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hotfix for pdnm test (#220)

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Nathan Lambert 2022-08-21 22:23:59 -07:00 committed by GitHub
parent 6a03060c45
commit 3f1861ee46
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1 changed files with 12 additions and 9 deletions
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21
tests/test_scheduler.py
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@ -426,16 +426,18 @@ class PNDMSchedulerTest(SchedulerCommonTest):
scheduler = scheduler_class(**scheduler_config) scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(num_inference_steps) scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residuals # copy over dummy past residuals (must be after setting timesteps)
scheduler.ets = dummy_past_residuals[:] scheduler.ets = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname: with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(tmpdirname) scheduler.save_config(tmpdirname)
new_scheduler = scheduler_class.from_config(tmpdirname) new_scheduler = scheduler_class.from_config(tmpdirname)
# copy over dummy past residuals # copy over dummy past residuals
new_scheduler.ets = dummy_past_residuals[:]
new_scheduler.set_timesteps(num_inference_steps) 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"] output = scheduler.step_prk(residual, time_step, sample, **kwargs)["prev_sample"]
new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs)["prev_sample"] new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs)["prev_sample"]
@ -461,12 +463,8 @@ class PNDMSchedulerTest(SchedulerCommonTest):
scheduler_config = self.get_scheduler_config() scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(tensor_format="np", **scheduler_config) scheduler = scheduler_class(tensor_format="np", **scheduler_config)
# copy over dummy past residuals
scheduler.ets = dummy_past_residuals[:]
scheduler_pt = scheduler_class(tensor_format="pt", **scheduler_config) scheduler_pt = scheduler_class(tensor_format="pt", **scheduler_config)
# copy over dummy past residuals
scheduler_pt.ets = dummy_past_residuals_pt[:]
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
scheduler.set_timesteps(num_inference_steps) scheduler.set_timesteps(num_inference_steps)
@ -474,6 +472,10 @@ class PNDMSchedulerTest(SchedulerCommonTest):
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
kwargs["num_inference_steps"] = num_inference_steps kwargs["num_inference_steps"] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
scheduler.ets = dummy_past_residuals[:]
scheduler_pt.ets = dummy_past_residuals_pt[:]
output = scheduler.step_prk(residual, 1, sample, **kwargs)["prev_sample"] output = scheduler.step_prk(residual, 1, sample, **kwargs)["prev_sample"]
output_pt = scheduler_pt.step_prk(residual_pt, 1, sample_pt, **kwargs)["prev_sample"] output_pt = scheduler_pt.step_prk(residual_pt, 1, sample_pt, **kwargs)["prev_sample"]
assert np.sum(np.abs(output - output_pt.numpy())) < 1e-4, "Scheduler outputs are not identical" assert np.sum(np.abs(output - output_pt.numpy())) < 1e-4, "Scheduler outputs are not identical"
@ -494,15 +496,16 @@ class PNDMSchedulerTest(SchedulerCommonTest):
sample = self.dummy_sample sample = self.dummy_sample
residual = 0.1 * sample residual = 0.1 * sample
# copy over dummy past residuals
dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
scheduler.ets = dummy_past_residuals[:]
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
scheduler.set_timesteps(num_inference_steps) scheduler.set_timesteps(num_inference_steps)
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
kwargs["num_inference_steps"] = num_inference_steps 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_0 = scheduler.step_prk(residual, 0, sample, **kwargs)["prev_sample"]
output_1 = scheduler.step_prk(residual, 1, sample, **kwargs)["prev_sample"] output_1 = scheduler.step_prk(residual, 1, sample, **kwargs)["prev_sample"]