# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import tempfile
import unittest
from typing import Dict, List, Tuple
from diffusers import FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxPNDMScheduler
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from jax import random
jax_device = jax.default_backend()
@require_flax
class FlaxSchedulerCommonTest(unittest.TestCase):
scheduler_classes = ()
forward_default_kwargs = ()
@property
def dummy_sample(self):
batch_size = 4
num_channels = 3
height = 8
width = 8
key1, key2 = random.split(random.PRNGKey(0))
sample = random.uniform(key1, (batch_size, num_channels, height, width))
return sample, key2
@property
def dummy_sample_deter(self):
batch_size = 4
num_channels = 3
height = 8
width = 8
num_elems = batch_size * num_channels * height * width
sample = jnp.arange(num_elems)
sample = sample.reshape(num_channels, height, width, batch_size)
sample = sample / num_elems
return jnp.transpose(sample, (3, 0, 1, 2))
def get_scheduler_config(self):
raise NotImplementedError
def dummy_model(self):
def model(sample, t, *args):
return sample * t / (t + 1)
return model
def check_over_configs(self, time_step=0, **config):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
for scheduler_class in self.scheduler_classes:
sample, key = self.dummy_sample
residual = 0.1 * sample
scheduler_config = self.get_scheduler_config(**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, key, **kwargs).prev_sample
new_output = new_scheduler.step(new_state, residual, time_step, sample, key, **kwargs).prev_sample
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def check_over_forward(self, time_step=0, **forward_kwargs):
kwargs = dict(self.forward_default_kwargs)
kwargs.update(forward_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
for scheduler_class in self.scheduler_classes:
sample, key = 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, key, **kwargs).prev_sample
new_output = new_scheduler.step(new_state, residual, time_step, sample, key, **kwargs).prev_sample
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def test_from_save_pretrained(self):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
for scheduler_class in self.scheduler_classes:
sample, key = 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, 1, sample, key, **kwargs).prev_sample
new_output = new_scheduler.step(new_state, residual, 1, sample, key, **kwargs).prev_sample
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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, key = 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, key, **kwargs).prev_sample
output_1 = scheduler.step(state, residual, 1, sample, key, **kwargs).prev_sample
self.assertEqual(output_0.shape, sample.shape)
self.assertEqual(output_0.shape, output_1.shape)
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, key = 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, key, **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, key, return_dict=False, **kwargs)
recursive_check(outputs_tuple[0], outputs_dict.prev_sample)
def test_deprecated_kwargs(self):
for scheduler_class in self.scheduler_classes:
has_kwarg_in_model_class = "kwargs" in inspect.signature(scheduler_class.__init__).parameters
has_deprecated_kwarg = len(scheduler_class._deprecated_kwargs) > 0
if has_kwarg_in_model_class and not has_deprecated_kwarg:
raise ValueError(
f"{scheduler_class} has `**kwargs` in its __init__ method but has not defined any deprecated"
" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if"
" there are no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
" [<deprecated_argument>]`"
)
if not has_kwarg_in_model_class and has_deprecated_kwarg:
raise ValueError(
f"{scheduler_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated"
" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs`"
f" argument to {self.model_class}.__init__ if there are deprecated arguments or remove the"
" deprecated argument from `_deprecated_kwargs = [<deprecated_argument>]`"
)
@require_flax
class FlaxDDPMSchedulerTest(FlaxSchedulerCommonTest):
scheduler_classes = (FlaxDDPMScheduler,)
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**kwargs)
return config
def test_timesteps(self):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=timesteps)
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_variance_type(self):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=variance)
def test_clip_sample(self):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=clip_sample)
def test_time_indices(self):
for t in [0, 500, 999]:
self.check_over_forward(time_step=t)
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(state, 0) - 0.0)) < 1e-5
assert jnp.sum(jnp.abs(scheduler._get_variance(state, 487) - 0.00979)) < 1e-5
assert jnp.sum(jnp.abs(scheduler._get_variance(state, 999) - 0.02)) < 1e-5
def test_full_loop_no_noise(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
state = scheduler.create_state()
num_trained_timesteps = len(scheduler)
model = self.dummy_model()
sample = self.dummy_sample_deter
key1, key2 = random.split(random.PRNGKey(0))
for t in reversed(range(num_trained_timesteps)):
# 1. predict noise residual
residual = model(sample, t)
# 2. predict previous mean of sample x_t-1
output = scheduler.step(state, residual, t, sample, key1)
pred_prev_sample = output.prev_sample
state = output.state
key1, key2 = random.split(key2)
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
sample = pred_prev_sample
result_sum = jnp.sum(jnp.abs(sample))
result_mean = jnp.mean(jnp.abs(sample))
if jax_device == "tpu":
assert abs(result_sum - 255.0714) < 1e-2
assert abs(result_mean - 0.332124) < 1e-3
else:
assert abs(result_sum - 255.1113) < 1e-2
assert abs(result_mean - 0.332176) < 1e-3
@require_flax
class FlaxDDIMSchedulerTest(FlaxSchedulerCommonTest):
scheduler_classes = (FlaxDDIMScheduler,)
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 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()
key1, key2 = random.split(random.PRNGKey(0))
num_inference_steps = 10
model = self.dummy_model()
sample = self.dummy_sample_deter
state = scheduler.set_timesteps(state, num_inference_steps)
for t in state.timesteps:
residual = model(sample, t)
output = scheduler.step(state, residual, t, sample)
sample = output.prev_sample
state = output.state
key1, key2 = random.split(key2)
return sample
def check_over_configs(self, time_step=0, **config):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
for scheduler_class in self.scheduler_classes:
sample, _ = self.dummy_sample
residual = 0.1 * sample
scheduler_config = self.get_scheduler_config(**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_from_save_pretrained(self):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
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, 1, sample, **kwargs).prev_sample
new_output = new_scheduler.step(new_state, residual, 1, sample, **kwargs).prev_sample
assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def check_over_forward(self, time_step=0, **forward_kwargs):
kwargs = dict(self.forward_default_kwargs)
kwargs.update(forward_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
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(state, 0, 0) - 0.0)) < 1e-5
assert jnp.sum(jnp.abs(scheduler._get_variance(state, 420, 400) - 0.14771)) < 1e-5
assert jnp.sum(jnp.abs(scheduler._get_variance(state, 980, 960) - 0.32460)) < 1e-5
assert jnp.sum(jnp.abs(scheduler._get_variance(state, 0, 0) - 0.0)) < 1e-5
assert jnp.sum(jnp.abs(scheduler._get_variance(state, 487, 486) - 0.00979)) < 1e-5
assert jnp.sum(jnp.abs(scheduler._get_variance(state, 999, 998) - 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)
@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