935 lines
41 KiB
Python
935 lines
41 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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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from diffusers import FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxPNDMScheduler
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from diffusers.utils import deprecate, is_flax_available
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from diffusers.utils.testing_utils import require_flax
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if is_flax_available():
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import jax
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import jax.numpy as jnp
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from jax import random
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jax_device = jax.default_backend()
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@require_flax
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class FlaxSchedulerCommonTest(unittest.TestCase):
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scheduler_classes = ()
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forward_default_kwargs = ()
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@property
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def dummy_sample(self):
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batch_size = 4
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num_channels = 3
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height = 8
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width = 8
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key1, key2 = random.split(random.PRNGKey(0))
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sample = random.uniform(key1, (batch_size, num_channels, height, width))
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return sample, key2
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@property
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def dummy_sample_deter(self):
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batch_size = 4
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num_channels = 3
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height = 8
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width = 8
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num_elems = batch_size * num_channels * height * width
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sample = jnp.arange(num_elems)
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sample = sample.reshape(num_channels, height, width, batch_size)
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sample = sample / num_elems
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return jnp.transpose(sample, (3, 0, 1, 2))
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def get_scheduler_config(self):
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raise NotImplementedError
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def dummy_model(self):
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def model(sample, t, *args):
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return sample * t / (t + 1)
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return model
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def check_over_configs(self, time_step=0, **config):
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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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sample, key = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config(**config)
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scheduler = scheduler_class(**scheduler_config)
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state = scheduler.create_state()
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output = scheduler.step(state, residual, time_step, sample, key, **kwargs).prev_sample
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new_output = new_scheduler.step(new_state, residual, time_step, sample, key, **kwargs).prev_sample
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assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def check_over_forward(self, time_step=0, **forward_kwargs):
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kwargs = dict(self.forward_default_kwargs)
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kwargs.update(forward_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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sample, key = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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state = scheduler.create_state()
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output = scheduler.step(state, residual, time_step, sample, key, **kwargs).prev_sample
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new_output = new_scheduler.step(new_state, residual, time_step, sample, key, **kwargs).prev_sample
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assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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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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sample, key = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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state = scheduler.create_state()
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output = scheduler.step(state, residual, 1, sample, key, **kwargs).prev_sample
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new_output = new_scheduler.step(new_state, residual, 1, sample, key, **kwargs).prev_sample
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assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def test_step_shape(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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state = scheduler.create_state()
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sample, key = self.dummy_sample
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residual = 0.1 * sample
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output_0 = scheduler.step(state, residual, 0, sample, key, **kwargs).prev_sample
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output_1 = scheduler.step(state, residual, 1, sample, key, **kwargs).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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def test_scheduler_outputs_equivalence(self):
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def set_nan_tensor_to_zero(t):
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return t.at[t != t].set(0)
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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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jnp.allclose(set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5),
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msg=(
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"Tuple and dict output are not equal. Difference:"
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f" {jnp.max(jnp.abs(tuple_object - dict_object))}. Tuple has `nan`:"
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f" {jnp.isnan(tuple_object).any()} and `inf`: {jnp.isinf(tuple_object)}. Dict has"
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f" `nan`: {jnp.isnan(dict_object).any()} and `inf`: {jnp.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", 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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state = scheduler.create_state()
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sample, key = self.dummy_sample
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residual = 0.1 * sample
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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outputs_dict = scheduler.step(state, residual, 0, sample, key, **kwargs)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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outputs_tuple = scheduler.step(state, residual, 0, sample, key, return_dict=False, **kwargs)
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recursive_check(outputs_tuple[0], outputs_dict.prev_sample)
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def test_deprecated_kwargs(self):
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for scheduler_class in self.scheduler_classes:
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has_kwarg_in_model_class = "kwargs" in inspect.signature(scheduler_class.__init__).parameters
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has_deprecated_kwarg = len(scheduler_class._deprecated_kwargs) > 0
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if has_kwarg_in_model_class and not has_deprecated_kwarg:
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raise ValueError(
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f"{scheduler_class} has `**kwargs` in its __init__ method but has not defined any deprecated"
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" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if"
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" there are no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
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" [<deprecated_argument>]`"
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)
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if not has_kwarg_in_model_class and has_deprecated_kwarg:
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raise ValueError(
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f"{scheduler_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated"
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" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs`"
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f" argument to {self.model_class}.__init__ if there are deprecated arguments or remove the"
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" deprecated argument from `_deprecated_kwargs = [<deprecated_argument>]`"
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)
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@require_flax
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class FlaxDDPMSchedulerTest(FlaxSchedulerCommonTest):
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scheduler_classes = (FlaxDDPMScheduler,)
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def get_scheduler_config(self, **kwargs):
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config = {
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"num_train_timesteps": 1000,
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"beta_start": 0.0001,
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"beta_end": 0.02,
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"beta_schedule": "linear",
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"variance_type": "fixed_small",
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"clip_sample": True,
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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 [1, 5, 100, 1000]:
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self.check_over_configs(num_train_timesteps=timesteps)
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def test_betas(self):
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for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
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self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
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def test_schedules(self):
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for schedule in ["linear", "squaredcos_cap_v2"]:
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self.check_over_configs(beta_schedule=schedule)
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def test_variance_type(self):
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for variance in ["fixed_small", "fixed_large", "other"]:
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self.check_over_configs(variance_type=variance)
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def test_clip_sample(self):
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for clip_sample in [True, False]:
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self.check_over_configs(clip_sample=clip_sample)
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def test_time_indices(self):
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for t in [0, 500, 999]:
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self.check_over_forward(time_step=t)
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def test_variance(self):
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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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assert jnp.sum(jnp.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
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assert jnp.sum(jnp.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5
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assert jnp.sum(jnp.abs(scheduler._get_variance(999) - 0.02)) < 1e-5
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def test_full_loop_no_noise(self):
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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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state = scheduler.create_state()
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num_trained_timesteps = len(scheduler)
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model = self.dummy_model()
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sample = self.dummy_sample_deter
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key1, key2 = random.split(random.PRNGKey(0))
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for t in reversed(range(num_trained_timesteps)):
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# 1. predict noise residual
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residual = model(sample, t)
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# 2. predict previous mean of sample x_t-1
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output = scheduler.step(state, residual, t, sample, key1)
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pred_prev_sample = output.prev_sample
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state = output.state
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key1, key2 = random.split(key2)
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# if t > 0:
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# noise = self.dummy_sample_deter
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# variance = scheduler.get_variance(t) ** (0.5) * noise
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#
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# sample = pred_prev_sample + variance
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sample = pred_prev_sample
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result_sum = jnp.sum(jnp.abs(sample))
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result_mean = jnp.mean(jnp.abs(sample))
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if jax_device == "tpu":
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assert abs(result_sum - 255.0714) < 1e-2
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assert abs(result_mean - 0.332124) < 1e-3
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else:
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assert abs(result_sum - 255.1113) < 1e-2
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assert abs(result_mean - 0.332176) < 1e-3
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@require_flax
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class FlaxDDIMSchedulerTest(FlaxSchedulerCommonTest):
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scheduler_classes = (FlaxDDIMScheduler,)
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forward_default_kwargs = (("num_inference_steps", 50),)
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def get_scheduler_config(self, **kwargs):
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config = {
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"num_train_timesteps": 1000,
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"beta_start": 0.0001,
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"beta_end": 0.02,
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"beta_schedule": "linear",
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}
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config.update(**kwargs)
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return config
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def full_loop(self, **config):
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scheduler_class = self.scheduler_classes[0]
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scheduler_config = self.get_scheduler_config(**config)
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scheduler = scheduler_class(**scheduler_config)
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state = scheduler.create_state()
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key1, key2 = random.split(random.PRNGKey(0))
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num_inference_steps = 10
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model = self.dummy_model()
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sample = self.dummy_sample_deter
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state = scheduler.set_timesteps(state, num_inference_steps)
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for t in state.timesteps:
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residual = model(sample, t)
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output = scheduler.step(state, residual, t, sample)
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sample = output.prev_sample
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state = output.state
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key1, key2 = random.split(key2)
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return sample
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def check_over_configs(self, time_step=0, **config):
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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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sample, _ = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config(**config)
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scheduler = scheduler_class(**scheduler_config)
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state = scheduler.create_state()
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output = scheduler.step(state, residual, time_step, sample, **kwargs).prev_sample
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new_output = new_scheduler.step(new_state, residual, time_step, sample, **kwargs).prev_sample
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assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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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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sample, _ = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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state = scheduler.create_state()
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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state = scheduler.set_timesteps(state, num_inference_steps)
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new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output = scheduler.step(state, residual, 1, sample, **kwargs).prev_sample
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new_output = new_scheduler.step(new_state, residual, 1, sample, **kwargs).prev_sample
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assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def check_over_forward(self, time_step=0, **forward_kwargs):
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kwargs = dict(self.forward_default_kwargs)
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kwargs.update(forward_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:
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
scheduler.save_config(tmpdirname)
|
|
new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
state = scheduler.set_timesteps(state, num_inference_steps)
|
|
new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
output = scheduler.step(state, residual, time_step, sample, **kwargs).prev_sample
|
|
new_output = new_scheduler.step(new_state, residual, time_step, sample, **kwargs).prev_sample
|
|
|
|
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
|
|
|
def test_scheduler_outputs_equivalence(self):
|
|
def set_nan_tensor_to_zero(t):
|
|
return t.at[t != t].set(0)
|
|
|
|
def recursive_check(tuple_object, dict_object):
|
|
if isinstance(tuple_object, (List, Tuple)):
|
|
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
|
|
recursive_check(tuple_iterable_value, dict_iterable_value)
|
|
elif isinstance(tuple_object, Dict):
|
|
for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
|
|
recursive_check(tuple_iterable_value, dict_iterable_value)
|
|
elif tuple_object is None:
|
|
return
|
|
else:
|
|
self.assertTrue(
|
|
jnp.allclose(set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5),
|
|
msg=(
|
|
"Tuple and dict output are not equal. Difference:"
|
|
f" {jnp.max(jnp.abs(tuple_object - dict_object))}. Tuple has `nan`:"
|
|
f" {jnp.isnan(tuple_object).any()} and `inf`: {jnp.isinf(tuple_object)}. Dict has"
|
|
f" `nan`: {jnp.isnan(dict_object).any()} and `inf`: {jnp.isinf(dict_object)}."
|
|
),
|
|
)
|
|
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
state = scheduler.set_timesteps(state, num_inference_steps)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
outputs_dict = scheduler.step(state, residual, 0, sample, **kwargs)
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
state = scheduler.set_timesteps(state, num_inference_steps)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
outputs_tuple = scheduler.step(state, residual, 0, sample, return_dict=False, **kwargs)
|
|
|
|
recursive_check(outputs_tuple[0], outputs_dict.prev_sample)
|
|
|
|
def test_step_shape(self):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
state = scheduler.set_timesteps(state, num_inference_steps)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
output_0 = scheduler.step(state, residual, 0, sample, **kwargs).prev_sample
|
|
output_1 = scheduler.step(state, residual, 1, sample, **kwargs).prev_sample
|
|
|
|
self.assertEqual(output_0.shape, sample.shape)
|
|
self.assertEqual(output_0.shape, output_1.shape)
|
|
|
|
def test_timesteps(self):
|
|
for timesteps in [100, 500, 1000]:
|
|
self.check_over_configs(num_train_timesteps=timesteps)
|
|
|
|
def test_steps_offset(self):
|
|
for steps_offset in [0, 1]:
|
|
self.check_over_configs(steps_offset=steps_offset)
|
|
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config(steps_offset=1)
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
state = scheduler.set_timesteps(state, 5)
|
|
assert jnp.equal(state.timesteps, jnp.array([801, 601, 401, 201, 1])).all()
|
|
|
|
def test_betas(self):
|
|
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
|
|
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
|
|
|
|
def test_schedules(self):
|
|
for schedule in ["linear", "squaredcos_cap_v2"]:
|
|
self.check_over_configs(beta_schedule=schedule)
|
|
|
|
def test_time_indices(self):
|
|
for t in [1, 10, 49]:
|
|
self.check_over_forward(time_step=t)
|
|
|
|
def test_inference_steps(self):
|
|
for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):
|
|
self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
|
|
|
|
def test_variance(self):
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
assert jnp.sum(jnp.abs(scheduler._get_variance(0, 0, state.alphas_cumprod) - 0.0)) < 1e-5
|
|
assert jnp.sum(jnp.abs(scheduler._get_variance(420, 400, state.alphas_cumprod) - 0.14771)) < 1e-5
|
|
assert jnp.sum(jnp.abs(scheduler._get_variance(980, 960, state.alphas_cumprod) - 0.32460)) < 1e-5
|
|
assert jnp.sum(jnp.abs(scheduler._get_variance(0, 0, state.alphas_cumprod) - 0.0)) < 1e-5
|
|
assert jnp.sum(jnp.abs(scheduler._get_variance(487, 486, state.alphas_cumprod) - 0.00979)) < 1e-5
|
|
assert jnp.sum(jnp.abs(scheduler._get_variance(999, 998, state.alphas_cumprod) - 0.02)) < 1e-5
|
|
|
|
def test_full_loop_no_noise(self):
|
|
sample = self.full_loop()
|
|
|
|
result_sum = jnp.sum(jnp.abs(sample))
|
|
result_mean = jnp.mean(jnp.abs(sample))
|
|
|
|
assert abs(result_sum - 172.0067) < 1e-2
|
|
assert abs(result_mean - 0.223967) < 1e-3
|
|
|
|
def test_full_loop_with_set_alpha_to_one(self):
|
|
# We specify different beta, so that the first alpha is 0.99
|
|
sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
|
|
result_sum = jnp.sum(jnp.abs(sample))
|
|
result_mean = jnp.mean(jnp.abs(sample))
|
|
|
|
if jax_device == "tpu":
|
|
assert abs(result_sum - 149.8409) < 1e-2
|
|
assert abs(result_mean - 0.1951) < 1e-3
|
|
else:
|
|
assert abs(result_sum - 149.8295) < 1e-2
|
|
assert abs(result_mean - 0.1951) < 1e-3
|
|
|
|
def test_full_loop_with_no_set_alpha_to_one(self):
|
|
# We specify different beta, so that the first alpha is 0.99
|
|
sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
|
|
result_sum = jnp.sum(jnp.abs(sample))
|
|
result_mean = jnp.mean(jnp.abs(sample))
|
|
|
|
if jax_device == "tpu":
|
|
pass
|
|
# FIXME: both result_sum and result_mean are nan on TPU
|
|
# assert jnp.isnan(result_sum)
|
|
# assert jnp.isnan(result_mean)
|
|
else:
|
|
assert abs(result_sum - 149.0784) < 1e-2
|
|
assert abs(result_mean - 0.1941) < 1e-3
|
|
|
|
def test_prediction_type(self):
|
|
for prediction_type in ["epsilon", "sample", "v_prediction"]:
|
|
self.check_over_configs(prediction_type=prediction_type)
|
|
|
|
def test_deprecated_predict_epsilon(self):
|
|
deprecate("remove this test", "0.11.0", "remove")
|
|
for predict_epsilon in [True, False]:
|
|
self.check_over_configs(predict_epsilon=predict_epsilon)
|
|
|
|
def test_deprecated_predict_epsilon_to_prediction_type(self):
|
|
deprecate("remove this test", "0.11.0", "remove")
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config(predict_epsilon=True)
|
|
scheduler = scheduler_class.from_config(scheduler_config)
|
|
assert scheduler.prediction_type == "epsilon"
|
|
|
|
scheduler_config = self.get_scheduler_config(predict_epsilon=False)
|
|
scheduler = scheduler_class.from_config(scheduler_config)
|
|
assert scheduler.prediction_type == "sample"
|
|
|
|
|
|
@require_flax
|
|
class FlaxPNDMSchedulerTest(FlaxSchedulerCommonTest):
|
|
scheduler_classes = (FlaxPNDMScheduler,)
|
|
forward_default_kwargs = (("num_inference_steps", 50),)
|
|
|
|
def get_scheduler_config(self, **kwargs):
|
|
config = {
|
|
"num_train_timesteps": 1000,
|
|
"beta_start": 0.0001,
|
|
"beta_end": 0.02,
|
|
"beta_schedule": "linear",
|
|
}
|
|
|
|
config.update(**kwargs)
|
|
return config
|
|
|
|
def check_over_configs(self, time_step=0, **config):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
dummy_past_residuals = jnp.array([residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05])
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config(**config)
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)
|
|
# copy over dummy past residuals
|
|
state = state.replace(ets=dummy_past_residuals[:])
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
scheduler.save_config(tmpdirname)
|
|
new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
|
|
new_state = new_scheduler.set_timesteps(new_state, num_inference_steps, shape=sample.shape)
|
|
# copy over dummy past residuals
|
|
new_state = new_state.replace(ets=dummy_past_residuals[:])
|
|
|
|
(prev_sample, state) = scheduler.step_prk(state, residual, time_step, sample, **kwargs)
|
|
(new_prev_sample, new_state) = new_scheduler.step_prk(new_state, residual, time_step, sample, **kwargs)
|
|
|
|
assert jnp.sum(jnp.abs(prev_sample - new_prev_sample)) < 1e-5, "Scheduler outputs are not identical"
|
|
|
|
output, _ = scheduler.step_plms(state, residual, time_step, sample, **kwargs)
|
|
new_output, _ = new_scheduler.step_plms(new_state, residual, time_step, sample, **kwargs)
|
|
|
|
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
|
|
|
def test_from_save_pretrained(self):
|
|
pass
|
|
|
|
def test_scheduler_outputs_equivalence(self):
|
|
def set_nan_tensor_to_zero(t):
|
|
return t.at[t != t].set(0)
|
|
|
|
def recursive_check(tuple_object, dict_object):
|
|
if isinstance(tuple_object, (List, Tuple)):
|
|
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
|
|
recursive_check(tuple_iterable_value, dict_iterable_value)
|
|
elif isinstance(tuple_object, Dict):
|
|
for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
|
|
recursive_check(tuple_iterable_value, dict_iterable_value)
|
|
elif tuple_object is None:
|
|
return
|
|
else:
|
|
self.assertTrue(
|
|
jnp.allclose(set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5),
|
|
msg=(
|
|
"Tuple and dict output are not equal. Difference:"
|
|
f" {jnp.max(jnp.abs(tuple_object - dict_object))}. Tuple has `nan`:"
|
|
f" {jnp.isnan(tuple_object).any()} and `inf`: {jnp.isinf(tuple_object)}. Dict has"
|
|
f" `nan`: {jnp.isnan(dict_object).any()} and `inf`: {jnp.isinf(dict_object)}."
|
|
),
|
|
)
|
|
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
outputs_dict = scheduler.step(state, residual, 0, sample, **kwargs)
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
outputs_tuple = scheduler.step(state, residual, 0, sample, return_dict=False, **kwargs)
|
|
|
|
recursive_check(outputs_tuple[0], outputs_dict.prev_sample)
|
|
|
|
def check_over_forward(self, time_step=0, **forward_kwargs):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
dummy_past_residuals = jnp.array([residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05])
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)
|
|
|
|
# copy over dummy past residuals (must be after setting timesteps)
|
|
scheduler.ets = dummy_past_residuals[:]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
scheduler.save_config(tmpdirname)
|
|
new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)
|
|
# copy over dummy past residuals
|
|
new_state = new_scheduler.set_timesteps(new_state, num_inference_steps, shape=sample.shape)
|
|
|
|
# copy over dummy past residual (must be after setting timesteps)
|
|
new_state.replace(ets=dummy_past_residuals[:])
|
|
|
|
output, state = scheduler.step_prk(state, residual, time_step, sample, **kwargs)
|
|
new_output, new_state = new_scheduler.step_prk(new_state, residual, time_step, sample, **kwargs)
|
|
|
|
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
|
|
|
output, _ = scheduler.step_plms(state, residual, time_step, sample, **kwargs)
|
|
new_output, _ = new_scheduler.step_plms(new_state, residual, time_step, sample, **kwargs)
|
|
|
|
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
|
|
|
def full_loop(self, **config):
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config(**config)
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
num_inference_steps = 10
|
|
model = self.dummy_model()
|
|
sample = self.dummy_sample_deter
|
|
state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)
|
|
|
|
for i, t in enumerate(state.prk_timesteps):
|
|
residual = model(sample, t)
|
|
sample, state = scheduler.step_prk(state, residual, t, sample)
|
|
|
|
for i, t in enumerate(state.plms_timesteps):
|
|
residual = model(sample, t)
|
|
sample, state = scheduler.step_plms(state, residual, t, sample)
|
|
|
|
return sample
|
|
|
|
def test_step_shape(self):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
# copy over dummy past residuals (must be done after set_timesteps)
|
|
dummy_past_residuals = jnp.array([residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05])
|
|
state = state.replace(ets=dummy_past_residuals[:])
|
|
|
|
output_0, state = scheduler.step_prk(state, residual, 0, sample, **kwargs)
|
|
output_1, state = scheduler.step_prk(state, residual, 1, sample, **kwargs)
|
|
|
|
self.assertEqual(output_0.shape, sample.shape)
|
|
self.assertEqual(output_0.shape, output_1.shape)
|
|
|
|
output_0, state = scheduler.step_plms(state, residual, 0, sample, **kwargs)
|
|
output_1, state = scheduler.step_plms(state, residual, 1, sample, **kwargs)
|
|
|
|
self.assertEqual(output_0.shape, sample.shape)
|
|
self.assertEqual(output_0.shape, output_1.shape)
|
|
|
|
def test_timesteps(self):
|
|
for timesteps in [100, 1000]:
|
|
self.check_over_configs(num_train_timesteps=timesteps)
|
|
|
|
def test_steps_offset(self):
|
|
for steps_offset in [0, 1]:
|
|
self.check_over_configs(steps_offset=steps_offset)
|
|
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config(steps_offset=1)
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
state = scheduler.set_timesteps(state, 10, shape=())
|
|
assert jnp.equal(
|
|
state.timesteps,
|
|
jnp.array([901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]),
|
|
).all()
|
|
|
|
def test_betas(self):
|
|
for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]):
|
|
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
|
|
|
|
def test_schedules(self):
|
|
for schedule in ["linear", "squaredcos_cap_v2"]:
|
|
self.check_over_configs(beta_schedule=schedule)
|
|
|
|
def test_time_indices(self):
|
|
for t in [1, 5, 10]:
|
|
self.check_over_forward(time_step=t)
|
|
|
|
def test_inference_steps(self):
|
|
for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):
|
|
self.check_over_forward(num_inference_steps=num_inference_steps)
|
|
|
|
def test_pow_of_3_inference_steps(self):
|
|
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
|
|
num_inference_steps = 27
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
sample, _ = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)
|
|
|
|
# before power of 3 fix, would error on first step, so we only need to do two
|
|
for i, t in enumerate(state.prk_timesteps[:2]):
|
|
sample, state = scheduler.step_prk(state, residual, t, sample)
|
|
|
|
def test_inference_plms_no_past_residuals(self):
|
|
with self.assertRaises(ValueError):
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
state = scheduler.create_state()
|
|
|
|
scheduler.step_plms(state, self.dummy_sample, 1, self.dummy_sample).prev_sample
|
|
|
|
def test_full_loop_no_noise(self):
|
|
sample = self.full_loop()
|
|
result_sum = jnp.sum(jnp.abs(sample))
|
|
result_mean = jnp.mean(jnp.abs(sample))
|
|
|
|
if jax_device == "tpu":
|
|
assert abs(result_sum - 198.1275) < 1e-2
|
|
assert abs(result_mean - 0.2580) < 1e-3
|
|
else:
|
|
assert abs(result_sum - 198.1318) < 1e-2
|
|
assert abs(result_mean - 0.2580) < 1e-3
|
|
|
|
def test_full_loop_with_set_alpha_to_one(self):
|
|
# We specify different beta, so that the first alpha is 0.99
|
|
sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
|
|
result_sum = jnp.sum(jnp.abs(sample))
|
|
result_mean = jnp.mean(jnp.abs(sample))
|
|
|
|
if jax_device == "tpu":
|
|
assert abs(result_sum - 186.83226) < 1e-2
|
|
assert abs(result_mean - 0.24327) < 1e-3
|
|
else:
|
|
assert abs(result_sum - 186.9466) < 1e-2
|
|
assert abs(result_mean - 0.24342) < 1e-3
|
|
|
|
def test_full_loop_with_no_set_alpha_to_one(self):
|
|
# We specify different beta, so that the first alpha is 0.99
|
|
sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
|
|
result_sum = jnp.sum(jnp.abs(sample))
|
|
result_mean = jnp.mean(jnp.abs(sample))
|
|
|
|
if jax_device == "tpu":
|
|
assert abs(result_sum - 186.83226) < 1e-2
|
|
assert abs(result_mean - 0.24327) < 1e-3
|
|
else:
|
|
assert abs(result_sum - 186.9482) < 1e-2
|
|
assert abs(result_mean - 0.2434) < 1e-3
|