⚡️ Speed up method Dinov2WithRegistersSelfAttention.transpose_for_scores by 25% in PR #1250 (feature/inference-v1-models)

[codeflash-ai]

⚡️ This pull request contains optimizations for PR #1250

If you approve this dependent PR, these changes will be merged into the original PR branch feature/inference-v1-models.

This PR will be automatically closed if the original PR is merged.

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📄 25% (0.25x) speedup for Dinov2WithRegistersSelfAttention.transpose_for_scores in inference/v1/models/rfdetr/dinov2_with_windowed_attn.py

⏱️ Runtime : 5.10 milliseconds → 4.08 milliseconds (best of 31 runs)

📝 Explanation and details

Here is an optimized version of your code, specifically targeting the runtime bottleneck revealed in the profiler: the transpose_for_scores function.
The main optimization is to replace view() and permute() with a single call to reshape() followed by transpose(), which is typically more efficient, especially for large tensors.
This avoids creating non-contiguous tensors, and, in many cases, can make better use of internal strides, minimizing unnecessary data movement.

No function signatures or return values are changed. All existing comments are preserved.

Explanation of optimizations:

• Instead of view() (which requires the tensor to be contiguous) and then permute(), using reshape() followed by transpose() is both faster and more robust, and preferred in PyTorch for this kind of operation.
• transpose(1, 2) directly swaps the sequence and head dimensions, achieving the same as permute(0, 2, 1, 3) but faster in practice for rank-4 tensors with the given dimensions.
• This eliminates the need for permuting two axes and maintains a more contiguous memory pattern.
• Comments were kept as per your requirement.

This version will have the exact same outputs and interface as your original, but with significantly improved runtime and memory handling for the "transpose_for_scores" function.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 1051 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 inference.v1.models.rfdetr.dinov2_with_windowed_attn import \
    Dinov2WithRegistersSelfAttention
from torch import nn

# function to test
# ------------------------------------------------------------------------
# RF-DETR
# Copyright (c) 2025 Roboflow. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from HuggingFace Dinov2 (https://github.com/huggingface/transformers)
# Copyright 2024 Meta Inc. and the HuggingFace Inc. team. All rights reserved.
# ------------------------------------------------------------------------

class WindowedDinov2WithRegistersConfig:
    def __init__(self, hidden_size, num_attention_heads, qkv_bias=True, attention_probs_dropout_prob=0.1):
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.qkv_bias = qkv_bias
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
from inference.v1.models.rfdetr.dinov2_with_windowed_attn import \
    Dinov2WithRegistersSelfAttention

# unit tests

@pytest.fixture
def attention_module():
    config = WindowedDinov2WithRegistersConfig(hidden_size=8, num_attention_heads=2)
    return Dinov2WithRegistersSelfAttention(config)

def test_standard_input(attention_module):
    # Test with standard input tensor
    x = torch.rand(2, 5, 8)  # batch_size=2, seq_length=5, hidden_size=8
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output


def test_large_batch_and_sequence():
    # Test with large batch and sequence
    config = WindowedDinov2WithRegistersConfig(hidden_size=1024, num_attention_heads=16)
    attention_module = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(64, 128, 1024)  # batch_size=64, seq_length=128, hidden_size=1024
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output

def test_large_hidden_size():
    # Test with large hidden size
    config = WindowedDinov2WithRegistersConfig(hidden_size=256, num_attention_heads=16)
    attention_module = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(4, 10, 256)  # batch_size=4, seq_length=10, hidden_size=256
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output



def test_zeros_and_ones(attention_module):
    # Test with tensor filled with zeros
    x = torch.zeros(2, 5, 8)
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output

    # Test with tensor filled with ones
    x = torch.ones(2, 5, 8)
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output

def test_random_values(attention_module):
    # Test with random values
    x = torch.rand(2, 5, 8)
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output

def test_maximal_and_minimal_values(attention_module):
    # Test with maximal float values
    x = torch.full((2, 5, 8), torch.finfo(torch.float32).max)
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output

    # Test with minimal float values
    x = torch.full((2, 5, 8), torch.finfo(torch.float32).min)
    codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output

def test_invalid_dimensions(attention_module):
    # Test with incorrect dimensions
    x = torch.rand(5, 8)  # Missing batch dimension
    with pytest.raises(RuntimeError):
        attention_module.transpose_for_scores(x)

def test_non_tensor_input(attention_module):
    # Test with non-tensor input
    with pytest.raises(AttributeError):
        attention_module.transpose_for_scores([1, 2, 3])

def test_repeated_calls(attention_module):
    # Stress test with repeated calls
    x = torch.rand(2, 5, 8)
    for _ in range(1000):
        codeflash_output = attention_module.transpose_for_scores(x); result = codeflash_output
# 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
from inference.v1.models.rfdetr.dinov2_with_windowed_attn import \
    Dinov2WithRegistersSelfAttention
from torch import nn

# function to test
# ------------------------------------------------------------------------
# RF-DETR
# Copyright (c) 2025 Roboflow. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from HuggingFace Dinov2 (https://github.com/huggingface/transformers)
# Copyright 2024 Meta Inc. and the HuggingFace Inc. team. All rights reserved.
# ------------------------------------------------------------------------

class WindowedDinov2WithRegistersConfig:
    def __init__(self, hidden_size, num_attention_heads, qkv_bias=True, attention_probs_dropout_prob=0.1):
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.qkv_bias = qkv_bias
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
from inference.v1.models.rfdetr.dinov2_with_windowed_attn import \
    Dinov2WithRegistersSelfAttention

# unit tests

def test_standard_input_shape():
    # Test with a standard input shape
    config = WindowedDinov2WithRegistersConfig(hidden_size=64, num_attention_heads=8)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(2, 10, 64)  # Batch size 2, sequence length 10, hidden size 64
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_minimum_input_dimensions():
    # Test with minimum input dimensions
    config = WindowedDinov2WithRegistersConfig(hidden_size=8, num_attention_heads=2)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(1, 1, 8)  # Batch size 1, sequence length 1, hidden size 8
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_single_attention_head():
    # Test with a single attention head
    config = WindowedDinov2WithRegistersConfig(hidden_size=32, num_attention_heads=1)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(3, 5, 32)  # Batch size 3, sequence length 5, hidden size 32
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_large_number_of_attention_heads():
    # Test with a large number of attention heads
    config = WindowedDinov2WithRegistersConfig(hidden_size=16, num_attention_heads=16)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(4, 10, 16)  # Batch size 4, sequence length 10, hidden size 16
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_non_divisible_hidden_size():
    # Test with non-divisible hidden size, should raise ValueError
    config = WindowedDinov2WithRegistersConfig(hidden_size=30, num_attention_heads=4)
    with pytest.raises(ValueError):
        Dinov2WithRegistersSelfAttention(config)

def test_large_batch_and_sequence_length():
    # Test with large batch and sequence length
    config = WindowedDinov2WithRegistersConfig(hidden_size=1024, num_attention_heads=16)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(70, 281, 563)  # Batch size 128, sequence length 512, hidden size 1024
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_maximal_hidden_size():
    # Test with maximal hidden size
    config = WindowedDinov2WithRegistersConfig(hidden_size=2048, num_attention_heads=32)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(8, 20, 2048)  # Batch size 8, sequence length 20, hidden size 2048
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_zero_batch_size():
    # Test with zero batch size
    config = WindowedDinov2WithRegistersConfig(hidden_size=64, num_attention_heads=8)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(0, 10, 64)  # Batch size 0, sequence length 10, hidden size 64
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_zero_sequence_length():
    # Test with zero sequence length
    config = WindowedDinov2WithRegistersConfig(hidden_size=64, num_attention_heads=8)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(5, 0, 64)  # Batch size 5, sequence length 0, hidden size 64
    codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_randomized_input_shapes():
    # Test with randomized input shapes
    config = WindowedDinov2WithRegistersConfig(hidden_size=128, num_attention_heads=8)
    attention = Dinov2WithRegistersSelfAttention(config)
    for _ in range(10):
        batch_size = torch.randint(1, 10, (1,)).item()
        seq_len = torch.randint(1, 20, (1,)).item()
        x = torch.rand(batch_size, seq_len, 128)  # Random batch size and sequence length
        codeflash_output = attention.transpose_for_scores(x); result = codeflash_output

def test_non_tensor_input():
    # Test with non-tensor input, should raise TypeError
    config = WindowedDinov2WithRegistersConfig(hidden_size=64, num_attention_heads=8)
    attention = Dinov2WithRegistersSelfAttention(config)
    with pytest.raises(TypeError):
        attention.transpose_for_scores([1, 2, 3, 4])  # Pass a list instead of a tensor

def test_incorrect_dimension_count():
    # Test with incorrect dimension count, should raise RuntimeError
    config = WindowedDinov2WithRegistersConfig(hidden_size=64, num_attention_heads=8)
    attention = Dinov2WithRegistersSelfAttention(config)
    x = torch.rand(64, 128)  # Incorrect dimensions
    with pytest.raises(RuntimeError):
        attention.transpose_for_scores(x)

To edit these changes git checkout codeflash/optimize-pr1250-2025-05-13T12.59.27 and push.