⚡️ Speed up function `retrieve_timesteps` by 78% by codeflash-ai[bot] · Pull Request #138 · aseembits93/diffusers

codeflash-ai · 2025-06-01T14:00:27Z

📄 78% (0.78x) speedup for `retrieve_timesteps` in `src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py`

⏱️ Runtime : 745 microseconds → 418 microseconds (best of 308 runs)

📝 Explanation and details

Here’s a rewritten, optimized version of your function.
The optimization targets the expensive repeated use of inspect.signature() (which is very slow).
Instead, we cache the parameter introspection on the scheduler’s type, so it's only done once per class.

Below is the code, with all existing comments preserved and only improved for the code that changes.

Optimization summary:

The repeated inspect.signature(...).parameters.keys() calls (previously measured as a major bottleneck) are now done once per scheduler class.
All logic and results remain fully equivalent.
All comments are retained (just clarified where modified).

This will substantially reduce per-call CPU time, especially when calling this function in a loop or across many batches.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 22 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

import inspect
# function to test
from typing import List, Optional, Union

# imports
import pytest  # used for our unit tests
import torch
from src.diffusers.pipelines.hidream_image.pipeline_hidream_image import \
    retrieve_timesteps

# --- Dummy Schedulers for Testing ---

class DummySchedulerBasic:
    """A scheduler that accepts num_inference_steps and device; returns range(num_inference_steps) as timesteps."""
    def __init__(self):
        self.timesteps = None
        self.last_kwargs = None

    def set_timesteps(self, num_inference_steps, device=None, **kwargs):
        self.last_kwargs = dict(num_inference_steps=num_inference_steps, device=device, **kwargs)
        self.timesteps = torch.arange(num_inference_steps, dtype=torch.long, device=device)

class DummySchedulerTimesteps:
    """A scheduler that accepts custom timesteps and device."""
    def __init__(self):
        self.timesteps = None
        self.last_kwargs = None

    def set_timesteps(self, timesteps, device=None, **kwargs):
        self.last_kwargs = dict(timesteps=timesteps, device=device, **kwargs)
        self.timesteps = torch.tensor(timesteps, dtype=torch.long, device=device)

class DummySchedulerSigmas:
    """A scheduler that accepts custom sigmas and device."""
    def __init__(self):
        self.timesteps = None
        self.last_kwargs = None

    def set_timesteps(self, sigmas, device=None, **kwargs):
        self.last_kwargs = dict(sigmas=sigmas, device=device, **kwargs)
        # For test, just store sigmas as float tensor
        self.timesteps = torch.tensor(sigmas, dtype=torch.float, device=device)

class DummySchedulerNoCustom:
    """A scheduler that only accepts num_inference_steps and device, NOT timesteps or sigmas."""
    def __init__(self):
        self.timesteps = None

    def set_timesteps(self, num_inference_steps, device=None, **kwargs):
        self.timesteps = torch.arange(num_inference_steps, dtype=torch.long, device=device)

class DummySchedulerBoth:
    """A scheduler that accepts both timesteps and sigmas (for completeness)."""
    def __init__(self):
        self.timesteps = None
        self.last_kwargs = None

    def set_timesteps(self, num_inference_steps=None, timesteps=None, sigmas=None, device=None, **kwargs):
        self.last_kwargs = dict(num_inference_steps=num_inference_steps, timesteps=timesteps, sigmas=sigmas, device=device, **kwargs)
        if timesteps is not None:
            self.timesteps = torch.tensor(timesteps, dtype=torch.long, device=device)
        elif sigmas is not None:
            self.timesteps = torch.tensor(sigmas, dtype=torch.float, device=device)
        else:
            self.timesteps = torch.arange(num_inference_steps, dtype=torch.long, device=device)

# --- Unit Tests ---

# 1. Basic Test Cases

def test_basic_num_inference_steps():
    # Test standard behavior with num_inference_steps
    scheduler = DummySchedulerBasic()
    timesteps, nsteps = retrieve_timesteps(scheduler, num_inference_steps=5)

def test_basic_timesteps():
    # Test custom timesteps with compatible scheduler
    scheduler = DummySchedulerTimesteps()
    custom = [3, 1, 0]
    timesteps, nsteps = retrieve_timesteps(scheduler, timesteps=custom)

def test_basic_sigmas():
    # Test custom sigmas with compatible scheduler
    scheduler = DummySchedulerSigmas()
    sigmas = [0.1, 0.2, 0.3]
    timesteps, nsteps = retrieve_timesteps(scheduler, sigmas=sigmas)

def test_basic_device_cpu():
    # Test device argument with CPU
    scheduler = DummySchedulerBasic()
    timesteps, nsteps = retrieve_timesteps(scheduler, num_inference_steps=3, device="cpu")


def test_basic_kwargs_forwarded():
    # Test that extra kwargs are forwarded to set_timesteps
    class SchedulerWithKwargs(DummySchedulerBasic):
        def set_timesteps(self, num_inference_steps, device=None, foo=None, **kwargs):
            super().set_timesteps(num_inference_steps, device=device)
            self.foo = foo

    scheduler = SchedulerWithKwargs()
    retrieve_timesteps(scheduler, num_inference_steps=2, foo="bar")

# 2. Edge Test Cases

def test_timesteps_and_sigmas_raises():
    # Both timesteps and sigmas provided: should raise ValueError
    scheduler = DummySchedulerBoth()
    with pytest.raises(ValueError) as excinfo:
        retrieve_timesteps(scheduler, timesteps=[1, 2], sigmas=[0.1, 0.2])

def test_timesteps_with_incompatible_scheduler_raises():
    # timesteps provided but scheduler does not accept timesteps: should raise ValueError
    scheduler = DummySchedulerNoCustom()
    with pytest.raises(ValueError) as excinfo:
        retrieve_timesteps(scheduler, timesteps=[1, 2, 3])

def test_sigmas_with_incompatible_scheduler_raises():
    # sigmas provided but scheduler does not accept sigmas: should raise ValueError
    scheduler = DummySchedulerNoCustom()
    with pytest.raises(ValueError) as excinfo:
        retrieve_timesteps(scheduler, sigmas=[0.1, 0.2])

def test_timesteps_none_and_sigmas_none_and_num_inference_steps_none():
    # All three are None: should raise TypeError from scheduler.set_timesteps
    scheduler = DummySchedulerBasic()
    with pytest.raises(TypeError):
        retrieve_timesteps(scheduler)

def test_empty_timesteps():
    # Empty timesteps: should return empty tensor and nsteps=0
    scheduler = DummySchedulerTimesteps()
    timesteps, nsteps = retrieve_timesteps(scheduler, timesteps=[])

def test_empty_sigmas():
    # Empty sigmas: should return empty tensor and nsteps=0
    scheduler = DummySchedulerSigmas()
    timesteps, nsteps = retrieve_timesteps(scheduler, sigmas=[])

def test_timesteps_with_negative_values():
    # Timesteps with negative values: should be accepted as is
    scheduler = DummySchedulerTimesteps()
    custom = [-1, -2, 0]
    timesteps, nsteps = retrieve_timesteps(scheduler, timesteps=custom)

def test_sigmas_with_negative_values():
    # Sigmas with negative values: should be accepted as is
    scheduler = DummySchedulerSigmas()
    sigmas = [-0.1, 0.0, 0.2]
    timesteps, nsteps = retrieve_timesteps(scheduler, sigmas=sigmas)

def test_timesteps_with_non_integer_values():
    # Timesteps as floats: should be cast to long by DummySchedulerTimesteps
    scheduler = DummySchedulerTimesteps()
    custom = [1.5, 2.7, 3.2]
    timesteps, nsteps = retrieve_timesteps(scheduler, timesteps=custom)

def test_sigmas_with_non_float_values():
    # Sigmas as integers: should be cast to float by DummySchedulerSigmas
    scheduler = DummySchedulerSigmas()
    sigmas = [1, 2, 3]
    timesteps, nsteps = retrieve_timesteps(scheduler, sigmas=sigmas)

def test_timesteps_with_large_values():
    # Timesteps with large integer values
    scheduler = DummySchedulerTimesteps()
    custom = [10**6, 10**7, 10**8]
    timesteps, nsteps = retrieve_timesteps(scheduler, timesteps=custom)

def test_sigmas_with_large_values():
    # Sigmas with large float values
    scheduler = DummySchedulerSigmas()
    sigmas = [1e10, 2e10, 3e10]
    timesteps, nsteps = retrieve_timesteps(scheduler, sigmas=sigmas)

def test_device_type_as_torch_device():
    # device as torch.device instance
    scheduler = DummySchedulerBasic()
    dev = torch.device("cpu")
    timesteps, nsteps = retrieve_timesteps(scheduler, num_inference_steps=2, device=dev)

def test_kwargs_are_passed_to_scheduler():
    # Test that arbitrary kwargs are passed to scheduler.set_timesteps
    class SchedulerWithExtra(DummySchedulerBasic):
        def set_timesteps(self, num_inference_steps, device=None, extra=None, **kwargs):
            super().set_timesteps(num_inference_steps, device=device)
            self.extra = extra

    scheduler = SchedulerWithExtra()
    retrieve_timesteps(scheduler, num_inference_steps=2, extra="foo")

# 3. Large Scale Test Cases

def test_large_num_inference_steps():
    # Test with a large number of inference steps (under 1000)
    scheduler = DummySchedulerBasic()
    N = 999
    timesteps, nsteps = retrieve_timesteps(scheduler, num_inference_steps=N)

def test_large_timesteps():
    # Test with a large custom timesteps list
    scheduler = DummySchedulerTimesteps()
    custom = list(range(1000))
    timesteps, nsteps = retrieve_timesteps(scheduler, timesteps=custom)

def test_large_sigmas():
    # Test with a large custom sigmas list
    scheduler = DummySchedulerSigmas()
    sigmas = [float(i)/1000 for i in range(1000)]
    timesteps, nsteps = retrieve_timesteps(scheduler, sigmas=sigmas)

def test_large_timesteps_negative_and_positive():
    # Large timesteps with negative and positive values
    scheduler = DummySchedulerTimesteps()
    custom = list(range(-500, 500))
    timesteps, nsteps = retrieve_timesteps(scheduler, timesteps=custom)

def test_large_sigmas_extremes():
    # Large sigmas with very small and very large floats
    scheduler = DummySchedulerSigmas()
    sigmas = [1e-10 * i for i in range(500)] + [1e10 * i for i in range(500)]
    timesteps, nsteps = retrieve_timesteps(scheduler, sigmas=sigmas)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import inspect
# function to test
from typing import List, Optional, Union

# imports
import pytest  # used for our unit tests
import torch
from src.diffusers.pipelines.hidream_image.pipeline_hidream_image import \
    retrieve_timesteps

# ---- Helper Classes for Testing ----

class DummySchedulerBasic:
    """Scheduler that accepts only num_inference_steps, returns range(num_inference_steps) as timesteps."""
    def __init__(self):
        self.timesteps = None
        self.set_timesteps_calls = []

    def set_timesteps(self, num_inference_steps, device=None, **kwargs):
        self.set_timesteps_calls.append((num_inference_steps, device, kwargs))
        # Simulate torch.Tensor output
        self.timesteps = torch.arange(num_inference_steps, dtype=torch.long, device=device)

class DummySchedulerWithTimesteps:
    """Scheduler that accepts custom timesteps."""
    def __init__(self):
        self.timesteps = None
        self.set_timesteps_calls = []

    def set_timesteps(self, timesteps, device=None, **kwargs):
        self.set_timesteps_calls.append((timesteps, device, kwargs))
        # Simulate torch.Tensor output
        self.timesteps = torch.tensor(timesteps, dtype=torch.long, device=device)

class DummySchedulerWithSigmas:
    """Scheduler that accepts custom sigmas."""
    def __init__(self):
        self.timesteps = None
        self.set_timesteps_calls = []

    def set_timesteps(self, sigmas, device=None, **kwargs):
        self.set_timesteps_calls.append((sigmas, device, kwargs))
        # Simulate torch.Tensor output
        self.timesteps = torch.tensor(sigmas, dtype=torch.float, device=device)

class DummySchedulerWithBoth:
    """Scheduler that accepts both timesteps and sigmas."""
    def __init__(self):
        self.timesteps = None
        self.set_timesteps_calls = []

    def set_timesteps(self, num_inference_steps=None, timesteps=None, sigmas=None, device=None, **kwargs):
        self.set_timesteps_calls.append((num_inference_steps, timesteps, sigmas, device, kwargs))
        if timesteps is not None:
            self.timesteps = torch.tensor(timesteps, dtype=torch.long, device=device)
        elif sigmas is not None:
            self.timesteps = torch.tensor(sigmas, dtype=torch.float, device=device)
        elif num_inference_steps is not None:
            self.timesteps = torch.arange(num_inference_steps, dtype=torch.long, device=device)
        else:
            raise ValueError("No valid input provided to set_timesteps")

class DummySchedulerNoTimesteps:
    """Scheduler that does NOT accept timesteps parameter."""
    def __init__(self):
        self.timesteps = None

    def set_timesteps(self, num_inference_steps, device=None, **kwargs):
        self.timesteps = torch.arange(num_inference_steps, dtype=torch.long, device=device)

class DummySchedulerNoSigmas:
    """Scheduler that does NOT accept sigmas parameter."""
    def __init__(self):
        self.timesteps = None

    def set_timesteps(self, num_inference_steps, device=None, **kwargs):
        self.timesteps = torch.arange(num_inference_steps, dtype=torch.long, device=device)

# ---- Unit Tests ----

# 1. Basic Test Cases

def test_basic_num_inference_steps():
    """Test with num_inference_steps only (basic usage)."""
    scheduler = DummySchedulerBasic()
    timesteps, steps = retrieve_timesteps(scheduler, num_inference_steps=5)

def test_basic_custom_timesteps():
    """Test with custom timesteps and scheduler that supports timesteps."""
    scheduler = DummySchedulerWithTimesteps()
    custom_ts = [10, 20, 30]
    timesteps, steps = retrieve_timesteps(scheduler, timesteps=custom_ts)

def test_basic_custom_sigmas():
    """Test with custom sigmas and scheduler that supports sigmas."""
    scheduler = DummySchedulerWithSigmas()
    custom_sigmas = [0.1, 0.2, 0.3]
    timesteps, steps = retrieve_timesteps(scheduler, sigmas=custom_sigmas)

def test_basic_device_cpu():
    """Test that the device argument is respected (CPU)."""
    scheduler = DummySchedulerBasic()
    timesteps, steps = retrieve_timesteps(scheduler, num_inference_steps=4, device="cpu")


def test_basic_kwargs_passed():
    """Test that extra kwargs are passed to scheduler.set_timesteps."""
    class SchedulerWithKwargs(DummySchedulerBasic):
        def set_timesteps(self, num_inference_steps, device=None, **kwargs):
            super().set_timesteps(num_inference_steps, device=device)
            self.extra = kwargs
    scheduler = SchedulerWithKwargs()
    timesteps, steps = retrieve_timesteps(scheduler, num_inference_steps=3, foo="bar")

# 2. Edge Test Cases

def test_timesteps_and_sigmas_mutual_exclusion():
    """Test that passing both timesteps and sigmas raises ValueError."""
    scheduler = DummySchedulerWithBoth()
    with pytest.raises(ValueError) as excinfo:
        retrieve_timesteps(scheduler, timesteps=[1,2], sigmas=[0.1, 0.2])

def test_timesteps_not_supported():
    """Test that passing timesteps to scheduler that doesn't support them raises ValueError."""
    scheduler = DummySchedulerNoTimesteps()
    with pytest.raises(ValueError) as excinfo:
        retrieve_timesteps(scheduler, timesteps=[1,2,3])

def test_sigmas_not_supported():
    """Test that passing sigmas to scheduler that doesn't support them raises ValueError."""
    scheduler = DummySchedulerNoSigmas()
    with pytest.raises(ValueError) as excinfo:
        retrieve_timesteps(scheduler, sigmas=[0.1, 0.2])

def test_empty_timesteps():
    """Test with empty timesteps list."""
    scheduler = DummySchedulerWithTimesteps()
    timesteps, steps = retrieve_timesteps(scheduler, timesteps=[])

def test_empty_sigmas():
    """Test with empty sigmas list."""
    scheduler = DummySchedulerWithSigmas()
    timesteps, steps = retrieve_timesteps(scheduler, sigmas=[])

def test_zero_num_inference_steps():
    """Test with num_inference_steps=0."""
    scheduler = DummySchedulerBasic()
    timesteps, steps = retrieve_timesteps(scheduler, num_inference_steps=0)


def test_non_integer_timesteps():
    """Test with non-integer timesteps (should be accepted and cast by torch.tensor)."""
    scheduler = DummySchedulerWithTimesteps()
    custom_ts = [1.5, 2.5, 3.5]
    timesteps, steps = retrieve_timesteps(scheduler, timesteps=custom_ts)

def test_non_integer_sigmas():
    """Test with integer sigmas (should be accepted and cast by torch.tensor)."""
    scheduler = DummySchedulerWithSigmas()
    custom_sigmas = [1, 2, 3]
    timesteps, steps = retrieve_timesteps(scheduler, sigmas=custom_sigmas)

def test_scheduler_with_both_timesteps_and_sigmas():
    """Test scheduler that supports both timesteps and sigmas."""
    scheduler = DummySchedulerWithBoth()
    # Test timesteps
    custom_ts = [5, 6, 7]
    timesteps, steps = retrieve_timesteps(scheduler, timesteps=custom_ts)
    # Test sigmas
    custom_sigmas = [0.5, 0.6, 0.7]
    timesteps, steps = retrieve_timesteps(scheduler, sigmas=custom_sigmas)

def test_device_none():
    """Test that device=None does not error and returns CPU tensor by default."""
    scheduler = DummySchedulerBasic()
    timesteps, steps = retrieve_timesteps(scheduler, num_inference_steps=2, device=None)

def test_kwargs_with_custom_timesteps():
    """Test that extra kwargs are passed with custom timesteps."""
    class SchedulerWithTimestepsAndKwargs(DummySchedulerWithTimesteps):
        def set_timesteps(self, timesteps, device=None, **kwargs):
            super().set_timesteps(timesteps, device=device)
            self.extra = kwargs
    scheduler = SchedulerWithTimestepsAndKwargs()
    timesteps, steps = retrieve_timesteps(scheduler, timesteps=[1,2,3], foo="bar")

def test_kwargs_with_custom_sigmas():
    """Test that extra kwargs are passed with custom sigmas."""
    class SchedulerWithSigmasAndKwargs(DummySchedulerWithSigmas):
        def set_timesteps(self, sigmas, device=None, **kwargs):
            super().set_timesteps(sigmas, device=device)
            self.extra = kwargs
    scheduler = SchedulerWithSigmasAndKwargs()
    timesteps, steps = retrieve_timesteps(scheduler, sigmas=[0.1,0.2], foo="baz")

# 3. Large Scale Test Cases

def test_large_num_inference_steps():
    """Test with a large number of inference steps (1000)."""
    scheduler = DummySchedulerBasic()
    timesteps, steps = retrieve_timesteps(scheduler, num_inference_steps=1000)

def test_large_custom_timesteps():
    """Test with a large custom timesteps list (1000 elements)."""
    scheduler = DummySchedulerWithTimesteps()
    custom_ts = list(range(1000, 2000))
    timesteps, steps = retrieve_timesteps(scheduler, timesteps=custom_ts)

def test_large_custom_sigmas():
    """Test with a large custom sigmas list (1000 elements)."""
    scheduler = DummySchedulerWithSigmas()
    custom_sigmas = [float(i)/1000 for i in range(1000)]
    timesteps, steps = retrieve_timesteps(scheduler, sigmas=custom_sigmas)

def test_large_scale_device_cpu():
    """Test large scale with device='cpu'."""
    scheduler = DummySchedulerBasic()
    timesteps, steps = retrieve_timesteps(scheduler, num_inference_steps=999, device="cpu")

To edit these changes git checkout codeflash/optimize-retrieve_timesteps-mbdqa6x5 and push.

Here’s a **rewritten, optimized version** of your function. The optimization targets the expensive repeated use of `inspect.signature()` (which is very slow). Instead, we **cache** the parameter introspection on the scheduler’s type, so it's only done once per class. Below is the code, with **all existing comments preserved** and only improved for the code that changes. **Optimization summary:** - The repeated `inspect.signature(...).parameters.keys()` calls (previously measured as a major bottleneck) are now done **once per scheduler class**. - All logic and results remain **fully equivalent**. - All comments are retained (just clarified where modified). This will substantially reduce per-call CPU time, especially when calling this function in a loop or across many batches.

codeflash-ai Bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jun 1, 2025

codeflash-ai Bot requested a review from aseembits93 June 1, 2025 14:00

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⚡️ Speed up function `retrieve_timesteps` by 78%#138

⚡️ Speed up function `retrieve_timesteps` by 78%#138
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codeflash-ai Bot commented Jun 1, 2025

📄 78% (0.78x) speedup for retrieve_timesteps in src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py

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📄 78% (0.78x) speedup for `retrieve_timesteps` in `src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py`