⚡️ Speed up method TensorChunker._split_value by 89% 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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📄 89% (0.89x) speedup for TensorChunker._split_value in src/ldp/nn/handlers/chunking.py

⏱️ Runtime : 2.60 milliseconds → 1.38 millisecond (best of 82 runs)

📝 Explanation and details

To optimize the existing code for speed, we can make use of more efficient operations for tensor handling and avoid unnecessary list operations within the function. Here is the rewritten program.

Changes Made

1. Directly used the torch.chunk function to split the tensor and handle the resulting chunks as a tuple.
2. Precomputed the number of real chunks and initialized the dummy_chunk_flags list with appropriate lengths to avoid list appends in a loop.
3. Used tuple concatenation to efficiently add the necessary dummy chunks.
4. Converted the chunks to a list only once, just before returning, to maintain the same return type as before.

These changes ensure that the operations, particularly list appending and tensor manipulations, are as efficient as possible.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 84 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
def test_basic_functionality():
    chunker = TensorChunker(2)
    tensor = torch.arange(10)
    chunks, dummy_flags = chunker._split_value(tensor)

def test_empty_tensor():
    chunker = TensorChunker(3)
    tensor = torch.tensor([])
    chunks, dummy_flags = chunker._split_value(tensor)

def test_single_element_tensor():
    chunker = TensorChunker(3)
    tensor = torch.tensor([1])
    chunks, dummy_flags = chunker._split_value(tensor)

def test_tensor_smaller_than_chunks():
    chunker = TensorChunker(5)
    tensor = torch.tensor([1, 2])
    chunks, dummy_flags = chunker._split_value(tensor)

def test_tensor_equal_to_chunks():
    chunker = TensorChunker(5)
    tensor = torch.tensor([1, 2, 3, 4, 5])
    chunks, dummy_flags = chunker._split_value(tensor)

def test_integer_input():
    chunker = TensorChunker(3)
    value = 5
    chunks, dummy_flags = chunker._split_value(value)

def test_string_input():
    chunker = TensorChunker(4)
    value = "test"
    chunks, dummy_flags = chunker._split_value(value)

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

def test_large_tensor():
    chunker = TensorChunker(100)
    tensor = torch.arange(10000)
    chunks, dummy_flags = chunker._split_value(tensor)
    for i in range(100):
        pass

def test_multi_dimensional_tensor():
    chunker = TensorChunker(2)
    tensor = torch.arange(50).reshape(10, 5)
    chunks, dummy_flags = chunker._split_value(tensor)

def test_tensor_with_one_chunk():
    chunker = TensorChunker(1)
    tensor = torch.arange(10)
    chunks, dummy_flags = chunker._split_value(tensor)

def test_tensor_with_max_chunks():
    chunker = TensorChunker(10)
    tensor = torch.arange(10)
    chunks, dummy_flags = chunker._split_value(tensor)
    for i in range(10):
        pass



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

# unit tests

# Basic Tensor Splitting
def test_tensor_split_even():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([1, 2, 3, 4])
    chunks, flags = chunker._split_value(value)

def test_tensor_split_uneven():
    chunker = TensorChunker(num_chunks=3)
    value = torch.tensor([1, 2, 3, 4, 5])
    chunks, flags = chunker._split_value(value)

# Edge Cases for Tensor Splitting
def test_empty_tensor():
    chunker = TensorChunker(num_chunks=3)
    value = torch.tensor([])
    chunks, flags = chunker._split_value(value)

def test_single_element_tensor():
    chunker = TensorChunker(num_chunks=3)
    value = torch.tensor([1])
    chunks, flags = chunker._split_value(value)

def test_fewer_elements_than_chunks():
    chunker = TensorChunker(num_chunks=4)
    value = torch.tensor([1, 2])
    chunks, flags = chunker._split_value(value)

# Non-Tensor Values
def test_integer_value():
    chunker = TensorChunker(num_chunks=3)
    value = 5
    chunks, flags = chunker._split_value(value)

def test_string_value():
    chunker = TensorChunker(num_chunks=3)
    value = "test"
    chunks, flags = chunker._split_value(value)

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

def test_dict_value():
    chunker = TensorChunker(num_chunks=3)
    value = {"key": "value"}
    chunks, flags = chunker._split_value(value)

# Large Scale Test Cases
def test_large_tensor():
    chunker = TensorChunker(num_chunks=100)
    value = torch.arange(10000)
    chunks, flags = chunker._split_value(value)

def test_very_large_num_chunks():
    chunker = TensorChunker(num_chunks=1000)
    value = torch.arange(100)
    chunks, flags = chunker._split_value(value)

# Special Tensor Types
def test_multi_dimensional_tensor():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([[1, 2], [3, 4], [5, 6]])
    chunks, flags = chunker._split_value(value)

def test_float_tensor():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([1.0, 2.0, 3.0])
    chunks, flags = chunker._split_value(value)

def test_boolean_tensor():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([True, False, True])
    chunks, flags = chunker._split_value(value)

# Boundary Conditions
def test_num_chunks_one():
    chunker = TensorChunker(num_chunks=1)
    value = torch.tensor([1, 2, 3])
    chunks, flags = chunker._split_value(value)

def test_num_chunks_zero():
    chunker = TensorChunker(num_chunks=0)
    value = torch.tensor([1, 2, 3])
    with pytest.raises(ValueError):
        chunker._split_value(value)

def test_negative_num_chunks():
    chunker = TensorChunker(num_chunks=-1)
    value = torch.tensor([1, 2, 3])
    with pytest.raises(ValueError):
        chunker._split_value(value)

# Consistency and Determinism
def test_deterministic_behavior():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([1, 2, 3, 4])
    chunks1, flags1 = chunker._split_value(value)
    chunks2, flags2 = chunker._split_value(value)

# Rare or Unexpected Edge Cases
def test_tensor_with_nan():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([float('nan'), 2, 3])
    chunks, flags = chunker._split_value(value)

def test_tensor_with_inf():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([float('inf'), 2, 3])
    chunks, flags = chunker._split_value(value)

def test_tensor_with_negative_values():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([-1, -2, -3, -4])
    chunks, flags = chunker._split_value(value)

def test_single_element_tensor():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([1])
    chunks, flags = chunker._split_value(value)

def test_high_dimensional_tensor():
    chunker = TensorChunker(num_chunks=3)
    value = torch.randn(2, 3, 4, 5)
    chunks, flags = chunker._split_value(value)

def test_tensor_with_mixed_data_types():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([1, 2, 3.5])
    chunks, flags = chunker._split_value(value)

def test_tensor_with_boolean_and_integer_values():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([True, 1, False])
    chunks, flags = chunker._split_value(value)

def test_ragged_tensor():
    chunker = TensorChunker(num_chunks=2)
    value = [torch.tensor([1, 2]), torch.tensor([3, 4, 5])]
    with pytest.raises(TypeError):
        chunker._split_value(value)

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

def test_nested_dicts():
    chunker = TensorChunker(num_chunks=2)
    value = {"a": {"key": "value"}, "b": {"key": "value"}}
    chunks, flags = chunker._split_value(value)

def test_scalar_tensor():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor(5)
    chunks, flags = chunker._split_value(value)

def test_sparse_tensor():
    chunker = TensorChunker(num_chunks=2)
    value = torch.tensor([0, 0, 0, 1, 0, 0, 0, 2])
    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.

To edit these changes git checkout codeflash/optimize-pr272-2025-04-07T15.04.58 and push.