⚡️ Speed up method Qwen3OmniMoeCausalConvNet._get_extra_padding_for_conv1d by 36%

[codeflash-ai]

📄 36% (0.36x) speedup for Qwen3OmniMoeCausalConvNet._get_extra_padding_for_conv1d in src/transformers/models/qwen3_omni_moe/modular_qwen3_omni_moe.py

⏱️ Runtime : 76.0 microseconds → 56.0 microseconds (best of 205 runs)

📝 Explanation and details

The optimized code achieves a 35% speedup by replacing expensive floating-point arithmetic with efficient integer operations and reducing attribute access overhead.

Key optimizations:

1. Integer arithmetic replaces float division and math.ceil: The original code uses (length - self.kernel_size + self.padding) / self.stride + 1 followed by math.ceil(), which involves floating-point division and a function call. The optimized version uses integer ceil division (numer + s - 1) // s + 1, which is significantly faster in Python.

2. Reduced attribute access: The optimized code caches self.kernel_size, self.padding, and self.stride as local variables k, p, and s. This eliminates repeated attribute lookups, which have overhead in Python's object model.

Why this leads to speedup:

• Integer operations are inherently faster than floating-point operations in CPUs
• The math.ceil() function call is eliminated entirely
• Attribute access in Python involves dictionary lookups, so caching these values as locals provides a modest but measurable improvement
• The integer ceil division pattern (numer + s - 1) // s is a well-known optimization that compilers and interpreters can handle efficiently

Performance characteristics:
Based on the test results, the optimization provides consistent speedups ranging from 16.8% to 52.6% across different scenarios, with larger improvements typically seen in simpler cases where the arithmetic operations represent a higher proportion of the total execution time. The optimization is particularly effective for edge cases with small tensors where the computational overhead is more significant relative to tensor shape access.

This optimization maintains identical mathematical correctness while improving performance through more efficient low-level operations.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 77 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

# imports
import pytest
import torch

from transformers.models.qwen3_omni_moe.modular_qwen3_omni_moe import Qwen3OmniMoeCausalConvNet


# unit tests

# ---- Basic Test Cases ----


def test_basic_no_padding_needed():
    # Simple case where no extra padding is required
    net = Qwen3OmniMoeCausalConvNet(2, 4, kernel_size=3, stride=1, dilation=1)
    # kernel_size = 3, dilation = 1 => effective kernel = 3, padding = 2
    # input length = 5
    x = torch.zeros(1, 2, 5)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.94μs -> 1.37μs (41.7% faster)


def test_basic_padding_needed():
    # Case where extra padding is needed
    net = Qwen3OmniMoeCausalConvNet(2, 4, kernel_size=3, stride=2, dilation=1)
    # effective kernel = 3, padding = 1
    x = torch.zeros(1, 2, 5)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.93μs -> 1.31μs (46.9% faster)


def test_basic_stride_equals_kernel():
    # Case where stride equals kernel size
    net = Qwen3OmniMoeCausalConvNet(2, 4, kernel_size=4, stride=4, dilation=1)
    x = torch.zeros(1, 2, 8)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.99μs -> 1.35μs (47.5% faster)


def test_basic_dilation():
    # Case with dilation
    net = Qwen3OmniMoeCausalConvNet(2, 4, kernel_size=3, stride=2, dilation=2)
    # effective kernel = (3-1)*2+1 = 5, padding = 5-2 = 3
    x = torch.zeros(1, 2, 10)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.87μs -> 1.35μs (38.6% faster)


# ---- Edge Test Cases ----


def test_edge_minimal_input():
    # Minimal possible input length (1)
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=1, stride=1, dilation=1)
    x = torch.zeros(1, 1, 1)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 2.03μs -> 1.49μs (36.2% faster)


def test_edge_input_length_less_than_kernel():
    # Input shorter than effective kernel size
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=5, stride=1, dilation=1)
    x = torch.zeros(1, 1, 3)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.93μs -> 1.35μs (43.0% faster)


def test_edge_zero_stride_raises():
    # Stride of zero should raise ZeroDivisionError
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=0, dilation=1)
    x = torch.zeros(1, 1, 5)
    with pytest.raises(ZeroDivisionError):
        net._get_extra_padding_for_conv1d(x)  # 1.73μs -> 1.86μs (7.10% slower)


def test_edge_large_dilation():
    # Large dilation, small kernel
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=2, stride=1, dilation=10)
    # effective kernel = (2-1)*10+1=11, padding=11-1=10
    x = torch.zeros(1, 1, 15)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.93μs -> 1.35μs (42.6% faster)


def test_edge_non_multiple_length_stride():
    # Input length not a multiple of stride
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=2, dilation=1)
    x = torch.zeros(1, 1, 7)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.84μs -> 1.35μs (35.8% faster)


# ---- Large Scale Test Cases ----


def test_large_scale_max_size():
    # Large input, but <100MB (float32, 1MB = 262144 elements, so 10MB = 2,621,440)
    # We'll use 500,000 elements, which is ~2MB
    net = Qwen3OmniMoeCausalConvNet(8, 16, kernel_size=17, stride=5, dilation=3)
    x = torch.zeros(2, 8, 500_000)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 4.95μs -> 4.24μs (16.8% faster)


def test_large_scale_non_multiple_stride():
    # Large input, length not a multiple of stride
    net = Qwen3OmniMoeCausalConvNet(4, 4, kernel_size=15, stride=7, dilation=2)
    x = torch.zeros(1, 4, 999)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 2.17μs -> 1.60μs (35.2% faster)


def test_large_scale_dilation_and_stride():
    # Large input with large dilation and stride
    net = Qwen3OmniMoeCausalConvNet(3, 6, kernel_size=7, stride=13, dilation=5)
    x = torch.zeros(1, 3, 800)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 2.14μs -> 1.60μs (33.6% faster)


# ---- Additional Edge Cases ----


def test_edge_negative_length_raises():
    # Negative input length should raise IndexError from torch
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=1)
    # Construct tensor with negative shape should raise error
    with pytest.raises(RuntimeError):
        x = torch.zeros(1, 1, -5)
        net._get_extra_padding_for_conv1d(x)


def test_edge_empty_tensor():
    # Empty tensor (length 0)
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=1)
    x = torch.zeros(1, 1, 0)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 2.10μs -> 1.57μs (33.5% faster)


def test_edge_single_channel():
    # Single channel, single batch, arbitrary kernel/stride
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=4, stride=2, dilation=2)
    x = torch.zeros(1, 1, 20)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.89μs -> 1.31μs (44.0% faster)


def test_edge_groups_parameter():
    # Test with groups > 1
    net = Qwen3OmniMoeCausalConvNet(4, 4, kernel_size=3, stride=1, dilation=1, groups=2)
    x = torch.zeros(1, 4, 10)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 1.78μs -> 1.35μs (32.3% faster)


# ---- Parameterized Tests for Variety ----


@pytest.mark.parametrize(
    "kernel_size,stride,dilation,length,expected",
    [
        # kernel_size, stride, dilation, input length, expected extra
        (3, 1, 1, 8, 0),  # no padding needed
        (5, 2, 1, 10, 1),  # padding needed
        (7, 3, 2, 30, 0),  # no padding needed
        (4, 2, 3, 25, 1),  # padding needed
        (2, 1, 10, 15, 0),  # edge case, large dilation
    ],
)
def test_parametrized_variety(kernel_size, stride, dilation, length, expected):
    net = Qwen3OmniMoeCausalConvNet(2, 4, kernel_size=kernel_size, stride=stride, dilation=dilation)
    x = torch.zeros(1, 2, length)
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    extra = codeflash_output  # 9.67μs -> 6.55μs (47.7% faster)


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

# imports
import pytest  # used for our unit tests
import torch

from transformers.models.qwen3_omni_moe.modular_qwen3_omni_moe import Qwen3OmniMoeCausalConvNet


# unit tests

# 1. Basic Test Cases


def test_basic_no_padding_needed():
    # kernel_size=3, stride=1, dilation=1, input length=10
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=1)
    x = torch.zeros(1, 1, 10)  # shape: (batch, channel, length)
    # For these parameters, no extra padding should be needed
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 1.92μs -> 1.32μs (45.9% faster)


def test_basic_padding_needed():
    # kernel_size=3, stride=2, dilation=1, input length=10
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=2, dilation=1)
    x = torch.zeros(1, 1, 10)
    # Should require padding to ensure output is integer number of frames
    codeflash_output = net._get_extra_padding_for_conv1d(x)
    result = codeflash_output  # 1.85μs -> 1.26μs (46.8% faster)


def test_basic_dilation_effect():
    # kernel_size=3, stride=1, dilation=2, input length=10
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=2)
    x = torch.zeros(1, 1, 10)
    # kernel_size = (3-1)*2+1 = 5
    # padding = 5-1 = 4
    # n_frames = (10-5+4)/1 + 1 = 9/1 + 1 = 10
    # ideal_length = (ceil(10)-1)*1 + (5-4) = 9 + 1 = 10
    # extra_padding = 10-10 = 0
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 1.90μs -> 1.34μs (41.4% faster)


def test_basic_stride_larger_than_kernel():
    # kernel_size=2, stride=5, dilation=1, input length=13
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=2, stride=5, dilation=1)
    x = torch.zeros(1, 1, 13)
    # kernel_size = 2, padding = 2-5 = -3
    # n_frames = (13-2+(-3))/5 + 1 = (8)/5 + 1 = 1.6 + 1 = 2.6
    # ideal_length = (ceil(2.6)-1)*5 + (2-(-3)) = (3-1)*5 + 5 = 10 + 5 = 15
    # extra_padding = 15-13 = 2
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 1.96μs -> 1.33μs (47.2% faster)


def test_basic_groups_unused():
    # kernel_size=4, stride=2, dilation=1, groups=2, input length=8
    net = Qwen3OmniMoeCausalConvNet(2, 2, kernel_size=4, stride=2, dilation=1, groups=2)
    x = torch.zeros(1, 2, 8)
    # kernel_size = 4, padding = 4-2 = 2
    # n_frames = (8-4+2)/2 + 1 = (6)/2 + 1 = 3 + 1 = 4
    # ideal_length = (ceil(4)-1)*2 + (4-2) = (4-1)*2 + 2 = 6 + 2 = 8
    # extra_padding = 8-8 = 0
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 1.91μs -> 1.32μs (44.8% faster)


# 2. Edge Test Cases


def test_edge_minimum_length():
    # kernel_size=1, stride=1, dilation=1, input length=1
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=1, stride=1, dilation=1)
    x = torch.zeros(1, 1, 1)
    # kernel_size = 1, padding = 0
    # n_frames = (1-1+0)/1 + 1 = (0)/1 + 1 = 1
    # ideal_length = (ceil(1)-1)*1 + (1-0) = 0 + 1 = 1
    # extra_padding = 1-1 = 0
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 1.99μs -> 1.42μs (40.6% faster)


def test_edge_length_smaller_than_kernel():
    # kernel_size=5, stride=1, dilation=1, input length=3
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=5, stride=1, dilation=1)
    x = torch.zeros(1, 1, 3)
    # kernel_size = 5, padding = 4
    # n_frames = (3-5+4)/1 + 1 = (2)/1 + 1 = 2 + 1 = 3
    # ideal_length = (ceil(3)-1)*1 + (5-4) = (3-1)*1 + 1 = 2 + 1 = 3
    # extra_padding = 3-3 = 0
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 1.95μs -> 1.34μs (46.0% faster)


def test_edge_zero_length_tensor():
    # kernel_size=3, stride=1, dilation=1, input length=0
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=1)
    x = torch.zeros(1, 1, 0)
    # kernel_size = 3, padding = 2
    # n_frames = (0-3+2)/1 + 1 = (-1)/1 + 1 = -1 + 1 = 0
    # ideal_length = (ceil(0)-1)*1 + (3-2) = (-1)*1 + 1 = -1 + 1 = 0
    # extra_padding = 0-0 = 0
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.03μs -> 1.47μs (37.7% faster)


def test_edge_stride_zero_raises():
    # kernel_size=3, stride=0, dilation=1, input length=10
    # Should raise ZeroDivisionError
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=1)
    net.stride = 0  # forcibly set stride to zero
    x = torch.zeros(1, 1, 10)
    with pytest.raises(ZeroDivisionError):
        net._get_extra_padding_for_conv1d(x)  # 1.77μs -> 1.86μs (4.89% slower)


def test_edge_negative_length_raises():
    # kernel_size=3, stride=1, dilation=1, input length=-5 (invalid tensor shape)
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=1)
    # PyTorch will not allow negative shape, so we test with an empty tensor and simulate
    x = torch.zeros(1, 1, 0)

    # Patch shape[-1] to -5
    class FakeTensor:
        shape = (1, 1, -5)

    with pytest.raises(ValueError):
        net._get_extra_padding_for_conv1d(FakeTensor())


def test_edge_large_dilation():
    # kernel_size=2, stride=1, dilation=5, input length=15
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=2, stride=1, dilation=5)
    x = torch.zeros(1, 1, 15)
    # kernel_size = (2-1)*5+1 = 6
    # padding = 6-1 = 5
    # n_frames = (15-6+5)/1 + 1 = (14)/1 + 1 = 15
    # ideal_length = (ceil(15)-1)*1 + (6-5) = (15-1)*1 + 1 = 14 + 1 = 15
    # extra_padding = 15-15 = 0
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.13μs -> 1.51μs (41.2% faster)


def test_edge_large_kernel_and_stride():
    # kernel_size=10, stride=10, dilation=1, input length=15
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=10, stride=10, dilation=1)
    x = torch.zeros(1, 1, 15)
    # kernel_size = 10, padding = 0
    # n_frames = (15-10+0)/10 + 1 = (5)/10 + 1 = 0.5 + 1 = 1.5
    # ideal_length = (ceil(1.5)-1)*10 + (10-0) = (2-1)*10 + 10 = 10 + 10 = 20
    # extra_padding = 20-15 = 5
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 1.95μs -> 1.36μs (43.1% faster)


def test_edge_non_contiguous_tensor():
    # kernel_size=3, stride=1, dilation=1, input length=10, use non-contiguous tensor
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1, dilation=1)
    x = torch.zeros(1, 1, 20)[:, :, ::2]  # shape (1, 1, 10), but non-contiguous
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.01μs -> 1.32μs (52.6% faster)


# 3. Large Scale Test Cases


def test_large_scale_long_sequence():
    # kernel_size=5, stride=2, dilation=3, input length=999
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=5, stride=2, dilation=3)
    x = torch.zeros(1, 1, 999)
    # kernel_size = (5-1)*3+1 = 13
    # padding = 13-2 = 11
    # n_frames = (999-13+11)/2 + 1 = (997)/2 + 1 = 498.5 + 1 = 499.5
    # ideal_length = (ceil(499.5)-1)*2 + (13-11) = (500-1)*2 + 2 = 998 + 2 = 1000
    # extra_padding = 1000-999 = 1
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.19μs -> 1.72μs (27.3% faster)


def test_large_scale_wide_tensor():
    # kernel_size=7, stride=3, dilation=2, input length=1000, batch=4, channels=8
    net = Qwen3OmniMoeCausalConvNet(8, 16, kernel_size=7, stride=3, dilation=2)
    x = torch.zeros(4, 8, 1000)
    # kernel_size = (7-1)*2+1 = 13
    # padding = 13-3 = 10
    # n_frames = (1000-13+10)/3 + 1 = (997)/3 + 1 = 332.333... + 1 = 333.333...
    # ideal_length = (ceil(333.333...)-1)*3 + (13-10) = (334-1)*3 + 3 = 333*3 + 3 = 999 + 3 = 1002
    # extra_padding = 1002-1000 = 2
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.25μs -> 1.68μs (34.3% faster)


def test_large_scale_maximum_tensor_size():
    # kernel_size=9, stride=5, dilation=4, input length=999
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=9, stride=5, dilation=4)
    x = torch.zeros(1, 1, 999)
    # kernel_size = (9-1)*4+1 = 33
    # padding = 33-5 = 28
    # n_frames = (999-33+28)/5 + 1 = (994)/5 + 1 = 198.8 + 1 = 199.8
    # ideal_length = (ceil(199.8)-1)*5 + (33-28) = (200-1)*5 + 5 = 995 + 5 = 1000
    # extra_padding = 1000-999 = 1
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.03μs -> 1.66μs (22.3% faster)


def test_large_scale_stride_equals_length():
    # kernel_size=3, stride=1000, dilation=1, input length=1000
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1000, dilation=1)
    x = torch.zeros(1, 1, 1000)
    # kernel_size = 3, padding = 3-1000 = -997
    # n_frames = (1000-3+(-997))/1000 + 1 = (0)/1000 + 1 = 1
    # ideal_length = (ceil(1)-1)*1000 + (3-(-997)) = 0 + 1000 = 1000
    # extra_padding = 1000-1000 = 0
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.14μs -> 1.55μs (38.2% faster)


def test_large_scale_stride_larger_than_length():
    # kernel_size=3, stride=1001, dilation=1, input length=1000
    net = Qwen3OmniMoeCausalConvNet(1, 1, kernel_size=3, stride=1001, dilation=1)
    x = torch.zeros(1, 1, 1000)
    # kernel_size = 3, padding = 3-1001 = -998
    # n_frames = (1000-3+(-998))/1001 + 1 = (-1)/1001 + 1 = ~0.999
    # ideal_length = (ceil(0.999)-1)*1001 + (3-(-998)) = (1-1)*1001 + 1001 = 0 + 1001 = 1001
    # extra_padding = 1001-1000 = 1
    codeflash_output = net._get_extra_padding_for_conv1d(x)  # 2.17μs -> 1.55μs (39.8% faster)


# 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-Qwen3OmniMoeCausalConvNet._get_extra_padding_for_conv1d-misr31pp and push.