⚡️ Speed up function compute_take_along_axis_output_shape by 25%

[codeflash-ai]

📄 25% (0.25x) speedup for compute_take_along_axis_output_shape in keras/src/ops/operation_utils.py

⏱️ Runtime : 125 microseconds → 100 microseconds (best of 77 runs)

📝 Explanation and details

The optimized code achieves a 24% speedup through two key optimizations that reduce computational overhead in shape broadcasting operations:

What was optimized:

1. Eliminated unnecessary list copying in broadcast_shapes: Instead of creating output_shape = list(shape1) and then modifying elements via indexing, the optimized version builds output_shape = [] directly using append() operations while iterating with zip(shape1, shape2).

2. Replaced expensive np.prod() with manual multiplication: In compute_take_along_axis_output_shape, when axis is None, the code now uses a simple loop to calculate the product instead of calling np.prod(input_shape), which has overhead from NumPy's C API and type conversions.

Why these optimizations work:

• List building vs. copying: Python's list.append() is more efficient than list copying + indexing when most elements will be replaced. The zip() approach also eliminates repeated len() calls and index bounds checking.

• Manual product calculation: For small lists (typical in shape operations), a simple Python loop with prod *= d is faster than NumPy's prod() function, which has significant overhead for small arrays due to function call costs and type checking.

Performance impact:

The function is called from take_along_axis in TensorFlow backend operations, making it part of tensor manipulation hot paths. The test results show consistent 2-10% improvements across most test cases, with the largest gains (up to 623% in one edge case) when avoiding the np.prod() call. Since tensor shape operations are fundamental to deep learning frameworks, these micro-optimizations compound across many operations to deliver meaningful performance gains.

The optimizations are particularly effective for typical ML workloads involving 2D-4D tensors with small dimension counts, where the overhead reduction becomes proportionally significant.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 38 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	92.6%

🌀 Generated Regression Tests and Runtime

import numpy as np
# imports
import pytest  # used for our unit tests
from keras.src.ops.operation_utils import compute_take_along_axis_output_shape

# unit tests

# Basic Test Cases

def test_basic_1d_shapes():
    # Simple 1D shape replacement
    input_shape = [5]
    indices_shape = [3]
    axis = 0
    # Output should be [3]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.98μs -> 2.83μs (5.29% faster)

def test_basic_2d_shapes_axis_0():
    # 2D shape, axis 0 replaced
    input_shape = [5, 4]
    indices_shape = [2, 4]
    axis = 0
    # Output should be [2, 4]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.93μs -> 2.66μs (10.1% faster)

def test_basic_2d_shapes_axis_1():
    # 2D shape, axis 1 replaced
    input_shape = [5, 4]
    indices_shape = [5, 2]
    axis = 1
    # Output should be [5, 2]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.67μs -> 2.57μs (4.09% faster)

def test_basic_broadcasting():
    # Broadcasting: input_shape has 1 in a dimension
    input_shape = [1, 4]
    indices_shape = [5, 4]
    axis = 0
    # Output should be [5, 4]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.60μs -> 2.52μs (3.25% faster)

def test_basic_broadcasting_ones():
    # Broadcasting: indices_shape has 1 in a dimension
    input_shape = [7, 1]
    indices_shape = [7, 3]
    axis = 1
    # Output should be [7, 3]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.50μs -> 2.30μs (8.97% faster)

def test_basic_equal_shapes():
    # Input and indices shapes are equal
    input_shape = [6, 6]
    indices_shape = [6, 6]
    axis = 1
    # Output should be [6, 6]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.59μs -> 2.46μs (5.54% faster)

# Edge Test Cases

def test_edge_mismatched_ndims():
    # Mismatched number of dimensions should raise ValueError
    input_shape = [5, 4]
    indices_shape = [5]
    axis = 0
    with pytest.raises(ValueError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.71μs -> 3.00μs (9.86% slower)

def test_edge_broadcast_fail():
    # Broadcasting should fail if dimensions cannot match
    input_shape = [2, 4]
    indices_shape = [3, 5]
    axis = 0
    with pytest.raises(ValueError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 4.99μs -> 4.59μs (8.67% faster)

def test_edge_none_in_indices_shape():
    # None in indices_shape, axis replacement
    input_shape = [5, None]
    indices_shape = [5, 3]
    axis = 1
    # Output should be [5, 3]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.79μs -> 2.74μs (1.83% faster)

def test_edge_none_broadcasting():
    # None in input_shape, broadcasting with 1
    input_shape = [None, 1]
    indices_shape = [7, 2]
    axis = 1
    # Output should be [None, 2]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.63μs -> 2.58μs (1.90% faster)

def test_edge_singleton_dim():
    # Singleton dimension in input_shape
    input_shape = [1, 1, 8]
    indices_shape = [5, 1, 8]
    axis = 0
    # Output should be [5, 1, 8]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.75μs -> 2.68μs (2.50% faster)

def test_edge_zero_dim():
    # Zero dimension in input_shape
    input_shape = [0, 4]
    indices_shape = [0, 3]
    axis = 1
    # Output should be [0, 3]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.64μs -> 2.51μs (5.10% faster)

def test_edge_axis_negative():
    # Negative axis
    input_shape = [5, 4, 3]
    indices_shape = [5, 2, 3]
    axis = -2  # should map to axis 1
    # Output should be [5, 2, 3]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.84μs -> 2.58μs (9.99% faster)

def test_edge_axis_out_of_bounds():
    # Axis out of bounds should raise IndexError
    input_shape = [5, 4]
    indices_shape = [5, 4]
    axis = 2
    with pytest.raises(IndexError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 1.06μs -> 1.07μs (1.49% slower)

def test_edge_axis_negative_out_of_bounds():
    # Negative axis out of bounds should raise IndexError
    input_shape = [5, 4]
    indices_shape = [5, 4]
    axis = -3
    with pytest.raises(IndexError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 1.10μs -> 1.09μs (0.735% faster)

# Large Scale Test Cases

def test_large_scale_1d():
    # Large 1D shapes
    input_shape = [1000]
    indices_shape = [900]
    axis = 0
    # Output should be [900]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.62μs -> 2.68μs (2.31% slower)

def test_large_scale_2d():
    # Large 2D shapes with broadcasting
    input_shape = [1, 500]
    indices_shape = [1000, 500]
    axis = 0
    # Output should be [1000, 500]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.67μs -> 2.62μs (1.98% faster)

def test_large_scale_3d():
    # Large 3D shapes with broadcasting
    input_shape = [1, 10, 100]
    indices_shape = [50, 10, 100]
    axis = 0
    # Output should be [50, 10, 100]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.74μs -> 2.72μs (0.698% faster)

def test_large_scale_none_broadcast():
    # Large shape with None and broadcasting
    input_shape = [None, 1, 100]
    indices_shape = [900, 10, 100]
    axis = 1
    # Output should be [None, 10, 100]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 3.48μs -> 3.27μs (6.51% faster)

def test_large_scale_mismatched_dims():
    # Large shapes with mismatched dims should raise ValueError
    input_shape = [900, 100]
    indices_shape = [900]
    axis = 0
    with pytest.raises(ValueError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.88μs -> 2.91μs (1.34% slower)

import numpy as np
# imports
import pytest  # used for our unit tests
from keras.src.ops.operation_utils import compute_take_along_axis_output_shape

# unit tests

# ----------- BASIC TEST CASES -----------

def test_basic_same_shape():
    # Simple case: input and indices have the same shape
    input_shape = (2, 3)
    indices_shape = (2, 3)
    axis = 1
    # Should return [2, 3]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 3.53μs -> 3.21μs (9.88% faster)

def test_basic_different_shape_axis():
    # Indices shape differs on axis
    input_shape = (4, 5, 6)
    indices_shape = (4, 2, 6)
    axis = 1
    # Should return [4, 2, 6]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.96μs -> 2.81μs (5.09% faster)

def test_basic_broadcastable_shape():
    # Indices shape can be broadcasted
    input_shape = (5, 1)
    indices_shape = (5, 3)
    axis = 1
    # Should return [5, 3]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.78μs -> 2.62μs (5.83% faster)

def test_basic_axis_zero():
    # Axis 0 replacement
    input_shape = (7, 2)
    indices_shape = (3, 2)
    axis = 0
    # Should return [3, 2]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.65μs -> 2.60μs (2.08% faster)

def test_basic_3d_broadcast():
    # 3D broadcast
    input_shape = (2, 1, 4)
    indices_shape = (2, 3, 4)
    axis = 1
    # Should return [2, 3, 4]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.74μs -> 2.64μs (3.79% faster)

# ----------- EDGE TEST CASES -----------

def test_edge_empty_shape():
    # Empty shapes (scalar)
    input_shape = ()
    indices_shape = ()
    axis = 0  # Invalid, but should raise error
    with pytest.raises(IndexError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 1.30μs -> 1.36μs (4.35% slower)

def test_edge_mismatched_ndim():
    # Mismatched number of dimensions
    input_shape = (2, 3)
    indices_shape = (2, 3, 4)
    axis = 1
    with pytest.raises(ValueError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.93μs -> 2.85μs (2.85% faster)

def test_edge_non_broadcastable():
    # Non-broadcastable shapes
    input_shape = (4, 5)
    indices_shape = (3, 6)
    axis = 1
    with pytest.raises(ValueError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 4.85μs -> 4.58μs (5.81% faster)

def test_edge_none_axis_out_of_bounds():
    # Axis out of bounds
    input_shape = (2, 3)
    indices_shape = (2, 3)
    axis = 2
    with pytest.raises(IndexError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 1.13μs -> 1.25μs (9.32% slower)

def test_edge_negative_axis():
    # Negative axis
    input_shape = (2, 3, 4)
    indices_shape = (2, 5, 4)
    axis = -2
    # Should replace dim 1 (second dim)
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 3.37μs -> 3.23μs (4.30% faster)

def test_edge_none_in_indices():
    # Indices shape contains None
    input_shape = (2, 3)
    indices_shape = (2, None)
    axis = 1
    # Should return [2, None]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.81μs -> 2.83μs (0.848% slower)

def test_edge_all_none():
    # All dims are None
    input_shape = (None, None)
    indices_shape = (None, None)
    axis = 1
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.50μs -> 2.54μs (1.65% slower)

# ----------- LARGE SCALE TEST CASES -----------

def test_large_scale_1d():
    # Large 1D shapes
    input_shape = (1000,)
    indices_shape = (1000,)
    axis = 0
    # Should return [1000]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.51μs -> 2.26μs (10.9% faster)

def test_large_scale_2d():
    # Large 2D shapes
    input_shape = (1000, 1)
    indices_shape = (1000, 999)
    axis = 1
    # Should return [1000, 999]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.71μs -> 2.58μs (4.84% faster)

def test_large_scale_3d_broadcast():
    # Large 3D shapes with broadcast
    input_shape = (1, 1000, 50)
    indices_shape = (20, 1000, 50)
    axis = 0
    # Should return [20, 1000, 50]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.82μs -> 2.74μs (2.84% faster)

def test_large_scale_none_dim():
    # Large scale with None
    input_shape = (None, 1000)
    indices_shape = (None, 1000)
    axis = 1
    # Should return [None, 1000]
    codeflash_output = compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 2.70μs -> 2.60μs (4.00% faster)

def test_edge_axis_negative_out_of_bounds():
    # Negative axis out of bounds
    input_shape = (2, 3)
    indices_shape = (2, 3)
    axis = -3
    with pytest.raises(IndexError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 1.29μs -> 1.41μs (8.49% slower)

def test_edge_axis_none_with_mismatched_ndim():
    # Axis None but mismatched ndim
    input_shape = (2, 3)
    indices_shape = (2, 3, 4)
    axis = None
    with pytest.raises(ValueError):
        compute_take_along_axis_output_shape(input_shape, indices_shape, axis) # 25.0μs -> 3.46μs (623% faster)

To edit these changes git checkout codeflash/optimize-compute_take_along_axis_output_shape-mirg1qie and push.