⚡️ Speed up function interpolate by 38%

[codeflash-ai]

📄 38% (0.38x) speedup for interpolate in src/transformers/models/clap/modeling_clap.py

⏱️ Runtime : 1.40 milliseconds → 1.01 milliseconds (best of 118 runs)

📝 Explanation and details

The optimization replaces PyTorch's repeat() method with a more efficient unsqueeze() + expand() combination for tensor interpolation.

Key changes:

• Line 2: Changed hidden_states[:, :, None, :].repeat(1, 1, ratio, 1) to hidden_states.unsqueeze(2).expand(batch_size, time_length, ratio, classes_num)
• Memory efficiency: repeat() allocates new memory and copies data, while expand() creates a memory-efficient view without copying data
• Computational efficiency: expand() avoids the expensive memory allocation and data copying operations

Why this leads to speedup:
The repeat() operation physically duplicates tensor data in memory (87.7% of original runtime), while expand() creates a broadcasted view that shares the underlying memory (58.2% of optimized runtime). This eliminates unnecessary memory allocation and copying, especially beneficial for larger tensors where the memory overhead becomes significant.

Performance characteristics:

• Small tensors: 76-113% speedup (edge cases with empty/singleton dimensions benefit most)
• Medium tensors: 20-47% speedup (typical use cases)
• Large tensors: 15-25% speedup (memory efficiency becomes more important)
• Ratio=1 cases: Up to 112% speedup (expand operation becomes nearly free)

The optimization is particularly effective for time-domain interpolation in CNN downsampling compensation, where the function likely processes feature maps of varying sizes during model inference.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 36 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
import torch  # torch is required for tensor operations
from transformers.models.clap.modeling_clap import interpolate

# unit tests

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

def test_basic_single_batch_single_time_single_class():
    # Single batch, single time, single class
    x = torch.tensor([[[1.0]]])
    codeflash_output = interpolate(x, 2); out = codeflash_output # 37.2μs -> 21.1μs (76.6% faster)
    # Should repeat the time dimension by ratio
    expected = torch.tensor([[[1.0], [1.0]]])

def test_basic_single_batch_multiple_time_single_class():
    # Single batch, multiple time steps, single class
    x = torch.tensor([[[1.0], [2.0], [3.0]]])
    codeflash_output = interpolate(x, 3); out = codeflash_output # 37.4μs -> 31.0μs (20.5% faster)
    # Each time step should be repeated 3 times
    expected = torch.tensor([[
        [1.0], [1.0], [1.0], 
        [2.0], [2.0], [2.0], 
        [3.0], [3.0], [3.0]
    ]])

def test_basic_multiple_batch_multiple_time_multiple_class():
    # Two batches, two time steps, two classes
    x = torch.tensor([
        [[1.0, 2.0], [3.0, 4.0]],
        [[5.0, 6.0], [7.0, 8.0]]
    ])
    codeflash_output = interpolate(x, 2); out = codeflash_output # 37.9μs -> 30.1μs (25.9% faster)
    # Each time step should be repeated twice
    expected = torch.tensor([
        [[1.0, 2.0], [1.0, 2.0], [3.0, 4.0], [3.0, 4.0]],
        [[5.0, 6.0], [5.0, 6.0], [7.0, 8.0], [7.0, 8.0]]
    ])

def test_basic_ratio_one():
    # Ratio of 1 should return the same shape and values
    x = torch.randn(2, 3, 4)
    codeflash_output = interpolate(x, 1); out = codeflash_output # 37.0μs -> 18.4μs (101% faster)

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



def test_edge_empty_tensor():
    # Empty tensor: batch_size=0, should return empty tensor
    x = torch.empty(0, 2, 3)
    codeflash_output = interpolate(x, 2); out = codeflash_output # 39.7μs -> 22.0μs (80.8% faster)

def test_edge_time_length_zero():
    # time_length=0, should return empty tensor
    x = torch.empty(2, 0, 3)
    codeflash_output = interpolate(x, 2); out = codeflash_output # 35.5μs -> 15.6μs (128% faster)

def test_edge_classes_num_zero():
    # classes_num=0, should return empty tensor
    x = torch.empty(2, 3, 0)
    codeflash_output = interpolate(x, 2); out = codeflash_output # 31.2μs -> 15.3μs (104% faster)

def test_edge_non_integer_ratio_raises():
    # Ratio must be integer
    x = torch.randn(1, 2, 3)
    with pytest.raises(TypeError):
        interpolate(x, 1.5) # 61.2μs -> 58.4μs (4.75% faster)

def test_edge_non_tensor_input_raises():
    # hidden_states must be a torch.Tensor
    with pytest.raises(AttributeError):
        interpolate([[1.0]], 2) # 1.23μs -> 1.16μs (6.30% faster)

def test_edge_large_ratio():
    # Large ratio, but small tensor
    x = torch.tensor([[[1.0], [2.0]]])
    codeflash_output = interpolate(x, 100); out = codeflash_output # 42.3μs -> 34.4μs (22.8% faster)
    # Each time step repeated 100 times
    expected = torch.cat([torch.full((1, 100, 1), 1.0), torch.full((1, 100, 1), 2.0)], dim=1)

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

def test_large_scale_max_batch():
    # Large batch size, reasonable time and classes
    x = torch.randn(1000, 2, 3)
    codeflash_output = interpolate(x, 2); out = codeflash_output # 65.7μs -> 56.9μs (15.5% faster)
    # Check that each time step is repeated
    for b in range(0, 1000, 100):  # Sample some batches
        pass

def test_large_scale_max_time():
    # Large time_length, small batch and classes
    x = torch.arange(20).float().reshape(1,10,2)
    codeflash_output = interpolate(x, 5); out = codeflash_output # 28.5μs -> 20.5μs (39.2% faster)
    # Each time step should be repeated 5 times
    for t in range(10):
        expected_block = x[0, t].unsqueeze(0).repeat(5,1)

def test_large_scale_max_classes():
    # Large classes_num, small batch and time
    x = torch.arange(200).float().reshape(1,2,100)
    codeflash_output = interpolate(x, 3); out = codeflash_output # 26.1μs -> 19.4μs (34.6% faster)

def test_large_scale_max_all_dimensions():
    # All dimensions large but within limits
    x = torch.randn(10, 10, 10)
    codeflash_output = interpolate(x, 10); out = codeflash_output # 40.8μs -> 32.5μs (25.8% faster)
    # Check a sample batch and time step
    for b in range(0, 10, 5):
        for t in range(0, 10, 5):
            expected_block = x[b, t].unsqueeze(0).repeat(10,1)

def test_large_scale_dtype_preserved():
    # Check that dtype is preserved
    x = torch.randint(0, 10, (2, 2, 2), dtype=torch.int32)
    codeflash_output = interpolate(x, 2); out = codeflash_output # 34.0μs -> 26.1μs (30.3% faster)

# --------------- Additional Edge Cases for Robustness -------------

def test_edge_ratio_greater_than_time_length():
    # Ratio greater than time_length
    x = torch.tensor([[[1.0], [2.0]]])
    codeflash_output = interpolate(x, 5); out = codeflash_output # 36.9μs -> 26.7μs (38.4% faster)

def test_edge_ratio_is_one_and_empty():
    # Ratio is 1 and tensor is empty in some dimension
    x = torch.empty(0, 0, 0)
    codeflash_output = interpolate(x, 1); out = codeflash_output # 34.0μs -> 16.4μs (108% faster)

def test_edge_ratio_is_large_and_empty():
    # Ratio is large and tensor is empty in some dimension
    x = torch.empty(0, 0, 0)
    codeflash_output = interpolate(x, 100); out = codeflash_output # 31.4μs -> 14.7μs (113% faster)
# 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  # torch is required for tensor operations
from transformers.models.clap.modeling_clap import interpolate

# unit tests

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

def test_interpolate_basic_identity():
    # Test with ratio=1, output should be identical to input
    x = torch.randn(2, 3, 4)
    codeflash_output = interpolate(x, 1); y = codeflash_output # 39.4μs -> 19.2μs (105% faster)

def test_interpolate_basic_upsample_by_2():
    # Test upsampling by a factor of 2
    x = torch.tensor([[[1.0, 2.0], [3.0, 4.0]]])  # shape (1, 2, 2)
    codeflash_output = interpolate(x, 2); y = codeflash_output # 33.3μs -> 31.4μs (6.09% faster)
    # Each time step should be repeated twice
    expected = torch.tensor([[
        [1.0, 2.0],
        [1.0, 2.0],
        [3.0, 4.0],
        [3.0, 4.0]
    ]])

def test_interpolate_basic_upsample_by_3():
    # Test upsampling by a factor of 3
    x = torch.tensor([[[5.0], [6.0]]])  # shape (1, 2, 1)
    codeflash_output = interpolate(x, 3); y = codeflash_output # 37.6μs -> 30.2μs (24.6% faster)
    expected = torch.tensor([[
        [5.0],
        [5.0],
        [5.0],
        [6.0],
        [6.0],
        [6.0]
    ]])

def test_interpolate_basic_batch_size():
    # Test with batch size > 1
    x = torch.tensor([
        [[1.0], [2.0]],
        [[3.0], [4.0]]
    ])  # shape (2, 2, 1)
    codeflash_output = interpolate(x, 2); y = codeflash_output # 35.9μs -> 30.1μs (19.4% faster)
    expected = torch.tensor([
        [[1.0], [1.0], [2.0], [2.0]],
        [[3.0], [3.0], [4.0], [4.0]]
    ])

def test_interpolate_basic_classes_num():
    # Test with multiple classes per time step
    x = torch.tensor([[[1.0, 2.0], [3.0, 4.0]]])
    codeflash_output = interpolate(x, 2); y = codeflash_output # 38.2μs -> 26.0μs (47.0% faster)
    expected = torch.tensor([
        [[1.0, 2.0], [1.0, 2.0], [3.0, 4.0], [3.0, 4.0]]
    ])

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

def test_interpolate_edge_zero_time_length():
    # time_length=0 should yield an empty output
    x = torch.randn(2, 0, 3)
    codeflash_output = interpolate(x, 2); y = codeflash_output # 33.0μs -> 17.9μs (83.6% faster)

def test_interpolate_edge_zero_classes_num():
    # classes_num=0 should yield an empty output
    x = torch.randn(2, 5, 0)
    codeflash_output = interpolate(x, 3); y = codeflash_output # 33.5μs -> 15.0μs (124% faster)

def test_interpolate_edge_zero_batch_size():
    # batch_size=0 should yield an empty output
    x = torch.randn(0, 4, 2)
    codeflash_output = interpolate(x, 2); y = codeflash_output # 34.1μs -> 17.3μs (96.4% faster)

def test_interpolate_edge_ratio_one():
    # ratio=1 should not change the shape or content
    x = torch.randn(3, 4, 5)
    codeflash_output = interpolate(x, 1); y = codeflash_output # 37.2μs -> 17.5μs (112% faster)

def test_interpolate_edge_ratio_large():
    # ratio is large but within reasonable memory constraints
    x = torch.tensor([[[1.0], [2.0]]])
    codeflash_output = interpolate(x, 100); y = codeflash_output # 37.7μs -> 29.5μs (27.8% faster)
    # Each time step should be repeated 100 times
    expected = torch.cat([x[:, 0:1, :].repeat(1, 100, 1), x[:, 1:2, :].repeat(1, 100, 1)], dim=1)


def test_interpolate_edge_negative_ratio():
    # negative ratio should raise an error
    x = torch.randn(1, 2, 3)
    with pytest.raises(RuntimeError):
        interpolate(x, -2) # 76.2μs -> 71.3μs (6.95% faster)

def test_interpolate_edge_non_integer_ratio():
    # ratio is not integer, should raise a TypeError
    x = torch.randn(1, 2, 3)
    with pytest.raises(TypeError):
        interpolate(x, 1.5) # 57.2μs -> 54.1μs (5.60% faster)

def test_interpolate_edge_non_tensor_input():
    # hidden_states is not a tensor, should raise AttributeError
    with pytest.raises(AttributeError):
        interpolate([[1, 2], [3, 4]], 2) # 1.21μs -> 1.17μs (3.08% faster)

def test_interpolate_edge_singleton_dimensions():
    # Test with singleton dimensions
    x = torch.randn(1, 1, 1)
    codeflash_output = interpolate(x, 2); y = codeflash_output # 39.6μs -> 22.6μs (75.3% faster)

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

def test_interpolate_large_batch():
    # Large batch size, but small enough to stay under 100MB
    batch_size = 128
    time_length = 8
    classes_num = 8
    x = torch.randn(batch_size, time_length, classes_num)
    codeflash_output = interpolate(x, 4); y = codeflash_output # 58.1μs -> 49.9μs (16.5% faster)
    # Check that each block of 4 is identical to the original time step
    for b in range(batch_size):
        for t in range(time_length):
            orig = x[b, t, :]
            upsampled_block = y[b, t*4:(t+1)*4, :]
            for i in range(4):
                pass

def test_interpolate_large_time_length():
    # Large time_length, but small enough to stay under 100MB
    batch_size = 2
    time_length = 500
    classes_num = 4
    x = torch.randn(batch_size, time_length, classes_num)
    codeflash_output = interpolate(x, 2); y = codeflash_output # 54.4μs -> 45.6μs (19.3% faster)
    # Spot check a few time steps
    for t in [0, 100, 250, 499]:
        orig = x[0, t, :]
        upsampled_block = y[0, t*2:(t+1)*2, :]
        for i in range(2):
            pass

def test_interpolate_large_classes_num():
    # Large classes_num, but small enough to stay under 100MB
    batch_size = 1
    time_length = 10
    classes_num = 1000
    x = torch.randn(batch_size, time_length, classes_num)
    codeflash_output = interpolate(x, 3); y = codeflash_output # 46.3μs -> 37.8μs (22.7% faster)

def test_interpolate_large_all_dims():
    # All dims large but under 100MB
    batch_size = 4
    time_length = 20
    classes_num = 50
    ratio = 5
    x = torch.randn(batch_size, time_length, classes_num)
    codeflash_output = interpolate(x, ratio); y = codeflash_output # 45.1μs -> 36.3μs (24.3% faster)
    # Spot check
    for t in [0, 10, 19]:
        orig = x[0, t, :]
        upsampled_block = y[0, t*ratio:(t+1)*ratio, :]
        for i in range(ratio):
            pass
# 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-interpolate-mhmrvepf and push.