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[Community Pipelines]Accelerate inference of stable diffusion by IPEX on CPU #3105

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Merged
merged 15 commits into from
May 23, 2023
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[Community Pipelines]Accelerate inference of stable diffusion by IPEX on CPU #3105

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add stable_diffusion_ipex community pipeline
yingjie-han Apr 14, 2023
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Update readme.md
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yingjie-han Apr 18, 2023
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yingjie-han Apr 18, 2023
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Update examples/community/README.md
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yingjie-han May 19, 2023
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Modified according to review comment
yingjie-han May 19, 2023
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yingjie-han May 23, 2023
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@yingjie-han
yingjie-han committed Apr 18, 2023
commit 2eab9c4daee8cfbfb0ac7f1314d6565eaf67c7f6
87 changes: 51 additions & 36 deletions examples/community/stable_diffusion_ipex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,7 +160,6 @@ def __init__(
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)


self.register_modules(
vae=vae,
text_encoder=text_encoder,
Expand All @@ -173,9 +172,7 @@ def __init__(
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)



def get_input_example(self,prompt,height = None,width = None,guidance_scale=7.5,num_images_per_prompt =1):
def get_input_example(self, prompt, height=None, width=None, guidance_scale=7.5, num_images_per_prompt=1):

prompt_embeds = None
negative_prompt_embeds = None
Expand Down Expand Up @@ -205,7 +202,6 @@ def get_input_example(self,prompt,height = None,width = None,guidance_scale=7.5,
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0


# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
Expand All @@ -217,7 +213,6 @@ def get_input_example(self,prompt,height = None,width = None,guidance_scale=7.5,
negative_prompt_embeds=negative_prompt_embeds,
)


# 5. Prepare latent variables
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
Expand All @@ -236,52 +231,78 @@ def get_input_example(self,prompt,height = None,width = None,guidance_scale=7.5,
unet_input_example = (latent_model_input, dummy, prompt_embeds)
vae_decoder_input_example = latents

return unet_input_example,vae_decoder_input_example


return unet_input_example, vae_decoder_input_example

def prepare_for_ipex(self,promt,infer_type = 'bf16',height = None,width = None,guidance_scale=7.5):
def prepare_for_ipex(self, promt, infer_type="bf16", height=None, width=None, guidance_scale=7.5):
self.unet = self.unet.to(memory_format=torch.channels_last)
self.vae.decoder = self.vae.decoder.to(memory_format=torch.channels_last)
self.text_encoder = self.text_encoder.to(memory_format=torch.channels_last)
if self.safety_checker != None:
self.safety_checker = self.safety_checker.to(memory_format=torch.channels_last)

unet_input_example,vae_decoder_input_example = self.get_input_example(promt,height,width,guidance_scale)
unet_input_example, vae_decoder_input_example = self.get_input_example(promt, height, width, guidance_scale)

# optimize with ipex
if infer_type == 'bf16':
self.unet = ipex.optimize(self.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=unet_input_example)
if infer_type == "bf16":
self.unet = ipex.optimize(
self.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=unet_input_example
)
self.vae.decoder = ipex.optimize(self.vae.decoder.eval(), dtype=torch.bfloat16, inplace=True)
self.text_encoder = ipex.optimize(self.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)
if self.safety_checker != None:
self.safety_checker = ipex.optimize(self.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)
elif infer_type == 'fp32':
self.unet = ipex.optimize(self.unet.eval(), dtype=torch.float32, inplace=True, sample_input=unet_input_example, level="O1", weights_prepack=True, auto_kernel_selection=False)
self.vae.decoder = ipex.optimize(self.vae.decoder.eval(), dtype=torch.float32, inplace=True, level="O1", weights_prepack=True, auto_kernel_selection=False)
self.text_encoder = ipex.optimize(self.text_encoder.eval(), dtype=torch.float32, inplace=True, level="O1", weights_prepack=True, auto_kernel_selection=False)
elif infer_type == "fp32":
self.unet = ipex.optimize(
self.unet.eval(),
dtype=torch.float32,
inplace=True,
sample_input=unet_input_example,
level="O1",
weights_prepack=True,
auto_kernel_selection=False,
)
self.vae.decoder = ipex.optimize(
self.vae.decoder.eval(),
dtype=torch.float32,
inplace=True,
level="O1",
weights_prepack=True,
auto_kernel_selection=False,
)
self.text_encoder = ipex.optimize(
self.text_encoder.eval(),
dtype=torch.float32,
inplace=True,
level="O1",
weights_prepack=True,
auto_kernel_selection=False,
)
if self.safety_checker != None:
self.safety_checker = ipex.optimize(self.safety_checker.eval(), dtype=torch.float32, inplace=True, level="O1", weights_prepack=True, auto_kernel_selection=False)
self.safety_checker = ipex.optimize(
self.safety_checker.eval(),
dtype=torch.float32,
inplace=True,
level="O1",
weights_prepack=True,
auto_kernel_selection=False,
)
else:
raise ValueError(
f" The value of infer_type should be 'bf16' or 'fp32' !"
)
raise ValueError(f" The value of infer_type should be 'bf16' or 'fp32' !")

# trace unet model to get better performance on IPEX
with torch.cpu.amp.autocast(enabled=infer_type=='bf16'), torch.no_grad():
with torch.cpu.amp.autocast(enabled=infer_type == "bf16"), torch.no_grad():
unet_trace_model = torch.jit.trace(self.unet, unet_input_example, check_trace=False, strict=False)
unet_trace_model = torch.jit.freeze(unet_trace_model)
self.unet.forward = unet_trace_model.forward

# trace vae.decoder model to get better performance on IPEX
with torch.cpu.amp.autocast(enabled=infer_type=='bf16'), torch.no_grad():
ave_decoder_trace_model = torch.jit.trace(self.vae.decoder, vae_decoder_input_example, check_trace=False, strict=False)
with torch.cpu.amp.autocast(enabled=infer_type == "bf16"), torch.no_grad():
ave_decoder_trace_model = torch.jit.trace(
self.vae.decoder, vae_decoder_input_example, check_trace=False, strict=False
)
ave_decoder_trace_model = torch.jit.freeze(ave_decoder_trace_model)
self.vae.decoder.forward = ave_decoder_trace_model.forward




def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding.
Expand Down Expand Up @@ -459,7 +480,6 @@ def _encode_prompt(
)
prompt_embeds = prompt_embeds[0]


prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

bs_embed, seq_len, _ = prompt_embeds.shape
Expand Down Expand Up @@ -767,7 +787,7 @@ def __call__(

# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
Expand All @@ -777,11 +797,7 @@ def __call__(
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds
)['sample']
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)["sample"]

# perform guidance
if do_classifier_free_guidance:
Expand Down Expand Up @@ -811,7 +827,7 @@ def __call__(
# 10. Convert to PIL
image = self.numpy_to_pil(image)
else:

# 8. Post-processing
image = self.decode_latents(latents)

Expand All @@ -826,4 +842,3 @@ def __call__(
return (image, has_nsfw_concept)

return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)