⚡️ Speed up method TensorChunker._split_value by 12% in PR #272 (14__robusttraining)

[codeflash-ai]

⚡️ This pull request contains optimizations for PR #272

If you approve this dependent PR, these changes will be merged into the original PR branch 14__robusttraining.

This PR will be automatically closed if the original PR is merged.

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📄 12% (0.12x) speedup for TensorChunker._split_value in src/ldp/nn/handlers/chunking.py

⏱️ Runtime : 670 microseconds → 600 microseconds (best of 103 runs)

📝 Explanation and details

Sure, I can make the given code more efficient. Here are the main improvements.

1. Simplify the chunk splitting and dummy chunk creation to use fewer operations.
2. Avoid repetitive appending in a loop by pre-determining the length and constructing the final list accordingly.

Here is the optimized version of the provided code.

Improvements made.

1. Instead of using a conditional and loop to append dummy chunks, I pre-determine the number of necessary dummy chunks and extend the list in one operation.
2. Created the dummy_chunk_flags list in one go, thus avoiding repeated appending operations.

With these changes, the function should run faster while maintaining the intended behavior.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 54 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage

🌀 Generated Regression Tests Details

import pytest  # used for our unit tests
import torch
from ldp.nn.handlers.chunking import TensorChunker

# unit tests

# Basic Tensor Splitting
def test_basic_tensor_splitting():
    chunker = TensorChunker(5)
    tensor = torch.randn(10, 5)
    chunks, flags = chunker._split_value(tensor)

# Uneven Tensor Splitting
def test_uneven_tensor_splitting():
    chunker = TensorChunker(3)
    tensor = torch.randn(7, 4)
    chunks, flags = chunker._split_value(tensor)

# Tensor Smaller Than Number of Chunks
def test_tensor_smaller_than_chunks():
    chunker = TensorChunker(5)
    tensor = torch.randn(2, 3)
    chunks, flags = chunker._split_value(tensor)

# Edge Case with One Element Tensor
def test_one_element_tensor():
    chunker = TensorChunker(2)
    tensor = torch.randn(1, 1)
    chunks, flags = chunker._split_value(tensor)

# Edge Case with Zero Elements Tensor
def test_zero_elements_tensor():
    chunker = TensorChunker(4)
    tensor = torch.empty(0, 3)
    chunks, flags = chunker._split_value(tensor)

# Non-Tensor Values
def test_non_tensor_values():
    chunker = TensorChunker(3)
    value = 42
    chunks, flags = chunker._split_value(value)

# Large Scale Test Cases
def test_large_scale_tensor():
    chunker = TensorChunker(10)
    tensor = torch.randn(10000, 100)
    chunks, flags = chunker._split_value(tensor)

# Mixed Data Types
def test_mixed_data_types():
    chunker = TensorChunker(5)
    tensor = torch.randint(0, 10, (10, 5), dtype=torch.int32)
    chunks, flags = chunker._split_value(tensor)

# High Dimensional Tensors
def test_high_dimensional_tensors():
    chunker = TensorChunker(3)
    tensor = torch.randn(6, 3, 2)
    chunks, flags = chunker._split_value(tensor)

# Tensor with Different Dimensions
def test_tensor_different_dimensions():
    chunker = TensorChunker(5)
    tensor = torch.randn(10, 5)
    chunks, flags = chunker._split_value(tensor)

# Edge Case with Zero Chunks
def test_zero_chunks():
    chunker = TensorChunker(0)
    tensor = torch.randn(10, 5)
    with pytest.raises(RuntimeError):
        chunker._split_value(tensor)
    value = 42
    chunks, flags = chunker._split_value(value)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
import torch
from ldp.nn.handlers.chunking import TensorChunker

# unit tests

# Basic Test Cases
def test_tensor_split_equal_chunks():
    tensor = torch.randn(10, 5)
    chunker = TensorChunker(2)
    chunks, flags = chunker._split_value(tensor)

def test_tensor_split_unequal_chunks():
    tensor = torch.randn(7, 2)
    chunker = TensorChunker(3)
    chunks, flags = chunker._split_value(tensor)

# Edge Test Cases
def test_empty_tensor():
    tensor = torch.randn(0, 5)
    chunker = TensorChunker(3)
    chunks, flags = chunker._split_value(tensor)

def test_single_element_tensor():
    tensor = torch.randn(1, 5)
    chunker = TensorChunker(3)
    chunks, flags = chunker._split_value(tensor)

def test_tensor_with_one_dimension():
    tensor = torch.randn(10)
    chunker = TensorChunker(2)
    chunks, flags = chunker._split_value(tensor)

def test_high_num_chunks():
    tensor = torch.randn(5, 4)
    chunker = TensorChunker(10)
    chunks, flags = chunker._split_value(tensor)

# Non-Tensor Inputs
def test_integer_input():
    value = 42
    chunker = TensorChunker(3)
    chunks, flags = chunker._split_value(value)

def test_string_input():
    value = "test"
    chunker = TensorChunker(2)
    chunks, flags = chunker._split_value(value)

def test_list_input():
    value = [1, 2, 3]
    chunker = TensorChunker(4)
    chunks, flags = chunker._split_value(value)

# Large Scale Test Cases
def test_large_tensor():
    tensor = torch.randn(10000, 100)
    chunker = TensorChunker(10)
    chunks, flags = chunker._split_value(tensor)

def test_high_num_chunks_large_tensor():
    tensor = torch.randn(1000, 100)
    chunker = TensorChunker(100)
    chunks, flags = chunker._split_value(tensor)

# Performance and Scalability
def test_performance_under_load():
    import time
    tensor = torch.randn(50000, 100)  # 50MB tensor
    chunker = TensorChunker(50)
    start_time = time.time()
    chunks, flags = chunker._split_value(tensor)
    end_time = time.time()

# Deterministic Behavior
def test_consistent_output():
    tensor = torch.randn(10, 5)
    chunker = TensorChunker(2)
    chunks1, flags1 = chunker._split_value(tensor)
    chunks2, flags2 = chunker._split_value(tensor)

# Real-World Data
def test_image_data():
    tensor = torch.randn(32, 3, 224, 224)
    chunker = TensorChunker(4)
    chunks, flags = chunker._split_value(tensor)

def test_text_data():
    tensor = torch.randn(100, 768)
    chunker = TensorChunker(5)
    chunks, flags = chunker._split_value(tensor)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-pr272-2025-04-07T18.53.17 and push.