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Add arg compile_unet #17

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merged 3 commits into from
Nov 8, 2022
Merged

Add arg compile_unet #17

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1f884da
add
jackalcooper Nov 8, 2022
ca8689c
rm blank
jackalcooper Nov 8, 2022
8d48722
refine
jackalcooper Nov 8, 2022
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3 changes: 2 additions & 1 deletion src/diffusers/models/unet_blocks_oneflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -371,7 +371,8 @@ def set_attention_slice(self, slice_size):

def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
resnets_list = [m for m in self.resnets]
for attn, resnet in zip(self.attentions, resnets_list[1:]):
hidden_states = attn(hidden_states, encoder_hidden_states)
hidden_states = resnet(hidden_states, temb)

Expand Down
31 changes: 19 additions & 12 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_oneflow.py
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Expand Up @@ -142,6 +142,7 @@ def __call__(
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
compile_unet: bool = True,
**kwargs,
):
r"""
Expand Down Expand Up @@ -179,6 +180,8 @@ def __call__(
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
compile_unet (`bool`, *optional*, defaults to `True`):
Whether or not to compile unet as nn.graph

Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
Expand Down Expand Up @@ -278,15 +281,16 @@ def __call__(

compilation_start = timer()
compilation_time = 0
if self.unet_compiled == False:
print("[oneflow]", "compiling unet beforehand to make sure the progress bar is more accurate")
i, t = list(enumerate(self.scheduler.timesteps))[0]
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
self.unet_graph._compile(latent_model_input, t, text_embeddings)
self.unet_compiled = True
self.unet_graph(latent_model_input, t, text_embeddings) # warmup
compilation_time = timer() - compilation_start
print("[oneflow]", "[elapsed(s)]", "[unet compilation]", compilation_time)
if compile_unet:
if self.unet_compiled == False:
print("[oneflow]", "compiling unet beforehand to make sure the progress bar is more accurate")
i, t = list(enumerate(self.scheduler.timesteps))[0]
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
self.unet_graph._compile(latent_model_input, t, text_embeddings)
self.unet_compiled = True
self.unet_graph(latent_model_input, t, text_embeddings) # warmup
compilation_time = timer() - compilation_start
print("[oneflow]", "[elapsed(s)]", "[unet compilation]", compilation_time)

for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
torch._oneflow_internal.profiler.RangePush(f"denoise-{i}")
Expand All @@ -298,9 +302,12 @@ def __call__(
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

# predict the noise residual
torch._oneflow_internal.profiler.RangePush(f"denoise-{i}-unet-graph")
noise_pred = self.unet_graph(latent_model_input, t, text_embeddings)
torch._oneflow_internal.profiler.RangePop()
if compile_unet:
torch._oneflow_internal.profiler.RangePush(f"denoise-{i}-unet-graph")
noise_pred = self.unet_graph(latent_model_input, t, text_embeddings)
torch._oneflow_internal.profiler.RangePop()
else:
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

# perform guidance
if do_classifier_free_guidance:
Expand Down