⚡️ Speed up function zero_module by 143%

[codeflash-ai]

📄 143% (1.43x) speedup for zero_module in src/diffusers/models/controlnets/controlnet_xs.py

⏱️ Runtime : 2.74 milliseconds → 1.13 milliseconds (best of 233 runs)

📝 Explanation and details

Here’s how you can optimize the provided program.

Analysis and Ideas:

• zero_module runs slow mostly because it loops over all parameters and calls nn.init.zeros_ on each one.
• nn.init.zeros_ is a simple wrapper over torch.Tensor.zero_, but there’s no need to call it indirectly per-parameter—you can just call zero_() on each parameter.
• Using torch.no_grad() will avoid unnecessary autograd overhead when zeroing parameters.
• You can iterate parameters, but calling zero_() directly is both faster and idiomatic.

Here is your optimized code.

Why is this faster?

• torch.no_grad(): greatly reduces overhead by disabling autograd tracking for the operation.
• Direct use of .zero_(): avoids the minor overhead of calling nn.init.zeros_, giving a direct fast call for each tensor in place.

Function return value and signature are preserved.
All logic is the same.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 35 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

import pytest  # used for our unit tests
import torch
from src.diffusers.models.controlnets.controlnet_xs import zero_module
from torch import nn

# unit tests

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

def test_zero_module_linear_basic():
    # Test that a simple Linear layer's weights and bias are zeroed
    linear = nn.Linear(4, 2)
    # Fill with nonzero values to ensure change
    nn.init.constant_(linear.weight, 5.0)
    nn.init.constant_(linear.bias, -3.0)
    zero_module(linear)

def test_zero_module_conv2d_basic():
    # Test that a Conv2d layer's weights and bias are zeroed
    conv = nn.Conv2d(3, 6, 3)
    nn.init.normal_(conv.weight)
    nn.init.normal_(conv.bias)
    zero_module(conv)

def test_zero_module_sequential_basic():
    # Test that all parameters in a Sequential model are zeroed
    model = nn.Sequential(
        nn.Linear(2, 3),
        nn.ReLU(),
        nn.Linear(3, 1)
    )
    for p in model.parameters():
        nn.init.uniform_(p, -2, 2)
    zero_module(model)
    for p in model.parameters():
        pass

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

def test_zero_module_no_parameters():
    # Test module with no parameters (e.g., nn.ReLU)
    relu = nn.ReLU()
    # Should not raise or modify anything
    zero_module(relu)
    # There are no parameters, so nothing to check

def test_zero_module_shared_parameters():
    # Test module with shared parameters
    linear = nn.Linear(3, 3)
    # Share the same parameter in two submodules
    class SharedModule(nn.Module):
        def __init__(self, linear):
            super().__init__()
            self.l1 = linear
            self.l2 = linear
        def forward(self, x):
            return self.l1(x) + self.l2(x)
    model = SharedModule(linear)
    nn.init.constant_(linear.weight, 7.0)
    zero_module(model)

def test_zero_module_parameter_requires_grad_false():
    # Test parameter with requires_grad=False
    linear = nn.Linear(2, 2)
    linear.weight.requires_grad_(False)
    linear.bias.requires_grad_(False)
    nn.init.constant_(linear.weight, 4.0)
    nn.init.constant_(linear.bias, -2.0)
    zero_module(linear)

def test_zero_module_parameter_is_buffer():
    # Ensure buffers are not zeroed (only parameters)
    class BufferModule(nn.Module):
        def __init__(self):
            super().__init__()
            self.linear = nn.Linear(2, 2)
            self.register_buffer("mybuf", torch.ones(2, 2))
    model = BufferModule()
    nn.init.constant_(model.linear.weight, 8.0)
    zero_module(model)

def test_zero_module_custom_parameter():
    # Test module with a custom parameter (not registered as a buffer)
    class MyModule(nn.Module):
        def __init__(self):
            super().__init__()
            self.myparam = nn.Parameter(torch.ones(5))
    model = MyModule()
    nn.init.constant_(model.myparam, 9.0)
    zero_module(model)

def test_zero_module_empty_parameter():
    # Test module with a parameter of size zero
    class EmptyParamModule(nn.Module):
        def __init__(self):
            super().__init__()
            self.empty = nn.Parameter(torch.empty(0))
    model = EmptyParamModule()
    zero_module(model)
    # Should not raise

def test_zero_module_parameter_dtype():
    # Test module with parameters of different dtypes (float32, float64)
    class DTypeModule(nn.Module):
        def __init__(self):
            super().__init__()
            self.p1 = nn.Parameter(torch.ones(3, dtype=torch.float32))
            self.p2 = nn.Parameter(torch.ones(3, dtype=torch.float64))
    model = DTypeModule()
    nn.init.constant_(model.p1, 1.5)
    nn.init.constant_(model.p2, 2.5)
    zero_module(model)

def test_zero_module_parameter_device():
    # Test module with parameters on different devices (if CUDA available)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    linear = nn.Linear(2, 2).to(device)
    nn.init.constant_(linear.weight, 3.0)
    zero_module(linear)

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

def test_zero_module_large_linear():
    # Test a large Linear layer (under 100MB)
    in_features = 300
    out_features = 300
    linear = nn.Linear(in_features, out_features)
    nn.init.normal_(linear.weight)
    nn.init.normal_(linear.bias)
    zero_module(linear)

def test_zero_module_large_sequential():
    # Test a Sequential model with many layers
    layers = []
    for _ in range(100):  # 100 layers, each with small weight/bias
        layers.append(nn.Linear(10, 10))
        layers.append(nn.ReLU())
    model = nn.Sequential(*layers)
    for p in model.parameters():
        nn.init.uniform_(p, -5, 5)
    zero_module(model)
    for p in model.parameters():
        pass

def test_zero_module_many_parameters():
    # Test a module with many small parameters
    class ManyParams(nn.Module):
        def __init__(self, n):
            super().__init__()
            self.params = nn.ParameterList([nn.Parameter(torch.ones(3)) for _ in range(n)])
    model = ManyParams(500)
    for p in model.parameters():
        nn.init.constant_(p, 6.0)
    zero_module(model)
    for p in model.parameters():
        pass

def test_zero_module_large_conv2d():
    # Test a Conv2d layer with large weight tensor (under 100MB)
    conv = nn.Conv2d(16, 32, kernel_size=7)
    nn.init.uniform_(conv.weight, 1, 2)
    nn.init.uniform_(conv.bias, -1, 1)
    zero_module(conv)

# ------------------------
# Mutation Testing: Negative Test
# ------------------------

def test_zero_module_does_not_zero_buffers():
    # Ensure that only parameters are zeroed, not buffers
    class BufferModule(nn.Module):
        def __init__(self):
            super().__init__()
            self.param = nn.Parameter(torch.ones(2, 2))
            self.register_buffer("buf", torch.ones(2, 2) * 7)
    model = BufferModule()
    zero_module(model)

def test_zero_module_returns_same_instance():
    # Test that the function returns the same module instance
    linear = nn.Linear(2, 2)
    codeflash_output = zero_module(linear); 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 src.diffusers.models.controlnets.controlnet_xs import zero_module
from torch import nn

# unit tests

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

def test_zero_module_linear_basic():
    # Test that all weights and biases of a simple Linear layer are set to zero
    layer = nn.Linear(4, 3)
    zero_module(layer)

def test_zero_module_conv2d_basic():
    # Test that all weights and biases of a Conv2d layer are set to zero
    conv = nn.Conv2d(2, 4, 3)
    zero_module(conv)

def test_zero_module_sequential_basic():
    # Test zeroing a Sequential model with multiple layers
    model = nn.Sequential(
        nn.Linear(5, 2),
        nn.ReLU(),
        nn.Linear(2, 1)
    )
    # Only Linear layers have parameters
    for m in model:
        if hasattr(m, 'weight'):
            pass
    zero_module(model)
    for m in model:
        if hasattr(m, 'weight'):
            if hasattr(m, 'bias'):
                pass

def test_zero_module_returns_module():
    # Test that the function returns the same module object
    layer = nn.Linear(2, 2)
    codeflash_output = zero_module(layer); result = codeflash_output

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

def test_zero_module_no_parameters():
    # Test a module with no parameters (e.g., nn.ReLU)
    relu = nn.ReLU()
    # Should not raise or fail
    codeflash_output = zero_module(relu); returned = codeflash_output

def test_zero_module_shared_parameters():
    # Test module with shared parameters (same tensor object in multiple places)
    linear = nn.Linear(3, 3)
    # Share the same weight tensor between two modules
    class SharedModule(nn.Module):
        def __init__(self, shared_weight):
            super().__init__()
            self.weight = shared_weight
        def forward(self, x):
            return x @ self.weight.t()
    shared = SharedModule(linear.weight)
    # Compose into a module
    class Container(nn.Module):
        def __init__(self, l, s):
            super().__init__()
            self.l = l
            self.s = s
        def forward(self, x):
            return self.l(x) + self.s(x)
    container = Container(linear, shared)
    # Set some weight to nonzero
    with torch.no_grad():
        linear.weight.fill_(2.0)
    # Zero all parameters
    zero_module(container)

def test_zero_module_parameter_requires_grad_false():
    # Test module with parameter that does not require grad
    class Custom(nn.Module):
        def __init__(self):
            super().__init__()
            self.weight = nn.Parameter(torch.ones(4, 4), requires_grad=False)
        def forward(self, x):
            return x @ self.weight
    mod = Custom()
    zero_module(mod)

def test_zero_module_empty_linear():
    # Test Linear with zero input or output features
    for in_f, out_f in [(0, 3), (3, 0), (0, 0)]:
        layer = nn.Linear(in_f, out_f)
        zero_module(layer)
        # If parameter exists, it should be zero
        if hasattr(layer, 'weight'):
            pass
        if hasattr(layer, 'bias'):
            pass

def test_zero_module_parameter_with_nan_inf():
    # Test that parameters with NaN or Inf are also zeroed
    layer = nn.Linear(2, 2)
    with torch.no_grad():
        layer.weight.fill_(float('nan'))
        layer.bias.fill_(float('inf'))
    zero_module(layer)

def test_zero_module_buffers_untouched():
    # Test that non-parameter buffers are not zeroed
    class WithBuffer(nn.Module):
        def __init__(self):
            super().__init__()
            self.register_buffer("buf", torch.ones(5))
            self.weight = nn.Parameter(torch.ones(5))
        def forward(self, x):
            return x
    mod = WithBuffer()
    zero_module(mod)

def test_zero_module_submodule_parameters():
    # Test that parameters in submodules are zeroed
    class Sub(nn.Module):
        def __init__(self):
            super().__init__()
            self.weight = nn.Parameter(torch.ones(3, 3))
    class Parent(nn.Module):
        def __init__(self):
            super().__init__()
            self.sub = Sub()
    parent = Parent()
    zero_module(parent)

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

def test_zero_module_large_linear():
    # Test a large Linear layer (but <100MB)
    in_f, out_f = 400, 200  # 400*200*4 = 320KB
    layer = nn.Linear(in_f, out_f)
    zero_module(layer)

def test_zero_module_large_sequential():
    # Test a Sequential with many layers
    num_layers = 50
    layers = []
    for _ in range(num_layers):
        layers.append(nn.Linear(10, 10))
        layers.append(nn.ReLU())
    model = nn.Sequential(*layers)
    zero_module(model)
    for m in model:
        if hasattr(m, 'weight'):
            if hasattr(m, 'bias'):
                pass

def test_zero_module_many_parameters():
    # Test a module with many parameters (all zeroed)
    class ManyParams(nn.Module):
        def __init__(self):
            super().__init__()
            for i in range(500):
                self.register_parameter(f'param_{i}', nn.Parameter(torch.randn(1)))
        def forward(self, x):
            return x
    mod = ManyParams()
    zero_module(mod)
    for name, param in mod.named_parameters():
        pass

def test_zero_module_large_conv2d():
    # Test a large Conv2d layer (but <100MB)
    conv = nn.Conv2d(16, 32, kernel_size=7)  # 16*32*7*7*4 = ~100KB
    zero_module(conv)

def test_zero_module_large_nested_modules():
    # Test a deeply nested module structure
    class Inner(nn.Module):
        def __init__(self):
            super().__init__()
            self.weight = nn.Parameter(torch.ones(10, 10))
    class Middle(nn.Module):
        def __init__(self):
            super().__init__()
            self.inner = Inner()
    class Outer(nn.Module):
        def __init__(self):
            super().__init__()
            self.middle = Middle()
    outer = Outer()
    zero_module(outer)
# 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-zero_module-mbduiowf and push.