diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md index 7c99219e01..c8227cdef4 100644 --- a/examples/stable-diffusion/README.md +++ b/examples/stable-diffusion/README.md @@ -245,6 +245,38 @@ python ../gaudi_spawn.py \ --distributed ``` +Here is how to generate SDXL images with optimized pipeline: +```bash +python text_to_image_generation.py \ + --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \ + --prompts "Sailing ship painting by Van Gogh" \ + --num_images_per_prompt 28 \ + --batch_size 7 \ + --image_save_dir /tmp/stable_diffusion_xl_images \ + --scheduler euler_discrete \ + --use_habana \ + --use_hpu_graphs \ + --gaudi_config Habana/stable-diffusion \ + --bf16 \ + --optimize +``` + +Here is how to generate SDXL images with optimized pipeline in fp8: +```bash +QUANT_CONFIG=./quantization/quant_config.json python text_to_image_generation.py \ + --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \ + --prompts "Sailing ship painting by Van Gogh" \ + --num_images_per_prompt 28 \ + --batch_size 7 \ + --image_save_dir /tmp/stable_diffusion_xl_images \ + --scheduler euler_discrete \ + --use_habana \ + --use_hpu_graphs \ + --gaudi_config Habana/stable-diffusion \ + --bf16 \ + --optimize +``` + > HPU graphs are recommended when generating images by batches to get the fastest possible generations. > The first batch of images entails a performance penalty. 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a/examples/stable-diffusion/quantization/quant_config.json b/examples/stable-diffusion/quantization/quant_config.json new file mode 100755 index 0000000000..b372905d7f --- /dev/null +++ b/examples/stable-diffusion/quantization/quant_config.json @@ -0,0 +1,7 @@ +{ + "method": "HOOKS", + "mode": "QUANTIZE", + "observer": "maxabs", + "scale_method": "maxabs_hw", + "dump_stats_path": "./quantization/measure/fp8" +} \ No newline at end of file diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py index c191b1982a..7e63b7c1f6 100755 --- a/examples/stable-diffusion/text_to_image_generation.py +++ b/examples/stable-diffusion/text_to_image_generation.py @@ -15,6 +15,7 @@ import argparse import logging +import os import sys from pathlib import Path @@ -452,6 +453,8 @@ def main(): elif args.optimize: # Import SDXL pipeline + # set PATCH_SDPA to enable fp8 varient of softmax in sdpa + os.environ["PATCH_SDPA"] = "1" import habana_frameworks.torch.hpu as torch_hpu from optimum.habana.diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_mlperf import ( @@ -463,10 +466,29 @@ def main(): **kwargs, ) - pipeline.to(torch.device("hpu")) pipeline.unet.set_default_attn_processor(pipeline.unet) + pipeline.to(torch.device("hpu")) + + quant_config_path = os.getenv("QUANT_CONFIG") + if quant_config_path: + import habana_frameworks.torch.core as htcore + from neural_compressor.torch.quantization import FP8Config, convert, prepare + + htcore.hpu_set_env() + + config = FP8Config.from_json_file(quant_config_path) + + if config.measure: + logger.info("Running measurements") + pipeline.unet = prepare(pipeline.unet, config) + elif config.quantize: + logger.info("Running quantization") + pipeline.unet = convert(pipeline.unet, config) + htcore.hpu_initialize(pipeline.unet, mark_only_scales_as_const=True) + if args.use_hpu_graphs: pipeline.unet = torch_hpu.wrap_in_hpu_graph(pipeline.unet) + else: from optimum.habana.diffusers import GaudiStableDiffusionXLPipeline @@ -636,6 +658,12 @@ def main(): else: outputs = pipeline(prompt=args.prompts, **kwargs_call) + if args.optimize and quant_config_path and config.measure: + from neural_compressor.torch.quantization import finalize_calibration + + logger.info("Finalizing calibration...") + finalize_calibration(pipeline.unet) + # Save the pipeline in the specified directory if not None if args.pipeline_save_dir is not None: save_dir = args.pipeline_save_dir diff --git a/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py b/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py index 0cd0cd28dd..f4a0dbd244 100644 --- a/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py +++ b/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py @@ -726,7 +726,6 @@ def __call__( continue timestep = timesteps[0] timesteps = torch.roll(timesteps, shifts=-1, dims=0) - # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents_batch] * 2) if self.do_classifier_free_guidance else latents_batch @@ -817,13 +816,17 @@ def __call__( hb_profiler.stop() speed_metrics_prefix = "generation" + if t1 == t0 or use_warmup_inference_steps: + num_samples = num_batches * batch_size + num_steps = (num_inference_steps - throughput_warmup_steps) * num_batches * batch_size + else: + num_samples = (num_batches - throughput_warmup_steps) * batch_size + num_steps = (num_batches - throughput_warmup_steps) * num_inference_steps * batch_size speed_measures = speed_metrics( split=speed_metrics_prefix, start_time=t0, - num_samples=num_batches * batch_size - if t1 == t0 or use_warmup_inference_steps - else (num_batches - throughput_warmup_steps) * batch_size, - num_steps=num_batches * batch_size * num_inference_steps, + num_samples=num_samples, + num_steps=num_steps, start_time_after_warmup=t1, ) logger.info(f"Speed metrics: {speed_measures}") diff --git a/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_mlperf.py b/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_mlperf.py index 72297d37d4..42c703b78b 100644 --- a/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_mlperf.py +++ b/optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_mlperf.py @@ -12,9 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. import os +import time +from math import ceil from typing import Any, Callable, Dict, List, Optional, Tuple, Union +import habana_frameworks.torch as ht import habana_frameworks.torch.core as htcore +import numpy as np import torch from diffusers import StableDiffusionXLPipeline from diffusers.image_processor import PipelineImageInput @@ -38,7 +42,7 @@ from optimum.utils import logging -from ....utils import HabanaProfile +from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment from ...models.attention_processor import ( AttentionProcessor, AttnProcessor2_0, @@ -47,6 +51,7 @@ from ...models.unet_2d_condition import gaudi_unet_2d_condition_model_forward +logging.set_verbosity_info() logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -187,7 +192,6 @@ def run_unet( negative_add_time_ids, negative_pooled_prompt_embeds, num_warmup_steps, - progress_bar, callback, callback_steps, ip_adapter_image, @@ -247,13 +251,127 @@ def run_unet( # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) return latents + @classmethod + def _split_inputs_into_batches( + cls, + batch_size, + latents, + prompt_embeds, + negative_prompt_embeds, + add_text_embeds, + negative_pooled_prompt_embeds, + add_time_ids, + negative_add_time_ids, + ): + # Use torch.split to generate num_batches batches of size batch_size + latents_batches = list(torch.split(latents, batch_size)) + prompt_embeds_batches = list(torch.split(prompt_embeds, batch_size)) + if negative_prompt_embeds is not None: + negative_prompt_embeds_batches = list(torch.split(negative_prompt_embeds, batch_size)) + if add_text_embeds is not None: + add_text_embeds_batches = list(torch.split(add_text_embeds, batch_size)) + if negative_pooled_prompt_embeds is not None: + negative_pooled_prompt_embeds_batches = list(torch.split(negative_pooled_prompt_embeds, batch_size)) + if add_time_ids is not None: + add_time_ids_batches = list(torch.split(add_time_ids, batch_size)) + if negative_add_time_ids is not None: + negative_add_time_ids_batches = list(torch.split(negative_add_time_ids, batch_size)) + + # If the last batch has less samples than batch_size, pad it with dummy samples + num_dummy_samples = 0 + if latents_batches[-1].shape[0] < batch_size: + num_dummy_samples = batch_size - latents_batches[-1].shape[0] + # Pad latents_batches + sequence_to_stack = (latents_batches[-1],) + tuple( + torch.zeros_like(latents_batches[-1][0][None, :]) for _ in range(num_dummy_samples) + ) + latents_batches[-1] = torch.vstack(sequence_to_stack) + # Pad prompt_embeds_batches + sequence_to_stack = (prompt_embeds_batches[-1],) + tuple( + torch.zeros_like(prompt_embeds_batches[-1][0][None, :]) for _ in range(num_dummy_samples) + ) + prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack) + # Pad negative_prompt_embeds_batches if necessary + if negative_prompt_embeds is not None: + sequence_to_stack = (negative_prompt_embeds_batches[-1],) + tuple( + torch.zeros_like(negative_prompt_embeds_batches[-1][0][None, :]) for _ in range(num_dummy_samples) + ) + negative_prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack) + # Pad add_text_embeds_batches if necessary + if add_text_embeds is not None: + sequence_to_stack = (add_text_embeds_batches[-1],) + tuple( + torch.zeros_like(add_text_embeds_batches[-1][0][None, :]) for _ in range(num_dummy_samples) + ) + add_text_embeds_batches[-1] = torch.vstack(sequence_to_stack) + # Pad negative_pooled_prompt_embeds_batches if necessary + if negative_pooled_prompt_embeds is not None: + sequence_to_stack = (negative_pooled_prompt_embeds_batches[-1],) + tuple( + torch.zeros_like(negative_pooled_prompt_embeds_batches[-1][0][None, :]) + for _ in range(num_dummy_samples) + ) + negative_pooled_prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack) + # Pad add_time_ids_batches if necessary + if add_time_ids is not None: + sequence_to_stack = (add_time_ids_batches[-1],) + tuple( + torch.zeros_like(add_time_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples) + ) + add_time_ids_batches[-1] = torch.vstack(sequence_to_stack) + # Pad negative_add_time_ids_batches if necessary + if negative_add_time_ids is not None: + sequence_to_stack = (negative_add_time_ids_batches[-1],) + tuple( + torch.zeros_like(negative_add_time_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples) + ) + negative_add_time_ids_batches[-1] = torch.vstack(sequence_to_stack) + + # Stack batches in the same tensor + latents_batches = torch.stack(latents_batches) + + if negative_prompt_embeds is not None: + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + for i, (negative_prompt_embeds_batch, prompt_embeds_batch) in enumerate( + zip(negative_prompt_embeds_batches, prompt_embeds_batches[:]) + ): + prompt_embeds_batches[i] = torch.cat([negative_prompt_embeds_batch, prompt_embeds_batch]) + prompt_embeds_batches = torch.stack(prompt_embeds_batches) + + if add_text_embeds is not None: + if negative_pooled_prompt_embeds is not None: + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + for i, (negative_pooled_prompt_embeds_batch, add_text_embeds_batch) in enumerate( + zip(negative_pooled_prompt_embeds_batches, add_text_embeds_batches[:]) + ): + add_text_embeds_batches[i] = torch.cat( + [negative_pooled_prompt_embeds_batch, add_text_embeds_batch] + ) + add_text_embeds_batches = torch.stack(add_text_embeds_batches) + else: + add_text_embeds_batches = None + + if add_time_ids is not None: + if negative_add_time_ids is not None: + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + for i, (negative_add_time_ids_batch, add_time_ids_batch) in enumerate( + zip(negative_add_time_ids_batches, add_time_ids_batches[:]) + ): + add_time_ids_batches[i] = torch.cat([negative_add_time_ids_batch, add_time_ids_batch]) + add_time_ids_batches = torch.stack(add_time_ids_batches) + else: + add_time_ids_batches = None + + return latents_batches, prompt_embeds_batches, add_text_embeds_batches, add_time_ids_batches, num_dummy_samples + @torch.no_grad() def __call__( self, @@ -268,6 +386,7 @@ def __call__( negative_prompt: Optional[Union[str, List[str]]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, + batch_size: int = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, @@ -482,11 +601,18 @@ def __call__( # 2. Define call parameters if prompt is not None and isinstance(prompt, str): - batch_size = 1 + num_prompts = 1 elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) + num_prompts = len(prompt) else: - batch_size = prompt_embeds.shape[0] + num_prompts = prompt_embeds.shape[0] + num_batches = ceil((num_images_per_prompt * num_prompts) / batch_size) + logger.info( + f"{num_prompts} prompt(s) received, {num_images_per_prompt} generation(s) per prompt," + f" {batch_size} sample(s) per batch, {num_batches} total batch(es)." + ) + if num_batches < 3: + logger.warning("The first two iterations are slower so it is recommended to feed more batches.") device = self._execution_device @@ -519,10 +645,15 @@ def __call__( # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps_hpu(self.scheduler, num_inference_steps, device, timesteps) + # Since the scheduler is reinitialized for each image generation, + # creating a separate copy of the timestep tensor prevents view-related issue + timesteps = timesteps.clone() + # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels + latents = self.prepare_latents( - batch_size * num_images_per_prompt, + num_prompts * num_images_per_prompt, num_channels_latents, height, width, @@ -560,20 +691,33 @@ def __call__( else: negative_add_time_ids = add_time_ids - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) - prompt_embeds = prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) - + add_time_ids = add_time_ids.to(device).repeat(num_prompts * num_images_per_prompt, 1) + negative_add_time_ids = negative_add_time_ids.to(device).repeat(num_prompts * num_images_per_prompt, 1) if ip_adapter_image is not None: image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, device, batch_size * num_images_per_prompt + ip_adapter_image, device, num_prompts * num_images_per_prompt ) + # 7.5 Split into batches (HPU-specific step) + ( + latents_batches, + text_embeddings_batches, + add_text_embeddings_batches, + add_time_ids_batches, + num_dummy_samples, + ) = self._split_inputs_into_batches( + batch_size, + latents, + prompt_embeds, + negative_prompt_embeds, + add_text_embeds, + negative_pooled_prompt_embeds, + add_time_ids, + negative_add_time_ids, + ) + hb_profiler = HabanaProfile( warmup=profiling_warmup_steps, active=profiling_steps, @@ -603,13 +747,45 @@ def __call__( # 9. Optionally get Guidance Scale Embedding timestep_cond = None if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) + guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(num_prompts * num_images_per_prompt) timestep_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) + t0 = time.time() + t1 = t0 + + throughput_warmup_steps = kwargs.get("throughput_warmup_steps", 3) + use_warmup_inference_steps = ( + num_batches < throughput_warmup_steps and num_inference_steps > throughput_warmup_steps + ) + self._num_timesteps = len(timesteps) - with self.progress_bar(total=num_inference_steps) as progress_bar: + + output_images = [] + for j in self.progress_bar(range(num_batches)): + # The throughput is calculated from the 3rd iteration + # because compilation occurs in the first two iterations + if j == throughput_warmup_steps: + ht.hpu.synchronize() + t1 = time.time() + if use_warmup_inference_steps: + ht.hpu.synchronize() + t0_inf = time.time() + + latents = latents_batches[0] + latents_batches = torch.roll(latents_batches, shifts=-1, dims=0) + prompt_embeds = text_embeddings_batches[0] + text_embeddings_batches = torch.roll(text_embeddings_batches, shifts=-1, dims=0) + add_text_embeds = add_text_embeddings_batches[0] + add_text_embeddings_batches = torch.roll(add_text_embeddings_batches, shifts=-1, dims=0) + add_time_ids = add_time_ids_batches[0] + add_time_ids_batches = torch.roll(add_time_ids_batches, shifts=-1, dims=0) + + if hasattr(self.scheduler, "_init_step_index"): + # Reset scheduler step index for next batch + self.scheduler._init_step_index(timesteps[0]) + if self.quantized: for i, t in enumerate(timesteps[0:-2]): if self.interrupt: @@ -628,7 +804,6 @@ def __call__( negative_add_time_ids, negative_pooled_prompt_embeds, num_warmup_steps, - progress_bar, callback, callback_steps, ip_adapter_image, @@ -655,7 +830,6 @@ def __call__( negative_add_time_ids, negative_pooled_prompt_embeds, num_warmup_steps, - progress_bar, callback, callback_steps, ip_adapter_image, @@ -667,10 +841,17 @@ def __call__( hb_profiler.step() else: for i in range(num_inference_steps): + if use_warmup_inference_steps and i == throughput_warmup_steps: + ht.hpu.synchronize() + t1_inf = time.time() + t1 += t1_inf - t0_inf + t = timesteps[0] timesteps = torch.roll(timesteps, shifts=-1, dims=0) + if self.interrupt: continue + latents = self.run_unet( self.unet, latents, @@ -685,7 +866,6 @@ def __call__( negative_add_time_ids, negative_pooled_prompt_embeds, num_warmup_steps, - progress_bar, callback, callback_steps, ip_adapter_image, @@ -695,34 +875,82 @@ def __call__( callback_on_step_end_tensor_inputs, ) hb_profiler.step() - hb_profiler.stop() - if not output_type == "latent": - # make sure the VAE is in float32 mode, as it overflows in float16 - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + if use_warmup_inference_steps: + ht.hpu.synchronize() + t1 = warmup_inference_steps_time_adjustment(t1, t1_inf, num_inference_steps, throughput_warmup_steps) + + if not output_type == "latent": + # make sure the VAE is in float32 mode, as it overflows in float16 + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + + if needs_upcasting: + self.upcast_vae() + latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + else: + image = latents - if needs_upcasting: - self.upcast_vae() - latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + output_images.append(image) - image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + hb_profiler.stop() - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) + speed_metrics_prefix = "generation" + ht.hpu.synchronize() + + if t1 == t0 or use_warmup_inference_steps: + num_samples = num_batches * batch_size + num_steps = (num_inference_steps - throughput_warmup_steps) * num_batches * batch_size else: - image = latents + num_samples = (num_batches - throughput_warmup_steps) * batch_size + num_steps = (num_batches - throughput_warmup_steps) * num_inference_steps * batch_size + + speed_measures = speed_metrics( + split=speed_metrics_prefix, + start_time=t0, + num_samples=num_samples, + num_steps=num_steps, + start_time_after_warmup=t1, + ) + logger.info(f"Speed metrics: {speed_measures}") + + # Remove dummy generations if needed + if num_dummy_samples > 0: + output_images[-1] = output_images[-1][:-num_dummy_samples] - if not output_type == "latent": - # apply watermark if available - if self.watermark is not None: - image = self.watermark.apply_watermark(image) + # Process generated images + for i, image in enumerate(output_images[:]): + if i == 0: + output_images.clear() + + if not output_type == "latent": + # apply watermark if available + if self.watermark is not None: + image = self.watermark.apply_watermark(image) image = self.image_processor.postprocess(image, output_type=output_type) + if output_type == "pil" and isinstance(image, list): + output_images += image + elif output_type in ["np", "numpy"] and isinstance(image, np.ndarray): + if len(output_images) == 0: + output_images = image + else: + output_images = np.concatenate((output_images, image), axis=0) + else: + if len(output_images) == 0: + output_images = image + else: + output_images = torch.cat((output_images, image), 0) + # Offload all models self.maybe_free_model_hooks() if not return_dict: - return (image,) + return (output_images,) - return StableDiffusionXLPipelineOutput(images=image) + return StableDiffusionXLPipelineOutput(images=output_images)