge_quantization_config( + quantization_config, + args, + config_arguments, + ) + except (TypeError, ValueError) as error: + parser.error(str(error)) + args.quantization_config = quantization_config + _set_quantization_argument_values(args, quantization_config) + return args def main() -> None: args = parse_args() - calibration = None - if args.algorithm == "gptq": - calibration = { - "gptq": { - "block_size": args.gptq_block_size, - "damp_percent": args.gptq_damp_percent, - } - } - - config = QuantizationConfig( - algorithm=args.algorithm, - group_size=args.group_size, - symmetric=not args.asymmetric, - scale_dtype=args.scale_dtype, - calibration=calibration, - source_checkpoint=args.model, - ) + config = args.quantization_config calibration_input_ids = None if args.calibration_file: diff --git a/src/lab5/src/hpc101_infer/quantization/config.py b/src/lab5/src/hpc101_infer/quantization/config.py index 31a6d984..db34be25 100644 --- a/src/lab5/src/hpc101_infer/quantization/config.py +++ b/src/lab5/src/hpc101_infer/quantization/config.py @@ -1,8 +1,68 @@ from __future__ import annotations -from dataclasses import asdict, dataclass +from collections.abc import Mapping +from dataclasses import asdict, dataclass, field, fields +from pathlib import Path from typing import Any +import yaml + + +def _load_yaml_mapping(path: str | Path) -> dict[str, object]: + try: + with Path(path).open() as stream: + raw = yaml.safe_load(stream) + except yaml.YAMLError as error: + raise ValueError(f"invalid YAML: {error}") from error + if raw is None: + return {} + if not isinstance(raw, dict): + raise ValueError("configuration root must be a YAML mapping") + return raw + + +def _require_mapping(value: object, location: str) -> Mapping[str, object]: + if value is None: + return {} + if not isinstance(value, dict): + raise ValueError(f"{location} must be a YAML mapping") + return value + + +def _reject_unknown_options( + values: Mapping[str, object], + allowed: set[str], + location: str, +) -> None: + unknown = set(values) - allowed + if unknown: + names = ", ".join(sorted(map(str, unknown))) + raise ValueError(f"unknown {location} option(s): {names}") + + +def _optional_string(value: object, location: str) -> str | None: + if value is None: + return None + if not isinstance(value, str): + raise ValueError(f"{location} must be a string") + return value + + +def _optional_integer(value: object, location: str) -> int | None: + if value is None: + return None + if not isinstance(value, int) or isinstance(value, bool): + raise ValueError(f"{location} must be an integer") + return value + + +def _optional_boolean(value: object, location: str) -> bool | None: + if value is None: + return None + if not isinstance(value, bool): + raise ValueError(f"{location} must be true or false") + return value + @dataclass(frozen=True) class QuantizationConfig: @@ -23,11 +83,73 @@ class QuantizationConfig: "down_proj", ) calibration: dict[str, Any] | None = None - source_checkpoint: str | None = None + source_checkpoint: str | None = field( + default=None, + metadata={"yaml": False}, + ) propagate_quantized: bool = True + @classmethod + def from_mapping(cls, raw: Mapping[str, object]) -> QuantizationConfig: + allowed_fields = { + config_field.name + for config_field in fields(cls) + if config_field.init and config_field.metadata.get("yaml", True) + } + _reject_unknown_options(raw, allowed_fields, "config.quantization") + values: dict[str, object] = { + key: value for key, value in raw.items() if value is not None + } + + for key in ("algorithm", "scale_dtype", "packing"): + value = _optional_string(raw.get(key), f"config.quantization.{key}") + if value is not None: + values[key] = value + + version = raw.get("version") + if version is not None: + if not isinstance(version, (str, int)) or isinstance(version, bool): + raise ValueError("config.quantization.version must be a string or integer") + values["version"] = str(version) + + for key in ("bits", "group_size"): + value = _optional_integer(raw.get(key), f"config.quantization.{key}") + if value is not None: + values[key] = value + + for key in ("symmetric", "propagate_quantized"): + value = _optional_boolean(raw.get(key), f"config.quantization.{key}") + if value is not None: + values[key] = value + + target_modules = raw.get("target_modules") + if target_modules is not None: + if not isinstance(target_modules, (list, tuple)) or not all( + isinstance(module, str) for module in target_modules + ): + raise ValueError( + "config.quantization.target_modules must be a list of strings" + ) + values["target_modules"] = tuple(target_modules) + + calibration = raw.get("calibration") + if calibration is not None: + if not isinstance(calibration, dict): + raise ValueError("config.quantization.calibration must be a mapping") + values["calibration"] = calibration + return cls(**values) + + @classmethod + def from_yaml(cls, path: str | Path) -> QuantizationConfig: + raw = _load_yaml_mapping(path) + quantization = _require_mapping( + raw.get("quantization"), + "config.quantization", + ) + return cls.from_mapping(quantization) + def __post_init__(self) -> None: - if self.algorithm not in {"rtn", "gptq"}: + if self.algorithm not in {"rtn", "awq", "gptq"}: raise ValueError(f"unsupported quantization algorithm: {self.algorithm}") if self.bits != 4: raise ValueError("only 4-bit weight quantization is supported") @@ -46,7 +168,7 @@ def to_dict(self) -> dict[str, Any]: return result @classmethod - def from_dict(cls, raw: dict[str, Any]) -> "QuantizationConfig": + def from_dict(cls, raw: dict[str, Any]) -> QuantizationConfig: values = dict(raw) if "target_modules" in values: values["target_modules"] = tuple(values["target_modules"]) diff --git a/src/lab5/src/hpc101_infer/scheduler/base.py b/src/lab5/src/hpc101_infer/scheduler/base.py index 667e5cd1..86643f6e 100644 --- a/src/lab5/src/hpc101_infer/scheduler/base.py +++ b/src/lab5/src/hpc101_infer/scheduler/base.py @@ -35,7 +35,7 @@ class ScheduledOutput: requests: list[ScheduledRequest] @property - def max_seq_len(self) -> int: + def batch_max_length(self) -> int: return max(scheduled.request.num_computed_tokens for scheduled in self.requests) From de4fbbac2c653a05e4a2b53bb621206db70ac8ff Mon Sep 17 00:00:00 2001 From: silvermilight Date: Sat, 15 Aug 2026 01:52:08 +0800 Subject: [PATCH 4/4] docs(lab5): score formula fix and submission guide --- docs/lab/Lab5-Gemma4/index.md | 51 ++++++++++++++++++++++------------- 1 file changed, 33 insertions(+), 18 deletions(-) diff --git a/docs/lab/Lab5-Gemma4/index.md b/docs/lab/Lab5-Gemma4/index.md index 65edebc7..c6802e89 100644 --- a/docs/lab/Lab5-Gemma4/index.md +++ b/docs/lab/Lab5-Gemma4/index.md @@ -691,21 +691,19 @@ Prefill 通常处理多个 token，计算量明显大于单步 decode。如果 ## 评测方式 -!!! danger "施工中" - - 目前 OJ 自动评测系统仍在建设中😭，我们会尽快发布。 - - -### 提交要求 +### 实验提交要求 需要提交的内容包括： - `src` 目录下的实验代码 - 配置文件 `config.yaml` - 实验报告 PDF -- 公开数据集上的精度评测结果和性能评测结果 JSON 文件 +- 公开数据集上的精度评测结果、性能测试输出和性能评测结果 - （可选）自定义的校准数据集 +!!! tip "注意事项" + 提交的测试结果与 OJ 评测结果相差过大时，优先以 OJ 评测结果为准，如果实在无法在 OJ 上复现，请提交你的最佳测试结果并在报告中说明。 + 提交目录结构如下： ``` @@ -717,8 +715,9 @@ Prefill 通常处理多个 token，计算量明显大于单步 decode。如果 │ └── calibration.jsonl ├── report.pdf └── results/ - ├── gptq-public-quality.json - └── gptq-public-performance.json + ├── gptq-public-quality.json # 精度评测结果 + ├── gptq-public-performance.json # 性能测试输出 + └── gptq-public-summary.json # 性能评测结果 ``` 请勿提交下列内容： @@ -728,7 +727,26 @@ Prefill 通常处理多个 token，计算量明显大于单步 decode。如果 - profiler 产物 - 任何中间结果或临时文件 -### 评测流程 +### OJ 提交要求 + +需要提交的内容包括： + +- `src` 目录下的实验代码 +- 配置文件 `config.yaml` +- （可选）自定义的校准数据集 + +提交目录结构如下： + +``` +. +├── src/ +│ └── ... +├── config.yaml +└── datasets/ # optimal + └── calibration.jsonl +``` + +### OJ 评测流程 OJ 评测时会经过三个阶段： @@ -754,7 +772,7 @@ OJ 会根据你提交的量化框架代码和配置文件中的量化选项， $$ S_1(x) = \begin{cases} 100, & x < 0.1 \\[0.9em] - 100 \cdot \dfrac{e^{20x} - e^{3.6}}{e^{2} - e^{3.6}}, & 0.1 \le x < 0.16 \\[0.9em] + 100 \cdot \dfrac{e^{20x} - e^{3.2}}{e^{2} - e^{3.2}}, & 0.1 \le x < 0.16 \\[0.9em] 0, & x \ge 0.16 \end{cases} $$ @@ -800,19 +818,19 @@ $$ $$ S_2(x) = \begin{cases} 100, & x < 36 \\[0.9em] - -\dfrac{5}{6} x + 30, & 36 \le x < 60 \\[0.9em] + -\dfrac{5}{6} x + 130, & 36 \le x < 60 \\[0.9em] \dfrac{5}{6} u \ln \dfrac{u}{x + u - 60} + 80, & 60 \le x < 150 \\[0.9em] -\dfrac{1}{3} x + 80, & 150 \le x < 240 \\[0.9em] 0, & x \ge 240 \end{cases} $$ - 其中 $x$ 是 `elapsed_s`，$u \approx -65.56$ 是方程 $u \ln \left( 1 + \dfrac{90}{u} \right) = 60$ 的正根。 + 其中 $x$ 是 `elapsed_s`，$u \approx 78.67$ 是方程 $u \ln \left( 1 + \dfrac{90}{u} \right) = 60$ 的正根。 - `performance_long.jsonl`：Bonus 测试集，考查长上下文压力下的性能表现 - !!! danger "Bonus 测评" - Bonus 数据集设计和测评方式尚未发布，敬请期待... + !!! warning "Bonus 测评" + Bonus 数据集设计和测评方式尚未发布... 除报告外，你的最终得分公式如下： @@ -827,9 +845,6 @@ $$ 2. 滑动窗口注意力实质上是一种静态稀疏注意力机制，根据经验设置了注意力掩码的形状。请你思考如果我们不事先设定注意力掩码的形状，可以如何实现稀疏注意力？你的实现相比滑动窗口注意力会对模型的性能和推理效率产生什么影响？ 3. 我们的量化框架已经实现了分层量化，请分析分层量化时对权重的管理策略与推理时的 Offloading 策略的异同点。 -!!! warning - 思考题可能还会继续补充，请大家保持关注... - ## 参考资料

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z93m8!d)L9vw)TY61u6=azFjlNG^ioX73lxY6UgYBeQo#`B4O!X8H*ngXLr9p=7!i; z%nzkzTy||vTzrGPL~7D9?n` 来提示模型进行深度思考，使用 `` 来提示模型不要进行深度思考。（上述代码中 `enable_thinking` 就是通过这种方式控制思考的） - - 你可以将自己的尝试和结果分享在实验报告中。 - -### 开发环境设置 - -本次实验使用 [UV](https://docs.astral.sh/uv/) 进行环境管理。UV 是一个现代化的 Python 环境管理工具，支持多种 Python 版本和依赖包管理。 - -首先，你需要安装 UV。参考 UV 文档中的 [安装手册](https://docs.astral.sh/uv/getting-started/installation/) 使用以下命令来安装 UV： - -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -``` - -本次实验提供了一个预配置的 UV 环境，你可以通过以下命令来加载环境: - -```bash -source /river/hpc101/2025/lab5/env/bin/activate -``` - -加载后直接使用 `python main.py` 命令即可运行实验代码。 - -### 提交作业 - -本实验需要在 GPU 平台上运行，你可以参考 [提交作业 - 提交 GPU 任务](https://hpc101.zjusct.io/guide/job/#%E6%8F%90%E4%BA%A4gpu%E4%BB%BB%E5%8A%A1) 来申请 GPU 资源并提交你的任务。 - -需要注意的是，你需要确保在运行前加载 UV 环境。你可以在 SBATCH 提交脚本中执行加载环境的命令，或者在使用 `srun` 命令前确保已经加载了 UV 环境。 - -## 实验任务和要求 - -### 实验任务 - -1. 根据 [知识讲解](#知识讲解qwen3-model-architecture) 中的内容，完成 `modules/attention.py` 中的 Qwen3Attention 类的实现和 `modules/mlp.py` 中的 Qwen3FFN 类的实现。 -2. 完成 [思考题](#思考题) （在实验报告中体现） -3. 完成实验报告，内容包括： - - 你的实现思路和对这两个模块前向过程的理解 - - 单元测试通过的截图和模型生成效果的截图 - - 思考题的答案和分析 - - （可选）实验感想和改进建议 -4. 在学在浙大中提交你的实验报告和两个模块的代码实现。 - -### 实验要求 - -1. 实现的模型能够正常进行前向推理，并生成正常的文本。 -2. 不得直接调用 Pytorch 中现成的 Attention、FFN、Transformer 等模块，只可使用实验框架中的模块和函数以及 Pytorch 的基础 Tensor 操作。 -3. 不得直接调用 torch.nn.functional 中的现成注意力函数。 - -## 参考资料与阅读材料 - -### 参考文献 - -- Qwen 3 官方博客: [Qwen3: Think Deeper, Act Faster](https://qwenlm.github.io/blog/qwen3/) -- Attention is All You Need: [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762) -- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints: [Ainslie et al., 2023](http://arxiv.org/abs/2305.13245) -- RoFormer: Enhanced Transformer with Rotary Position Embedding [Su et al., 2021](https://arxiv.org/abs/2104.09864) -- LLaMA: Open and Efficient Foundation Language Models [Touvron et al., 2023](https://arxiv.org/abs/2302.13971) - -### 推荐阅读材料 - -- [UV 文档](https://docs.astral.sh/uv/): 本实验只使用 UV 进行环境管理，并没有完全发挥 UV 在项目管理上的强大功能，感兴趣的同学可以在后续的学习中深入了解 UV 的更多功能。 -- PyTorch 文档: [Getting Started with PyTorch](https://docs.pytorch.org/tutorials/beginner/basics/intro.html) 这一篇文档可以帮你回顾我们在理论课上学过的 PyTorch 构建模型流程。而 [torch.Tensor API](https://docs.pytorch.org/docs/2.6/tensors.html) 则是 PyTorch 的核心 API 文档，包含了所有 Tensor 操作的详细说明，可以帮助你理解如何使用 Tensor 计算来完成本实验。 -- [:simple-youtube: How DeepSeek Rewrote the Transformer](https://www.youtube.com/watch?v=0VLAoVGf_74): 感兴趣的同学可以观看这个视频来理解 MHA, MQA, GQA 以及 DeepSeek 提出的 MLA（Multi-Layer Attention）等注意力机制的区别。 diff --git a/zensical.toml b/zensical.toml index d0ccd163..6b4ab74c 100644 --- a/zensical.toml +++ b/zensical.toml @@ -14,12 +14,20 @@ nav = [ { "报名与选课" = "enroll.md" }, { "支持单位" = "sponsors.md" }, ] }, + { "集群使用" = [ + { "登录平台" = "guide/index.md" }, + { "提交计算任务" = "guide/job.md" }, + { "提交实验报告" = "guide/submit.md" }, + { "在线测评 OJ" = "guide/oj.md" }, + { "集群概况" = "guide/overview.md" }, + ] }, { "课程实验" = [ { "Lab 0: Linux 快速入门" = "lab/Lab0-LinuxCrashCourse/index.md" }, { "Lab 1: 简单集群搭建" = "lab/Lab1-MiniCluster/index.md" }, { "Lab 2: MoE 的向量化计算" = "lab/Lab2-Vectorization/index.md"}, { "Lab 3: GDN Prefill 前向优化" = "lab/Lab3-GDN-Prefill/index.md" }, - { "Lab 4: AMSS-NCKU 数值相对论程序优化" = "lab/Lab4-AMSS-NCKU/index.md"} + { "Lab 4: AMSS-NCKU 数值相对论程序优化" = "lab/Lab4-AMSS-NCKU/index.md"}, + { "Lab 5: Gemma4 端到端推理优化" = "lab/Lab5-Gemma4/index.md"} ] }, ] From 19f8f9421f7bc6df8b5f1c45134e7d92d0a28b57 Mon Sep 17 00:00:00 2001 From: silvermilight Date: Fri, 24 Jul 2026 02:05:13 +0800 Subject: [PATCH 3/4] feat(lab5): score policy, submit guide and framework guide --- docs/lab/Lab5-Gemma4/image/task1_score.png | Bin 0 -> 243315 bytes docs/lab/Lab5-Gemma4/image/task2_score.png | Bin 0 -> 252325 bytes docs/lab/Lab5-Gemma4/index.md | 228 ++++++++++++++---- src/lab5/README.md | 182 ++++++++------ src/lab5/config.yaml | 38 +++ src/lab5/datasets/performance_public.jsonl | 9 +- src/lab5/pyproject.toml | 8 + 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    2. Gemma4-12B 模型结构。图源。

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