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Running
on
Zero
| import tempfile | |
| import torch | |
| from diffusers import PNDMScheduler | |
| from .test_schedulers import SchedulerCommonTest | |
| class PNDMSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (PNDMScheduler,) | |
| 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 = [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) | |
| scheduler.set_timesteps(num_inference_steps) | |
| # copy over dummy past residuals | |
| scheduler.ets = dummy_past_residuals[:] | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| scheduler.save_config(tmpdirname) | |
| new_scheduler = scheduler_class.from_pretrained(tmpdirname) | |
| new_scheduler.set_timesteps(num_inference_steps) | |
| # copy over dummy past residuals | |
| new_scheduler.ets = dummy_past_residuals[:] | |
| output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| def test_from_save_pretrained(self): | |
| pass | |
| 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 = [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) | |
| scheduler.set_timesteps(num_inference_steps) | |
| # 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 = scheduler_class.from_pretrained(tmpdirname) | |
| # copy over dummy past residuals | |
| new_scheduler.set_timesteps(num_inference_steps) | |
| # copy over dummy past residual (must be after setting timesteps) | |
| new_scheduler.ets = dummy_past_residuals[:] | |
| output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.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) | |
| num_inference_steps = 10 | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| scheduler.set_timesteps(num_inference_steps) | |
| for i, t in enumerate(scheduler.prk_timesteps): | |
| residual = model(sample, t) | |
| sample = scheduler.step_prk(residual, t, sample).prev_sample | |
| for i, t in enumerate(scheduler.plms_timesteps): | |
| residual = model(sample, t) | |
| sample = scheduler.step_plms(residual, t, sample).prev_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) | |
| sample = self.dummy_sample | |
| residual = 0.1 * sample | |
| if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): | |
| scheduler.set_timesteps(num_inference_steps) | |
| 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 = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] | |
| scheduler.ets = dummy_past_residuals[:] | |
| output_0 = scheduler.step_prk(residual, 0, sample, **kwargs).prev_sample | |
| output_1 = scheduler.step_prk(residual, 1, sample, **kwargs).prev_sample | |
| self.assertEqual(output_0.shape, sample.shape) | |
| self.assertEqual(output_0.shape, output_1.shape) | |
| output_0 = scheduler.step_plms(residual, 0, sample, **kwargs).prev_sample | |
| output_1 = scheduler.step_plms(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, 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) | |
| scheduler.set_timesteps(10) | |
| assert torch.equal( | |
| scheduler.timesteps, | |
| torch.LongTensor( | |
| [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] | |
| ), | |
| ) | |
| 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_prediction_type(self): | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs(prediction_type=prediction_type) | |
| 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) | |
| scheduler.set_timesteps(num_inference_steps) | |
| # before power of 3 fix, would error on first step, so we only need to do two | |
| for i, t in enumerate(scheduler.prk_timesteps[:2]): | |
| sample = scheduler.step_prk(residual, t, sample).prev_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) | |
| scheduler.step_plms(self.dummy_sample, 1, self.dummy_sample).prev_sample | |
| def test_full_loop_no_noise(self): | |
| sample = self.full_loop() | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 198.1318) < 1e-2 | |
| assert abs(result_mean.item() - 0.2580) < 1e-3 | |
| def test_full_loop_with_v_prediction(self): | |
| sample = self.full_loop(prediction_type="v_prediction") | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 67.3986) < 1e-2 | |
| assert abs(result_mean.item() - 0.0878) < 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 = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 230.0399) < 1e-2 | |
| assert abs(result_mean.item() - 0.2995) < 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 = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 186.9482) < 1e-2 | |
| assert abs(result_mean.item() - 0.2434) < 1e-3 | |