⚡️ Speed up method MraOutput.forward by 5%

[codeflash-ai]

📄 5% (0.05x) speedup for MraOutput.forward in src/transformers/models/mra/modeling_mra.py

⏱️ Runtime : 1.79 milliseconds → 1.70 milliseconds (best of 138 runs)

📝 Explanation and details

The optimized code achieves a 5% speedup through two key optimizations:

1. Conditional Dropout Application
The original code always calls self.dropout(hidden_states) regardless of training mode or dropout probability. The optimization adds a conditional check if self.training and self.dropout.p > 0: to only apply dropout when actually needed. This eliminates unnecessary computation when:

• The model is in evaluation mode (self.training = False)
• Dropout probability is 0 (self.dropout.p == 0)

From the line profiler, dropout execution time drops from 2.55M ns (33.5% of total) to 2.27M ns (29.9%), and the condition check adds only 129K ns (1.7%).

2. In-place Addition with add_()
The original code creates a new tensor with hidden_states + input_tensor. The optimization uses hidden_states.add_(input_tensor) to perform in-place addition, reducing memory allocation overhead. This saves approximately 100K ns in the LayerNorm line.

Test Case Performance Analysis:

• Zero dropout cases show consistent 2-8% improvements (most common scenario in production)
• Non-zero dropout cases show slight slowdown (4-5%) due to the conditional overhead, but this is acceptable since dropout is typically only used during training
• Large tensor cases benefit most (up to 11.3% improvement) as memory allocation savings become more significant
• Edge cases (empty batches, single elements) maintain good performance gains (1-7%)

The optimization is particularly effective for inference scenarios where dropout is disabled, which represents the majority of production use cases.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 52 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  # used for tensor operations
# Copied from transformers.models.bert.modeling_bert.BertOutput
from torch import nn
from transformers.models.mra.modeling_mra import MraOutput

# function to test
# coding=utf-8
# Copyright 2023 University of Wisconsin-Madison and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


class DummyConfig:
    """Minimal config class for MraOutput instantiation in tests."""
    def __init__(self,
                 intermediate_size=4,
                 hidden_size=4,
                 layer_norm_eps=1e-12,
                 hidden_dropout_prob=0.0):
        self.intermediate_size = intermediate_size
        self.hidden_size = hidden_size
        self.layer_norm_eps = layer_norm_eps
        self.hidden_dropout_prob = hidden_dropout_prob
from transformers.models.mra.modeling_mra import MraOutput

# unit tests

# --- Basic Test Cases ---

def test_forward_basic_shape_and_type():
    """Test that output shape and type are correct for basic inputs."""
    config = DummyConfig(intermediate_size=8, hidden_size=8, hidden_dropout_prob=0.0)
    module = MraOutput(config)
    batch_size, seq_len = 2, 5
    hidden_states = torch.ones((batch_size, seq_len, config.intermediate_size))
    input_tensor = torch.zeros((batch_size, seq_len, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 85.3μs -> 79.3μs (7.52% faster)

def test_forward_basic_addition_effect():
    """Test that adding input_tensor affects the output."""
    config = DummyConfig(intermediate_size=4, hidden_size=4, hidden_dropout_prob=0.0)
    module = MraOutput(config)
    hidden_states = torch.ones((1, 2, config.intermediate_size))
    input_tensor = torch.zeros((1, 2, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out1 = codeflash_output # 82.5μs -> 79.5μs (3.82% faster)
    input_tensor2 = torch.ones((1, 2, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor2); out2 = codeflash_output # 30.2μs -> 29.0μs (4.33% faster)

def test_forward_dropout_zero():
    """Test that dropout=0 does not change the output between calls."""
    config = DummyConfig(intermediate_size=3, hidden_size=3, hidden_dropout_prob=0.0)
    module = MraOutput(config)
    hidden_states = torch.randn((1, 2, config.intermediate_size))
    input_tensor = torch.randn((1, 2, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out1 = codeflash_output # 77.0μs -> 74.8μs (2.87% faster)
    codeflash_output = module.forward(hidden_states, input_tensor); out2 = codeflash_output # 28.9μs -> 27.4μs (5.58% faster)

def test_forward_dropout_nonzero():
    """Test that dropout>0 introduces stochasticity in output."""
    config = DummyConfig(intermediate_size=3, hidden_size=3, hidden_dropout_prob=0.5)
    module = MraOutput(config)
    hidden_states = torch.randn((1, 2, config.intermediate_size))
    input_tensor = torch.randn((1, 2, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out1 = codeflash_output # 98.5μs -> 103μs (4.88% slower)
    codeflash_output = module.forward(hidden_states, input_tensor); out2 = codeflash_output # 37.6μs -> 39.3μs (4.27% slower)

# --- Edge Test Cases ---

def test_forward_zero_batch():
    """Test with zero batch size."""
    config = DummyConfig(intermediate_size=6, hidden_size=6)
    module = MraOutput(config)
    hidden_states = torch.randn((0, 10, config.intermediate_size))
    input_tensor = torch.randn((0, 10, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 61.2μs -> 60.4μs (1.39% faster)

def test_forward_zero_seq_len():
    """Test with zero sequence length."""
    config = DummyConfig(intermediate_size=5, hidden_size=5)
    module = MraOutput(config)
    hidden_states = torch.randn((2, 0, config.intermediate_size))
    input_tensor = torch.randn((2, 0, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 62.3μs -> 60.4μs (3.10% faster)

def test_forward_single_element():
    """Test with single element (batch=1, seq_len=1)."""
    config = DummyConfig(intermediate_size=2, hidden_size=2)
    module = MraOutput(config)
    hidden_states = torch.randn((1, 1, config.intermediate_size))
    input_tensor = torch.randn((1, 1, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 76.1μs -> 74.0μs (2.97% faster)

def test_forward_mismatched_shapes_raises():
    """Test that mismatched shapes raise an error."""
    config = DummyConfig(intermediate_size=4, hidden_size=4)
    module = MraOutput(config)
    hidden_states = torch.randn((1, 2, config.intermediate_size))
    input_tensor = torch.randn((1, 3, config.hidden_size))
    with pytest.raises(RuntimeError):
        module.forward(hidden_states, input_tensor) # 117μs -> 109μs (7.67% faster)

def test_forward_non_float_tensor():
    """Test that non-float tensors raise an error in LayerNorm."""
    config = DummyConfig(intermediate_size=4, hidden_size=4)
    module = MraOutput(config)
    hidden_states = torch.randint(0, 10, (1, 2, config.intermediate_size), dtype=torch.int32)
    input_tensor = torch.randint(0, 10, (1, 2, config.hidden_size), dtype=torch.int32)
    with pytest.raises(RuntimeError):
        module.forward(hidden_states, input_tensor) # 85.7μs -> 85.4μs (0.323% faster)

def test_forward_large_eps():
    """Test with large layer_norm_eps."""
    config = DummyConfig(intermediate_size=4, hidden_size=4, layer_norm_eps=1.0)
    module = MraOutput(config)
    hidden_states = torch.randn((1, 2, config.intermediate_size))
    input_tensor = torch.randn((1, 2, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 82.0μs -> 79.3μs (3.34% faster)


def test_forward_non_contiguous_input():
    """Test with non-contiguous tensors."""
    config = DummyConfig(intermediate_size=4, hidden_size=4)
    module = MraOutput(config)
    hidden_states = torch.randn((2, 3, config.intermediate_size)).transpose(0, 1)
    input_tensor = torch.randn((2, 3, config.hidden_size)).transpose(0, 1)
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 91.5μs -> 82.2μs (11.3% faster)

# --- Large Scale Test Cases ---

def test_forward_large_batch():
    """Test with large batch size."""
    config = DummyConfig(intermediate_size=16, hidden_size=16)
    module = MraOutput(config)
    batch_size, seq_len = 512, 2  # 512*2*16*4 ~ 64KB per tensor
    hidden_states = torch.randn((batch_size, seq_len, config.intermediate_size))
    input_tensor = torch.randn((batch_size, seq_len, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 122μs -> 116μs (5.60% faster)

def test_forward_large_seq_len():
    """Test with large sequence length."""
    config = DummyConfig(intermediate_size=8, hidden_size=8)
    module = MraOutput(config)
    batch_size, seq_len = 2, 512  # 2*512*8*4 ~ 32KB per tensor
    hidden_states = torch.randn((batch_size, seq_len, config.intermediate_size))
    input_tensor = torch.randn((batch_size, seq_len, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 120μs -> 115μs (4.31% faster)

def test_forward_large_hidden_size():
    """Test with large hidden and intermediate sizes."""
    config = DummyConfig(intermediate_size=256, hidden_size=256)
    module = MraOutput(config)
    batch_size, seq_len = 2, 2  # 2*2*256*4 ~ 4KB per tensor
    hidden_states = torch.randn((batch_size, seq_len, config.intermediate_size))
    input_tensor = torch.randn((batch_size, seq_len, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 89.7μs -> 86.7μs (3.42% faster)

def test_forward_maximum_tensor_size():
    """Test with tensors just under 100MB."""
    config = DummyConfig(intermediate_size=128, hidden_size=128)
    module = MraOutput(config)
    batch_size, seq_len = 64, 12  # 64*12*128*4 = ~393KB per tensor
    hidden_states = torch.randn((batch_size, seq_len, config.intermediate_size))
    input_tensor = torch.randn((batch_size, seq_len, config.hidden_size))
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 352μs -> 317μs (11.2% faster)

def test_forward_gradients():
    """Test that gradients can be computed through the module."""
    config = DummyConfig(intermediate_size=8, hidden_size=8)
    module = MraOutput(config)
    hidden_states = torch.randn((2, 3, config.intermediate_size), requires_grad=True)
    input_tensor = torch.randn((2, 3, config.hidden_size), requires_grad=True)
    codeflash_output = module.forward(hidden_states, input_tensor); out = codeflash_output # 85.8μs -> 80.8μs (6.20% faster)
    loss = out.sum()
    loss.backward()
# 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  # used for tensor creation and manipulation
from torch import nn
from transformers.models.mra.modeling_mra import MraOutput

# function to test
# coding=utf-8
# Copyright 2023 University of Wisconsin-Madison and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


class DummyConfig:
    """A minimal config class for testing MraOutput."""
    def __init__(
        self,
        intermediate_size=8,
        hidden_size=8,
        layer_norm_eps=1e-5,
        hidden_dropout_prob=0.1
    ):
        self.intermediate_size = intermediate_size
        self.hidden_size = hidden_size
        self.layer_norm_eps = layer_norm_eps
        self.hidden_dropout_prob = hidden_dropout_prob
from transformers.models.mra.modeling_mra import MraOutput

# unit tests

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

def test_forward_basic_shapes():
    """Test basic functionality with small tensors of matching shapes."""
    config = DummyConfig(intermediate_size=4, hidden_size=4)
    mra = MraOutput(config)
    # Create input tensors with batch_size=2, seq_len=3, hidden/intermediate sizes
    hidden_states = torch.ones((2, 3, 4))
    input_tensor = torch.zeros((2, 3, 4))
    # Should run without error and output shape should match input_tensor's last dim
    output = mra(hidden_states, input_tensor)

def test_forward_basic_values():
    """Test output values for known input (all ones and zeros)."""
    config = DummyConfig(intermediate_size=2, hidden_size=2, hidden_dropout_prob=0.0)
    mra = MraOutput(config)
    # Set weights and bias to known values for deterministic output
    mra.dense.weight.data.fill_(1.0)
    mra.dense.bias.data.fill_(0.0)
    mra.LayerNorm.weight.data.fill_(1.0)
    mra.LayerNorm.bias.data.fill_(0.0)
    hidden_states = torch.ones((1, 1, 2))
    input_tensor = torch.zeros((1, 1, 2))
    output = mra(hidden_states, input_tensor)

def test_forward_dropout_zero():
    """Test that dropout does not affect output when probability is set to zero."""
    config = DummyConfig(intermediate_size=3, hidden_size=3, hidden_dropout_prob=0.0)
    mra = MraOutput(config)
    hidden_states = torch.rand((2, 2, 3))
    input_tensor = torch.rand((2, 2, 3))
    output1 = mra(hidden_states, input_tensor)
    output2 = mra(hidden_states, input_tensor)

def test_forward_dropout_nonzero():
    """Test that dropout introduces stochasticity when probability > 0."""
    config = DummyConfig(intermediate_size=3, hidden_size=3, hidden_dropout_prob=0.5)
    mra = MraOutput(config)
    mra.train()  # Enable dropout
    hidden_states = torch.ones((1, 1, 3))
    input_tensor = torch.zeros((1, 1, 3))
    output1 = mra(hidden_states, input_tensor)
    output2 = mra(hidden_states, input_tensor)

def test_forward_eval_mode_no_dropout():
    """Test that dropout does not affect output in eval mode."""
    config = DummyConfig(intermediate_size=3, hidden_size=3, hidden_dropout_prob=0.5)
    mra = MraOutput(config)
    mra.eval()  # Disable dropout
    hidden_states = torch.ones((1, 1, 3))
    input_tensor = torch.zeros((1, 1, 3))
    output1 = mra(hidden_states, input_tensor)
    output2 = mra(hidden_states, input_tensor)

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

def test_forward_empty_batch():
    """Test with zero batch size."""
    config = DummyConfig(intermediate_size=5, hidden_size=5)
    mra = MraOutput(config)
    hidden_states = torch.rand((0, 2, 5))
    input_tensor = torch.rand((0, 2, 5))
    output = mra(hidden_states, input_tensor)

def test_forward_empty_sequence():
    """Test with zero sequence length."""
    config = DummyConfig(intermediate_size=5, hidden_size=5)
    mra = MraOutput(config)
    hidden_states = torch.rand((2, 0, 5))
    input_tensor = torch.rand((2, 0, 5))
    output = mra(hidden_states, input_tensor)

def test_forward_1d_input():
    """Test with 1D input tensors (single vector)."""
    config = DummyConfig(intermediate_size=4, hidden_size=4)
    mra = MraOutput(config)
    hidden_states = torch.rand((4,))
    input_tensor = torch.rand((4,))
    output = mra(hidden_states, input_tensor)

def test_forward_mismatched_shapes():
    """Test with mismatched shapes (should raise error)."""
    config = DummyConfig(intermediate_size=4, hidden_size=4)
    mra = MraOutput(config)
    hidden_states = torch.rand((2, 3, 4))
    input_tensor = torch.rand((2, 2, 4))  # mismatched sequence length
    with pytest.raises(RuntimeError):
        mra(hidden_states, input_tensor)

def test_forward_wrong_hidden_size():
    """Test with wrong hidden size for input_tensor (should raise error)."""
    config = DummyConfig(intermediate_size=4, hidden_size=4)
    mra = MraOutput(config)
    hidden_states = torch.rand((2, 3, 4))
    input_tensor = torch.rand((2, 3, 5))  # hidden_size mismatch
    with pytest.raises(RuntimeError):
        mra(hidden_states, input_tensor)

def test_forward_non_float_tensor():
    """Test with non-float input tensors (should raise error)."""
    config = DummyConfig(intermediate_size=2, hidden_size=2)
    mra = MraOutput(config)
    hidden_states = torch.randint(0, 10, (1, 1, 2), dtype=torch.int32)
    input_tensor = torch.randint(0, 10, (1, 1, 2), dtype=torch.int32)
    with pytest.raises(RuntimeError):
        mra(hidden_states, input_tensor)

def test_forward_large_eps():
    """Test with very large layer_norm_eps."""
    config = DummyConfig(intermediate_size=2, hidden_size=2, layer_norm_eps=1.0)
    mra = MraOutput(config)
    hidden_states = torch.ones((1, 1, 2))
    input_tensor = torch.zeros((1, 1, 2))
    output = mra(hidden_states, input_tensor)



def test_forward_large_batch():
    """Test with large batch size."""
    config = DummyConfig(intermediate_size=8, hidden_size=8)
    mra = MraOutput(config)
    batch_size = 512
    seq_len = 2
    hidden_states = torch.rand((batch_size, seq_len, 8))
    input_tensor = torch.rand((batch_size, seq_len, 8))
    output = mra(hidden_states, input_tensor)

def test_forward_large_sequence():
    """Test with large sequence length."""
    config = DummyConfig(intermediate_size=8, hidden_size=8)
    mra = MraOutput(config)
    batch_size = 2
    seq_len = 512
    hidden_states = torch.rand((batch_size, seq_len, 8))
    input_tensor = torch.rand((batch_size, seq_len, 8))
    output = mra(hidden_states, input_tensor)

def test_forward_large_hidden():
    """Test with large hidden/intermediate size, but <100MB total."""
    # 1000 x 2 x 32 floats = 256,000 floats = 1MB (well below limit)
    config = DummyConfig(intermediate_size=32, hidden_size=32)
    mra = MraOutput(config)
    batch_size = 100
    seq_len = 10
    hidden_states = torch.rand((batch_size, seq_len, 32))
    input_tensor = torch.rand((batch_size, seq_len, 32))
    output = mra(hidden_states, input_tensor)

def test_forward_max_tensor_size():
    """Test with maximum allowed tensor size (<100MB)."""
    config = DummyConfig(intermediate_size=128, hidden_size=128)
    mra = MraOutput(config)
    batch_size = 32
    seq_len = 24
    # 32 x 24 x 128 x 4 bytes = 393,216 bytes = ~0.4MB
    hidden_states = torch.rand((batch_size, seq_len, 128))
    input_tensor = torch.rand((batch_size, seq_len, 128))
    output = mra(hidden_states, input_tensor)

def test_forward_performance():
    """Test that forward completes within reasonable time for large input."""
    import time
    config = DummyConfig(intermediate_size=64, hidden_size=64)
    mra = MraOutput(config)
    batch_size = 64
    seq_len = 16
    hidden_states = torch.rand((batch_size, seq_len, 64))
    input_tensor = torch.rand((batch_size, seq_len, 64))
    start = time.time()
    output = mra(hidden_states, input_tensor)
    elapsed = time.time() - start
# 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-MraOutput.forward-mhjy695j and push.