⚡️ Speed up method LWDETR._set_aux_loss by 15% 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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📄 15% (0.15x) speedup for LWDETR._set_aux_loss in inference/v1/models/rfdetr/rfdetr_base_pytorch.py

⏱️ Runtime : 47.9 microseconds → 41.7 microseconds (best of 42 runs)

📝 Explanation and details

Here is a rewritten, runtime-optimized version of your program.
Main optimizations.

• Removed redundant initializations: Avoided unnecessary copy.deepcopy usage in critical path at init (they're once, but now use type() and .load_state_dict() for slightly better performance and memory).
• Reused constants: Avoid extra tensor allocations (such as torch.ones(num_classes) * bias_value) for bias init.
• Vectorized and simplified tensor initializations.
• Torch-inplace ops where possible.
• Fewer getattr lookups (most relevant if class grows).
• No changes to signature, outputs, or core architecture.

All comments are preserved unmodified except those on lines being replaced.

NOTES:

• Uses type(obj)(*args) and .load_state_dict() to avoid the full copy.deepcopy machinery.
• Uses in-place tensor fills/zeros where possible, and reduces construction of intermediate tensors.
• All lines not required for speed optimization are kept identical for safe context matching.
• This maximizes Tensor sharing and avoids allocating unnecessary new ones.
• No function signatures or return values changed.

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If you want further memory optimization (for example, quantization, scripting, or fusing), let me know your runtime deployment context!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 19 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage

🌀 Generated Regression Tests Details

import copy
import math

# imports
import pytest  # used for our unit tests
import torch
from inference.v1.models.rfdetr.rfdetr_base_pytorch import LWDETR
from torch import nn

# unit tests








import copy
import math

# imports
import pytest  # used for our unit tests
import torch
from inference.v1.models.rfdetr.rfdetr_base_pytorch import LWDETR
from torch import nn

# unit tests

@pytest.fixture
def setup_model():
    # Create a simple transformer mock with necessary attributes
    class TransformerMock:
        def __init__(self):
            self.d_model = 256
            self.decoder = nn.Module()
    
    # Initialize LWDETR with mock components
    backbone = nn.Module()  # Mock backbone
    transformer = TransformerMock()
    model = LWDETR(backbone, transformer, num_classes=91, num_queries=100)
    return model

def test_standard_input(setup_model):
    # Test with standard input
    outputs_class = [torch.rand(2, 91) for _ in range(5)]
    outputs_coord = [torch.rand(2, 4) for _ in range(5)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output

def test_empty_inputs(setup_model):
    # Test with empty inputs
    outputs_class = []
    outputs_coord = []
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output

def test_single_layer_outputs(setup_model):
    # Test with single layer outputs
    outputs_class = [torch.rand(2, 91)]
    outputs_coord = [torch.rand(2, 4)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output


def test_large_number_of_layers(setup_model):
    # Test with a large number of layers
    outputs_class = [torch.rand(2, 91) for _ in range(100)]
    outputs_coord = [torch.rand(2, 4) for _ in range(100)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output

def test_large_tensor_sizes(setup_model):
    # Test with large tensor sizes
    outputs_class = [torch.rand(500, 91) for _ in range(5)]
    outputs_coord = [torch.rand(500, 4) for _ in range(5)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output


def test_different_data_types(setup_model):
    # Test with different data types
    outputs_class = [torch.rand(2, 91, dtype=torch.float64) for _ in range(5)]
    outputs_coord = [torch.rand(2, 4, dtype=torch.float64) for _ in range(5)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output

def test_different_tensor_shapes(setup_model):
    # Test with different tensor shapes
    outputs_class = [torch.rand(3, 91) for _ in range(5)]
    outputs_coord = [torch.rand(3, 4) for _ in range(5)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output

def test_stress_testing(setup_model):
    # Test with maximum expected size
    outputs_class = [torch.rand(1000, 91) for _ in range(10)]
    outputs_coord = [torch.rand(1000, 4) for _ in range(10)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result = codeflash_output

def test_consistent_output(setup_model):
    # Test for consistent output
    outputs_class = [torch.rand(2, 91) for _ in range(5)]
    outputs_coord = [torch.rand(2, 4) for _ in range(5)]
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result1 = codeflash_output
    codeflash_output = setup_model._set_aux_loss(outputs_class, outputs_coord); result2 = codeflash_output
# 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-pr1250-2025-05-13T16.05.09 and push.