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204 lines
8.1 KiB
Python
204 lines
8.1 KiB
Python
import inspect
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import tempfile
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import unittest
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from typing import Dict, List, Tuple
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import torch
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from diffusers import EDMEulerScheduler
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from .test_schedulers import SchedulerCommonTest
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class EDMEulerSchedulerTest(SchedulerCommonTest):
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scheduler_classes = (EDMEulerScheduler,)
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forward_default_kwargs = (("num_inference_steps", 10),)
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def get_scheduler_config(self, **kwargs):
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config = {
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"num_train_timesteps": 256,
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"sigma_min": 0.002,
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"sigma_max": 80.0,
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}
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config.update(**kwargs)
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return config
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def test_timesteps(self):
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for timesteps in [10, 50, 100, 1000]:
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self.check_over_configs(num_train_timesteps=timesteps)
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def test_prediction_type(self):
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for prediction_type in ["epsilon", "v_prediction"]:
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self.check_over_configs(prediction_type=prediction_type)
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def test_full_loop_no_noise(self, num_inference_steps=10, seed=0):
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scheduler_class = self.scheduler_classes[0]
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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scheduler.set_timesteps(num_inference_steps)
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model = self.dummy_model()
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sample = self.dummy_sample_deter * scheduler.init_noise_sigma
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for i, t in enumerate(scheduler.timesteps):
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scaled_sample = scheduler.scale_model_input(sample, t)
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model_output = model(scaled_sample, t)
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output = scheduler.step(model_output, t, sample)
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sample = output.prev_sample
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result_sum = torch.sum(torch.abs(sample))
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result_mean = torch.mean(torch.abs(sample))
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assert abs(result_sum.item() - 34.1855) < 1e-3
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assert abs(result_mean.item() - 0.044) < 1e-3
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def test_full_loop_device(self, num_inference_steps=10, seed=0):
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scheduler_class = self.scheduler_classes[0]
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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scheduler.set_timesteps(num_inference_steps)
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model = self.dummy_model()
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sample = self.dummy_sample_deter * scheduler.init_noise_sigma
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for i, t in enumerate(scheduler.timesteps):
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scaled_sample = scheduler.scale_model_input(sample, t)
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model_output = model(scaled_sample, t)
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output = scheduler.step(model_output, t, sample)
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sample = output.prev_sample
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result_sum = torch.sum(torch.abs(sample))
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result_mean = torch.mean(torch.abs(sample))
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assert abs(result_sum.item() - 34.1855) < 1e-3
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assert abs(result_mean.item() - 0.044) < 1e-3
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# Override test_from_save_pretrained to use EDMEulerScheduler-specific logic
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def test_from_save_pretrained(self):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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for scheduler_class in self.scheduler_classes:
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler = scheduler_class.from_pretrained(tmpdirname)
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scheduler.set_timesteps(num_inference_steps)
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new_scheduler.set_timesteps(num_inference_steps)
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timestep = scheduler.timesteps[0]
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sample = self.dummy_sample
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scaled_sample = scheduler.scale_model_input(sample, timestep)
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residual = 0.1 * scaled_sample
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new_scaled_sample = new_scheduler.scale_model_input(sample, timestep)
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new_residual = 0.1 * new_scaled_sample
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if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
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kwargs["generator"] = torch.manual_seed(0)
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output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample
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if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
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kwargs["generator"] = torch.manual_seed(0)
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new_output = new_scheduler.step(new_residual, timestep, sample, **kwargs).prev_sample
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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# Override test_from_save_pretrained to use EDMEulerScheduler-specific logic
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def test_step_shape(self):
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num_inference_steps = 10
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scheduler_config = self.get_scheduler_config()
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scheduler = self.scheduler_classes[0](**scheduler_config)
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scheduler.set_timesteps(num_inference_steps)
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timestep_0 = scheduler.timesteps[0]
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timestep_1 = scheduler.timesteps[1]
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sample = self.dummy_sample
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scaled_sample = scheduler.scale_model_input(sample, timestep_0)
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residual = 0.1 * scaled_sample
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output_0 = scheduler.step(residual, timestep_0, sample).prev_sample
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output_1 = scheduler.step(residual, timestep_1, sample).prev_sample
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self.assertEqual(output_0.shape, sample.shape)
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self.assertEqual(output_0.shape, output_1.shape)
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# Override test_from_save_pretrained to use EDMEulerScheduler-specific logic
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def test_scheduler_outputs_equivalence(self):
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def set_nan_tensor_to_zero(t):
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t[t != t] = 0
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return t
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def recursive_check(tuple_object, dict_object):
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if isinstance(tuple_object, (List, Tuple)):
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for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif isinstance(tuple_object, Dict):
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for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif tuple_object is None:
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return
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else:
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self.assertTrue(
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torch.allclose(
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set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
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),
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msg=(
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"Tuple and dict output are not equal. Difference:"
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f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
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f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
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f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
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),
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)
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", 50)
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timestep = 0
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for scheduler_class in self.scheduler_classes:
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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scheduler.set_timesteps(num_inference_steps)
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timestep = scheduler.timesteps[0]
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sample = self.dummy_sample
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scaled_sample = scheduler.scale_model_input(sample, timestep)
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residual = 0.1 * scaled_sample
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# Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
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if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
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kwargs["generator"] = torch.manual_seed(0)
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outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)
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scheduler.set_timesteps(num_inference_steps)
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scaled_sample = scheduler.scale_model_input(sample, timestep)
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residual = 0.1 * scaled_sample
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# Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
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if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
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kwargs["generator"] = torch.manual_seed(0)
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outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)
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recursive_check(outputs_tuple, outputs_dict)
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@unittest.skip(reason="EDMEulerScheduler does not support beta schedules.")
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def test_trained_betas(self):
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pass
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