diff --git a/src/nemotron/cli/bin/nemotron.py b/src/nemotron/cli/bin/nemotron.py index 73b779ee5..6464ffdd7 100644 --- a/src/nemotron/cli/bin/nemotron.py +++ b/src/nemotron/cli/bin/nemotron.py @@ -89,11 +89,13 @@ def _register_groups() -> None: from nemotron.cli.commands.super3 import super3_app from nemotron.cli.kit import kit_app from nemotron.cli.commands.embed import embed_app + from nemotron.cli.commands.rerank import rerank_app app.add_typer(nano3_app, name="nano3") app.add_typer(super3_app, name="super3") app.add_typer(kit_app, name="kit") app.add_typer(embed_app, name="embed") + app.add_typer(rerank_app, name="rerank") # Register groups on import diff --git a/src/nemotron/cli/commands/embed/eval.py b/src/nemotron/cli/commands/embed/eval.py index 0a9eb0d84..491ebbe7e 100644 --- a/src/nemotron/cli/commands/embed/eval.py +++ b/src/nemotron/cli/commands/embed/eval.py @@ -101,33 +101,20 @@ def _execute_eval(cfg: RecipeConfig, *, experiment=None): def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: """Execute eval locally via UV isolated environment.""" - import os - import shutil - import subprocess - - uv_cmd = shutil.which("uv") - if not uv_cmd: - typer.echo("Error: 'uv' command not found. Please install uv.", err=True) - raise typer.Exit(1) + from nemotron.kit.uv_local import execute_uv_local script_abs = SPEC.script_path stage_dir = script_abs.parent repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] - cmd = [ - uv_cmd, "run", - "--with", str(repo_root), - "--project", str(stage_dir), - "python", str(script_abs), - "--config", str(train_path), - *passthrough, - ] - - env = os.environ.copy() - env.pop("VIRTUAL_ENV", None) - - typer.echo(f"Executing with uv isolated environment: {' '.join(cmd)}") - result = subprocess.run(cmd, env=env) - raise typer.Exit(result.returncode) + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + ) + raise typer.Exit(rc) def _execute_remote( diff --git a/src/nemotron/cli/commands/embed/export.py b/src/nemotron/cli/commands/embed/export.py index 1ec6a3215..0775fb499 100644 --- a/src/nemotron/cli/commands/embed/export.py +++ b/src/nemotron/cli/commands/embed/export.py @@ -102,41 +102,26 @@ def _execute_uv_local(train_path: Path, passthrough: list[str], job_config) -> N Conditionally includes TensorRT dependency based on config. """ - import os - import shutil - import subprocess - - uv_cmd = shutil.which("uv") - if not uv_cmd: - typer.echo("Error: 'uv' command not found. Please install uv.", err=True) - raise typer.Exit(1) + from nemotron.kit.uv_local import execute_uv_local script_abs = SPEC.script_path stage_dir = script_abs.parent repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] - cmd = [ - uv_cmd, "run", - "--with", str(repo_root), - "--project", str(stage_dir), - ] - # Conditionally include TensorRT dependency + extra_with = [] export_to_trt = job_config.get("export_to_trt", False) if export_to_trt: - cmd += ["--extra", "tensorrt"] - - cmd += [ - "python", str(script_abs), - "--config", str(train_path), - *passthrough, - ] - - env = os.environ.copy() - env.pop("VIRTUAL_ENV", None) - - typer.echo(f"Executing with uv isolated environment: {' '.join(cmd)}") - result = subprocess.run(cmd, env=env) - raise typer.Exit(result.returncode) + extra_with.append("tensorrt") + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + extra_with=extra_with, + ) + raise typer.Exit(rc) def _execute_remote( diff --git a/src/nemotron/cli/commands/embed/finetune.py b/src/nemotron/cli/commands/embed/finetune.py index b9e1ca241..eab5aead5 100644 --- a/src/nemotron/cli/commands/embed/finetune.py +++ b/src/nemotron/cli/commands/embed/finetune.py @@ -99,33 +99,20 @@ def _execute_finetune(cfg: RecipeConfig, *, experiment=None): def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: """Execute finetune locally via UV isolated environment.""" - import os - import shutil - import subprocess - - uv_cmd = shutil.which("uv") - if not uv_cmd: - typer.echo("Error: 'uv' command not found. Please install uv.", err=True) - raise typer.Exit(1) + from nemotron.kit.uv_local import execute_uv_local script_abs = SPEC.script_path stage_dir = script_abs.parent repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] - cmd = [ - uv_cmd, "run", - "--with", str(repo_root), - "--project", str(stage_dir), - "python", str(script_abs), - "--config", str(train_path), - *passthrough, - ] - - env = os.environ.copy() - env.pop("VIRTUAL_ENV", None) - - typer.echo(f"Executing with uv isolated environment: {' '.join(cmd)}") - result = subprocess.run(cmd, env=env) - raise typer.Exit(result.returncode) + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + ) + raise typer.Exit(rc) def _execute_remote( diff --git a/src/nemotron/cli/commands/embed/prep.py b/src/nemotron/cli/commands/embed/prep.py index 809f7a0d0..d45c895b2 100644 --- a/src/nemotron/cli/commands/embed/prep.py +++ b/src/nemotron/cli/commands/embed/prep.py @@ -100,33 +100,20 @@ def _execute_prep(cfg: RecipeConfig, *, experiment=None): def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: """Execute data prep locally via UV isolated environment.""" - import os - import shutil - import subprocess - - uv_cmd = shutil.which("uv") - if not uv_cmd: - typer.echo("Error: 'uv' command not found. Please install uv.", err=True) - raise typer.Exit(1) + from nemotron.kit.uv_local import execute_uv_local script_abs = SPEC.script_path stage_dir = script_abs.parent repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] - cmd = [ - uv_cmd, "run", - "--with", str(repo_root), - "--project", str(stage_dir), - "python", str(script_abs), - "--config", str(train_path), - *passthrough, - ] - - env = os.environ.copy() - env.pop("VIRTUAL_ENV", None) - - typer.echo(f"Executing with uv isolated environment: {' '.join(cmd)}") - result = subprocess.run(cmd, env=env) - raise typer.Exit(result.returncode) + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + ) + raise typer.Exit(rc) def _execute_remote( diff --git a/src/nemotron/cli/commands/embed/sdg.py b/src/nemotron/cli/commands/embed/sdg.py index 5e0f06041..8fd33c6b9 100644 --- a/src/nemotron/cli/commands/embed/sdg.py +++ b/src/nemotron/cli/commands/embed/sdg.py @@ -107,33 +107,20 @@ def _execute_sdg(cfg: RecipeConfig, *, experiment=None): def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: """Execute SDG locally via UV isolated environment.""" - import os - import shutil - import subprocess - - uv_cmd = shutil.which("uv") - if not uv_cmd: - typer.echo("Error: 'uv' command not found. Please install uv.", err=True) - raise typer.Exit(1) + from nemotron.kit.uv_local import execute_uv_local script_abs = SPEC.script_path stage_dir = script_abs.parent repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] - cmd = [ - uv_cmd, "run", - "--with", str(repo_root), - "--project", str(stage_dir), - "python", str(script_abs), - "--config", str(train_path), - *passthrough, - ] - - env = os.environ.copy() - env.pop("VIRTUAL_ENV", None) - - typer.echo(f"Executing with uv isolated environment: {' '.join(cmd)}") - result = subprocess.run(cmd, env=env) - raise typer.Exit(result.returncode) + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + ) + raise typer.Exit(rc) def _execute_remote( diff --git a/src/nemotron/cli/commands/rerank/__init__.py b/src/nemotron/cli/commands/rerank/__init__.py new file mode 100644 index 000000000..302a94423 --- /dev/null +++ b/src/nemotron/cli/commands/rerank/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Rerank CLI command group. + +Exports the rerank_app typer group. +""" + +from nemotron.cli.commands.rerank._typer_group import rerank_app + +__all__ = ["rerank_app"] diff --git a/src/nemotron/cli/commands/rerank/_typer_group.py b/src/nemotron/cli/commands/rerank/_typer_group.py new file mode 100644 index 000000000..a17a73c68 --- /dev/null +++ b/src/nemotron/cli/commands/rerank/_typer_group.py @@ -0,0 +1,87 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Rerank Typer group. + +Contains the rerank command group with subcommands for cross-encoder +reranking model fine-tuning workflow: +- sdg: Generate synthetic Q&A pairs from documents +- prep: Prepare training data (convert, mine, unroll) +- finetune: Fine-tune the cross-encoder reranking model +- eval: Evaluate models on reranking metrics +- export: Export model to ONNX/TensorRT for optimized inference +- deploy: Deploy NIM container with custom model +""" + +from __future__ import annotations + +from rich.console import Console + +from nemotron.cli.commands.rerank.sdg import META as SDG_META, sdg +from nemotron.cli.commands.rerank.prep import META as PREP_META, prep +from nemotron.cli.commands.rerank.finetune import META as FINETUNE_META, finetune +from nemotron.cli.commands.rerank.eval import META as EVAL_META, eval as eval_cmd +from nemotron.cli.commands.rerank.export import META as EXPORT_META, export +from nemotron.cli.commands.rerank.deploy import META as DEPLOY_META, deploy +from nemotron.cli.commands.rerank.run import run as run_cmd +from nemo_runspec.recipe_typer import RecipeTyper + +console = Console() + +# Create rerank app using RecipeTyper +rerank_app = RecipeTyper( + name="rerank", + help="Cross-encoder reranking model fine-tuning recipe", + no_args_is_help=True, + rich_markup_mode="rich", +) + + +@rerank_app.command(name="info") +def info() -> None: + """Display rerank workspace information.""" + console.print("[bold green]Rerank Workspace[/bold green]") + console.print(" Fine-tune cross-encoder reranking models for domain-adapted re-ranking.") + console.print() + console.print("[bold]Workflow Stages:[/bold]") + console.print(" 1. [cyan]sdg[/] - Generate synthetic Q&A pairs from documents") + console.print(" 2. [cyan]prep[/] - Prepare training data (convert, mine, unroll)") + console.print(" 3. [cyan]finetune[/] - Fine-tune the cross-encoder reranking model") + console.print(" 4. [cyan]eval[/] - Evaluate base vs fine-tuned rerankers") + console.print(" 5. [cyan]export[/] - Export model to ONNX/TensorRT") + console.print(" 6. [cyan]deploy[/] - Deploy NIM with custom model") + console.print() + console.print("[bold]Key Components:[/bold]") + console.print(" - retriever-sdg (synthetic data generation)") + console.print(" - Automodel (cross-encoder model training)") + console.print(" - BEIR (reranking evaluation framework)") + console.print() + console.print("[bold]Base Model:[/bold]") + console.print(" - nvidia/llama-nemotron-rerank-1b-v2") + + +# Register stage commands +rerank_app.add_recipe_command(sdg, meta=SDG_META, rich_help_panel="Data") +rerank_app.add_recipe_command(prep, meta=PREP_META, rich_help_panel="Data") +rerank_app.add_recipe_command(finetune, meta=FINETUNE_META, rich_help_panel="Training") +rerank_app.add_recipe_command(eval_cmd, meta=EVAL_META, rich_help_panel="Evaluation") +rerank_app.add_recipe_command(export, meta=EXPORT_META, rich_help_panel="Deployment") +rerank_app.add_recipe_command(deploy, meta=DEPLOY_META, rich_help_panel="Deployment") + +# Register run (pipeline) command +rerank_app.command( + "run", + context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, + rich_help_panel="Pipeline", +)(run_cmd) diff --git a/src/nemotron/cli/commands/rerank/deploy.py b/src/nemotron/cli/commands/rerank/deploy.py new file mode 100644 index 000000000..9eb03e882 --- /dev/null +++ b/src/nemotron/cli/commands/rerank/deploy.py @@ -0,0 +1,90 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Deploy command implementation. + +Launches NIM container with custom fine-tuned reranking model for inference. +Deploy is local-only (it's just a Docker wrapper). +""" + +from __future__ import annotations + +import subprocess +import sys + +import typer + +from nemo_runspec import parse as parse_runspec +from nemo_runspec.config import ( + build_job_config, + extract_train_config, + generate_job_dir, + parse_config, + save_configs, +) +from nemo_runspec.display import display_job_config +from nemo_runspec.recipe_config import RecipeConfig, parse_recipe_config +from nemo_runspec.recipe_typer import RecipeMeta +from nemotron.recipes.rerank.stage5_deploy.deploy import DeployConfig + +SCRIPT_PATH = "src/nemotron/recipes/rerank/stage5_deploy/deploy.py" +SPEC = parse_runspec(SCRIPT_PATH) + +META = RecipeMeta( + name=SPEC.name, + script_path=SCRIPT_PATH, + config_dir=str(SPEC.config_dir), + config_model=DeployConfig, + default_config=SPEC.config.default, + input_artifacts={"model": "Exported model directory (ONNX/TensorRT)"}, +) + + +def _execute_deploy(cfg: RecipeConfig): + """Execute deploy locally.""" + train_config = parse_config(cfg.ctx, SPEC.config_dir, SPEC.config.default) + + job_config = build_job_config( + train_config, + cfg.ctx, + SPEC.name, + SCRIPT_PATH, + cfg.argv, + ) + + display_job_config(job_config, for_remote=False) + + if cfg.dry_run: + return + + job_dir = generate_job_dir(SPEC.name) + train_config_for_script = extract_train_config(job_config, for_remote=False) + job_path, train_path = save_configs(job_config, train_config_for_script, job_dir) + + cmd = [ + sys.executable, + str(SPEC.script_path), + "--config", str(train_path), + *cfg.passthrough, + ] + + typer.echo(f"Executing deploy script: {' '.join(cmd)}") + result = subprocess.run(cmd) + raise typer.Exit(result.returncode) + + +def deploy(ctx: typer.Context) -> None: + """Deploy NIM container with custom fine-tuned reranking model for inference.""" + cfg = parse_recipe_config(ctx) + _execute_deploy(cfg) diff --git a/src/nemotron/cli/commands/rerank/eval.py b/src/nemotron/cli/commands/rerank/eval.py new file mode 100644 index 000000000..81f8827d5 --- /dev/null +++ b/src/nemotron/cli/commands/rerank/eval.py @@ -0,0 +1,182 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Evaluation command implementation. + +Evaluates cross-encoder reranking models on reranking metrics. +""" + +from __future__ import annotations + +from pathlib import Path + +import typer + +from nemo_runspec import parse as parse_runspec +from nemo_runspec.config import ( + build_job_config, + extract_train_config, + generate_job_dir, + parse_config, + save_configs, +) +from nemo_runspec.display import display_job_config, display_job_submission +from nemo_runspec.env import parse_env +from nemo_runspec.execution import build_env_vars +from nemo_runspec.recipe_config import RecipeConfig, parse_recipe_config +from nemo_runspec.recipe_typer import RecipeMeta +from nemotron.recipes.rerank.stage3_eval.eval import EvalConfig + +SCRIPT_PATH = "src/nemotron/recipes/rerank/stage3_eval/eval.py" +SCRIPT_REMOTE = "src/nemotron/recipes/rerank/stage3_eval/run_uv.py" +SPEC = parse_runspec(SCRIPT_PATH) + +META = RecipeMeta( + name=SPEC.name, + script_path=SCRIPT_PATH, + config_dir=str(SPEC.config_dir), + config_model=EvalConfig, + default_config=SPEC.config.default, + input_artifacts={ + "model": "Fine-tuned reranker checkpoint", + "data": "Evaluation data (BEIR format)", + }, +) + + +def _execute_eval(cfg: RecipeConfig, *, experiment=None): + """Execute eval with visible execution logic.""" + train_config = parse_config(cfg.ctx, SPEC.config_dir, SPEC.config.default) + env = parse_env(cfg.ctx) + + script_path = SCRIPT_PATH if cfg.mode == "local" else SCRIPT_REMOTE + + job_config = build_job_config( + train_config, + cfg.ctx, + SPEC.name, + script_path, + cfg.argv, + env_profile=env, + ) + + display_job_config(job_config, for_remote=False) + + if cfg.dry_run: + return + + job_dir = generate_job_dir(SPEC.name) + train_config_for_script = extract_train_config(job_config, for_remote=False) + job_path, train_path = save_configs(job_config, train_config_for_script, job_dir) + + env_for_executor = job_config.run.env if hasattr(job_config.run, "env") else None + env_vars = build_env_vars(job_config, env_for_executor) + + display_job_submission(job_path, train_path, env_vars, cfg.mode) + + if cfg.mode == "local": + _execute_uv_local(train_path, cfg.passthrough) + else: + _execute_remote( + train_path=train_path, + env=env_for_executor, + passthrough=cfg.passthrough, + attached=cfg.attached, + env_vars=env_vars, + force_squash=cfg.force_squash, + experiment=experiment, + ) + + +def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: + """Execute eval locally via UV isolated environment.""" + from nemotron.kit.uv_local import execute_uv_local + + script_abs = SPEC.script_path + stage_dir = script_abs.parent + repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + ) + raise typer.Exit(rc) + + +def _execute_remote( + train_path: Path, + env, + passthrough: list[str], + attached: bool, + env_vars: dict[str, str], + force_squash: bool, + experiment=None, +): + """Execute eval via nemo-run with remote backend.""" + try: + import nemo_run as run + except ImportError: + typer.echo("Error: nemo-run is required for --run/--batch execution", err=True) + raise typer.Exit(1) + + from nemo_runspec.packaging import CodePackager + from nemo_runspec.execution import create_executor + from nemo_runspec.run import ( + patch_nemo_run_ray_template_for_cpu, + patch_nemo_run_rsync_accept_new_host_keys, + ) + + patch_nemo_run_rsync_accept_new_host_keys() + patch_nemo_run_ray_template_for_cpu() + + packager = CodePackager( + script_path=str(SCRIPT_REMOTE), + train_path=str(train_path), + ) + + executor = create_executor( + env=env, + env_vars=env_vars, + packager=packager, + attached=attached, + force_squash=force_squash, + default_image=SPEC.image, + ) + + recipe_name = SPEC.name.replace("/", "-") + script_args = [*passthrough] + + if experiment is not None: + return experiment.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + + with run.Experiment(recipe_name) as exp: + exp.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + exp.run(detach=not attached, tail_logs=attached) + + +def eval(ctx: typer.Context) -> None: + """Evaluate reranking models on nDCG and Recall metrics.""" + cfg = parse_recipe_config(ctx) + _execute_eval(cfg) diff --git a/src/nemotron/cli/commands/rerank/export.py b/src/nemotron/cli/commands/rerank/export.py new file mode 100644 index 000000000..d8da29dcb --- /dev/null +++ b/src/nemotron/cli/commands/rerank/export.py @@ -0,0 +1,186 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Export command implementation. + +Exports reranking models to ONNX and TensorRT for optimized inference. +""" + +from __future__ import annotations + +from pathlib import Path + +import typer + +from nemo_runspec import parse as parse_runspec +from nemo_runspec.config import ( + build_job_config, + extract_train_config, + generate_job_dir, + parse_config, + save_configs, +) +from nemo_runspec.display import display_job_config, display_job_submission +from nemo_runspec.env import parse_env +from nemo_runspec.execution import build_env_vars +from nemo_runspec.recipe_config import RecipeConfig, parse_recipe_config +from nemo_runspec.recipe_typer import RecipeMeta +from nemotron.recipes.rerank.stage4_export.export import ExportConfig + +SCRIPT_PATH = "src/nemotron/recipes/rerank/stage4_export/export.py" +SCRIPT_REMOTE = "src/nemotron/recipes/rerank/stage4_export/run_uv.py" +SPEC = parse_runspec(SCRIPT_PATH) + +META = RecipeMeta( + name=SPEC.name, + script_path=SCRIPT_PATH, + config_dir=str(SPEC.config_dir), + config_model=ExportConfig, + default_config=SPEC.config.default, + input_artifacts={"model": "Fine-tuned reranker checkpoint to export"}, + output_artifacts={"model": "Exported model (ONNX / TensorRT)"}, +) + + +def _execute_export(cfg: RecipeConfig, *, experiment=None): + """Execute export with visible execution logic.""" + train_config = parse_config(cfg.ctx, SPEC.config_dir, SPEC.config.default) + env = parse_env(cfg.ctx) + + script_path = SCRIPT_PATH if cfg.mode == "local" else SCRIPT_REMOTE + + job_config = build_job_config( + train_config, + cfg.ctx, + SPEC.name, + script_path, + cfg.argv, + env_profile=env, + ) + + display_job_config(job_config, for_remote=False) + + if cfg.dry_run: + return + + job_dir = generate_job_dir(SPEC.name) + train_config_for_script = extract_train_config(job_config, for_remote=False) + job_path, train_path = save_configs(job_config, train_config_for_script, job_dir) + + env_for_executor = job_config.run.env if hasattr(job_config.run, "env") else None + env_vars = build_env_vars(job_config, env_for_executor) + + display_job_submission(job_path, train_path, env_vars, cfg.mode) + + if cfg.mode == "local": + _execute_uv_local(train_path, cfg.passthrough, job_config) + else: + _execute_remote( + train_path=train_path, + env=env_for_executor, + passthrough=cfg.passthrough, + attached=cfg.attached, + env_vars=env_vars, + force_squash=cfg.force_squash, + experiment=experiment, + ) + + +def _execute_uv_local(train_path: Path, passthrough: list[str], job_config) -> None: + """Execute export locally via UV isolated environment.""" + from nemotron.kit.uv_local import execute_uv_local + + script_abs = SPEC.script_path + stage_dir = script_abs.parent + repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] + + extra_with = [] + export_to_trt = job_config.get("export_to_trt", False) + if export_to_trt: + extra_with.append("tensorrt") + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + extra_with=extra_with, + ) + raise typer.Exit(rc) + + +def _execute_remote( + train_path: Path, + env, + passthrough: list[str], + attached: bool, + env_vars: dict[str, str], + force_squash: bool, + experiment=None, +): + """Execute export via nemo-run with remote backend.""" + try: + import nemo_run as run + except ImportError: + typer.echo("Error: nemo-run is required for --run/--batch execution", err=True) + raise typer.Exit(1) + + from nemo_runspec.packaging import CodePackager + from nemo_runspec.execution import create_executor + from nemo_runspec.run import ( + patch_nemo_run_ray_template_for_cpu, + patch_nemo_run_rsync_accept_new_host_keys, + ) + + patch_nemo_run_rsync_accept_new_host_keys() + patch_nemo_run_ray_template_for_cpu() + + packager = CodePackager( + script_path=str(SCRIPT_REMOTE), + train_path=str(train_path), + ) + + executor = create_executor( + env=env, + env_vars=env_vars, + packager=packager, + attached=attached, + force_squash=force_squash, + default_image=SPEC.image, + ) + + recipe_name = SPEC.name.replace("/", "-") + script_args = [*passthrough] + + if experiment is not None: + return experiment.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + + with run.Experiment(recipe_name) as exp: + exp.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + exp.run(detach=not attached, tail_logs=attached) + + +def export(ctx: typer.Context) -> None: + """Export reranking models to ONNX and TensorRT for optimized inference.""" + cfg = parse_recipe_config(ctx) + _execute_export(cfg) diff --git a/src/nemotron/cli/commands/rerank/finetune.py b/src/nemotron/cli/commands/rerank/finetune.py new file mode 100644 index 000000000..990d739ad --- /dev/null +++ b/src/nemotron/cli/commands/rerank/finetune.py @@ -0,0 +1,181 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Finetune command implementation. + +Fine-tunes a cross-encoder reranking model using classification loss. +""" + +from __future__ import annotations + +from pathlib import Path + +import typer + +from nemo_runspec import parse as parse_runspec +from nemo_runspec.config import ( + build_job_config, + extract_train_config, + generate_job_dir, + parse_config, + save_configs, +) +from nemo_runspec.display import display_job_config, display_job_submission +from nemo_runspec.env import parse_env +from nemo_runspec.execution import build_env_vars +from nemo_runspec.recipe_config import RecipeConfig, parse_recipe_config +from nemo_runspec.recipe_typer import RecipeMeta +from nemotron.recipes.rerank.stage2_finetune.train import FinetuneConfig + +SCRIPT_PATH = "src/nemotron/recipes/rerank/stage2_finetune/train.py" +SCRIPT_REMOTE = "src/nemotron/recipes/rerank/stage2_finetune/run_uv.py" +SPEC = parse_runspec(SCRIPT_PATH) + +META = RecipeMeta( + name=SPEC.name, + script_path=SCRIPT_PATH, + config_dir=str(SPEC.config_dir), + config_model=FinetuneConfig, + default_config=SPEC.config.default, + input_artifacts={"data": "Training data (from embed prep stage)"}, + output_artifacts={"model": "Fine-tuned reranking model checkpoint"}, +) + + +def _execute_finetune(cfg: RecipeConfig, *, experiment=None): + """Execute finetune with visible execution logic.""" + train_config = parse_config(cfg.ctx, SPEC.config_dir, SPEC.config.default) + env = parse_env(cfg.ctx) + + script_path = SCRIPT_PATH if cfg.mode == "local" else SCRIPT_REMOTE + + job_config = build_job_config( + train_config, + cfg.ctx, + SPEC.name, + script_path, + cfg.argv, + env_profile=env, + ) + + display_job_config(job_config, for_remote=False) + + if cfg.dry_run: + return + + job_dir = generate_job_dir(SPEC.name) + train_config_for_script = extract_train_config(job_config, for_remote=False) + job_path, train_path = save_configs(job_config, train_config_for_script, job_dir) + + env_for_executor = job_config.run.env if hasattr(job_config.run, "env") else None + env_vars = build_env_vars(job_config, env_for_executor) + + display_job_submission(job_path, train_path, env_vars, cfg.mode) + + if cfg.mode == "local": + _execute_uv_local(train_path, cfg.passthrough) + else: + _execute_remote( + train_path=train_path, + env=env_for_executor, + passthrough=cfg.passthrough, + attached=cfg.attached, + env_vars=env_vars, + force_squash=cfg.force_squash, + experiment=experiment, + ) + + +def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: + """Execute finetune locally via UV isolated environment.""" + from nemotron.kit.uv_local import execute_uv_local + + script_abs = SPEC.script_path + stage_dir = script_abs.parent + repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + pre_script_args=["-m", "torch.distributed.run", "--nproc_per_node=gpu"], + ) + raise typer.Exit(rc) + + +def _execute_remote( + train_path: Path, + env, + passthrough: list[str], + attached: bool, + env_vars: dict[str, str], + force_squash: bool, + experiment=None, +): + """Execute finetune via nemo-run with remote backend.""" + try: + import nemo_run as run + except ImportError: + typer.echo("Error: nemo-run is required for --run/--batch execution", err=True) + raise typer.Exit(1) + + from nemo_runspec.packaging import CodePackager + from nemo_runspec.execution import create_executor + from nemo_runspec.run import ( + patch_nemo_run_ray_template_for_cpu, + patch_nemo_run_rsync_accept_new_host_keys, + ) + + patch_nemo_run_rsync_accept_new_host_keys() + patch_nemo_run_ray_template_for_cpu() + + packager = CodePackager( + script_path=str(SCRIPT_REMOTE), + train_path=str(train_path), + ) + + executor = create_executor( + env=env, + env_vars=env_vars, + packager=packager, + attached=attached, + force_squash=force_squash, + default_image=SPEC.image, + ) + + recipe_name = SPEC.name.replace("/", "-") + script_args = [*passthrough] + + if experiment is not None: + return experiment.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + + with run.Experiment(recipe_name) as exp: + exp.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + exp.run(detach=not attached, tail_logs=attached) + + +def finetune(ctx: typer.Context) -> None: + """Fine-tune cross-encoder reranking model with classification loss.""" + cfg = parse_recipe_config(ctx) + _execute_finetune(cfg) diff --git a/src/nemotron/cli/commands/rerank/prep.py b/src/nemotron/cli/commands/rerank/prep.py new file mode 100644 index 000000000..818228838 --- /dev/null +++ b/src/nemotron/cli/commands/rerank/prep.py @@ -0,0 +1,183 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Data preparation command for rerank recipe. + +Runs the same data prep pipeline as embed but with rerank-specific config +that reads/writes to output/rerank/ instead of output/embed/. +""" + +from __future__ import annotations + +from pathlib import Path + +import typer + +from nemo_runspec import parse as parse_runspec +from nemo_runspec.config import ( + build_job_config, + extract_train_config, + generate_job_dir, + parse_config, + save_configs, +) +from nemo_runspec.display import display_job_config, display_job_submission +from nemo_runspec.env import parse_env +from nemo_runspec.execution import build_env_vars +from nemo_runspec.recipe_config import RecipeConfig, parse_recipe_config +from nemo_runspec.recipe_typer import RecipeMeta +from nemotron.recipes.embed.stage1_data_prep.data_prep import DataPrepConfig + +# Use embed's script but rerank's config directory +SCRIPT_PATH = "src/nemotron/recipes/embed/stage1_data_prep/data_prep.py" +SCRIPT_REMOTE = "src/nemotron/recipes/embed/stage1_data_prep/run_uv.py" +SPEC = parse_runspec(SCRIPT_PATH) +RERANK_CONFIG_DIR = Path("src/nemotron/recipes/rerank/stage1_prep/config").resolve() + +META = RecipeMeta( + name="rerank/prep", + script_path=SCRIPT_PATH, + config_dir=str(RERANK_CONFIG_DIR), + config_model=DataPrepConfig, + default_config="default", + input_artifacts={"data": "SDG output (synthetic Q&A pairs)"}, + output_artifacts={"data": "Training data (mined + unrolled, Automodel format)"}, +) + + +def _execute_prep(cfg: RecipeConfig, *, experiment=None): + """Execute data prep with rerank output paths.""" + train_config = parse_config(cfg.ctx, RERANK_CONFIG_DIR, "default") + env = parse_env(cfg.ctx) + + script_path = SCRIPT_PATH if cfg.mode == "local" else SCRIPT_REMOTE + + job_config = build_job_config( + train_config, + cfg.ctx, + "rerank/prep", + script_path, + cfg.argv, + env_profile=env, + ) + + display_job_config(job_config, for_remote=False) + + if cfg.dry_run: + return + + job_dir = generate_job_dir("rerank/prep") + train_config_for_script = extract_train_config(job_config, for_remote=False) + job_path, train_path = save_configs(job_config, train_config_for_script, job_dir) + + env_for_executor = job_config.run.env if hasattr(job_config.run, "env") else None + env_vars = build_env_vars(job_config, env_for_executor) + + display_job_submission(job_path, train_path, env_vars, cfg.mode) + + if cfg.mode == "local": + _execute_uv_local(train_path, cfg.passthrough) + else: + _execute_remote( + train_path=train_path, + env=env_for_executor, + passthrough=cfg.passthrough, + attached=cfg.attached, + env_vars=env_vars, + force_squash=cfg.force_squash, + experiment=experiment, + ) + + +def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: + """Execute data prep locally via UV isolated environment.""" + from nemotron.kit.uv_local import execute_uv_local + + script_abs = SPEC.script_path + stage_dir = script_abs.parent + repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + ) + raise typer.Exit(rc) + + +def _execute_remote( + train_path: Path, + env, + passthrough: list[str], + attached: bool, + env_vars: dict[str, str], + force_squash: bool, + experiment=None, +): + """Execute data prep via nemo-run with remote backend.""" + try: + import nemo_run as run + except ImportError: + typer.echo("Error: nemo-run is required for --run/--batch execution", err=True) + raise typer.Exit(1) + + from nemo_runspec.packaging import CodePackager + from nemo_runspec.execution import create_executor + from nemo_runspec.run import ( + patch_nemo_run_ray_template_for_cpu, + patch_nemo_run_rsync_accept_new_host_keys, + ) + + patch_nemo_run_rsync_accept_new_host_keys() + patch_nemo_run_ray_template_for_cpu() + + packager = CodePackager( + script_path=str(SCRIPT_REMOTE), + train_path=str(train_path), + ) + + executor = create_executor( + env=env, + env_vars=env_vars, + packager=packager, + attached=attached, + force_squash=force_squash, + default_image=SPEC.image, + ) + + recipe_name = "rerank-prep" + script_args = [*passthrough] + + if experiment is not None: + return experiment.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + + with run.Experiment(recipe_name) as exp: + exp.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + exp.run(detach=not attached, tail_logs=attached) + + +def prep(ctx: typer.Context) -> None: + """Prepare training data: convert to BEIR format, mine hard negatives, unroll multi-hop.""" + cfg = parse_recipe_config(ctx) + _execute_prep(cfg) diff --git a/src/nemotron/cli/commands/rerank/run.py b/src/nemotron/cli/commands/rerank/run.py new file mode 100644 index 000000000..347bf128f --- /dev/null +++ b/src/nemotron/cli/commands/rerank/run.py @@ -0,0 +1,190 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Pipeline orchestration for the rerank recipe.""" + +from __future__ import annotations + +import shutil +import subprocess +import sys + +import typer + +from nemo_runspec.cli_context import GlobalContext +from nemo_runspec.recipe_config import RecipeConfig, parse_recipe_config + +# Stage order defines the DAG +STAGE_ORDER = ["sdg", "prep", "finetune", "eval", "export", "deploy"] +DEFAULT_TO = "eval" # export/deploy are opt-in + +# Map stage names to their modules (lazy import to avoid circular deps) +STAGE_MODULES = { + "sdg": "nemotron.cli.commands.rerank.sdg", + "prep": "nemotron.cli.commands.rerank.prep", + "finetune": "nemotron.cli.commands.rerank.finetune", + "eval": "nemotron.cli.commands.rerank.eval", + "export": "nemotron.cli.commands.rerank.export", + # deploy has no remote execution support +} + +# Map stage names to their execution function names +STAGE_RUN_FNS = { + "sdg": "_execute_sdg", + "prep": "_execute_prep", + "finetune": "_execute_finetune", + "eval": "_execute_eval", + "export": "_execute_export", + # deploy has no remote execution support +} + + +def _resolve_stages(from_stage: str, to_stage: str) -> list[str]: + """Return the ordered list of stages to run.""" + if from_stage not in STAGE_ORDER: + raise typer.BadParameter(f"Unknown stage: {from_stage}") + if to_stage not in STAGE_ORDER: + raise typer.BadParameter(f"Unknown stage: {to_stage}") + start = STAGE_ORDER.index(from_stage) + end = STAGE_ORDER.index(to_stage) + if start > end: + raise typer.BadParameter(f"--from {from_stage} is after --to {to_stage}") + return STAGE_ORDER[start : end + 1] + + +def _split_stage_overrides(dotlist: list[str]) -> tuple[dict[str, list[str]], list[str]]: + """Split dotlist into per-stage overrides and global overrides. + + 'finetune.learning_rate=1e-5' -> stage='finetune', override='learning_rate=1e-5' + 'num_epochs=5' -> global override (applied to all stages) + """ + stage_overrides: dict[str, list[str]] = {} + global_overrides: list[str] = [] + + for item in dotlist: + key = item.split("=", 1)[0] + parts = key.split(".", 1) + if len(parts) == 2 and parts[0] in STAGE_ORDER: + stage_name = parts[0] + rest = parts[1] + "=" + item.split("=", 1)[1] if "=" in item else parts[1] + stage_overrides.setdefault(stage_name, []).append(rest) + else: + global_overrides.append(item) + + return stage_overrides, global_overrides + + +def _run_pipeline_local( + stages: list[str], + base_options: RecipeConfig, + stage_overrides: dict[str, list[str]], + global_overrides: list[str], +) -> None: + """Run stages sequentially as local subprocesses.""" + for stage_name in stages: + print(f"\n{'=' * 60}") + print(f" Stage: {stage_name}") + print(f"{'=' * 60}\n") + + stage_dotlist = global_overrides + stage_overrides.get(stage_name, []) + + uv_cmd = shutil.which("uv") + cmd = [uv_cmd or "uv", "run", "nemotron", "rerank", stage_name] + if base_options.config: + cmd.extend(["-c", base_options.config]) + cmd.extend(stage_dotlist) + cmd.extend(base_options.passthrough) + + result = subprocess.run(cmd) + if result.returncode != 0: + print(f"\nError: Stage '{stage_name}' failed with exit code {result.returncode}") + raise typer.Exit(result.returncode) + + +def _run_pipeline_remote( + stages: list[str], + base_options: RecipeConfig, + stage_overrides: dict[str, list[str]], + global_overrides: list[str], +) -> None: + """Run stages as tasks in a single nemo-run Experiment.""" + import nemo_run as run + from importlib import import_module + + experiment_name = "rerank-" + "-".join(stages) + + with run.Experiment(experiment_name) as exp: + for stage_name in stages: + if stage_name not in STAGE_RUN_FNS: + print(f"Warning: Stage '{stage_name}' does not support remote execution, skipping") + continue + + stage_dotlist = global_overrides + stage_overrides.get(stage_name, []) + + ctx = GlobalContext( + config=base_options.ctx.config, + run=base_options.ctx.run, + batch=base_options.ctx.batch, + dry_run=base_options.ctx.dry_run, + force_squash=base_options.ctx.force_squash, + ) + ctx.dotlist = stage_dotlist + ctx.passthrough = base_options.passthrough + + options = RecipeConfig( + ctx=ctx, + argv=sys.argv, + dotlist=stage_dotlist, + passthrough=base_options.passthrough, + ) + + mod = import_module(STAGE_MODULES[stage_name]) + run_fn = getattr(mod, STAGE_RUN_FNS[stage_name]) + run_fn(options, experiment=exp) + + exp.run(detach=not base_options.attached, tail_logs=base_options.attached) + + +def run( + ctx: typer.Context, + from_stage: str = typer.Option("sdg", "--from", help="Stage to start from"), + to_stage: str = typer.Option(DEFAULT_TO, "--to", help="Stage to stop at (inclusive)"), +) -> None: + """Run the rerank pipeline end-to-end. + + Executes stages sequentially from --from to --to (default: sdg through eval). + Supports local, Docker, and Slurm execution via --run/--batch. + + Per-stage overrides use stage prefix: sdg.num_pairs=20, finetune.learning_rate=1e-5 + """ + options = parse_recipe_config(ctx) + stages = _resolve_stages(from_stage, to_stage) + stage_overrides, global_overrides = _split_stage_overrides(options.dotlist) + + print(f"Rerank pipeline: {' -> '.join(stages)}") + if options.config: + print(f"Config: {options.config}") + print() + + if options.dry_run: + print("Dry run — would execute these stages:") + for s in stages: + overrides = global_overrides + stage_overrides.get(s, []) + print(f" {s}: {overrides or '(no overrides)'}") + return + + if options.mode == "local": + _run_pipeline_local(stages, options, stage_overrides, global_overrides) + else: + _run_pipeline_remote(stages, options, stage_overrides, global_overrides) diff --git a/src/nemotron/cli/commands/rerank/sdg.py b/src/nemotron/cli/commands/rerank/sdg.py new file mode 100644 index 000000000..c0041a763 --- /dev/null +++ b/src/nemotron/cli/commands/rerank/sdg.py @@ -0,0 +1,183 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""SDG command for rerank recipe. + +Runs the same SDG pipeline as embed but with rerank-specific config +that writes output to output/rerank/ instead of output/embed/. +""" + +from __future__ import annotations + +from pathlib import Path + +import typer + +from nemo_runspec import parse as parse_runspec +from nemo_runspec.config import ( + build_job_config, + extract_train_config, + generate_job_dir, + parse_config, + save_configs, +) +from nemo_runspec.display import display_job_config, display_job_submission +from nemo_runspec.env import parse_env +from nemo_runspec.execution import build_env_vars +from nemo_runspec.recipe_config import RecipeConfig, parse_recipe_config +from nemo_runspec.recipe_typer import RecipeMeta +from nemotron.recipes.embed.stage0_sdg.data_prep import SDGConfig + +# Use embed's script but rerank's config directory +SCRIPT_PATH = "src/nemotron/recipes/embed/stage0_sdg/data_prep.py" +SCRIPT_REMOTE = "src/nemotron/recipes/embed/stage0_sdg/run_uv.py" +SPEC = parse_runspec(SCRIPT_PATH) +RERANK_CONFIG_DIR = Path("src/nemotron/recipes/rerank/stage0_sdg/config").resolve() + +META = RecipeMeta( + name="rerank/sdg", + script_path=SCRIPT_PATH, + config_dir=str(RERANK_CONFIG_DIR), + config_model=SDGConfig, + default_config="default", + input_artifacts={"corpus": "Document corpus directory"}, + output_artifacts={"data": "Synthetic Q&A pairs (JSON)"}, +) + + +def _execute_sdg(cfg: RecipeConfig, *, experiment=None): + """Execute SDG with rerank output paths.""" + train_config = parse_config(cfg.ctx, RERANK_CONFIG_DIR, "default") + env = parse_env(cfg.ctx) + + script_path = SCRIPT_PATH if cfg.mode == "local" else SCRIPT_REMOTE + + job_config = build_job_config( + train_config, + cfg.ctx, + "rerank/sdg", + script_path, + cfg.argv, + env_profile=env, + ) + + display_job_config(job_config, for_remote=False) + + if cfg.dry_run: + return + + job_dir = generate_job_dir("rerank/sdg") + train_config_for_script = extract_train_config(job_config, for_remote=False) + job_path, train_path = save_configs(job_config, train_config_for_script, job_dir) + + env_for_executor = job_config.run.env if hasattr(job_config.run, "env") else None + env_vars = build_env_vars(job_config, env_for_executor) + + display_job_submission(job_path, train_path, env_vars, cfg.mode) + + if cfg.mode == "local": + _execute_uv_local(train_path, cfg.passthrough) + else: + _execute_remote( + train_path=train_path, + env=env_for_executor, + passthrough=cfg.passthrough, + attached=cfg.attached, + env_vars=env_vars, + force_squash=cfg.force_squash, + experiment=experiment, + ) + + +def _execute_uv_local(train_path: Path, passthrough: list[str]) -> None: + """Execute SDG locally via UV isolated environment.""" + from nemotron.kit.uv_local import execute_uv_local + + script_abs = SPEC.script_path + stage_dir = script_abs.parent + repo_root = SPEC.script_path.parents[len(Path(SCRIPT_PATH).parts) - 1] + + rc = execute_uv_local( + script_path=str(script_abs), + stage_dir=stage_dir, + repo_root=repo_root, + train_path=train_path, + passthrough=passthrough, + ) + raise typer.Exit(rc) + + +def _execute_remote( + train_path: Path, + env, + passthrough: list[str], + attached: bool, + env_vars: dict[str, str], + force_squash: bool, + experiment=None, +): + """Execute SDG via nemo-run with remote backend.""" + try: + import nemo_run as run + except ImportError: + typer.echo("Error: nemo-run is required for --run/--batch execution", err=True) + raise typer.Exit(1) + + from nemo_runspec.packaging import CodePackager + from nemo_runspec.execution import create_executor + from nemo_runspec.run import ( + patch_nemo_run_ray_template_for_cpu, + patch_nemo_run_rsync_accept_new_host_keys, + ) + + patch_nemo_run_rsync_accept_new_host_keys() + patch_nemo_run_ray_template_for_cpu() + + packager = CodePackager( + script_path=str(SCRIPT_REMOTE), + train_path=str(train_path), + ) + + executor = create_executor( + env=env, + env_vars=env_vars, + packager=packager, + attached=attached, + force_squash=force_squash, + default_image=SPEC.image, + ) + + recipe_name = "rerank-sdg" + script_args = [*passthrough] + + if experiment is not None: + return experiment.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + + with run.Experiment(recipe_name) as exp: + exp.add( + run.Script(path="main.py", args=script_args, entrypoint="python"), + executor=executor, + name=recipe_name, + ) + exp.run(detach=not attached, tail_logs=attached) + + +def sdg(ctx: typer.Context) -> None: + """Generate synthetic Q&A pairs from document corpus.""" + cfg = parse_recipe_config(ctx) + _execute_sdg(cfg) diff --git a/src/nemotron/kit/artifacts/rerank.py b/src/nemotron/kit/artifacts/rerank.py new file mode 100644 index 000000000..80501580b --- /dev/null +++ b/src/nemotron/kit/artifacts/rerank.py @@ -0,0 +1,37 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Rerank artifacts - fine-tuned reranking model artifacts.""" + +from __future__ import annotations + +from typing import Annotated + +from pydantic import Field + +from nemotron.kit.artifacts.base import Artifact + + +class RerankModelArtifact(Artifact): + """Fine-tuned cross-encoder reranking model checkpoint (output of stage2_finetune). + + The path points to the final model checkpoint directory. + """ + + base_model: Annotated[str, Field(description="Base model that was fine-tuned")] + training_examples: Annotated[int, Field(ge=0, description="Number of training examples")] + num_epochs: Annotated[int, Field(ge=0, description="Training epochs")] + global_batch_size: Annotated[int, Field(ge=0, description="Global batch size")] + learning_rate: Annotated[float, Field(description="Learning rate")] + num_labels: Annotated[int, Field(ge=1, description="Number of classification labels")] diff --git a/src/nemotron/kit/uv_local.py b/src/nemotron/kit/uv_local.py new file mode 100644 index 000000000..c346ec641 --- /dev/null +++ b/src/nemotron/kit/uv_local.py @@ -0,0 +1,202 @@ +"""Shared helper for local UV execution with torch detection. + +When torch is already importable (e.g., inside an NVIDIA container), we create +a venv with --system-site-packages and exclude torch from UV resolution. This +avoids the CUDA version mismatch where UV's torch-backend=auto detects the +kernel driver's CUDA version (via nvidia-smi) but the container's libcuda.so +is an older version. + +When torch is NOT importable (bare machine), we fall back to the standard +``uv run --with torch`` approach with ``UV_TORCH_BACKEND=auto``. +""" + +from __future__ import annotations + +import os +import shutil +import subprocess +import sys +import tempfile +import tomllib +from pathlib import Path + + +def _torch_is_importable() -> bool: + """Check if torch is importable in the current Python.""" + result = subprocess.run( + [sys.executable, "-c", "import torch"], + capture_output=True, + ) + return result.returncode == 0 + + +def execute_uv_local( + *, + script_path: str, + stage_dir: Path, + repo_root: Path, + train_path: Path, + passthrough: list[str], + extra_with: list[str] | None = None, + pre_script_args: list[str] | None = None, +) -> int: + """Execute a stage script locally, handling torch installation correctly. + + When torch is already available in the current Python (e.g., inside a + container), creates a venv with --system-site-packages and excludes torch + from resolution. Otherwise, uses ``uv run --with torch``. + + Args: + script_path: Relative path to the stage script. + stage_dir: Absolute path to the stage directory (contains pyproject.toml). + repo_root: Absolute path to the repo root (installed via --with). + train_path: Path to the resolved training config YAML. + passthrough: Extra CLI arguments to forward to the script. + extra_with: Additional ``--with`` packages for uv run (e.g., ["tensorrt"]). + pre_script_args: Arguments inserted before the script path + (e.g., ["-m", "torch.distributed.run", "--nproc_per_node=gpu"]). + + Returns: + Process exit code. + """ + uv_cmd = shutil.which("uv") + if not uv_cmd: + print("Error: 'uv' command not found. Please install uv.", file=sys.stderr) + return 1 + + script_abs = (stage_dir / Path(script_path).name) if not Path(script_path).is_absolute() else Path(script_path) + + if _torch_is_importable(): + return _execute_with_system_torch( + uv_cmd=uv_cmd, + stage_dir=stage_dir, + repo_root=repo_root, + script_abs=script_abs, + train_path=train_path, + passthrough=passthrough, + pre_script_args=pre_script_args or [], + ) + else: + return _execute_with_uv_torch( + uv_cmd=uv_cmd, + stage_dir=stage_dir, + repo_root=repo_root, + script_abs=script_abs, + train_path=train_path, + passthrough=passthrough, + extra_with=extra_with or [], + pre_script_args=pre_script_args or [], + ) + + +def _execute_with_system_torch( + *, + uv_cmd: str, + stage_dir: Path, + repo_root: Path, + script_abs: Path, + train_path: Path, + passthrough: list[str], + pre_script_args: list[str], +) -> int: + """Execute using system torch via --system-site-packages venv.""" + print("Detected system torch — using system-site-packages to avoid CUDA mismatch") + + # Read stage pyproject.toml for exclude-dependencies + pyproject_path = stage_dir / "pyproject.toml" + with open(pyproject_path, "rb") as f: + pyproject_data = tomllib.load(f) + + nemotron_cfg = pyproject_data.get("tool", {}).get("nemotron", {}) + exclude_deps = nemotron_cfg.get("container-exclude-dependencies", [ + "torch", "torchvision", "flash-attn", "triton", + "pyarrow", "scipy", "opencv-python-headless", + ]) + + # Import the temp pyproject writer from run_uv + from nemotron.kit.run_uv import _write_temp_pyproject + + temp_dir = _write_temp_pyproject(pyproject_data, stage_dir, exclude_deps) + + # Create venv with system-site-packages + venv_path = Path(tempfile.mkdtemp()) / "venv" + result = subprocess.run([ + uv_cmd, "venv", + "--system-site-packages", + "--seed", + str(venv_path), + ]) + if result.returncode != 0: + print("Error: Failed to create venv", file=sys.stderr) + return 1 + + venv_python = str(venv_path / "bin" / "python3") + env = os.environ.copy() + env.pop("VIRTUAL_ENV", None) + env["VIRTUAL_ENV"] = str(venv_path) + env["UV_PROJECT_ENVIRONMENT"] = str(venv_path) + env["PATH"] = f"{venv_path / 'bin'}:{env.get('PATH', '')}" + + # Sync stage dependencies (excluding torch) + sync_cmd = [ + uv_cmd, "sync", + "--active", + "--project", str(temp_dir), + ] + print(f"Syncing dependencies (torch excluded): {' '.join(sync_cmd)}") + result = subprocess.run(sync_cmd, env=env, cwd=str(temp_dir)) + if result.returncode != 0: + print("Error: Package sync failed", file=sys.stderr) + return 1 + + # Install the repo package + result = subprocess.run( + [uv_cmd, "pip", "install", "--no-deps", str(repo_root)], + env=env, + ) + if result.returncode != 0: + print("Error: Failed to install repo package", file=sys.stderr) + return 1 + + # Run the script + cmd = [venv_python, *pre_script_args, str(script_abs), "--config", str(train_path), *passthrough] + print(f"Executing: {' '.join(cmd)}") + result = subprocess.run(cmd, env=env) + return result.returncode + + +def _execute_with_uv_torch( + *, + uv_cmd: str, + stage_dir: Path, + repo_root: Path, + script_abs: Path, + train_path: Path, + passthrough: list[str], + extra_with: list[str], + pre_script_args: list[str], +) -> int: + """Execute using uv run with torch installed via UV_TORCH_BACKEND.""" + torch_spec = os.environ.get("NEMOTRON_LOCAL_TORCH_SPEC", "torch<=2.10.0") + cmd = [ + uv_cmd, "run", + "--with", str(repo_root), + "--with", torch_spec, + ] + for pkg in extra_with: + cmd += ["--with", pkg] + + if pre_script_args: + cmd += ["--project", str(stage_dir), *pre_script_args] + else: + cmd += ["--project", str(stage_dir), "python"] + + cmd += [str(script_abs), "--config", str(train_path), *passthrough] + + env = os.environ.copy() + env.pop("VIRTUAL_ENV", None) + env.setdefault("UV_TORCH_BACKEND", "auto") + + print(f"Executing with uv isolated environment: {' '.join(cmd)}") + result = subprocess.run(cmd, env=env) + return 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b/src/nemotron/recipes/embed/stage0_sdg/vendor/retriever-sdg/src/retriever_sdg/deduplication.py index 49a9a7291..de9357e63 100644 --- a/src/nemotron/recipes/embed/stage0_sdg/vendor/retriever-sdg/src/retriever_sdg/deduplication.py +++ b/src/nemotron/recipes/embed/stage0_sdg/vendor/retriever-sdg/src/retriever_sdg/deduplication.py @@ -38,16 +38,9 @@ def embedder(self): model_alias=self.config.embedding_alias) def _embed(self, text: str) -> list[float]: - """Calculate an embedding of the text - """ - response = self.embedder._router.embedding( - input=text, - model=self.embedder.model_name, - encoding_format="float", - extra_body=self.embedder._model_config.inference_parameters. - extra_body) - - return response.data[0]["embedding"] + """Calculate an embedding of the text.""" + embeddings = self.embedder.generate_text_embeddings([text]) + return embeddings[0] def dedupe_qa_pairs(self, embeddings: list[list[float]]) -> list[int]: """Run a semantic dedupe of the qa pairs. diff --git a/src/nemotron/recipes/embed/stage1_data_prep/pyproject.toml b/src/nemotron/recipes/embed/stage1_data_prep/pyproject.toml index ec71699e8..bf0afd8a1 100644 --- a/src/nemotron/recipes/embed/stage1_data_prep/pyproject.toml +++ b/src/nemotron/recipes/embed/stage1_data_prep/pyproject.toml @@ -18,6 +18,11 @@ dependencies = [ "pandas>=2.0.0", ] +[tool.uv] +# Exclude torch/torchvision from resolution — supplied via --with at runtime +# with UV_TORCH_BACKEND=auto so the correct CUDA variant is resolved. +exclude-dependencies = ["torch", "torchvision"] + [tool.nemotron] entry-point = "data_prep.py" container-exclude-dependencies = [ diff --git a/src/nemotron/recipes/embed/stage1_data_prep/scripts/convert_to_retriever_data.py b/src/nemotron/recipes/embed/stage1_data_prep/scripts/convert_to_retriever_data.py index 62ded9ce1..e65c07a9c 100644 --- a/src/nemotron/recipes/embed/stage1_data_prep/scripts/convert_to_retriever_data.py +++ 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b/src/nemotron/recipes/rerank/README.md new file mode 100644 index 000000000..3871bc8c7 --- /dev/null +++ b/src/nemotron/recipes/rerank/README.md @@ -0,0 +1,670 @@ +# Reranking Model Fine-Tuning Recipe + +A complete 6-stage pipeline for fine-tuning and deploying cross-encoder reranking models on domain-specific data using synthetic data generation. + +## Overview + +This recipe fine-tunes NVIDIA's [Llama-Nemotron-Rerank-1B-v2](https://huggingface.co/nvidia/llama-nemotron-rerank-1b-v2) cross-encoder reranking model on your own domain data. By the end of this pipeline, you'll have a domain-adapted reranker that improves retrieval precision by re-scoring candidate documents returned by a first-stage retriever. + +### Why Fine-Tune Reranking Models? + +In a typical retrieval pipeline, a fast embedding model retrieves a broad set of candidate documents, then a cross-encoder reranker re-scores each query–document pair to improve ranking quality. Fine-tuning the reranker adapts it to: + +- Understand domain-specific relevance signals and terminology +- Better discriminate between subtly relevant and irrelevant documents +- Improve precision at the top of the ranked list (nDCG@k) on your specific corpus + +### Embedding vs. Reranking + +| Aspect | Embedding Model | Reranking Model | +|--------|----------------|-----------------| +| **Architecture** | Bi-encoder (encodes query and document separately) | Cross-encoder (encodes query and document together) | +| **Speed** | Fast (single encoding per document, offline indexable) | Slower (joint encoding per query–document pair) | +| **Accuracy** | Good for broad recall | Higher precision at top ranks | +| **Role** | First-stage retrieval | Second-stage re-ranking | + +The two models are complementary — use the embedding model to cast a wide net, then the reranker to sort the catch. + +## Training Pipeline + +``` +┌─────────────────────────────────────────────────────────────────────────────┐ +│ YOUR DOCUMENT CORPUS │ +│ (Text files: .txt, .md, etc.) │ +└──────────────────────────────────┬──────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ STAGE 0: SYNTHETIC DATA GENERATION (retriever-sdg) │ +│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────────────────┐ │ +│ │ Document Chunks │ → │ LLM Generation │ → │ Q&A Pairs + Evaluations │ │ +│ │ │ │ (NVIDIA API) │ │ │ │ +│ └─────────────────┘ └─────────────────┘ └──────────────────────────┘ │ +└──────────────────────────────────┬──────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ STAGE 1: TRAINING DATA PREPARATION │ +│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────────────────┐ │ +│ │ Train/Val/Test │ → │ Hard Negative │ → │ Multi-hop Unrolling │ │ +│ │ Split │ │ Mining │ │ │ │ +│ └─────────────────┘ └─────────────────┘ └──────────────────────────┘ │ +└──────────────────────────────────┬──────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ STAGE 2: MODEL FINE-TUNING (Cross-Encoder, Automodel) │ +│ ┌─────────────────────────────────────────────────────────────────────────┐│ +│ │ Classification Loss: Query+Passage → Relevance Score ││ +│ └─────────────────────────────────────────────────────────────────────────┘│ +└──────────────────────────────────┬──────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ STAGE 3: EVALUATION (BEIR + Reranking) │ +│ ┌─────────────────────────────────────────────────────────────────────────┐│ +│ │ Dense Retrieval → Re-rank → Measure nDCG@k Improvement ││ +│ └─────────────────────────────────────────────────────────────────────────┘│ +└──────────────────────────────────┬──────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ STAGE 4: EXPORT (ONNX/TensorRT) │ +│ ┌─────────────────────────────────────────────────────────────────────────┐│ +│ │ Export Model to ONNX and TensorRT for Optimized Inference ││ +│ └─────────────────────────────────────────────────────────────────────────┘│ +└──────────────────────────────────┬──────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ STAGE 5: DEPLOY (NIM) │ +│ ┌─────────────────────────────────────────────────────────────────────────┐│ +│ │ Launch NIM Container with Custom Model for Ranking API ││ +│ └─────────────────────────────────────────────────────────────────────────┘│ +└─────────────────────────────────────────────────────────────────────────────┘ +``` + +| Stage | Command | Description | Output | +|-------|---------|-------------|--------| +| [Stage 0: SDG](./stage0_sdg/) | `nemotron rerank sdg` | Validate corpus, generate synthetic Q&A pairs from documents | Q&A pairs with quality scores | +| [Stage 1: Data Prep](./stage1_prep/) | `nemotron rerank prep` | Convert, mine hard negatives, unroll | Training-ready data | +| [Stage 2: Finetune](./stage2_finetune/) | `nemotron rerank finetune` | Fine-tune cross-encoder reranking model | Model checkpoint | +| [Stage 3: Eval](./stage3_eval/) | `nemotron rerank eval` | Evaluate reranking improvement over first-stage retrieval | Metrics comparison | +| [Stage 4: Export](./stage4_export/) | `nemotron rerank export` | Export to ONNX/TensorRT | Optimized inference models | +| [Stage 5: Deploy](./stage5_deploy/) | `nemotron rerank deploy` | Deploy NIM ranking service | Running inference service | + +## Installation + +### 1. Install UV Package Manager + +This project **requires [UV](https://docs.astral.sh/uv/)** as its package manager. UV automatically creates and manages a virtual environment under the repository root, and each pipeline stage uses its own isolated environment as well. **Do not use `pip install`** — the project relies on UV workspaces and per-stage dependency isolation. + +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +### 2. Clone and Install Nemotron + +```bash +# Clone the repository +git clone https://github.com/NVIDIA-NeMo/Nemotron.git +cd Nemotron + +# UV creates a virtual environment at .venv/ and installs all dependencies +uv sync --all-extras +``` + +### 3. Get Your NVIDIA API Key + +The SDG stage (Stage 0) uses NVIDIA's hosted LLM APIs for synthetic data generation. + +1. Sign up at [build.nvidia.com](https://build.nvidia.com) +2. Create an API key +3. Set the environment variable: + +```bash +export NVIDIA_API_KEY=nvapi-your_key_here +``` + +### 4. Configure Execution Profiles (Optional) + +For Docker or Slurm execution, create `env.toml` in the **repository root directory**. + +**Minimal configuration (local execution only):** +```toml +[wandb] +project = "my-reranking-project" +entity = "my-username" +``` + +**Full configuration with Docker and Slurm support:** +See the [Execution Profiles](#execution-profiles) section below. + +## Preparing Your Corpus + +### Supported Formats + +- Any text files, default: .txt, .md, .text, and files with no extension +- Documents should be UTF-8 encoded +- Files are processed recursively from the corpus directory + +### Corpus Size Recommendations + +| Corpus Size | Documents | Expected Results | +|-------------|-----------|------------------| +| **Minimum** | 50-100 docs (~50K tokens) | Basic domain adaptation | +| **Recommended** | 500+ docs | Good domain coverage | +| **Optimal** | 1000+ docs | Best performance | + +### Document Organization + +Organize your documents in a directory structure: + +```bash +data/corpus/ +├── doc1.txt +├── doc2.md +└── subdirectory/ + └── doc3.txt +``` + +All files matching the `file_extensions` config (default: `.txt,.md`) will be processed recursively. + +## Prerequisites + +### Hardware Requirements + +- **GPU**: NVIDIA GPU with at least 80GB VRAM (e.g., A100, H100) + - Stage 0 uses NVIDIA API (no GPU required) + - Stages 1-5: Require GPU for model inference and training +- **CPU**: Modern multi-core processor (16+ cores recommended) +- **Memory**: 128GB+ RAM recommended +- **Storage**: ~50GB free disk space for outputs, models, and containers + +### Software Requirements + +- **Python**: 3.12 or later +- **UV**: Package manager (installation instructions above) +- **NVIDIA API Key**: Required for synthetic data generation +- **NVIDIA GPU Drivers**: Latest drivers for your GPU +- **Docker** (optional): For containerized execution or NIM deployment +- **Slurm** (optional): For cluster execution + +### Expected Runtime & Resources + +| Stage | GPU VRAM | CPU | Notes | +|-------|----------|-----|-------| +| Stage 0 (SDG) | N/A | 8+ cores | Uses API (no local GPU); runtime varies by dataset size | +| Stage 1 (Data Prep) | 40GB | 16+ cores | Hard negative mining on GPU; runtime varies by dataset size | +| Stage 2 (Finetune) | 80GB | 16+ cores | Runtime varies by dataset size and epochs | +| Stage 3 (Eval) | 40GB | 8+ cores | Dense retrieval + reranking; runtime varies by dataset size | +| Stage 4 (Export) | 40GB | 8+ cores | TensorRT export requires NGC container | +| Stage 5 (Deploy) | 40GB | 4+ cores | NIM container initialization | + +### LLM API Usage (Stage 0) + +Stage 0 uses LLM APIs for synthetic data generation. By default, it uses NVIDIA's hosted LLMs: + +- **Default provider**: NVIDIA API (free tier available at [build.nvidia.com](https://build.nvidia.com)) +- **Default model**: `nvidia/nemotron-3-nano-30b-a3b` (fast, reliable for structured generation) +- **Usage**: ~4 API calls per document (artifact extraction, QA generation, dedup, quality eval) +- **Cost**: Free tier has rate limits; contact NVIDIA for production usage + +## Quick Start + +### Local Execution + +```bash +# Set environment (important for CUDA compatibility) +export LD_LIBRARY_PATH="" +export NVIDIA_API_KEY=nvapi-your_key_here + +# Stage 0: Generate synthetic Q&A pairs from your documents +nemotron rerank sdg -c default corpus_dir=/path/to/your/docs + +# Stage 1: Prepare training data (convert, mine hard negatives, unroll) +nemotron rerank prep -c default + +# Stage 2: Fine-tune the cross-encoder reranking model +nemotron rerank finetune -c default + +# Stage 3: Evaluate base vs fine-tuned reranker +nemotron rerank eval -c default + +# Stage 4: Export to ONNX/TensorRT for deployment +nemotron rerank export -c default + +# Stage 5: Deploy NIM with custom model +nemotron rerank deploy -c default + +# Optional: Verify NIM accuracy matches checkpoint +nemotron rerank eval -c default eval_nim=true eval_base=false +``` + +### Preview Commands (Dry Run) + +```bash +# See what would be executed without running +nemotron rerank finetune -c default --dry-run +``` + +## Pipeline Flexibility + +Stages are designed to run sequentially, but you can start from any stage if you have the required inputs: + +| Start From | Requirement | Use Case | +|------------|-------------|----------| +| **Stage 0** | Document corpus | Full pipeline from scratch | +| **Stage 1** | Q&A pairs (JSON) | Skip SDG if you have labeled data or use [NVIDIA's pre-generated dataset](#using-nvidias-pre-generated-dataset) | +| **Stage 2** | Training data (Automodel format) | Skip data prep if data is ready | +| **Stage 3** | Model checkpoint | Evaluate existing checkpoint | +| **Stage 4** | Model checkpoint | Export existing model | +| **Stage 5** | Exported model (ONNX/TensorRT) | Deploy existing model | + +See individual stage configs for input format requirements. + +### Using NVIDIA's Pre-Generated Dataset + +NVIDIA provides a ready-to-use synthetic retrieval dataset on Hugging Face: [Retrieval-Synthetic-NVDocs-v1](https://huggingface.co/datasets/nvidia/Retrieval-Synthetic-NVDocs-v1). This dataset was generated from NVIDIA's publicly available content using the same SDG pipeline (Stage 0) in this recipe, and contains ~15K documents with 105K+ question-answer pairs across multiple reasoning types. + +If you want to fine-tune a reranking model on NVIDIA-related content, you can **skip Stage 0 entirely** and start directly from Stage 1: + +```bash +# Download the pre-generated dataset +python -c " +from datasets import load_dataset +ds = load_dataset('nvidia/Retrieval-Synthetic-NVDocs-v1', split='train') +ds.to_json('./output/rerank/stage0_sdg/nv_docs_sdg.json') +" + +# Start from Stage 1 (data preparation) using the downloaded data +nemotron rerank prep -c default sdg_input_path=./output/rerank/stage0_sdg + +# Continue with the rest of the pipeline +nemotron rerank finetune -c default +nemotron rerank eval -c default +``` + +## Execution Modes + +The rerank recipe supports multiple execution modes for flexibility between local development and production cluster runs. + +### Local Execution (Default) + +Run directly on your local machine with GPU: + +```bash +nemotron rerank finetune -c default +nemotron rerank eval -c default +``` + +### Docker Execution + +Run inside a Docker container with GPU passthrough using `--run local-docker`: + +```bash +# Runs the command inside a Docker container with GPU access +nemotron rerank finetune -c default --run local-docker + +# All stages support Docker execution +nemotron rerank sdg -c default --run local-docker +nemotron rerank prep -c default --run local-docker +nemotron rerank eval -c default --run local-docker +``` + +> **Note**: Requires `local-docker` profile in `env.toml` (see [Execution Profiles](#execution-profiles) below) + +### Slurm Batch Execution + +Submit jobs to a Slurm cluster for production workloads: + +```bash +# Attached execution (waits for completion, streams logs via SSH) +nemotron rerank finetune -c default --run my-cluster + +# Detached execution (submits job and exits immediately) +nemotron rerank finetune -c default --batch my-cluster + +# Run full pipeline on cluster +nemotron rerank sdg -c default --batch my-cluster +nemotron rerank prep -c default --batch my-cluster +nemotron rerank finetune -c default --batch my-cluster +nemotron rerank eval -c default --batch my-cluster +``` + +### Execution Profiles + +Execution profiles are defined in `env.toml` in the **repository root directory**. + +**Example `env.toml` for local and cluster execution:** + +```toml +# Weights & Biases configuration (optional but recommended) +[wandb] +project = "my-reranking-project" +entity = "my-team" + +# Local Docker execution profile +[local-docker] +executor = "docker" +container_image = "nvcr.io/nvidia/pytorch:25.12-py3" +runtime = "nvidia" # Enable GPU passthrough +ipc_mode = "host" +shm_size = "16g" +mounts = [ + "./data:/workspace/data", + "./output:/workspace/output" +] + +# Slurm cluster execution profile +[my-cluster] +executor = "slurm" +account = "my-account" +partition = "interactive" +batch_partition = "batch" +container_image = "nvcr.io/nvidia/pytorch:25.12-py3" +tunnel = "ssh" +host = "cluster.example.com" +user = "username" +remote_job_dir = "/shared/path/to/jobs" +mounts = ["/shared:/shared"] +``` + +### Runtime Overrides + +Override execution settings on the command line: + +```bash +# Use more GPUs +nemotron rerank finetune -c default --run my-cluster run.env.gpus_per_node=4 + +# Use different partition +nemotron rerank finetune -c default --batch my-cluster run.env.partition=batch + +# Override time limit +nemotron rerank finetune -c default --batch my-cluster run.env.time=08:00:00 +``` + +### Interactive Debugging + +Stage files to the cluster for interactive debugging: + +```bash +# Stage files without executing +nemotron rerank finetune -c default --run my-cluster --stage + +# Then SSH to cluster and run manually +ssh cluster.example.com +cd /path/to/staged/files +./run.sh +``` + +## Configuration + +Each stage has a `config/` directory with YAML configuration files. + +| File | Purpose | +|------|---------| +| `default.yaml` | Production-ready configuration | + +### Key Configuration Options + +**Stage 0: SDG** +```yaml +corpus_id: my_corpus # Identifier for your corpus +corpus_dir: ./data/corpus # Path to your documents +file_extensions: ".txt,.md" # File types to process +output_dir: ./output/rerank/stage0_sdg # Path to save the generated data +artifact_extraction_model: nvidia/nemotron-3-nano-30b-a3b # LLM for document artifacts extraction +qa_generation_model: nvidia/nemotron-3-nano-30b-a3b # LLM for QA generation +quality_judge_model: nvidia/nemotron-3-nano-30b-a3b # LLM for QA quality evaluation +``` + +**Stage 1: Data Prep** +```yaml +base_model: nvidia/llama-nemotron-embed-1b-v2 # Embedding model for hard negative mining +quality_threshold: 7.0 # Minimum Q&A quality score (0-10) +hard_negatives_to_mine: 5 # Number of hard negatives per query +query_max_length: 512 # Max query tokens +passage_max_length: 512 # Max passage tokens +train_ratio: 0.8 # Training data split (80%) +val_ratio: 0 # Validation split (0% — maximizes train/test for small data) +test_ratio: 0.2 # Test split (20%) +``` + +**Stage 2: Finetune** +```yaml +base_model: nvidia/llama-nemotron-rerank-1b-v2 +num_epochs: 3 +global_batch_size: 128 +learning_rate: 3.0e-6 +lr_warmup_steps: 100 +lr_decay_style: cosine +weight_decay: 0.01 +rerank_max_length: 512 # Max tokens for concatenated query+passage +prompt_template: "question:{query} \n \n passage:{passage}" +train_n_passages: 5 # 1 positive + 4 hard negatives +num_labels: 1 +temperature: 1.0 +force_fp32_parameters: true # Keep torch AdamW parameters and optimizer state in fp32 +checkpoint_every_steps: 100 +restore_from: null # Optional checkpoint path or name for explicit resume +``` + +> **Warning — Overfitting risk**: The default `num_epochs: 3` is set for the small example dataset shipped with this recipe, where fewer epochs may not produce a visible training signal. For most real-world datasets, **1–2 epochs is sufficient** and 3 epochs carries a high risk of overfitting. Lower this value when working with your own data (e.g., `nemotron rerank finetune -c default num_epochs=1`). + +> **Precision note**: Local fine-tuning keeps parameters in fp32 when `force_fp32_parameters: true` so native torch AdamW creates and checkpoints fp32 Adam moments. This matches the Automodel POC behavior more closely than bf16 optimizer state and is also enforced on explicit resume before optimizer state is loaded. + +> **Tokenizer note**: Reranker train and eval tokenization must stay aligned. Training pins `add_eos_token: false` in the Automodel tokenizer config, and Stage 3 eval pins the same value when loading the fine-tuned tokenizer. Keep this setting and `prompt_template` matched between stages when customizing the recipe. + +**Stage 3: Eval** +```yaml +base_model: nvidia/llama-nemotron-rerank-1b-v2 # Base reranker for comparison +retrieval_model: nvidia/llama-nemotron-embed-1b-v2 # First-stage retriever +k_values: [1, 5, 10, 100] # K values for nDCG@k, Recall@k +top_k: 100 # Number of first-stage candidates to re-rank +max_length: 512 # Max sequence length +eval_base: true # Evaluate base reranker +eval_finetuned: true # Evaluate fine-tuned reranker +eval_nim: false # Evaluate NIM endpoint +``` + +**Stage 4: Export** +```yaml +model_path: ./output/rerank/stage2_finetune/checkpoints/LATEST/model/consolidated +export_to_trt: false # Export to TensorRT (requires nemo:25.07+ container) +quant_cfg: null # Quantization: null, "fp8", "int8_sq" +trt_opt_batch: 16 # Optimal batch size for TRT +trt_opt_seq_len: 256 # Optimal sequence length for TRT +``` + +**Stage 5: Deploy** +```yaml +nim_image: nvcr.io/nim/nvidia/llama-nemotron-rerank-1b-v2:1.10.0 +model_dir: ./output/rerank/stage4_export/onnx # Path to exported model +host_port: 8000 # Port for NIM ranking API +detach: false # Run in background +``` + +### Overriding Configuration + +Override config values on the command line: + +```bash +# Override training epochs +nemotron rerank finetune -c default num_epochs=5 + +# Override learning rate +nemotron rerank finetune -c default learning_rate=1e-5 + +# Override multiple values +nemotron rerank finetune -c default num_epochs=2 learning_rate=1e-5 + +# Force specific attention implementation +nemotron rerank finetune -c default attn_implementation=flash_attention_2 +``` + +## CLI Commands + +### Workspace Info + +```bash +# Display workflow overview +nemotron rerank info +``` + +### Data + +```bash +# Generate synthetic Q&A pairs from documents +nemotron rerank sdg -c default corpus_dir=/path/to/docs + +# Prepare training data (convert, mine, unroll) +nemotron rerank prep -c default sdg_input_path=/path/to/sdg +``` + +### Training + +```bash +# Fine-tune the cross-encoder reranking model +nemotron rerank finetune -c default train_data_path=/path/to/data +``` + +### Evaluation + +```bash +# Evaluate base and fine-tuned rerankers +nemotron rerank eval -c default finetuned_model_path=/path/to/checkpoint +``` + +### Export + +```bash +# Export model to ONNX +nemotron rerank export -c default model_path=/path/to/checkpoint + +# Export to ONNX only (skip TensorRT) +nemotron rerank export -c default export_to_trt=false + +# Export with FP8 quantization +nemotron rerank export -c default quant_cfg=fp8 +``` + +### Deploy + +```bash +# Deploy NIM with custom ONNX model (foreground) +nemotron rerank deploy -c default + +# Deploy in background (detached mode) +nemotron rerank deploy -c default detach=true + +# Deploy with custom model directory +nemotron rerank deploy -c default model_dir=./output/rerank/stage4_export/onnx + +# Stop the NIM container +docker stop nemotron-rerank-nim +``` + +### Verify NIM Accuracy + +```bash +# Evaluate NIM endpoint against fine-tuned checkpoint +nemotron rerank eval -c default eval_nim=true eval_base=false + +# The output will show if NIM metrics match the checkpoint +# ok indicates metrics match within tolerance (0.03 for @1, 0.01 for @5+) +# MISMATCH indicates potential accuracy loss beyond ONNX/TensorRT conversion noise +``` + +### Test the Deployed Service + +```bash +curl -X POST http://localhost:8000/v1/ranking \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "nvidia/llama-nemotron-rerank-1b-v2", + "query": {"text": "what is AI?"}, + "passages": [ + {"text": "AI is artificial intelligence"}, + {"text": "The weather is sunny today"} + ] + }' +``` + +## Output Structure + +After running the full pipeline: + +``` +output/rerank/ +├── stage0_sdg/ # Synthetic Q&A pairs +│ └── generated_batch*.json +├── stage1_prep/ # Training-ready data +│ ├── train.json # Original training data +│ ├── train_mined.automodel.json # With hard negatives +│ ├── train_mined.automodel_unrolled.json # Final training file +│ ├── corpus/ # Document corpus +│ └── eval_beir/ # BEIR-format evaluation data +├── stage2_finetune/ # Model checkpoints +│ └── checkpoints/ +│ └── LATEST/model/consolidated/ # Final model +├── stage3_eval/ # Evaluation results +│ └── eval_results.json +└── stage4_export/ # Exported models + ├── onnx/ # ONNX model files + │ └── model.onnx + └── tensorrt/ # TensorRT engine (if enabled) + └── model.plan +``` + +## Evaluation Metrics + +The evaluation stage measures reranking quality using a two-stage approach: first-stage dense retrieval followed by cross-encoder re-ranking. This mirrors how rerankers are used in production. + +| Metric | Description | Range | +|--------|-------------|-------| +| **nDCG@k** | Normalized Discounted Cumulative Gain (ranking quality) | 0.0-1.0 | +| **Recall@k** | Fraction of relevant documents in top-k results | 0.0-1.0 | +| **Precision@k** | Fraction of retrieved documents that are relevant | 0.0-1.0 | +| **MAP@k** | Mean Average Precision | 0.0-1.0 | + +Higher scores indicate better re-ranking performance. The key metric to watch is **nDCG@10**, which captures how well the reranker promotes relevant documents to the top of the list. + + +## Key Components + +| Component | Purpose | Repository | +|-----------|---------|------------| +| retriever-sdg | Synthetic data generation using NeMo Data Designer | [GitHub](https://github.com/NVIDIA-NeMo/DataDesigner) | +| Automodel | Cross-encoder model training framework | [GitHub](https://github.com/NVIDIA-NeMo/Automodel) | +| BEIR | Evaluation framework for information retrieval | [GitHub](https://github.com/beir-cellar/beir) | +| NeMo Export-Deploy | ONNX/TensorRT export for optimized inference | [GitHub](https://github.com/NVIDIA-NeMo/Export-Deploy) | +| NVIDIA NIM | Production inference microservice with ranking API | [Developer Site](https://developer.nvidia.com/nim) | + +## Base Model + +| Property | Value | +|----------|-------| +| Model | nvidia/llama-nemotron-rerank-1b-v2 | +| Parameters | ~1B | +| Architecture | Cross-encoder (sequence classification) | +| Max Sequence Length | 512 (concatenated query + passage) | +| Pooling | Average | +| HuggingFace | [Model Card](https://huggingface.co/nvidia/llama-nemotron-rerank-1b-v2) | + +## Further Reading + +- [NeMo Data Designer Documentation](https://github.com/NVIDIA-NeMo/DataDesigner) - Synthetic data generation framework +- [Automodel Documentation](https://github.com/NVIDIA-NeMo/Automodel) - Model training framework +- [BEIR Benchmark](https://github.com/beir-cellar/beir) - Information retrieval evaluation +- [NVIDIA NIM Documentation](https://developer.nvidia.com/nim) - Production inference microservices +- [Llama-Nemotron-Rerank-1B-v2 Model Card](https://huggingface.co/nvidia/llama-nemotron-rerank-1b-v2) - Base model details +- [Retrieval-Synthetic-NVDocs-v1 Dataset](https://huggingface.co/datasets/nvidia/Retrieval-Synthetic-NVDocs-v1) - Pre-generated synthetic retrieval dataset + +## Support + +For issues, questions, or contributions: +- **Issues**: [GitHub Issues](https://github.com/NVIDIA-NeMo/Nemotron/issues) +- **Discussions**: [GitHub Discussions](https://github.com/NVIDIA-NeMo/Nemotron/discussions) +- **Documentation**: [Nemotron Documentation](https://github.com/NVIDIA-NeMo/Nemotron) diff --git a/src/nemotron/recipes/rerank/__init__.py b/src/nemotron/recipes/rerank/__init__.py new file mode 100644 index 000000000..33c48b5b2 --- /dev/null +++ b/src/nemotron/recipes/rerank/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Rerank recipe for fine-tuning cross-encoder reranking models. + +Workflow stages: +- stage0_sdg: Generate synthetic Q&A pairs from documents +- stage1_prep: Prepare training data (convert, mine, unroll) +- stage2_finetune: Fine-tune cross-encoder reranking model +- stage3_eval: Evaluate base vs fine-tuned reranker +- stage4_export: Export model to ONNX/TensorRT +- stage5_deploy: Deploy NIM reranker service +""" diff --git a/src/nemotron/recipes/rerank/stage0_sdg/config/default.yaml b/src/nemotron/recipes/rerank/stage0_sdg/config/default.yaml new file mode 100644 index 000000000..5e3997599 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage0_sdg/config/default.yaml @@ -0,0 +1,58 @@ +# Stage 0: Synthetic Data Generation Configuration (Rerank) +# +# Generates synthetic Q&A pairs from document corpus using retriever-sdg. +# Uses the same SDG pipeline as the embed recipe but outputs to rerank directory. +# +# Usage: +# nemotron rerank sdg -c default +# nemotron rerank sdg -c default corpus_dir=/path/to/docs + +# Execution environment configuration +run: + env: + container: nvcr.io/nvidia/pytorch:25.12-py3 + +# Core paths +corpus_id: nv_pp_random +corpus_dir: hf://nvidia/Retrieval-Synthetic-NVDocs-v1@1c0d1856f3fb595b2dda98d4b61061fa6d782d51/sample_corpus/nv_pp_random + +# Output directory for generated synthetic data +output_dir: ./output/rerank/stage0_sdg + +# Document processing +min_text_length: 50 +sentences_per_chunk: 5 +num_sections: 1 + +# Generation parameters +max_artifacts_per_type: 2 +num_pairs: 10 +min_hops: 1 +max_hops: 3 +min_complexity: 2 + +# Batch processing +batch_size: 200 +start_batch_index: 0 +end_batch_index: -1 + +# Multi-document bundling +multi_doc: false +bundle_size: 2 +bundle_strategy: sequential +max_docs_per_bundle: 3 + +# Model configuration +artifact_extraction_model: nvidia/nemotron-3-nano-30b-a3b +artifact_extraction_provider: nvidia +qa_generation_model: nvidia/nemotron-3-nano-30b-a3b +qa_generation_provider: nvidia +quality_judge_model: nvidia/nemotron-3-nano-30b-a3b +quality_judge_provider: nvidia +embed_model: nvidia/llama-3.2-nv-embedqa-1b-v2 +embed_provider: nvidia + +# Runtime options +artifact_path: ./output/rerank/stage0_sdg/artifacts +preview: false +log_level: INFO diff --git a/src/nemotron/recipes/rerank/stage1_prep/config/default.yaml b/src/nemotron/recipes/rerank/stage1_prep/config/default.yaml new file mode 100644 index 000000000..df2aa3bb6 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage1_prep/config/default.yaml @@ -0,0 +1,50 @@ +# Stage 1: Data Preparation Configuration (Rerank) +# +# Prepares training data from SDG output: +# 1. Convert to BEIR format (train/val/test split) +# 2. Mine hard negatives using base embedding model +# 3. Unroll multi-hop positives +# +# Uses the same prep pipeline as the embed recipe but reads/writes +# to the rerank output directory. +# +# Usage: +# nemotron rerank prep -c default +# nemotron rerank prep -c default sdg_input_path=/path/to/sdg + +# Execution environment configuration +run: + env: + container: nvcr.io/nvidia/pytorch:25.12-py3 + +# Corpus identifier +corpus_id: nv_pp_random + +# Path to SDG output directory (from rerank sdg stage) +sdg_input_path: ./output/rerank/stage0_sdg + +# Output directory for prepared training data +output_dir: ./output/rerank/stage1_prep + +# Base embedding model for hard negative mining +base_model: nvidia/llama-nemotron-embed-1b-v2 + +# Quality filtering threshold (0-10 scale) +quality_threshold: 7.0 + +# Train/val/test split ratios +train_ratio: 0.8 +val_ratio: 0 +test_ratio: 0.2 + +# Hard negative mining settings +attn_implementation: sdpa +hard_negatives_to_mine: 5 +hard_neg_margin: 0.95 +mining_batch_size: 128 + +# Tokenization settings +query_max_length: 512 +passage_max_length: 512 +query_prefix: "query:" +passage_prefix: "passage:" diff --git a/src/nemotron/recipes/rerank/stage2_finetune/__init__.py b/src/nemotron/recipes/rerank/stage2_finetune/__init__.py new file mode 100644 index 000000000..76d6a5ad5 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage2_finetune/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Stage 0: Fine-tune cross-encoder reranking model.""" diff --git a/src/nemotron/recipes/rerank/stage2_finetune/config/default.yaml b/src/nemotron/recipes/rerank/stage2_finetune/config/default.yaml new file mode 100644 index 000000000..b28f969d7 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage2_finetune/config/default.yaml @@ -0,0 +1,55 @@ +# Stage 2: Fine-tuning Configuration +# +# Fine-tunes a cross-encoder reranking model using classification loss. +# +# Usage: +# nemotron rerank finetune -c default +# nemotron rerank finetune -c default base_model=nvidia/llama-nemotron-rerank-1b-v2 +# +# Remote execution (Slurm): +# nemotron rerank finetune -c default --run my-cluster +# nemotron rerank finetune -c default --batch my-cluster + +# Execution environment configuration +run: + env: + # Container image for remote execution (Docker/Slurm) + container: nvcr.io/nvidia/pytorch:25.12-py3 + +# Base reranking model to fine-tune +base_model: nvidia/llama-nemotron-rerank-1b-v2 + +# Path to training data (output from embed stage1_data_prep) +train_data_path: ./output/rerank/stage1_prep/train_mined.automodel_unrolled.json + +# Directory for saving checkpoints +checkpoint_dir: ./output/rerank/stage2_finetune/checkpoints + +# Training hyperparameters +num_epochs: 3 +global_batch_size: 128 +local_batch_size: 4 +learning_rate: 3.0e-6 +lr_warmup_steps: 100 +lr_decay_style: cosine +weight_decay: 0.01 +# Optional absolute optimizer step limit. Leave null for full epoch-based training. +max_steps: null +# Keep torch AdamW state fp32 in local mode. +force_fp32_parameters: true + +# Model architecture +# attn_implementation: null # Auto-detects: flash_attention_2 if available, else sdpa +train_n_passages: 5 # 1 positive + 4 negatives +num_labels: 1 +temperature: 1.0 +pooling: avg + +# Tokenization +rerank_max_length: 512 +prompt_template: "question:{query} \n \n passage:{passage}" + +# Checkpointing +checkpoint_every_steps: 100 +val_every_steps: 100 +restore_from: null diff --git a/src/nemotron/recipes/rerank/stage2_finetune/crossencoder_base.yaml b/src/nemotron/recipes/rerank/stage2_finetune/crossencoder_base.yaml new file mode 100644 index 000000000..be13674fa --- /dev/null +++ b/src/nemotron/recipes/rerank/stage2_finetune/crossencoder_base.yaml @@ -0,0 +1,96 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Base configuration for cross-encoder fine-tuning using nemo-automodel +# This file provides defaults that will be overridden by the Nemotron CLI +# Source: https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/retrieval/cross_encoder/llama3_2_1b.yaml + +seed: 42 + +step_scheduler: + global_batch_size: 128 + local_batch_size: 4 + ckpt_every_steps: 100 + val_every_steps: 100 + num_epochs: 1 + +dist_env: + backend: nccl + timeout_minutes: 30 + +model: + _target_: nemo_automodel.NeMoAutoModelCrossEncoder.from_pretrained + pretrained_model_name_or_path: nvidia/llama-nemotron-rerank-1b-v2 + trust_remote_code: true + num_labels: 1 + temperature: 1.0 + pooling: avg + use_liger_kernel: true + use_sdpa_patching: true + # Load weights in fp32 so the FSDP policy below gives torch AdamW fp32 state. + torch_dtype: float32 + +tokenizer: + _target_: nemo_automodel.NeMoAutoTokenizer.from_pretrained + pretrained_model_name_or_path: nvidia/llama-nemotron-rerank-1b-v2 + trust_remote_code: true + add_eos_token: false + +dataloader: + _target_: torchdata.stateful_dataloader.StatefulDataLoader + dataset: + _target_: nemo_automodel.components.datasets.llm.make_retrieval_dataset + model_type: cross_encoder + data_dir_list: + - training_datasets/train.json + data_type: train + n_passages: 5 + seed: 42 + do_shuffle: true + collate_fn: + _target_: nemo_automodel.components.datasets.llm.CrossEncoderCollator + rerank_max_length: 512 + prompt_template: "question:{query} \n \n passage:{passage}" + pad_to_multiple_of: 8 + shuffle: true + num_workers: 0 + +optimizer: + _target_: torch.optim.AdamW + lr: 3.0e-6 + weight_decay: 0.01 + betas: [0.9, 0.999] + eps: 1.0e-8 + fused: true + +lr_scheduler: + lr_warmup_steps: 100 + +checkpoint: + enabled: true + checkpoint_dir: ./output/rerank/stage2_finetune/checkpoints + model_save_format: safetensors + save_consolidated: true + +distributed: + strategy: fsdp2 + dp_size: none + tp_size: 1 + cp_size: 1 + sequence_parallel: false + mp_policy: + _target_: torch.distributed.fsdp.MixedPrecisionPolicy + param_dtype: float32 + reduce_dtype: float32 + output_dtype: float32 diff --git a/src/nemotron/recipes/rerank/stage2_finetune/pyproject.toml b/src/nemotron/recipes/rerank/stage2_finetune/pyproject.toml new file mode 100644 index 000000000..0b2893033 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage2_finetune/pyproject.toml @@ -0,0 +1,33 @@ +[project] +name = "recipe-runner" +version = "0.0.0" +requires-python = ">=3.12" +dependencies = [ + "nemo-automodel @ git+https://github.com/NVIDIA-NeMo/Automodel.git@897ebedfa6264225d2c0e1e3f7ed289da7803f1a", + # Needed by Nemotron library (added via --with flag) + "omegaconf>=2.3.0", + "pydantic-settings>=2.0.0", +] + +# Note: exclude-dependencies are injected dynamically by run_uv.py for Docker/Slurm +# This base config works for local mode where all packages need to be installed + +[tool.uv] +# Override automodel's transformers pin to use 5.5.x +override-dependencies = ["transformers>=5.5.0,<5.6.0"] +# Exclude torch/torchvision from resolution; local mode supplies torch<=2.10.0 +# at runtime, with UV_TORCH_BACKEND=auto selecting a compatible CUDA variant. +exclude-dependencies = ["torch", "torchvision"] + +[tool.nemotron] +entry-point = "train.py" +container-exclude-dependencies = [ + "torch", + "torchao", + "torchvision", + "flash-attn", + "triton", + "pyarrow", + "scipy", + "opencv-python-headless", +] diff --git a/src/nemotron/recipes/rerank/stage2_finetune/run_uv.py b/src/nemotron/recipes/rerank/stage2_finetune/run_uv.py new file mode 100644 index 000000000..3209e26e3 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage2_finetune/run_uv.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python3 +"""UV dependency wrapper — delegates to shared nemotron.kit.run_uv.""" +import sys +from pathlib import Path + +# Ensure nemotron package is importable (for container execution) +_src = str(Path(__file__).resolve().parents[4]) # -> .../src +if _src not in sys.path: + sys.path.insert(0, _src) + +from nemotron.kit.run_uv import main + +main(stage_dir=Path(__file__).parent) diff --git a/src/nemotron/recipes/rerank/stage2_finetune/train.py b/src/nemotron/recipes/rerank/stage2_finetune/train.py new file mode 100644 index 000000000..4ea37b12b --- /dev/null +++ b/src/nemotron/recipes/rerank/stage2_finetune/train.py @@ -0,0 +1,448 @@ +#!/usr/bin/env python3 +# /// script +# [tool.runspec] +# schema = "1" +# docs = "https://raw.githubusercontent.com/NVIDIA-NeMo/Nemotron/main/docs/runspec/v1/spec.md" +# name = "rerank/finetune" +# image = "nvcr.io/nvidia/pytorch:25.12-py3" +# setup = "PyTorch pre-installed. Stage dependencies resolved via UV at runtime." +# +# [tool.runspec.run] +# launch = "torchrun" +# +# [tool.runspec.config] +# dir = "./config" +# default = "default" +# +# [tool.runspec.resources] +# nodes = 1 +# gpus_per_node = 1 +# /// +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Fine-tuning script for cross-encoder reranking models. + +Fine-tunes a reranking model using cross-entropy classification loss +with prepared training data (from embed stage1_data_prep). + +Usage: + # With default config + nemotron rerank finetune -c default + + # With custom config + nemotron rerank finetune -c /path/to/config.yaml + + # With CLI overrides + nemotron rerank finetune -c default base_model=nvidia/llama-nemotron-rerank-1b-v2 +""" + +from __future__ import annotations + +import json +import os +import pickle +import sys +from pathlib import Path +from types import MethodType +from typing import Literal + +from pydantic import ConfigDict, Field + +from nemo_runspec.config.pydantic_loader import RecipeSettings, load_config, parse_config_and_overrides + +STAGE_PATH = Path(__file__).parent +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "default.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + + +class FinetuneConfig(RecipeSettings): + """Fine-tuning configuration for cross-encoder reranking models.""" + + model_config = ConfigDict(extra="forbid") + + # Model settings + base_model: str = Field(default="nvidia/llama-nemotron-rerank-1b-v2", description="Base reranking model to fine-tune.") + + # Data paths + train_data_path: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage1_prep/train_mined.automodel_unrolled.json", description="Path to training data file.") + + # Output settings + checkpoint_dir: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage2_finetune/checkpoints", description="Directory for saving checkpoints.") + + # Training hyperparameters + num_epochs: int = Field(default=3, gt=0, description="Number of training epochs.") + global_batch_size: int = Field(default=128, gt=0, description="Global batch size across all GPUs.") + local_batch_size: int = Field(default=4, gt=0, description="Per-GPU batch size.") + learning_rate: float = Field(default=3e-6, gt=0, description="Learning rate.") + lr_warmup_steps: int = Field(default=100, ge=0, description="Learning rate warmup steps.") + lr_decay_style: Literal["cosine", "linear"] = Field(default="cosine", description="LR decay schedule (cosine, linear).") + weight_decay: float = Field(default=0.01, ge=0, description="Weight decay for optimizer.") + max_steps: int | None = Field(default=None, gt=0, description="Optional absolute maximum optimizer step to run to. Useful for short smoke tests and controlled resume runs.") + force_fp32_parameters: bool = Field(default=True, description="Cast model parameters to fp32 before training so torch AdamW creates fp32 optimizer state.") + + # Model architecture + attn_implementation: Literal["sdpa", "flash_attention_2", "eager"] | None = Field(default=None, description="Attention implementation (sdpa, flash_attention_2, eager). None auto-detects.") + train_n_passages: int = Field(default=5, ge=2, description="Number of passages per query during training (1 pos + n-1 neg).") + num_labels: int = Field(default=1, ge=1, description="Number of classification labels.") + temperature: float = Field(default=1.0, gt=0, description="Temperature for cross-entropy loss.") + pooling: Literal["avg", "cls", "last"] = Field(default="avg", description="Pooling strategy.") + + # Tokenization + rerank_max_length: int = Field(default=512, gt=0, description="Maximum sequence length for concatenated query+passage.") + prompt_template: str = Field(default="question:{query} \n \n passage:{passage}", description="Template for formatting query-passage pairs.") + + # Checkpointing + checkpoint_every_steps: int = Field(default=100, gt=0, description="Save checkpoint every N steps.") + val_every_steps: int = Field(default=100, gt=0, description="Run validation every N steps.") + restore_from: str | None = Field(default=None, description="Optional checkpoint directory or name to restore from.") + + +_ADAM_METADATA_KEYS = ("exp_avg", "exp_avg_sq", "step") + + +def _cast_model_parts_to_fp32(model_parts) -> None: + """Cast model parameters in-place so torch AdamW keeps fp32 params/state.""" + print("Casting model parameters to fp32 before training") + for model_part in model_parts: + model_part.float() + + +def _optimizer_metadata_dtype_counts(metadata) -> dict[str, dict[str, int]]: + """Summarize optimizer state dtypes from torch distributed checkpoint metadata.""" + counts: dict[str, dict[str, int]] = {} + state_metadata = getattr(metadata, "state_dict_metadata", {}) + for key, value in state_metadata.items(): + if not str(key).startswith("optim.state."): + continue + state_name = str(key).rsplit(".", 1)[-1] + if state_name not in _ADAM_METADATA_KEYS: + continue + + properties = getattr(value, "properties", None) + dtype = getattr(properties, "dtype", None) + if dtype is None: + continue + + dtype_counts = counts.setdefault(state_name, {}) + dtype_name = str(dtype) + dtype_counts[dtype_name] = dtype_counts.get(dtype_name, 0) + 1 + return counts + + +def _read_optimizer_metadata_dtype_counts(checkpoint_dir: Path) -> dict[str, dict[str, int]]: + metadata_path = checkpoint_dir / "optim" / ".metadata" + if not metadata_path.exists(): + raise FileNotFoundError(f"Optimizer metadata not found: {metadata_path}") + + with metadata_path.open("rb") as f: + return _optimizer_metadata_dtype_counts(pickle.load(f)) + + +def _assert_optimizer_metadata_fp32(checkpoint_dir: Path) -> dict[str, dict[str, int]]: + """Assert Adam optimizer checkpoint metadata contains only fp32 state tensors.""" + counts = _read_optimizer_metadata_dtype_counts(checkpoint_dir) + missing = [state_name for state_name in _ADAM_METADATA_KEYS if state_name not in counts] + non_fp32 = { + state_name: dtype_counts + for state_name, dtype_counts in counts.items() + if set(dtype_counts) != {"torch.float32"} + } + if missing or non_fp32: + raise AssertionError( + "Expected resumed optimizer checkpoint metadata to be fp32. " + f"missing={missing}, non_fp32={non_fp32}, counts={counts}" + ) + return counts + + +def _install_fp32_restore_hook(recipe) -> None: + """Load restored model in fp32 before loading optimizer state. + + Automodel default restore order is model plus optimizer load inside setup. + If we cast after setup, optimizer state has already been attached and fused + AdamW can fail because param, grad, and moment tensor lists no longer have + matching dtypes. This hook keeps the load order explicit for fp32-state + resumes: load model, cast params to fp32, then load optimizer and scheduler. + """ + + def load_checkpoint_with_fp32_cast(self, restore_from: str | None = None): + if not self.checkpointer.config.enabled: + if restore_from is not None: + print("Enable checkpointing to resume from a checkpoint, skipping...", flush=True) + return + if restore_from is None: + # Keep Automodel auto-detect path unchanged for non-explicit resumes. + return original_load_checkpoint(restore_from) + + import torch + + is_rank_0 = not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0 + if str(restore_from).upper() == "LATEST": + from nemo_automodel.recipes.base_recipe import _find_latest_checkpoint + + ckpt_dir = _find_latest_checkpoint(self.checkpointer.config.checkpoint_dir) + if ckpt_dir is None: + if is_rank_0: + print( + "restore_from=LATEST specified but no checkpoint found in " + f"{self.checkpointer.config.checkpoint_dir}. Starting fresh.", + flush=True, + ) + return + ckpt_dir = str(ckpt_dir) + elif os.path.sep not in str(restore_from) and not os.path.isabs(str(restore_from)): + ckpt_dir = os.path.join(self.checkpointer.config.checkpoint_dir, str(restore_from)) + else: + ckpt_dir = str(restore_from) + + self._validate_checkpoint_dir_exists(ckpt_dir, restore_from=str(restore_from), is_rank_0=is_rank_0) + if is_rank_0: + print(f"Loading checkpoint from {ckpt_dir}", flush=True) + print("Restoring model before fp32 cast, then loading optimizer state", flush=True) + + model, optimizer, scheduler = self._load_checkpoint_tracked_state(ckpt_dir) + self.checkpointer.load_model(model, os.path.join(ckpt_dir, "model")) + _cast_model_parts_to_fp32(self.model_parts) + self.checkpointer.load_optimizer(optimizer, model, ckpt_dir, scheduler) + + original_load_checkpoint = recipe.load_checkpoint + recipe.load_checkpoint = MethodType(load_checkpoint_with_fp32_cast, recipe) + +def _count_training_examples(train_data_path: Path) -> int: + """Count the number of training examples in a training data file.""" + with open(train_data_path) as f: + data = json.load(f) + return len(data.get("data", [])) + + +def _auto_scale_hyperparams( + cfg: FinetuneConfig, num_examples: int +) -> tuple[int, int, int, int]: + """Auto-scale training hyperparameters based on dataset size. + + Args: + cfg: Fine-tuning configuration (with user-specified or default values). + num_examples: Number of training examples. + + Returns: + Tuple of (global_batch_size, num_epochs, checkpoint_every_steps, val_every_steps). + """ + if cfg.global_batch_size == 128 and num_examples < 2000: + global_batch_size = max(16, min(64, num_examples // 8)) + else: + global_batch_size = cfg.global_batch_size + + steps_per_epoch = max(1, num_examples // global_batch_size) + num_epochs = cfg.num_epochs + total_steps = steps_per_epoch * num_epochs + if cfg.max_steps is not None: + total_steps = min(total_steps, cfg.max_steps) + + if total_steps < cfg.checkpoint_every_steps * 3: + checkpoint_every_steps = max(1, total_steps // 3) + else: + checkpoint_every_steps = cfg.checkpoint_every_steps + + if total_steps < cfg.val_every_steps * 3: + val_every_steps = max(1, total_steps // 3) + else: + val_every_steps = cfg.val_every_steps + + return global_batch_size, num_epochs, checkpoint_every_steps, val_every_steps + + +def run_finetune(cfg: FinetuneConfig) -> Path: + """Run cross-encoder reranking model fine-tuning using nemo-automodel. + + Args: + cfg: Fine-tuning configuration. + + Returns: + Path to final checkpoint directory. + """ + # Validate inputs + if not cfg.train_data_path.exists(): + print(f"Error: Training data not found: {cfg.train_data_path}", file=sys.stderr) + print(" Please run 'nemotron embed prep' first.", file=sys.stderr) + sys.exit(1) + + # Count training examples + num_examples = _count_training_examples(cfg.train_data_path) + + global_batch_size, num_epochs, ckpt_every, val_every = _auto_scale_hyperparams( + cfg, num_examples + ) + + steps_per_epoch = max(1, num_examples // global_batch_size) + total_steps = steps_per_epoch * num_epochs + if cfg.max_steps is not None: + total_steps = min(total_steps, cfg.max_steps) + + # Print training plan + print(f"Training plan:") + print(f" Dataset: {num_examples:,} examples") + + if global_batch_size != cfg.global_batch_size: + print(f" Batch size: {global_batch_size} (auto-scaled from {cfg.global_batch_size} — dataset < 2000 examples)") + else: + print(f" Batch size: {global_batch_size}") + + print(f" Epochs: {num_epochs}") + print(f" Steps/epoch: ~{steps_per_epoch}") + print(f" Total steps: ~{total_steps}") + if cfg.max_steps is not None: + print(f" Max steps: {cfg.max_steps}") + lr_warmup_steps = min(cfg.lr_warmup_steps, max(1, total_steps - 1)) + print(f" LR schedule: {cfg.lr_decay_style}, warmup={lr_warmup_steps}, peak={cfg.learning_rate}") + print(f" Checkpoint every: {ckpt_every} steps") + print(f" Validate every: {val_every} steps") + print() + + if total_steps < 50: + print(f"Warning: Only ~{total_steps} total training steps. " + f"Dataset may be too small for meaningful fine-tuning.", file=sys.stderr) + print(f" Consider adding more documents to your corpus.", file=sys.stderr) + print() + + print(f"Base model: {cfg.base_model}") + print(f"Training data: {cfg.train_data_path}") + print(f"Checkpoint dir: {cfg.checkpoint_dir}") + print() + + # Import nemo-automodel components + try: + from nemo_automodel.components.config.loader import load_yaml_config + from nemo_automodel.recipes.retrieval import TrainCrossEncoderRecipe + except ImportError as e: + print(f"Error: Failed to import nemo-automodel. Is it installed?", file=sys.stderr) + print(f" Install with: pip install nemo-automodel", file=sys.stderr) + print(f" Error: {e}", file=sys.stderr) + sys.exit(1) + + # Load base config from nemo-automodel defaults + base_config_path = STAGE_PATH / "crossencoder_base.yaml" + automodel_cfg = load_yaml_config(str(base_config_path)) + + # Apply overrides from our config + # Model settings + automodel_cfg.model.pretrained_model_name_or_path = cfg.base_model + automodel_cfg.tokenizer.pretrained_model_name_or_path = cfg.base_model + automodel_cfg.model.num_labels = cfg.num_labels + automodel_cfg.model.temperature = cfg.temperature + automodel_cfg.model.pooling = cfg.pooling + + # Auto-detect attention implementation if not explicitly set + if cfg.attn_implementation is not None: + attn_impl = cfg.attn_implementation + else: + try: + import flash_attn # noqa: F401 + attn_impl = "flash_attention_2" + except ImportError: + attn_impl = "sdpa" + print(f" Attention: {attn_impl} (auto-detected)") + + # Data settings + automodel_cfg.dataloader.dataset.data_dir_list = [str(cfg.train_data_path)] + automodel_cfg.dataloader.dataset.n_passages = cfg.train_n_passages + automodel_cfg.dataloader.collate_fn.rerank_max_length = cfg.rerank_max_length + automodel_cfg.dataloader.collate_fn.prompt_template = cfg.prompt_template + + # Training settings — use auto-scaled values + automodel_cfg.step_scheduler.num_epochs = num_epochs + automodel_cfg.step_scheduler.global_batch_size = global_batch_size + automodel_cfg.step_scheduler.local_batch_size = cfg.local_batch_size + automodel_cfg.step_scheduler.ckpt_every_steps = ckpt_every + automodel_cfg.step_scheduler.val_every_steps = val_every + if cfg.max_steps is not None: + automodel_cfg.step_scheduler.max_steps = cfg.max_steps + + # Optimizer settings + automodel_cfg.optimizer.lr = cfg.learning_rate + automodel_cfg.optimizer.weight_decay = cfg.weight_decay + automodel_cfg.lr_scheduler.lr_warmup_steps = lr_warmup_steps + + # Checkpoint settings + automodel_cfg.checkpoint.checkpoint_dir = str(cfg.checkpoint_dir) + if cfg.restore_from is not None: + automodel_cfg.checkpoint.restore_from = cfg.restore_from + + # Create and run the cross-encoder recipe + recipe = TrainCrossEncoderRecipe(automodel_cfg) + if cfg.force_fp32_parameters and cfg.restore_from is not None: + _install_fp32_restore_hook(recipe) + recipe.setup() + if cfg.force_fp32_parameters and cfg.restore_from is None: + _cast_model_parts_to_fp32(recipe.model_parts) + recipe.run_train_validation_loop() + if cfg.force_fp32_parameters and cfg.restore_from is not None: + counts = _assert_optimizer_metadata_fp32(cfg.checkpoint_dir / "LATEST") + print(f"Verified resumed optimizer checkpoint metadata is fp32: {counts}") + + # Find the final checkpoint + final_model_dir = cfg.checkpoint_dir / "LATEST" / "model" / "consolidated" + + print(f"\nFine-tuning complete!") + print(f" Checkpoint: {cfg.checkpoint_dir}") + print(f" Model: {final_model_dir}") + + # Save artifact (registers with artifact registry if kit.init() was called) + try: + from nemotron.kit.artifacts.rerank import RerankModelArtifact + + artifact = RerankModelArtifact( + path=final_model_dir, + base_model=cfg.base_model, + training_examples=num_examples, + num_epochs=num_epochs, + global_batch_size=global_batch_size, + learning_rate=cfg.learning_rate, + num_labels=cfg.num_labels, + ) + artifact.save(name="rerank/model") + except Exception: + pass # Artifact save is best-effort — don't break the pipeline + + return final_model_dir + + +def main(cfg: FinetuneConfig | None = None) -> Path: + """Entry point for fine-tuning. + + Args: + cfg: Config from CLI framework, or None when run directly as script. + + Returns: + Path to final model checkpoint. + """ + if cfg is None: + # Called directly as script - parse config ourselves + config_path, cli_overrides = parse_config_and_overrides( + default_config=DEFAULT_CONFIG_PATH + ) + + try: + cfg = load_config(config_path, cli_overrides, FinetuneConfig) + 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Stage 1: Evaluate cross-encoder reranking models.""" diff --git a/src/nemotron/recipes/rerank/stage3_eval/config/default.yaml b/src/nemotron/recipes/rerank/stage3_eval/config/default.yaml new file mode 100644 index 000000000..82d89587f --- /dev/null +++ b/src/nemotron/recipes/rerank/stage3_eval/config/default.yaml @@ -0,0 +1,66 @@ +# Stage 3: Evaluation Configuration +# +# Evaluates cross-encoder reranking models by re-ranking first-stage +# retrieval results and measuring nDCG@k improvement. +# +# Usage: +# nemotron rerank eval -c default +# nemotron rerank eval -c default finetuned_model_path=/path/to/model +# +# Remote execution (Slurm): +# nemotron rerank eval -c default --run my-cluster + +# Execution environment configuration +run: + env: + container: nvcr.io/nvidia/pytorch:25.12-py3 + +# Base reranking model for comparison +base_model: nvidia/llama-nemotron-rerank-1b-v2 + +# Path to fine-tuned model checkpoint +finetuned_model_path: ./output/rerank/stage2_finetune/checkpoints/LATEST/model/consolidated + +# First-stage retrieval model (for generating candidates) +retrieval_model: nvidia/llama-nemotron-embed-1b-v2 + +# Path to BEIR-formatted evaluation data (from embed stage1_data_prep) +eval_data_path: ./output/rerank/stage1_prep/eval_beir + +# Output directory for evaluation results +output_dir: ./output/rerank/stage3_eval + +# K values for nDCG@k metrics +k_values: + - 1 + - 5 + - 10 + +# Number of first-stage candidates to re-rank +top_k: 10 + +# Encoding settings +batch_size: 128 +retrieval_batch_size: 32 +max_length: 512 +corpus_chunk_size: 50000 + +# Retrieval model settings +retrieval_pooling: avg +retrieval_normalize: true +query_prefix: "query:" +passage_prefix: "passage:" + +# Reranker prompt template (must match training config) +prompt_template: "question:{query} \n \n passage:{passage}" + +# Evaluation mode +eval_base: true +eval_finetuned: true + +# NIM API evaluation settings +eval_nim: false +nim_url: http://localhost:8000 +nim_model: nvidia/llama-nemotron-rerank-1b-v2 +nim_batch_size: 32 +nim_timeout: 60 diff --git a/src/nemotron/recipes/rerank/stage3_eval/eval.py b/src/nemotron/recipes/rerank/stage3_eval/eval.py new file mode 100644 index 000000000..ddc63c0fb --- /dev/null +++ b/src/nemotron/recipes/rerank/stage3_eval/eval.py @@ -0,0 +1,685 @@ +#!/usr/bin/env python3 +# /// script +# [tool.runspec] +# schema = "1" +# docs = "https://raw.githubusercontent.com/NVIDIA-NeMo/Nemotron/main/docs/runspec/v1/spec.md" +# name = "rerank/eval" +# image = "nvcr.io/nvidia/pytorch:25.12-py3" +# setup = "PyTorch pre-installed. Stage dependencies resolved via UV at runtime." +# +# [tool.runspec.run] +# launch = "direct" +# +# [tool.runspec.config] +# dir = "./config" +# default = "default" +# +# [tool.runspec.resources] +# nodes = 1 +# gpus_per_node = 1 +# /// + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Evaluation script for cross-encoder reranking models. + +Evaluates reranking models by first running dense retrieval to get candidates, +then re-ranking with the cross-encoder and measuring nDCG@k improvement. + +Supports evaluation of: +- Local HuggingFace models (base and fine-tuned) +- NIM API endpoints + +Usage: + # With default config + nemotron rerank eval -c default + + # With CLI overrides + nemotron rerank eval -c default finetuned_model_path=/path/to/model + + # Evaluate NIM endpoint + nemotron rerank eval -c default eval_nim=true nim_url=http://localhost:8000 +""" + +from __future__ import annotations + +import json +import os +import sys +from pathlib import Path +from typing import Literal + +# Must be set before transformers is imported (BEIR imports it at module level) +os.environ.setdefault("HF_HUB_TRUST_REMOTE_CODE", "1") +os.environ.setdefault("TRUST_REMOTE_CODE", "True") + +from pydantic import ConfigDict, Field + +from nemo_runspec.config.pydantic_loader import RecipeSettings, load_config, parse_config_and_overrides + +STAGE_PATH = Path(__file__).parent +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "default.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + + +class EvalConfig(RecipeSettings): + """Evaluation configuration for cross-encoder reranking models.""" + + model_config = ConfigDict(extra="forbid") + + # Model paths + base_model: str = Field(default="nvidia/llama-nemotron-rerank-1b-v2", description="Base reranking model for comparison.") + finetuned_model_path: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage2_finetune/checkpoints/LATEST/model/consolidated", description="Path to fine-tuned model checkpoint.") + + # First-stage retrieval model (for generating candidates to re-rank) + retrieval_model: str = Field(default="nvidia/llama-nemotron-embed-1b-v2", description="Dense retrieval model for first-stage candidate generation.") + + # Evaluation data + eval_data_path: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage1_prep/eval_beir", description="Path to BEIR-formatted evaluation data.") + + # Output settings + output_dir: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage3_eval", description="Directory for saving evaluation results.") + + # Evaluation settings + k_values: list[int] = Field(default_factory=lambda: [1, 5, 10], description="K values for nDCG@k metrics.") + top_k: int = Field(default=10, gt=0, description="Number of first-stage candidates to re-rank.") + batch_size: int = Field(default=128, gt=0, description="Batch size for reranker scoring.") + retrieval_batch_size: int = Field(default=32, gt=0, description="Batch size for first-stage retrieval encoding. Lower than batch_size because the embedding model processes longer sequences.") + max_length: int = Field(default=512, gt=0, description="Maximum sequence length.") + corpus_chunk_size: int = Field(default=50000, gt=0, description="Chunk size for corpus encoding.") + + # Retrieval model settings + retrieval_pooling: Literal["avg", "cls", "last"] = Field(default="avg", description="Pooling strategy for retrieval model.") + retrieval_normalize: bool = Field(default=True, description="Whether to L2 normalize retrieval embeddings.") + query_prefix: str = Field(default="query:", description="Prefix for query inputs (retrieval model).") + passage_prefix: str = Field(default="passage:", description="Prefix for passage inputs (retrieval model).") + + # Evaluation mode + eval_base: bool = Field(default=True, description="Whether to evaluate the base reranker.") + eval_finetuned: bool = Field(default=True, description="Whether to evaluate the fine-tuned reranker.") + + # Reranker prompt template (must match training config) + prompt_template: str = Field(default="question:{query} \n \n passage:{passage}", description="Template for formatting query-passage pairs. Must match the template used during training.") + + # NIM API evaluation settings + eval_nim: bool = Field(default=False, description="Whether to evaluate a NIM API endpoint.") + nim_url: str = Field(default="http://localhost:8000", description="NIM API base URL.") + nim_model: str = Field(default="nvidia/llama-nemotron-rerank-1b-v2", description="Model name for NIM API requests.") + nim_batch_size: int = Field(default=32, gt=0, description="Batch size for NIM API requests.") + nim_timeout: int = Field(default=60, gt=0, description="Timeout in seconds for NIM API requests.") + + +class _SentenceTransformerRetriever: + """BEIR-compatible retriever using SentenceTransformer. + + Wraps SentenceTransformer to provide encode_queries() and encode_corpus() + methods expected by BEIR's DenseRetrievalExactSearch. Unlike BEIR's + built-in models.HuggingFace, SentenceTransformer correctly passes + trust_remote_code=True when loading models with custom code. + """ + + def __init__( + self, + model_path: str, + query_prefix: str = "query:", + passage_prefix: str = "passage:", + ): + import torch + from sentence_transformers import SentenceTransformer + + self.model = SentenceTransformer( + model_path, + trust_remote_code=True, + model_kwargs={"torch_dtype": "bfloat16"}, + ) + self.query_prefix = query_prefix + self.passage_prefix = passage_prefix + + def encode_queries(self, queries: list[str], batch_size: int = 128, **kwargs): + prompts = [f"{self.query_prefix} {q}" for q in queries] + return self.model.encode(prompts, batch_size=batch_size, **kwargs) + + def encode_corpus(self, corpus: list[dict[str, str]], batch_size: int = 128, **kwargs): + texts = [] + for doc in corpus: + title = doc.get("title", "") + text = doc.get("text", "") + texts.append(f"{self.passage_prefix} {title} {text}".strip()) + return self.model.encode(texts, batch_size=batch_size, **kwargs) + + +def _get_first_stage_results( + retrieval_model: str, + dataset_path: Path, + batch_size: int = 32, + corpus_chunk_size: int = 50000, + k_values: list[int] | None = None, + query_prefix: str = "query:", + passage_prefix: str = "passage:", +) -> tuple[dict, dict, dict, dict]: + """Run first-stage dense retrieval to get candidates for re-ranking. + + Returns: + Tuple of (corpus, queries, qrels, first_stage_results). + """ + try: + from beir.datasets.data_loader import GenericDataLoader + from beir.retrieval.evaluation import EvaluateRetrieval + from beir.retrieval.search.dense.exact_search import ( + DenseRetrievalExactSearch as DRES, + ) + except ImportError: + print("Error: BEIR is required for evaluation. Install with: pip install beir") + sys.exit(1) + + if k_values is None: + k_values = [1, 5, 10, 100] + + dense_model = _SentenceTransformerRetriever( + model_path=str(retrieval_model), + query_prefix=query_prefix, + passage_prefix=passage_prefix, + ) + + dres_model = DRES( + dense_model, + corpus_chunk_size=corpus_chunk_size, + batch_size=batch_size, + ) + + retriever = EvaluateRetrieval( + dres_model, + score_function="dot", + k_values=k_values, + ) + + corpus, queries, qrels = GenericDataLoader(str(dataset_path)).load(split="test") + results = retriever.retrieve(corpus, queries) + + return corpus, queries, qrels, results + + +def evaluate_reranker( + model_path: str | Path, + corpus: dict, + queries: dict, + qrels: dict, + first_stage_results: dict, + top_k: int = 100, + batch_size: int = 128, + max_length: int = 512, + k_values: list[int] | None = None, + prompt_template: str | None = None, +) -> tuple[dict, dict]: + """Evaluate a cross-encoder reranker on first-stage retrieval results. + + Args: + model_path: Path to the cross-encoder model. + corpus: BEIR corpus dict. + queries: BEIR queries dict. + qrels: BEIR relevance judgments. + first_stage_results: First-stage retrieval results to re-rank. + top_k: Number of candidates to re-rank per query. + batch_size: Batch size for cross-encoder scoring. + max_length: Maximum sequence length. + k_values: K values for metrics. + prompt_template: Template for formatting query-passage pairs (e.g. + "question:{query} \\n \\n passage:{passage}"). When set, inputs + are formatted with this template to match training. When None, + falls back to BEIR's default pair formatting. + + Returns: + Tuple of (metrics dict, reranked results dict). + """ + import torch + from transformers import AutoModelForSequenceClassification, AutoTokenizer + + try: + from beir.retrieval.evaluation import EvaluateRetrieval + except ImportError: + print("Error: BEIR is required for evaluation.") + print(" Install with: pip install beir") + sys.exit(1) + + if k_values is None: + k_values = [1, 5, 10, 100] + + # Load model and tokenizer directly to control input formatting + tokenizer = AutoTokenizer.from_pretrained( + str(model_path), + trust_remote_code=True, + padding_side="left", + add_eos_token=False, + ) + if hasattr(tokenizer, "add_eos_token"): + tokenizer.add_eos_token = False + model = AutoModelForSequenceClassification.from_pretrained(str(model_path), trust_remote_code=True, torch_dtype=torch.bfloat16) + model.eval() + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = model.to(device) + if torch.cuda.device_count() > 1: + print(f" Using {torch.cuda.device_count()} GPUs with DataParallel") + model = torch.nn.DataParallel(model) + + # Build inputs + formatted_inputs = [] + pair_info = [] + for qid in queries: + if qid not in first_stage_results: + continue + sorted_candidates = sorted( + first_stage_results[qid].items(), + key=lambda x: x[1], + reverse=True, + )[:top_k] + for doc_id, _ in sorted_candidates: + pair_info.append((qid, doc_id)) + doc = corpus[doc_id] + title = doc.get("title", "") + text = doc.get("text", "") + passage = f"{title} {text}".strip() if title else text + if prompt_template: + formatted_inputs.append( + prompt_template.format(query=queries[qid], passage=passage) + ) + else: + formatted_inputs.append((queries[qid], passage)) + + # Score in batches + all_scores = [] + num_batches = (len(formatted_inputs) + batch_size - 1) // batch_size + print(f" Scoring {len(formatted_inputs)} query-passage pairs ({num_batches} batches, batch_size={batch_size})") + from tqdm import tqdm + for batch_start in tqdm(range(0, len(formatted_inputs), batch_size), total=num_batches, desc=" Reranking"): + batch = formatted_inputs[batch_start:batch_start + batch_size] + if prompt_template: + features = tokenizer( + batch, padding=True, truncation=True, + max_length=max_length, return_tensors="pt", + ) + else: + texts_a = [pair[0] for pair in batch] + texts_b = [pair[1] for pair in batch] + features = tokenizer( + texts_a, texts_b, padding=True, truncation=True, + max_length=max_length, return_tensors="pt", + ) + features = {k: v.to(device) for k, v in features.items()} + with torch.no_grad(): + logits = model(**features).logits.squeeze(-1) + if logits.dim() == 0: + all_scores.append(logits.item()) + else: + all_scores.extend(logits.cpu().tolist()) + + reranked_results: dict[str, dict[str, float]] = {} + for (qid, doc_id), score in zip(pair_info, all_scores): + if qid not in reranked_results: + reranked_results[qid] = {} + reranked_results[qid][doc_id] = float(score) + + # Evaluate re-ranked results + evaluator = EvaluateRetrieval(k_values=k_values) + metrics = evaluator.evaluate(qrels, reranked_results, k_values) + + return metrics, reranked_results + + +def evaluate_nim_reranker( + nim_url: str, + nim_model: str, + corpus: dict, + queries: dict, + qrels: dict, + first_stage_results: dict, + top_k: int = 100, + batch_size: int = 32, + timeout: int = 60, + k_values: list[int] | None = None, +) -> tuple[dict, dict]: + """Evaluate a NIM reranker endpoint on first-stage retrieval results. + + Args: + nim_url: Base URL for NIM API. + nim_model: Model name for API requests. + corpus: BEIR corpus dict. + queries: BEIR queries dict. + qrels: BEIR relevance judgments. + first_stage_results: First-stage retrieval results to re-rank. + top_k: Number of candidates to re-rank per query. + batch_size: Batch size for API requests. + timeout: Request timeout in seconds. + k_values: K values for metrics. + + Returns: + Tuple of (metrics dict, reranked results dict). + """ + import urllib.request + import urllib.error + + try: + from beir.retrieval.evaluation import EvaluateRetrieval + except ImportError: + print("Error: BEIR is required for evaluation.") + sys.exit(1) + + if k_values is None: + k_values = [1, 5, 10, 100] + + ranking_url = f"{nim_url.rstrip('/')}/v1/ranking" + reranked_results: dict[str, dict[str, float]] = {} + + query_ids = list(queries.keys()) + for i, qid in enumerate(query_ids): + query_text = queries[qid] + + # Get top-k candidates from first-stage results + candidate_ids = sorted( + first_stage_results.get(qid, {}).items(), + key=lambda x: x[1], + reverse=True, + )[:top_k] + + if not candidate_ids: + reranked_results[qid] = {} + continue + + # Build passages list + doc_ids = [did for did, _ in candidate_ids] + passages = [] + for did in doc_ids: + doc = corpus[did] + title = doc.get("title", "") + text = doc.get("text", "") + passages.append({"text": f"{title} {text}".strip() if title else text}) + + # Score in batches + all_scores = [] + for batch_start in range(0, len(passages), batch_size): + batch_passages = passages[batch_start:batch_start + batch_size] + + payload = json.dumps({ + "model": nim_model, + "query": {"text": query_text}, + "passages": batch_passages, + }).encode("utf-8") + + req = urllib.request.Request( + ranking_url, + data=payload, + headers={"Content-Type": "application/json"}, + method="POST", + ) + + try: + with urllib.request.urlopen(req, timeout=timeout) as response: + result = json.loads(response.read().decode("utf-8")) + rankings = sorted(result["rankings"], key=lambda x: x["index"]) + all_scores.extend([r["logit"] for r in rankings]) + except urllib.error.HTTPError as e: + error_body = e.read().decode("utf-8") if e.fp else "" + raise RuntimeError(f"NIM API error {e.code}: {error_body}") from e + + reranked_results[qid] = {did: score for did, score in zip(doc_ids, all_scores)} + + if (i + 1) % 50 == 0: + print(f" Re-ranked {i + 1}/{len(query_ids)} queries...") + + # Evaluate re-ranked results + evaluator = EvaluateRetrieval(k_values=k_values) + metrics = evaluator.evaluate(qrels, reranked_results, k_values) + + return metrics, reranked_results + + +def _print_summary_metrics(metrics: tuple, k_values: list[int]) -> None: + """Print NDCG and Recall at the highest available k value.""" + k = max(k_values) + for name, idx in [("NDCG", 0), ("Recall", 2)]: + key = f"{name}@{k}" + val = metrics[idx].get(key) + if val is not None: + print(f" {key}:{' ' * (10 - len(key))}{val:.5f}") + else: + print(f" {key}:{' ' * (10 - len(key))}N/A") + + +def run_eval(cfg: EvalConfig) -> dict: + """Run cross-encoder reranking model evaluation. + + Args: + cfg: Evaluation configuration. + + Returns: + Dictionary with evaluation results. + """ + print(f"Reranking Model Evaluation") + print(f"=" * 60) + print(f"Eval data: {cfg.eval_data_path}") + print(f"Retrieval model: {cfg.retrieval_model}") + print(f"Base reranker: {cfg.base_model}") + print(f"Finetuned reranker:{cfg.finetuned_model_path}") + print(f"Top-k to re-rank: {cfg.top_k}") + print(f"K values: {cfg.k_values}") + print(f"=" * 60) + print() + + # Validate inputs + if not cfg.eval_data_path.exists(): + print(f"Error: Eval data path not found: {cfg.eval_data_path}", file=sys.stderr) + print(" Please run 'nemotron embed prep' first or provide eval data.", file=sys.stderr) + sys.exit(1) + + # Create output directory + cfg.output_dir.mkdir(parents=True, exist_ok=True) + + results = {} + + # Step 1: Run first-stage dense retrieval + print(f"Running first-stage retrieval with: {cfg.retrieval_model}") + corpus, queries, qrels, first_stage_results = _get_first_stage_results( + retrieval_model=cfg.retrieval_model, + dataset_path=cfg.eval_data_path, + batch_size=cfg.retrieval_batch_size, + corpus_chunk_size=cfg.corpus_chunk_size, + k_values=cfg.k_values, + query_prefix=cfg.query_prefix, + passage_prefix=cfg.passage_prefix, + ) + print(f" Retrieved candidates for {len(queries)} queries") + print() + + # Step 2: Evaluate base reranker + if cfg.eval_base: + print(f"Evaluating base reranker: {cfg.base_model}") + base_metrics, _ = evaluate_reranker( + model_path=cfg.base_model, + corpus=corpus, + queries=queries, + qrels=qrels, + first_stage_results=first_stage_results, + top_k=cfg.top_k, + batch_size=cfg.batch_size, + max_length=cfg.max_length, + k_values=cfg.k_values, + prompt_template=cfg.prompt_template, + ) + results["base"] = base_metrics + _print_summary_metrics(base_metrics, cfg.k_values) + print() + + # Step 3: Evaluate fine-tuned reranker + if cfg.eval_finetuned: + if not cfg.finetuned_model_path.exists(): + print(f"Warning: Fine-tuned model not found at {cfg.finetuned_model_path}") + print(" Skipping fine-tuned model evaluation.") + else: + print(f"Evaluating fine-tuned reranker: {cfg.finetuned_model_path}") + ft_metrics, _ = evaluate_reranker( + model_path=cfg.finetuned_model_path, + corpus=corpus, + queries=queries, + qrels=qrels, + first_stage_results=first_stage_results, + top_k=cfg.top_k, + batch_size=cfg.batch_size, + max_length=cfg.max_length, + k_values=cfg.k_values, + prompt_template=cfg.prompt_template, + ) + results["finetuned"] = ft_metrics + _print_summary_metrics(ft_metrics, cfg.k_values) + print() + + # Step 4: Evaluate NIM reranker endpoint + if cfg.eval_nim: + print(f"Evaluating NIM reranker endpoint: {cfg.nim_url}") + + import urllib.request + import urllib.error + + nim_healthy = False + try: + health_url = f"{cfg.nim_url.rstrip('/')}/v1/health/ready" + with urllib.request.urlopen(health_url, timeout=10) as response: + nim_healthy = response.status == 200 + except (urllib.error.URLError, urllib.error.HTTPError, TimeoutError): + pass + + if not nim_healthy: + print(f" Error: NIM endpoint is not reachable at {cfg.nim_url}", file=sys.stderr) + print(f" Ensure the NIM service is running and healthy before evaluating.", file=sys.stderr) + print() + else: + try: + nim_metrics, _ = evaluate_nim_reranker( + nim_url=cfg.nim_url, + nim_model=cfg.nim_model, + corpus=corpus, + queries=queries, + qrels=qrels, + first_stage_results=first_stage_results, + top_k=cfg.top_k, + batch_size=cfg.nim_batch_size, + timeout=cfg.nim_timeout, + k_values=cfg.k_values, + ) + results["nim"] = nim_metrics + _print_summary_metrics(nim_metrics, cfg.k_values) + print() + except Exception as e: + print(f" Error evaluating NIM: {e}") + print() + + # Print comparison + if "base" in results and "finetuned" in results: + print(f"Comparison (Base -> Fine-tuned)") + print(f"=" * 60) + + metric_names = ["NDCG", "Recall"] + metric_indices = [0, 2] + + for name, idx in zip(metric_names, metric_indices): + print(f" {name}:") + for k in results["base"][idx]: + base_val = results["base"][idx][k] + ft_val = results["finetuned"][idx][k] + diff = ft_val - base_val + sign = "+" if diff > 0 else "" + pct = (diff / base_val * 100) if base_val != 0 else float("inf") + print(f" {k}: {base_val:.5f} -> {ft_val:.5f} ({sign}{diff:.5f}, {sign}{pct:.1f}%)") + print() + + # Print NIM vs Fine-tuned comparison (accuracy check for export) + if "finetuned" in results and "nim" in results: + print(f"Comparison (Fine-tuned -> NIM)") + print(f"=" * 60) + print(f" This verifies the exported model matches the checkpoint accuracy.") + print() + + metric_names = ["NDCG", "Recall"] + metric_indices = [0, 2] + + for name, idx in zip(metric_names, metric_indices): + print(f" {name}:") + for k in results["finetuned"][idx]: + ft_val = results["finetuned"][idx][k] + nim_val = results["nim"][idx][k] + diff = nim_val - ft_val + sign = "+" if diff > 0 else "" + at_k = int(k.split("@")[1]) if "@" in k else 1 + threshold = 0.03 if at_k < 5 else 0.01 + status = "ok" if abs(diff) < threshold else "MISMATCH" + pct = (diff / ft_val * 100) if ft_val != 0 else float("inf") + print(f" {k}: {ft_val:.5f} -> {nim_val:.5f} ({sign}{diff:.5f}, {sign}{pct:.1f}%) {status}") + print() + + # Save results + results_file = cfg.output_dir / "eval_results.json" + + serializable_results = {} + for model_name, metrics in results.items(): + serializable_results[model_name] = { + "NDCG": metrics[0], + "MAP": metrics[1], + "Recall": metrics[2], + "Precision": metrics[3], + } + + with open(results_file, "w") as f: + json.dump(serializable_results, f, indent=2) + + print(f"Evaluation complete!") + print(f" Results saved to: {results_file}") + + # Save artifact + try: + from nemotron.kit.artifacts.base import Artifact + + artifact = Artifact(path=cfg.output_dir) + artifact.save(name="rerank/eval") + except Exception: + pass + + return results + + +def main(cfg: EvalConfig | None = None) -> dict: + """Entry point for evaluation. + + Args: + cfg: Config from CLI framework, or None when run directly as script. + + Returns: + Dictionary with evaluation results. + """ + if cfg is None: + config_path, cli_overrides = parse_config_and_overrides( + default_config=DEFAULT_CONFIG_PATH + ) + + try: + cfg = load_config(config_path, cli_overrides, EvalConfig) + except FileNotFoundError as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + + return run_eval(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/rerank/stage3_eval/pyproject.toml b/src/nemotron/recipes/rerank/stage3_eval/pyproject.toml new file mode 100644 index 000000000..b9bd6c9b9 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage3_eval/pyproject.toml @@ -0,0 +1,33 @@ +[project] +name = "recipe-runner-eval" +version = "0.0.0" +requires-python = ">=3.12" +dependencies = [ + "beir>=2.0.0", + "sentence-transformers>=3.0.0", + "transformers>=5,<6", + "requests>=2.32.0", + # Needed by Nemotron library (added via --with flag) + "omegaconf>=2.3.0", + "pydantic-settings>=2.0.0", +] + +# Note: exclude-dependencies injected dynamically by run_uv.py for Docker/Slurm + +[tool.uv] +# Exclude torch/torchvision from resolution — supplied via --with at runtime +# with UV_TORCH_BACKEND=auto so the correct CUDA variant is resolved. +exclude-dependencies = ["torch", "torchvision"] + +[tool.nemotron] +entry-point = "eval.py" +container-exclude-dependencies = [ + "torch", + "torchao", + "torchvision", + "flash-attn", + "triton", + "pyarrow", + "scipy", + "opencv-python-headless", +] diff --git a/src/nemotron/recipes/rerank/stage3_eval/run_uv.py b/src/nemotron/recipes/rerank/stage3_eval/run_uv.py new file mode 100644 index 000000000..3209e26e3 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage3_eval/run_uv.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python3 +"""UV dependency wrapper — delegates to shared nemotron.kit.run_uv.""" +import sys +from pathlib import Path + +# Ensure nemotron package is importable (for container execution) +_src = str(Path(__file__).resolve().parents[4]) # -> .../src +if _src not in sys.path: + sys.path.insert(0, _src) + +from nemotron.kit.run_uv import main + +main(stage_dir=Path(__file__).parent) diff --git a/src/nemotron/recipes/rerank/stage3_eval/uv.lock b/src/nemotron/recipes/rerank/stage3_eval/uv.lock new file 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Stage 2: Export reranking model to ONNX/TensorRT.""" diff --git a/src/nemotron/recipes/rerank/stage4_export/config/default.yaml b/src/nemotron/recipes/rerank/stage4_export/config/default.yaml new file mode 100644 index 000000000..11caf019b --- /dev/null +++ b/src/nemotron/recipes/rerank/stage4_export/config/default.yaml @@ -0,0 +1,55 @@ +# Stage 4: Export Configuration +# +# Exports fine-tuned reranking models to ONNX and optionally TensorRT +# for optimized inference deployment. +# +# Usage: +# nemotron rerank export -c default +# nemotron rerank export -c default export_to_trt=false +# nemotron rerank export -c default model_path=/path/to/model +# +# Remote execution (Slurm): +# nemotron rerank export -c default --run my-cluster + +# Execution environment configuration +run: + env: + container: nvcr.io/nvidia/nemo:25.07 + +# Path to fine-tuned HuggingFace model checkpoint +model_path: ./output/rerank/stage2_finetune/checkpoints/LATEST/model/consolidated + +# Model settings +attn_implementation: eager # Use eager for ONNX export compatibility + +# Quantization settings (optional) +quant_cfg: null # Options: null, "fp8", "int8_sq" +calibration_batch_size: 64 + +# ONNX export settings +onnx_export_path: ./output/rerank/stage4_export/onnx +opset: 17 +export_dtype: fp32 + +# TensorRT export settings +export_to_trt: false +trt_model_path: ./output/rerank/stage4_export/tensorrt + +# TensorRT precision overrides for numerical stability +override_layernorm_precision_to_fp32: true +override_layers_to_fp32: + - "/model/norm/" +profiling_verbosity: layer_names_only + +# TensorRT optimization profiles (batch size) +trt_min_batch: 1 +trt_opt_batch: 16 +trt_max_batch: 64 + +# TensorRT optimization profiles (sequence length) +trt_min_seq_len: 3 +trt_opt_seq_len: 256 +trt_max_seq_len: 512 + +# Base output directory +output_dir: ./output/rerank/stage4_export diff --git a/src/nemotron/recipes/rerank/stage4_export/export.py b/src/nemotron/recipes/rerank/stage4_export/export.py new file mode 100644 index 000000000..aa8dce871 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage4_export/export.py @@ -0,0 +1,412 @@ +#!/usr/bin/env python3 +# /// script +# [tool.runspec] +# schema = "1" +# docs = "https://raw.githubusercontent.com/NVIDIA-NeMo/Nemotron/main/docs/runspec/v1/spec.md" +# name = "rerank/export" +# image = "nvcr.io/nvidia/nemo:25.07" +# setup = "NeMo and export dependencies are pre-installed in the image." +# +# [tool.runspec.run] +# launch = "direct" +# +# [tool.runspec.config] +# dir = "./config" +# default = "default" +# +# [tool.runspec.resources] +# nodes = 1 +# gpus_per_node = 1 +# /// + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Export script for reranking models to ONNX and TensorRT. + +Exports fine-tuned cross-encoder reranking models to ONNX format and +optionally converts to TensorRT for optimized inference. + +Usage: + # With default config + nemotron rerank export -c default + + # Export to ONNX only + nemotron rerank export -c default export_to_trt=false + + # With custom model path + nemotron rerank export -c default model_path=/path/to/model +""" + +from __future__ import annotations + +import os +import sys +from pathlib import Path +from typing import Any, Literal + +from pydantic import ConfigDict, Field, model_validator + +from nemo_runspec.config.pydantic_loader import RecipeSettings, load_config, parse_config_and_overrides + +STAGE_PATH = Path(__file__).parent +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "default.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + + +class ExportConfig(RecipeSettings): + """Export configuration for reranking models to ONNX/TensorRT.""" + + model_config = ConfigDict(extra="forbid") + + # Model path + model_path: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage2_finetune/checkpoints/LATEST/model/consolidated", description="Path to fine-tuned HuggingFace model checkpoint.") + + # Model settings + attn_implementation: Literal["eager", "sdpa", "flash_attention_2"] = Field(default="eager", description="Attention implementation: 'eager', 'sdpa', or 'flash_attention_2'.") + + # Quantization settings + quant_cfg: Literal["fp8", "int8_sq"] | None = Field(default=None, description="Quantization config: 'fp8', 'int8_sq', or None (no quantization).") + calibration_batch_size: int = Field(default=64, gt=0, description="Batch size for quantization calibration.") + + # ONNX export settings + onnx_export_path: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage4_export/onnx", description="Output path for ONNX model.") + opset: int = Field(default=17, gt=0, description="ONNX opset version.") + export_dtype: Literal["fp32", "fp16"] = Field(default="fp32", description="ONNX export data precision (fp32, fp16).") + + # TensorRT settings + export_to_trt: bool = Field(default=False, description="Whether to export ONNX model to TensorRT.") + trt_model_path: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage4_export/tensorrt", description="Output path for TensorRT .plan file.") + override_layernorm_precision_to_fp32: bool = Field(default=True, description="Whether to override LayerNorm precision to fp32 for stability.") + override_layers_to_fp32: list[str] = Field(default_factory=lambda: ["/model/norm/"], description="Layer patterns to override precision to fp32.") + profiling_verbosity: str = Field(default="layer_names_only", description="TensorRT profiling verbosity level.") + + # TensorRT input profiles (min, opt, max shapes) + trt_min_batch: int = Field(default=1, gt=0, description="Minimum batch size for TensorRT optimization.") + trt_opt_batch: int = Field(default=16, gt=0, description="Optimal batch size for TensorRT optimization.") + trt_max_batch: int = Field(default=64, gt=0, description="Maximum batch size for TensorRT optimization.") + trt_min_seq_len: int = Field(default=3, gt=0, description="Minimum sequence length for TensorRT optimization.") + trt_opt_seq_len: int = Field(default=256, gt=0, description="Optimal sequence length for TensorRT optimization.") + trt_max_seq_len: int = Field(default=512, gt=0, description="Maximum sequence length for TensorRT optimization.") + + @model_validator(mode="after") + def _check_trt_profile_order(self): + if self.trt_min_batch > self.trt_opt_batch: + raise ValueError(f"trt_min_batch ({self.trt_min_batch}) must be <= trt_opt_batch ({self.trt_opt_batch})") + if self.trt_opt_batch > self.trt_max_batch: + raise ValueError(f"trt_opt_batch ({self.trt_opt_batch}) must be <= trt_max_batch ({self.trt_max_batch})") + if self.trt_min_seq_len > self.trt_opt_seq_len: + raise ValueError(f"trt_min_seq_len ({self.trt_min_seq_len}) must be <= trt_opt_seq_len ({self.trt_opt_seq_len})") + if self.trt_opt_seq_len > self.trt_max_seq_len: + raise ValueError(f"trt_opt_seq_len ({self.trt_opt_seq_len}) must be <= trt_max_seq_len ({self.trt_max_seq_len})") + return self + + # Output settings + output_dir: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage4_export", description="Base output directory for export artifacts.") + + +def load_reranker_model( + model_path: str | Path, + attn_implementation: str = "eager", + trust_remote_code: bool = True, +) -> tuple[Any, Any]: + """Load a reranker model for export. + + Args: + model_path: Path to the HuggingFace model. + attn_implementation: Attention implementation to use. + trust_remote_code: Whether to trust remote code. + + Returns: + Tuple of (model, tokenizer). + """ + from nemo_export.model_adapters.reranker.reranker_adapter import get_llama_reranker_hf_model + + model, tokenizer = get_llama_reranker_hf_model( + model_name_or_path=str(model_path), + trust_remote_code=trust_remote_code, + attn_implementation=attn_implementation, + ) + return model, tokenizer + + +def export_to_onnx( + model: Any, + tokenizer: Any, + cfg: ExportConfig, +) -> Any: + """Export model to ONNX format. + + Args: + model: The reranker model. + tokenizer: The tokenizer. + cfg: Export configuration. + + Returns: + OnnxLLMExporter instance. + """ + try: + from nemo_export.onnx_llm_exporter import OnnxLLMExporter + except ImportError: + print( + "Error: nemo_export is required. Please install from NeMo Export-Deploy:\n" + " cd /opt/Export-Deploy && uv sync --inexact --link-mode symlink --locked --extra trt-onnx" + ) + sys.exit(1) + + # Reranker uses input_ids and attention_mask (no dimensions arg) + input_names = ["input_ids", "attention_mask"] + dynamic_axes_input = { + "input_ids": {0: "batch_size", 1: "seq_length"}, + "attention_mask": {0: "batch_size", 1: "seq_length"}, + } + + # Check if model uses token_type_ids + if "token_type_ids" in tokenizer.model_input_names: + input_names = ["input_ids", "token_type_ids", "attention_mask"] + dynamic_axes_input["token_type_ids"] = {0: "batch_size", 1: "seq_length"} + + output_names = ["logits"] + dynamic_axes_output = {"logits": {0: "batch_size", 1: "num_labels"}} + + # Create exporter + onnx_exporter = OnnxLLMExporter( + onnx_model_dir=str(cfg.onnx_export_path), + model=model, + tokenizer=tokenizer, + ) + + # Disable dynamo export + import torch + import torch.onnx + + original_export = torch.onnx.export + + def forced_legacy_export(*args, **kwargs): + kwargs['dynamo'] = False + return original_export(*args, **kwargs) + + # Monkeypatch create_bidirectional_mask to be ONNX-trace-friendly. + # The transformers masking_utils.sdpa_mask uses tensor shape indexing + # that fails during JIT tracing. This replacement produces the same + # bidirectional attention mask without the incompatible operations. + # The model's custom code does `from transformers.masking_utils import + # create_bidirectional_mask`, so we must patch it in the model's own + # module namespace where it holds the direct reference. + import sys + + def _onnx_safe_bidirectional_mask(config=None, input_embeds=None, attention_mask=None, **kwargs): + dtype = input_embeds.dtype + batch_size, seq_length, _ = input_embeds.shape + # Bidirectional mask: all tokens attend to all non-padded tokens + # attention_mask shape: (batch_size, seq_length) with 1=attend, 0=pad + mask = attention_mask[:, None, None, :].expand(batch_size, 1, seq_length, seq_length) + # Convert to additive mask: 0 for attend, large negative for ignore + mask = (1.0 - mask.to(dtype)) * torch.finfo(dtype).min + return mask + + # Find all loaded modules that imported create_bidirectional_mask and patch them + _patched_modules = {} + for mod_name, mod in list(sys.modules.items()): + if hasattr(mod, 'create_bidirectional_mask') and callable(getattr(mod, 'create_bidirectional_mask', None)): + _patched_modules[mod_name] = getattr(mod, 'create_bidirectional_mask') + setattr(mod, 'create_bidirectional_mask', _onnx_safe_bidirectional_mask) + + torch.onnx.export = forced_legacy_export + try: + print(f" Exporting to ONNX (opset {cfg.opset}, dtype {cfg.export_dtype})...") + onnx_exporter.export( + input_names=input_names, + output_names=output_names, + opset=cfg.opset, + dynamic_axes_input=dynamic_axes_input, + dynamic_axes_output=dynamic_axes_output, + export_dtype=cfg.export_dtype, + ) + finally: + torch.onnx.export = original_export + for mod_name, orig_fn in _patched_modules.items(): + if mod_name in sys.modules: + setattr(sys.modules[mod_name], 'create_bidirectional_mask', orig_fn) + + return onnx_exporter + + +def export_onnx_to_tensorrt(onnx_exporter: Any, cfg: ExportConfig) -> None: + """Convert ONNX model to TensorRT. + + Args: + onnx_exporter: The ONNX exporter instance. + cfg: Export configuration. + """ + try: + import tensorrt as trt + except ImportError: + print("Error: TensorRT is required for TRT export. Please install tensorrt.") + sys.exit(1) + + input_profiles = [ + { + "input_ids": [ + [cfg.trt_min_batch, cfg.trt_min_seq_len], + [cfg.trt_opt_batch, cfg.trt_opt_seq_len], + [cfg.trt_max_batch, cfg.trt_max_seq_len], + ], + "attention_mask": [ + [cfg.trt_min_batch, cfg.trt_min_seq_len], + [cfg.trt_opt_batch, cfg.trt_opt_seq_len], + [cfg.trt_max_batch, cfg.trt_max_seq_len], + ], + } + ] + + print(f" Converting to TensorRT...") + print(f" Batch sizes: min={cfg.trt_min_batch}, opt={cfg.trt_opt_batch}, max={cfg.trt_max_batch}") + print(f" Seq lengths: min={cfg.trt_min_seq_len}, opt={cfg.trt_opt_seq_len}, max={cfg.trt_max_seq_len}") + + onnx_exporter.export_onnx_to_trt( + trt_model_dir=str(cfg.trt_model_path), + profiles=input_profiles, + override_layernorm_precision_to_fp32=cfg.override_layernorm_precision_to_fp32, + override_layers_to_fp32=cfg.override_layers_to_fp32, + profiling_verbosity=cfg.profiling_verbosity, + trt_builder_flags=[trt.BuilderFlag.VERSION_COMPATIBLE], + ) + + +def verify_onnx_export(onnx_exporter: Any) -> bool: + """Verify ONNX export with a simple forward pass. + + Args: + onnx_exporter: The ONNX exporter instance. + + Returns: + True if verification passed, False otherwise. + """ + print(" Verifying ONNX export...") + prompt = ["query: what is AI? passage: Artificial intelligence is a field of computer science."] + + try: + result = onnx_exporter.forward(prompt) + print(f" Test logits shape: {result.shape if hasattr(result, 'shape') else 'OK'}") + return True + except Exception as e: + print(f" Error: ONNX verification failed: {e}", file=sys.stderr) + return False + + +def run_export(cfg: ExportConfig) -> dict: + """Run reranking model export to ONNX/TensorRT. + + Args: + cfg: Export configuration. + + Returns: + Dictionary with export paths. + """ + print(f"Reranking Model Export to ONNX/TensorRT") + print(f"=" * 60) + print(f"Model path: {cfg.model_path}") + print(f"Attention impl: {cfg.attn_implementation}") + print(f"Quantization: {cfg.quant_cfg or 'None'}") + print(f"ONNX output: {cfg.onnx_export_path}") + print(f"Export to TRT: {cfg.export_to_trt}") + if cfg.export_to_trt: + print(f"TRT output: {cfg.trt_model_path}") + print(f"=" * 60) + print() + + # Validate model path exists + if not cfg.model_path.exists(): + print(f"Error: Model not found at {cfg.model_path}") + print(" Please run stage2_finetune first or specify a valid model_path.") + sys.exit(1) + + # Create output directories + cfg.output_dir.mkdir(parents=True, exist_ok=True) + cfg.onnx_export_path.mkdir(parents=True, exist_ok=True) + if cfg.export_to_trt: + cfg.trt_model_path.mkdir(parents=True, exist_ok=True) + + results = { + "model_path": str(cfg.model_path), + "onnx_path": str(cfg.onnx_export_path), + } + + # Step 1: Load the reranker model + print(f"Loading reranker model from: {cfg.model_path}") + model, tokenizer = load_reranker_model( + model_path=cfg.model_path, + attn_implementation=cfg.attn_implementation, + ) + print(f" Model loaded successfully") + print() + + # Step 2: Export to ONNX + print(f"Exporting to ONNX...") + onnx_exporter = export_to_onnx(model, tokenizer, cfg) + print(f" ONNX model saved to: {cfg.onnx_export_path}") + print() + + # Step 3: Verify ONNX export + onnx_ok = verify_onnx_export(onnx_exporter) + print() + + # Step 4: Export to TensorRT (optional) + if cfg.export_to_trt and not onnx_ok: + print("Skipping TensorRT conversion: ONNX verification failed.", file=sys.stderr) + print("Fix the ONNX export before converting to TensorRT.", file=sys.stderr) + sys.exit(1) + if cfg.export_to_trt: + print(f"Exporting to TensorRT...") + export_onnx_to_tensorrt(onnx_exporter, cfg) + results["trt_path"] = str(cfg.trt_model_path) + print(f" TensorRT engine saved to: {cfg.trt_model_path}") + print() + + print(f"Export complete!") + print(f" ONNX model: {cfg.onnx_export_path}") + if cfg.export_to_trt: + print(f" TensorRT model: {cfg.trt_model_path}") + + return results + + +def main(cfg: ExportConfig | None = None) -> dict: + """Entry point for export. + + Args: + cfg: Config from CLI framework, or None when run directly as script. + + Returns: + Dictionary with export paths. + """ + if cfg is None: + config_path, cli_overrides = parse_config_and_overrides( + default_config=DEFAULT_CONFIG_PATH + ) + + try: + cfg = load_config(config_path, cli_overrides, ExportConfig) + except FileNotFoundError as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + + return run_export(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/rerank/stage4_export/pyproject.toml b/src/nemotron/recipes/rerank/stage4_export/pyproject.toml new file mode 100644 index 000000000..ed3853520 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage4_export/pyproject.toml @@ -0,0 +1,40 @@ +[project] +name = "recipe-runner-export" +version = "0.0.0" +requires-python = ">=3.12" +dependencies = [ + "onnx>=1.16.0,<1.20", + "onnxruntime>=1.18.0", + "optimum>=1.20.0", + "nemo-export-deploy>=0.3.1", + "onnxscript>=0.5.0", + # Needed by Nemotron library (added via --with flag) + "omegaconf>=2.3.0", + "pydantic-settings>=2.0.0", +] + +[project.optional-dependencies] +tensorrt = [ + "tensorrt>=10.13,<10.15", +] + +[tool.uv] +override-dependencies = ["transformers>=5,<6"] +# Exclude torch/torchvision from resolution — supplied via --with at runtime +# with UV_TORCH_BACKEND=auto so the correct CUDA variant is resolved. +exclude-dependencies = ["torch", "torchvision", "transformer-engine", "nvidia-pytriton"] +environments = ["python_version >= '3.12' and python_version < '3.14'"] + +[tool.nemotron] +entry-point = "export.py" +container-exclude-dependencies = [ + "torch", + "torchvision", + "flash-attn", + "triton", + "pyarrow", + "scipy", + "opencv-python-headless", + "tensorrt", + "transformer-engine", +] diff --git a/src/nemotron/recipes/rerank/stage4_export/run_uv.py b/src/nemotron/recipes/rerank/stage4_export/run_uv.py new file mode 100644 index 000000000..3209e26e3 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage4_export/run_uv.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python3 +"""UV dependency wrapper — delegates to shared nemotron.kit.run_uv.""" +import sys +from pathlib import Path + +# Ensure nemotron package is importable (for container execution) +_src = str(Path(__file__).resolve().parents[4]) # -> .../src +if _src not in sys.path: + sys.path.insert(0, _src) + +from nemotron.kit.run_uv import main + +main(stage_dir=Path(__file__).parent) diff --git a/src/nemotron/recipes/rerank/stage4_export/uv.lock b/src/nemotron/recipes/rerank/stage4_export/uv.lock new file mode 100644 index 000000000..523c9fdf5 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage4_export/uv.lock @@ -0,0 +1,5279 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version == '3.13.*'", + "python_full_version < '3.13'", +] +supported-markers = [ + "python_full_version < '3.14'", +] + +[manifest] +overrides = [{ name = "transformers", specifier = 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Stage 3: Deploy NIM reranker service.""" diff --git a/src/nemotron/recipes/rerank/stage5_deploy/config/default.yaml b/src/nemotron/recipes/rerank/stage5_deploy/config/default.yaml new file mode 100644 index 000000000..4ef5342f8 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage5_deploy/config/default.yaml @@ -0,0 +1,51 @@ +# Stage 5: Deploy Configuration +# +# Launches NIM reranking service with a custom fine-tuned model. +# The NIM provides a ranking API for re-ranking query-document pairs. +# +# Usage: +# nemotron rerank deploy -c default +# nemotron rerank deploy -c default detach=true +# nemotron rerank deploy -c default model_dir=/path/to/onnx +# +# Test the deployed service: +# curl -X POST http://localhost:8000/v1/ranking \ +# -H 'Content-Type: application/json' \ +# -d '{"model": "nvidia/llama-nemotron-rerank-1b-v2", "query": {"text": "what is AI?"}, "passages": [{"text": "AI is artificial intelligence"}]}' + +# NIM container image +nim_image: nvcr.io/nim/nvidia/llama-nemotron-rerank-1b-v2:1.10.0 + +# Container name (for management) +container_name: nemotron-rerank-nim + +# Path to custom model (ONNX or TensorRT from stage4_export) +model_dir: ./output/rerank/stage4_export/onnx + +# Use ONNX instead of TensorRT +use_onnx: true + +# Container internal paths +container_model_path: /opt/nim/custom_model +container_cache_path: /opt/nim/.cache + +# Network settings +host_port: 8000 +container_port: 8000 + +# GPU allocation +gpus: all + +# Shared memory size +shm_size: 2gb + +# Runtime settings +detach: false +remove_on_exit: true + +# Health check settings +health_check_timeout: 120 +health_check_interval: 5 + +# Environment variable for NGC API key +ngc_api_key_env: NGC_API_KEY diff --git a/src/nemotron/recipes/rerank/stage5_deploy/deploy.py b/src/nemotron/recipes/rerank/stage5_deploy/deploy.py new file mode 100644 index 000000000..8e734ec4e --- /dev/null +++ b/src/nemotron/recipes/rerank/stage5_deploy/deploy.py @@ -0,0 +1,309 @@ +#!/usr/bin/env python3 +# /// script +# [tool.runspec] +# schema = "1" +# docs = "https://raw.githubusercontent.com/NVIDIA-NeMo/Nemotron/main/docs/runspec/v1/spec.md" +# name = "rerank/deploy" +# setup = "Local-only Docker wrapper. Launches a NIM container for inference." +# +# [tool.runspec.run] +# launch = "direct" +# +# [tool.runspec.config] +# dir = "./config" +# default = "default" +# /// + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Deploy script for NIM reranking service with custom model. + +Launches the NVIDIA NIM container with a custom ONNX/TensorRT model +exported from stage4_export. The NIM provides a ranking API with +the custom fine-tuned reranking model. + +Usage: + # With default config (launches NIM in foreground) + nemotron rerank deploy -c default + + # With custom model path + nemotron rerank deploy -c default model_dir=/path/to/onnx + + # Detached mode (background) + nemotron rerank deploy -c default detach=true +""" + +from __future__ import annotations + +import os +import signal +import subprocess +import sys +import time +from pathlib import Path + +from typing import Annotated + +from pydantic import BeforeValidator, ConfigDict, Field + +from nemo_runspec.config.pydantic_loader import RecipeSettings, load_config, parse_config_and_overrides + +STAGE_PATH = Path(__file__).parent +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "default.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + + +class DeployConfig(RecipeSettings): + """Deployment configuration for NIM reranking service.""" + + model_config = ConfigDict(extra="forbid") + + # Container settings + nim_image: str = Field(default="nvcr.io/nim/nvidia/llama-nemotron-rerank-1b-v2:1.10.0", description="NIM container image to use.") + container_name: str = Field(default="nemotron-rerank-nim", description="Name for the Docker container.") + + # Model settings + model_dir: Path = Field(default_factory=lambda: _OUTPUT_BASE / "output/rerank/stage4_export/onnx", description="Path to custom model directory (ONNX or TensorRT).") + use_onnx: bool = Field(default=True, description="Use ONNX model instead of TensorRT.") + + # Container paths + container_model_path: str = Field(default="/opt/nim/custom_model", description="Path inside container where model will be mounted.") + container_cache_path: str = Field(default="/opt/nim/.cache", description="Path inside container for NIM cache.") + + # Network settings + host_port: int = Field(default=8000, ge=1, le=65535, description="Port to expose on host.") + container_port: int = Field(default=8000, ge=1, le=65535, description="Port inside container.") + + # Resource settings + gpus: Annotated[str, BeforeValidator(str)] = Field(default="all", description="Number of GPUs to use for the container.") + shm_size: str = Field(default="2gb", description="Shared memory size.") + + # Runtime settings + detach: bool = Field(default=False, description="Run container in detached mode.") + remove_on_exit: bool = Field(default=True, description="Remove container when it exits.") + health_check_timeout: int = Field(default=120, gt=0, description="Timeout in seconds for health check.") + health_check_interval: int = Field(default=5, gt=0, description="Interval in seconds between health checks.") + + # Environment + ngc_api_key_env: str = Field(default="NGC_API_KEY", description="Environment variable name for NGC API key.") + + +def check_docker() -> bool: + """Check if Docker is available.""" + try: + result = subprocess.run( + ["docker", "--version"], + capture_output=True, + text=True, + ) + return result.returncode == 0 + except FileNotFoundError: + return False + + +def check_nvidia_docker() -> bool: + """Check if NVIDIA Container Runtime is available.""" + try: + result = subprocess.run( + ["docker", "info", "--format", "{{.Runtimes}}"], + capture_output=True, + text=True, + ) + return "nvidia" in result.stdout.lower() + except FileNotFoundError: + return False + + +def stop_existing_container(container_name: str) -> None: + """Stop and remove existing container with the same name.""" + subprocess.run(["docker", "stop", container_name], capture_output=True) + subprocess.run(["docker", "rm", container_name], capture_output=True) + + +def build_docker_command(cfg: DeployConfig) -> list[str]: + """Build the Docker run command.""" + cmd = ["docker", "run"] + + if cfg.detach: + cmd.append("-d") + else: + cmd.extend(["-it"]) + + cmd.extend(["--name", cfg.container_name]) + + if cfg.remove_on_exit and not cfg.detach: + cmd.append("--rm") + + cmd.extend(["--gpus", cfg.gpus]) + cmd.extend(["--shm-size", cfg.shm_size]) + cmd.extend(["-u", "root"]) + cmd.extend(["-p", f"{cfg.host_port}:{cfg.container_port}"]) + + ngc_key = os.environ.get(cfg.ngc_api_key_env) + if ngc_key: + cmd.extend(["-e", f"NGC_API_KEY={ngc_key}"]) + else: + print(f"Warning: {cfg.ngc_api_key_env} not set. NIM may not authenticate properly.") + + cmd.extend(["-e", f"NIM_CUSTOM_MODEL={cfg.container_model_path}"]) + + model_dir_abs = cfg.model_dir.resolve() + cmd.extend(["-v", f"{model_dir_abs}:{cfg.container_model_path}:ro"]) + + cache_dir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) + nim_cache = Path(cache_dir) / "nim" + nim_cache.mkdir(parents=True, exist_ok=True) + cmd.extend(["-v", f"{nim_cache}:{cfg.container_cache_path}"]) + + cmd.append(cfg.nim_image) + + return cmd + + +def wait_for_health(cfg: DeployConfig) -> bool: + """Wait for NIM to become healthy.""" + import urllib.request + import urllib.error + + health_url = f"http://localhost:{cfg.host_port}/v1/health/ready" + start_time = time.time() + + print(f" Waiting for NIM to become healthy (timeout: {cfg.health_check_timeout}s)...") + + while time.time() - start_time < cfg.health_check_timeout: + try: + with urllib.request.urlopen(health_url, timeout=5) as response: + if response.status == 200: + return True + except (urllib.error.URLError, urllib.error.HTTPError, TimeoutError): + pass + + time.sleep(cfg.health_check_interval) + elapsed = int(time.time() - start_time) + print(f" ... still waiting ({elapsed}s)") + + return False + + +def run_deploy(cfg: DeployConfig) -> dict: + """Run NIM reranker deployment.""" + print(f"NIM Reranking Service Deployment") + print(f"=" * 60) + print(f"NIM image: {cfg.nim_image}") + print(f"Container name: {cfg.container_name}") + print(f"Model directory: {cfg.model_dir}") + print(f"Host port: {cfg.host_port}") + print(f"GPUs: {cfg.gpus}") + print(f"Detached: {cfg.detach}") + print(f"=" * 60) + print() + + if not check_docker(): + print("Error: Docker is not installed or not running.") + sys.exit(1) + + if not check_nvidia_docker(): + print("Warning: NVIDIA Container Runtime may not be available.") + + if not cfg.model_dir.exists(): + print(f"Error: Model directory not found: {cfg.model_dir}") + print(" Please run stage4_export first.") + sys.exit(1) + + model_files = list(cfg.model_dir.glob("*.onnx")) + list(cfg.model_dir.glob("*.plan")) + if not model_files: + print(f"Warning: No ONNX or TensorRT files found in {cfg.model_dir}") + + print(f"Stopping existing container (if any)...") + stop_existing_container(cfg.container_name) + + docker_cmd = build_docker_command(cfg) + print(f"Starting NIM container...") + print(f" Command: {' '.join(docker_cmd)}") + print() + + result = { + "container_name": cfg.container_name, + "host_port": cfg.host_port, + "model_dir": str(cfg.model_dir), + "api_url": f"http://localhost:{cfg.host_port}/v1/ranking", + } + + if cfg.detach: + proc = subprocess.run(docker_cmd, capture_output=True, text=True) + if proc.returncode != 0: + print(f"Error starting container: {proc.stderr}") + sys.exit(1) + + container_id = proc.stdout.strip() + result["container_id"] = container_id + print(f" Container ID: {container_id[:12]}") + + if wait_for_health(cfg): + print() + print(f"NIM is ready!") + print(f" API endpoint: {result['api_url']}") + print() + print(f" Test with:") + print(f" curl -X POST http://localhost:{cfg.host_port}/v1/ranking \\") + print(f" -H 'Content-Type: application/json' \\") + print(f" -d '{{\"model\": \"nvidia/llama-nemotron-rerank-1b-v2\", \"query\": {{\"text\": \"what is AI?\"}}, \"passages\": [{{\"text\": \"AI is artificial intelligence\"}}]}}'") + print() + print(f" Stop with: docker stop {cfg.container_name}") + else: + print() + print(f"Error: Health check timed out after {cfg.health_check_timeout}s.", file=sys.stderr) + print(f" Check logs with: docker logs {cfg.container_name}", file=sys.stderr) + sys.exit(1) + else: + print(f" Running in foreground. Press Ctrl+C to stop.") + print() + + def signal_handler(signum, frame): + print("\n Shutting down...") + stop_existing_container(cfg.container_name) + sys.exit(0) + + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) + + try: + subprocess.run(docker_cmd) + except KeyboardInterrupt: + pass + + return result + + +def main(cfg: DeployConfig | None = None) -> dict: + """Entry point for deployment.""" + if cfg is None: + config_path, cli_overrides = parse_config_and_overrides( + default_config=DEFAULT_CONFIG_PATH + ) + + try: + cfg = load_config(config_path, cli_overrides, DeployConfig) + except FileNotFoundError as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + + return run_deploy(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/rerank/stage5_deploy/run_uv.py b/src/nemotron/recipes/rerank/stage5_deploy/run_uv.py new file mode 100644 index 000000000..3209e26e3 --- /dev/null +++ b/src/nemotron/recipes/rerank/stage5_deploy/run_uv.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python3 +"""UV dependency wrapper — delegates to shared nemotron.kit.run_uv.""" +import sys +from pathlib import Path + +# Ensure nemotron package is importable (for container execution) +_src = str(Path(__file__).resolve().parents[4]) # -> .../src +if _src not in sys.path: + sys.path.insert(0, _src) + +from nemotron.kit.run_uv import main + +main(stage_dir=Path(__file__).parent) diff --git a/tests/recipes/rerank/__init__.py b/tests/recipes/rerank/__init__.py new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/tests/recipes/rerank/__init__.py @@ -0,0 +1 @@ + diff --git a/tests/recipes/rerank/test_stage2_finetune.py b/tests/recipes/rerank/test_stage2_finetune.py new file mode 100644 index 000000000..27892c60c --- /dev/null +++ b/tests/recipes/rerank/test_stage2_finetune.py @@ -0,0 +1,117 @@ +from __future__ import annotations + +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from nemotron.recipes.rerank.stage2_finetune.train import ( + FinetuneConfig, + _assert_optimizer_metadata_fp32, + _install_fp32_restore_hook, + _optimizer_metadata_dtype_counts, +) + + +def _metadata_entry(dtype: str) -> SimpleNamespace: + return SimpleNamespace(properties=SimpleNamespace(dtype=dtype)) + + +def test_finetune_config_keeps_default_checkpoint_frequency(): + assert FinetuneConfig().checkpoint_every_steps == 100 + + +def test_optimizer_metadata_dtype_counts_only_adam_state(): + metadata = SimpleNamespace( + state_dict_metadata={ + "optim.state.model.score.weight.step": _metadata_entry("torch.float32"), + "optim.state.model.score.weight.exp_avg": _metadata_entry("torch.float32"), + "optim.state.model.score.weight.exp_avg_sq": _metadata_entry("torch.float32"), + "optim.param_groups.0.lr": _metadata_entry("torch.float64"), + "model.score.weight": _metadata_entry("torch.bfloat16"), + } + ) + + assert _optimizer_metadata_dtype_counts(metadata) == { + "step": {"torch.float32": 1}, + "exp_avg": {"torch.float32": 1}, + "exp_avg_sq": {"torch.float32": 1}, + } + + +def test_optimizer_metadata_assertion_rejects_bf16_resume_checkpoint(tmp_path, monkeypatch): + metadata = SimpleNamespace( + state_dict_metadata={ + "optim.state.model.score.weight.step": _metadata_entry("torch.float32"), + "optim.state.model.score.weight.exp_avg": _metadata_entry("torch.bfloat16"), + "optim.state.model.score.weight.exp_avg_sq": _metadata_entry("torch.float32"), + } + ) + + monkeypatch.setattr( + "nemotron.recipes.rerank.stage2_finetune.train._read_optimizer_metadata_dtype_counts", + lambda checkpoint_dir: _optimizer_metadata_dtype_counts(metadata), + ) + with pytest.raises(AssertionError, match="Expected resumed optimizer checkpoint metadata to be fp32"): + _assert_optimizer_metadata_fp32(tmp_path) + + +def test_fp32_restore_hook_casts_model_before_optimizer_load(monkeypatch, tmp_path): + events: list[str] = [] + + class ModelPart: + dtype = "torch.bfloat16" + + def float(self): + events.append("cast_model_to_fp32") + self.dtype = "torch.float32" + + model_part = ModelPart() + + class Checkpointer: + config = SimpleNamespace(enabled=True, checkpoint_dir=str(tmp_path)) + + def load_model(self, model, model_dir): + events.append("load_model") + + def load_optimizer(self, optimizer, model, ckpt_dir, scheduler): + events.append(f"load_optimizer_with_{model_part.dtype}") + + class Recipe: + def __init__(self): + self.checkpointer = Checkpointer() + self.model_parts = [model_part] + + def load_checkpoint(self, restore_from=None): + events.append(f"original_load_checkpoint:{restore_from}") + + def _validate_checkpoint_dir_exists(self, ckpt_dir, restore_from, is_rank_0): + events.append(f"validate:{Path(ckpt_dir).name}") + + def _load_checkpoint_tracked_state(self, ckpt_dir): + events.append(f"load_tracked_state:{Path(ckpt_dir).name}") + return object(), object(), object() + + monkeypatch.setitem( + sys.modules, + "torch", + SimpleNamespace( + distributed=SimpleNamespace( + is_initialized=lambda: False, + get_rank=lambda: 0, + ) + ), + ) + + recipe = Recipe() + _install_fp32_restore_hook(recipe) + recipe.load_checkpoint(str(tmp_path / "epoch_0_step_1")) + + assert events == [ + "validate:epoch_0_step_1", + "load_tracked_state:epoch_0_step_1", + "load_model", + "cast_model_to_fp32", + "load_optimizer_with_torch.float32", + ]