Add the MC example

Signed-off-by: yuanwu <[email protected]>

examples/multi-card-inference/README.md

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+<!---
+Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Stable Diffusion Examples
+
+This directory contains a script that showcases how to run distributed inferenc of text-to-image generation using Stable Diffusion on Habana Gaudi.
+
+Stable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team.
+
+
+## Distributed inference with multiple HPUs
+
+Here is how to generate two images with two prompts on two HPUs :
+```python
+python ../gaudi_spawn.py \
+ --world_size 2 run_distributed.py \
+ --model_name_or_path runwayml/stable-diffusion-v1-5 \
+ --prompts "a cat" "a dog" \
+ --use_habana \
+ --use_hpu_graphs \
+ --gaudi_config Habana/stable-diffusion \
+ --bf16
+```
+

examples/multi-card-inference/run_distributed.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Adapted from: https://huggingface.co/docs/diffusers/en/training/distributed_inference
+ - Use the GaudiStableDiffusionPipeline
+"""
+import torch
+import logging
+import argparse
+from accelerate import PartialState
+from optimum.habana.diffusers import GaudiStableDiffusionPipeline
+from optimum.habana.utils import set_seed
+
+logger = logging.getLogger(__name__)
+
+def main():
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument(
+ "--model_name_or_path",
+ default="runwayml/stable-diffusion-v1-5",
+ type=str,
+ help="Path to pre-trained model",
+ )
+ # Pipeline arguments
+ parser.add_argument(
+ "--prompts",
+ type=str,
+ nargs="*",
+ default=["a dog", "a cat"],
+ help="The prompt or prompts to guide the image generation.",
+ )
+ parser.add_argument(
+ "--num_images_per_prompt", type=int, default=1, help="The number of images to generate per prompt."
+ )
+ parser.add_argument("--seed", type=int, default=None, help="Random seed for initialization.")
+ parser.add_argument("--bf16", action="store_true", help="Whether to perform generation in bf16 precision.")
+ parser.add_argument(
+ "--gaudi_config",
+ type=str,
+ default="Habana/stable-diffusion",
+ help=(
+ "Name or path of the Gaudi configuration. In particular, it enables to specify how to apply Habana Mixed"
+ " Precision."
+ ),
+ )
+ # HPU-specific arguments
+ parser.add_argument("--use_habana", action="store_true", help="Use HPU.")
+ parser.add_argument(
+ "--use_hpu_graphs", action="store_true", help="Use HPU graphs on HPU. This should lead to faster generations."
+ )
+ args = parser.parse_args()
+ # Set seed before running the model
+ if args.seed:
+ logger.info("Set the random seed {}!".format(args.seed))
+ set_seed(args.seed)
+
+ kwargs = {
+ "use_habana": args.use_habana,
+ "use_hpu_graphs": args.use_hpu_graphs,
+ "gaudi_config": args.gaudi_config,
+ "torch_dtype": torch.bfloat16 if args.bf16 else None
+ }
+ print(f"kwargs={kwargs}")
+ pipeline = GaudiStableDiffusionPipeline.from_pretrained(
+ args.model_name_or_path, use_safetensors=True, **kwargs
+ )
+ distributed_state = PartialState()
+ kwargs= {
+ "num_images_per_prompt": args.num_images_per_prompt
+ }
+ with distributed_state.split_between_processes(args.prompts) as prompt:
+ outputs = pipeline(prompt, **kwargs)
+ for i, image in enumerate(outputs.images):
+ image.save(f"result_{distributed_state.process_index}_{i}.png")
+
+if __name__ == "__main__":
+ main()