⚡️ Speed up method JanusVQVAEAttnBlock.forward by 23%

[codeflash-ai]

📄 23% (0.23x) speedup for JanusVQVAEAttnBlock.forward in src/transformers/models/janus/modeling_janus.py

⏱️ Runtime : 316 milliseconds → 257 milliseconds (best of 26 runs)

📝 Explanation and details

The optimized code achieves a 22% speedup through several memory-efficient tensor operations optimizations in the attention mechanism:

Key Optimizations:

1. Efficient tensor reshaping: Replaced .reshape().permute() with .view().transpose(), which avoids unnecessary memory copies when the tensor is contiguous. This is faster because .view() creates a new view of the same data without copying, while .reshape() may need to copy data.

2. In-place scaling: Instead of creating a new tensor with attn_weights * (int(channels) ** (-0.5)), the code now pre-computes the scale factor and uses in-place multiplication with attn_weights.mul_(scale). This eliminates one temporary tensor allocation and reduces memory bandwidth usage.

3. Streamlined transpose operations: Eliminated an unnecessary permute(0, 2, 1) operation by restructuring the computation flow. The original code permuted attention weights before the final bmm, but the optimized version uses transpose(1, 2) directly in the bmm call.

Performance Impact Analysis:
The line profiler shows the most significant improvements in:

• Attention weight scaling: Reduced from 90.4ms to 15.4ms (83% faster) due to in-place operations
• Final bmm operation: Slightly improved due to better memory layout from optimized reshaping

Test Case Performance:
The optimization particularly excels with larger inputs:

• Large spatial dimensions (64x64): 27.7% faster
• Large batch + spatial (32x32): 14.2% faster
• Performance scales well as tensor sizes increase, indicating the memory efficiency gains compound with larger workloads

These optimizations are especially valuable for transformer-based vision models where attention blocks are called frequently during inference and training.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 92 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest
import torch

from transformers.models.janus.modeling_janus import JanusVQVAEAttnBlock


# unit tests

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


def test_forward_output_shape_basic():
    # Test that output shape matches input shape for a typical input
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 219μs -> 221μs (0.786% slower)


def test_forward_batch_size_1():
    # Test with batch size 1
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(1, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 200μs -> 199μs (0.580% faster)


def test_forward_channels_32():
    # Test with 32 channels (GroupNorm default)
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(4, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 250μs -> 249μs (0.014% faster)


def test_forward_channels_multiple_of_32():
    # Test with 64 channels (multiple of 32)
    block = JanusVQVAEAttnBlock(in_channels=64)
    block.norm = torch.nn.GroupNorm(num_groups=32, num_channels=64, eps=1e-6, affine=True)
    x = torch.randn(2, 64, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 247μs -> 243μs (1.43% faster)


def test_forward_non_square_input():
    # Test with non-square spatial dimensions
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 12)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 228μs -> 234μs (2.81% slower)


def test_forward_different_dtype_float32():
    # Test with float32 dtype
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8, dtype=torch.float32)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 209μs -> 210μs (0.704% slower)


def test_forward_requires_grad():
    # Test that gradients can flow through the block
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8, requires_grad=True)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 209μs -> 215μs (2.63% slower)
    y.mean().backward()


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


def test_forward_minimum_spatial_size():
    # Test with minimum spatial size (1x1)
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 1, 1)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 170μs -> 179μs (5.17% slower)


def test_forward_single_channel_groupnorm():
    # Test with 32 channels, 1 group (GroupNorm fallback)
    block = JanusVQVAEAttnBlock(in_channels=32)
    block.norm = torch.nn.GroupNorm(num_groups=1, num_channels=32, eps=1e-6, affine=True)
    x = torch.randn(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 214μs -> 218μs (1.82% slower)


def test_forward_zero_tensor():
    # Test with all-zero input
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.zeros(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 216μs -> 219μs (1.37% slower)


def test_forward_inf_nan_tensor():
    # Test with inf and nan values in input
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)
    x[0, 0, 0, 0] = float("inf")
    x[1, 1, 1, 1] = float("nan")
    codeflash_output = block.forward(x)
    y = codeflash_output  # 197μs -> 207μs (4.94% slower)


def test_forward_gradient_check():
    # Check that backward works for edge values
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8, requires_grad=True)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 212μs -> 208μs (1.65% faster)
    grad = torch.autograd.grad(outputs=y.sum(), inputs=x, retain_graph=True)[0]


def test_forward_large_height_width():
    # Test with large spatial dimensions but within 100MB
    # 1 * 32 * 64 * 64 * 4 bytes = 524288 bytes = 0.5MB
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(1, 32, 64, 64)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 77.9ms -> 64.7ms (20.4% faster)


def test_forward_non_contiguous_input():
    # Test with non-contiguous input tensor
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)
    x = x.transpose(2, 3)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 242μs -> 240μs (0.813% faster)


def test_forward_different_device_cpu():
    # Test on CPU (default)
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 218μs -> 215μs (1.42% faster)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
def test_forward_large_batch():
    # Test with large batch size, but within memory constraints
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(32, 32, 8, 8)  # 32*32*8*8*4 = 65,536 bytes = 0.0625MB
    codeflash_output = block.forward(x)
    y = codeflash_output  # 1.09ms -> 1.10ms (0.834% slower)


def test_forward_large_channels():
    # Test with large number of channels (128), but within memory constraints
    block = JanusVQVAEAttnBlock(in_channels=128)
    block.norm = torch.nn.GroupNorm(num_groups=32, num_channels=128, eps=1e-6, affine=True)
    x = torch.randn(2, 128, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 342μs -> 343μs (0.292% slower)


def test_forward_large_spatial_and_batch():
    # Test with large batch and spatial dimensions, but under 100MB
    # 8*32*16*16*4 = 524,288 bytes = 0.5MB
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(8, 32, 16, 16)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 1.38ms -> 1.29ms (6.95% faster)


def test_forward_multiple_runs_consistency():
    # Test that multiple runs with same input and weights yield same output
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y1 = codeflash_output  # 213μs -> 214μs (0.281% slower)
    codeflash_output = block.forward(x)
    y2 = codeflash_output  # 124μs -> 121μs (1.94% faster)


def test_forward_performance_large_input():
    # Test that forward pass completes in reasonable time for large input
    import time

    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(4, 32, 32, 32)  # 4*32*32*32*4 = 524288 bytes = 0.5MB
    start = time.time()
    codeflash_output = block.forward(x)
    y = codeflash_output  # 12.0ms -> 9.17ms (31.2% faster)
    elapsed = time.time() - start


# ---------------- NEGATIVE TEST CASES (for robustness) ----------------


def test_forward_wrong_input_shape_raises():
    # Test that input with wrong number of dimensions raises
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8)  # Missing one spatial dimension
    with pytest.raises(RuntimeError):
        block.forward(x)  # 168μs -> 175μs (4.13% slower)


def test_forward_wrong_channel_count_raises():
    # Test that input with wrong channel count raises
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 16, 8, 8)  # 16 != 32
    with pytest.raises(RuntimeError):
        block.forward(x)  # 75.3μs -> 77.6μs (2.99% slower)


def test_forward_groupnorm_incompatible_channels():
    # Test that GroupNorm with incompatible num_groups/channels raises
    with pytest.raises(ValueError):
        # 30 channels cannot be divided into 32 groups
        JanusVQVAEAttnBlock(in_channels=30)


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

import pytest
import torch

from transformers.models.janus.modeling_janus import JanusVQVAEAttnBlock


# function to test
# (see provided code above for JanusVQVAEAttnBlock)

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


def test_forward_identity_shape_and_type():
    """Test that output shape and dtype match input for a simple case."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)  # batch=2, channels=32, 8x8 spatial
    codeflash_output = block.forward(x)
    y = codeflash_output  # 215μs -> 227μs (5.01% slower)


def test_forward_different_batch_sizes():
    """Test with varying batch sizes."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    for batch in [1, 4]:
        x = torch.randn(batch, 32, 8, 8)
        codeflash_output = block.forward(x)
        y = codeflash_output  # 350μs -> 355μs (1.31% slower)


def test_forward_different_spatial_sizes():
    """Test with different spatial dimensions."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    for h, w in [(4, 4), (16, 8), (8, 16)]:
        x = torch.randn(2, 32, h, w)
        codeflash_output = block.forward(x)
        y = codeflash_output  # 520μs -> 522μs (0.491% slower)


def test_forward_gradients():
    """Test that gradients flow through the block."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8, requires_grad=True)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 210μs -> 220μs (4.18% slower)
    loss = y.sum()
    loss.backward()


def test_forward_deterministic_on_same_input():
    """Test that repeated calls with the same input yield same output (eval mode)."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    block.eval()
    x = torch.randn(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y1 = codeflash_output  # 216μs -> 215μs (0.447% faster)
    codeflash_output = block.forward(x)
    y2 = codeflash_output  # 123μs -> 120μs (2.25% faster)


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


def test_forward_single_pixel():
    """Edge: 1x1 spatial input."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 1, 1)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 178μs -> 173μs (2.93% faster)


def test_forward_single_channel():
    """Edge: single channel (must be compatible with GroupNorm)."""
    # GroupNorm requires num_channels >= num_groups
    # So for in_channels=32, num_groups=32 is valid, but for 1 it's not.
    # Should raise an error.
    with pytest.raises(ValueError):
        JanusVQVAEAttnBlock(in_channels=1)


def test_forward_non_square_spatial():
    """Edge: non-square spatial dimensions."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 7, 5)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 209μs -> 209μs (0.242% faster)


def test_forward_zero_input():
    """Edge: All zeros input should not produce NaNs."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.zeros(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 206μs -> 216μs (4.59% slower)


def test_forward_extreme_values():
    """Edge: Very large and very small values should not produce NaNs or Infs."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8) * 1e6
    codeflash_output = block.forward(x)
    y = codeflash_output  # 199μs -> 196μs (1.35% faster)
    x = torch.randn(2, 32, 8, 8) * 1e-6
    codeflash_output = block.forward(x)
    y = codeflash_output  # 119μs -> 116μs (2.73% faster)


def test_forward_channels_not_multiple_of_groups():
    """Edge: in_channels not divisible by num_groups should raise error."""
    # GroupNorm with num_channels=34 and num_groups=32 is invalid
    with pytest.raises(ValueError):
        JanusVQVAEAttnBlock(in_channels=34)


def test_forward_channels_smaller_than_groups():
    """Edge: in_channels < num_groups (32) should raise error."""
    with pytest.raises(ValueError):
        JanusVQVAEAttnBlock(in_channels=16)


def test_forward_requires_grad_false():
    """Edge: Input does not require grad, output should also not require grad."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8, requires_grad=False)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 215μs -> 211μs (1.92% faster)


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


def test_forward_large_batch():
    """Large: Large batch size."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(32, 32, 8, 8)  # 32*32*8*8*4 = 262144 bytes = 0.25MB
    codeflash_output = block.forward(x)
    y = codeflash_output  # 1.07ms -> 1.01ms (5.87% faster)


def test_forward_large_spatial():
    """Large: Large spatial dimensions, but under 100MB."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    # 2*32*64*64*4 = 1,048,576 bytes = 1MB
    x = torch.randn(2, 32, 64, 64)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 175ms -> 137ms (27.7% faster)


def test_forward_large_channels():
    """Large: Large number of channels, must be divisible by 32."""
    block = JanusVQVAEAttnBlock(in_channels=128)
    # 2*128*8*8*4 = 65,536 bytes = 0.06MB
    x = torch.randn(2, 128, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 380μs -> 394μs (3.45% slower)


def test_forward_performance_large():
    """Large: Ensure forward pass is reasonably fast for large input."""
    import time

    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(8, 32, 32, 32)  # 8*32*32*32*4 = 1,048,576 bytes = 1MB
    start = time.time()
    codeflash_output = block.forward(x)
    y = codeflash_output  # 38.7ms -> 33.9ms (14.2% faster)
    elapsed = time.time() - start


def test_forward_output_is_sum_of_residual_and_attn():
    """Test that output equals input + something (i.e., residual connection is present)."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 228μs -> 232μs (2.03% slower)
    # If block.forward returned only attn_output, this test would fail
    diff = (y - x).abs().sum().item()


def test_forward_backward_multiple_steps():
    """Test that backward works for multiple forward/backward passes."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8, requires_grad=True)
    for _ in range(3):
        codeflash_output = block.forward(x)
        y = codeflash_output  # 528μs -> 540μs (2.29% slower)
        loss = y.pow(2).mean()
        loss.backward(retain_graph=True)


def test_forward_requires_grad_output():
    """Test that output requires grad if input does."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8, requires_grad=True)
    codeflash_output = block.forward(x)
    y = codeflash_output  # 218μs -> 221μs (1.31% slower)


def test_forward_no_side_effects():
    """Test that input tensor is not modified in-place."""
    block = JanusVQVAEAttnBlock(in_channels=32)
    x = torch.randn(2, 32, 8, 8)
    x_clone = x.clone()
    codeflash_output = block.forward(x)
    _ = codeflash_output  # 208μs -> 209μs (0.554% slower)


# 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-JanusVQVAEAttnBlock.forward-mi9u4ux4 and push.