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| <!--- | ||
| Copyright 2024 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. | ||
| --> | ||
|
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| # Examples | ||
|
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| This directory contains example scripts that demonstrate how to perform video comprehension on Gaudi with graph mode. | ||
|
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| ## Single-HPU inference | ||
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| ### Video-LLaVA Model | ||
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| ```bash | ||
| python3 run_example.py \ | ||
| --model_name_or_path "LanguageBind/Video-LLaVA-7B-hf" \ | ||
| --warmup 3 \ | ||
| --n_iterations 5 \ | ||
| --batch_size 1 \ | ||
| --use_hpu_graphs \ | ||
| --bf16 \ | ||
| --output_dir ./ | ||
| ``` | ||
| Models that have been validated: | ||
| - [LanguageBind/Video-LLaVA-7B-hf ](https://huggingface.co/LanguageBind/Video-LLaVA-7B-hf) |
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| av == 12.1.0 | ||
| sentencepiece == 0.2.0 |
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| #!/usr/bin/env python | ||
| # coding=utf-8 | ||
| # Copyright 2021 The HuggingFace Inc. 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 | ||
|
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| import argparse | ||
| import json | ||
| import logging | ||
| import os | ||
| import time | ||
| from pathlib import Path | ||
|
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| import av | ||
| import numpy as np | ||
| import torch | ||
| from huggingface_hub import hf_hub_download | ||
| from transformers import VideoLlavaProcessor | ||
|
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| from optimum.habana.transformers.modeling_utils import ( | ||
| GaudiVideoLlavaForConditionalGeneration, | ||
| adapt_transformers_to_gaudi, | ||
| ) | ||
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|
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| logging.basicConfig( | ||
| format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | ||
| datefmt="%m/%d/%Y %H:%M:%S", | ||
| level=logging.INFO, | ||
| ) | ||
| logger = logging.getLogger(__name__) | ||
|
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|
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| def read_video_pyav(container, indices): | ||
| frames = [] | ||
| container.seek(0) | ||
| start_index = indices[0] | ||
| end_index = indices[-1] | ||
| for i, frame in enumerate(container.decode(video=0)): | ||
| if i > end_index: | ||
| break | ||
| if i >= start_index and i in indices: | ||
| frames.append(frame) | ||
| return np.stack([x.to_ndarray(format="rgb24") for x in frames]) | ||
|
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|
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| def main(): | ||
| parser = argparse.ArgumentParser() | ||
|
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| parser.add_argument( | ||
| "--model_name_or_path", | ||
| default=None, | ||
| type=str, | ||
| help="Path to pre-trained model", | ||
| ) | ||
| parser.add_argument( | ||
| "--video_path", | ||
| default=None, | ||
| type=str, | ||
| nargs="*", | ||
| help='Path to video as input. Can be a single string (eg: --image_path "URL1"), or a list of space-separated strings (eg: --video_path "URL1" "URL2")', | ||
| ) | ||
| parser.add_argument( | ||
| "--prompt", | ||
| default=None, | ||
| type=str, | ||
| help='Optional argument to give a prompt of your choice as input. is a single string (eg: --prompt "Hello world")', | ||
| ) | ||
| parser.add_argument( | ||
| "--use_hpu_graphs", | ||
| action="store_true", | ||
| help="Whether to use HPU graphs or not. Using HPU graphs should give better latencies.", | ||
| ) | ||
| parser.add_argument("--max_new_tokens", type=int, default=100, help="Number of tokens to generate.") | ||
| parser.add_argument( | ||
| "--bf16", | ||
| action="store_true", | ||
| help="Whether to perform generation in bf16 precision.", | ||
| ) | ||
| parser.add_argument( | ||
| "--output_dir", | ||
| default=None, | ||
| type=str, | ||
| help="Output directory to store results in.", | ||
| ) | ||
| parser.add_argument( | ||
| "--token", | ||
| default=None, | ||
| type=str, | ||
| help="The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " | ||
| "generated when running `huggingface-cli login` (stored in `~/.huggingface`).", | ||
| ) | ||
| parser.add_argument("--batch_size", type=int, default=1, help="Input batch size.") | ||
| parser.add_argument("--warmup", type=int, default=3, help="Number of warmup iterations for benchmarking.") | ||
| parser.add_argument("--n_iterations", type=int, default=5, help="Number of inference iterations for benchmarking.") | ||
| parser.add_argument( | ||
| "--ignore_eos", | ||
| action="store_true", | ||
| help="Whether to disable stopping with eos token when calling `generate`.", | ||
| ) | ||
| parser.add_argument( | ||
| "--use_flash_attention", | ||
| action="store_true", | ||
| help="Whether to enable Habana Flash Attention, provided that the model supports it.", | ||
| ) | ||
| parser.add_argument( | ||
| "--flash_attention_recompute", | ||
| action="store_true", | ||
| help="Whether to enable Habana Flash Attention in recompute mode on first token generation. This gives an opportunity of splitting graph internally which helps reduce memory consumption.", | ||
| ) | ||
|
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| args = parser.parse_args() | ||
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| os.environ.setdefault("EXPERIMENTAL_WEIGHT_SHARING", "FALSE") | ||
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| if args.video_path is None: | ||
| args.video_path = [ | ||
| hf_hub_download( | ||
| repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset" | ||
| ) | ||
| ] | ||
|
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| if args.prompt is None: | ||
| args.prompt = ["USER: <video>Why is this video funny? ASSISTANT:"] | ||
| video_paths = args.video_path | ||
| video_paths_len = len(video_paths) | ||
|
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| prompts = args.prompt | ||
| if args.batch_size > video_paths_len: | ||
| # Dynamically extends to support larger batch sizes | ||
| num_path_to_add = args.batch_size - video_paths_len | ||
| for i in range(num_path_to_add): | ||
| video_paths.append(video_paths[i % video_paths_len]) | ||
| prompts.append(prompts[i % video_paths_len]) | ||
| elif args.batch_size < video_paths_len: | ||
| video_paths = video_paths[: args.batch_size] | ||
|
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| video_clips = [] | ||
|
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| for video_path in video_paths: | ||
| container = av.open(video_path) | ||
| num_frames = container.streams.video[0].frames | ||
| indices = np.arange(0, num_frames, num_frames / 8).astype(int) | ||
| clip = read_video_pyav(container, indices) | ||
| video_clips.append(clip) | ||
|
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| if args.bf16: | ||
| model_dtype = torch.bfloat16 | ||
| else: | ||
| model_dtype = torch.float32 | ||
|
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| adapt_transformers_to_gaudi() | ||
| model = GaudiVideoLlavaForConditionalGeneration.from_pretrained(args.model_name_or_path) | ||
| model = model.to(model_dtype) | ||
| device = torch.device("hpu") | ||
| model = model.to(device) | ||
| if args.use_hpu_graphs: | ||
| from habana_frameworks.torch.hpu import wrap_in_hpu_graph | ||
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| model = wrap_in_hpu_graph(model) | ||
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| processor = VideoLlavaProcessor.from_pretrained(args.model_name_or_path) | ||
| processor.tokenizer.padding_side = "left" | ||
| inputs = processor(text=prompts, videos=video_clips, return_tensors="pt") | ||
| inputs = inputs.to(device) | ||
|
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| # warm up | ||
| for i in range(args.warmup): | ||
| generate_ids = model.generate( | ||
| **inputs, | ||
| lazy_mode=True, | ||
| hpu_graphs=args.use_hpu_graphs, | ||
| max_new_tokens=args.max_new_tokens, | ||
| ignore_eos=args.ignore_eos, | ||
| use_flash_attention=args.use_flash_attention, | ||
| flash_attention_recompute=args.flash_attention_recompute, | ||
| ) | ||
| torch.hpu.synchronize() | ||
|
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| start = time.perf_counter() | ||
| for i in range(args.n_iterations): | ||
| generate_ids = model.generate( | ||
| **inputs, | ||
| lazy_mode=True, | ||
| hpu_graphs=args.use_hpu_graphs, | ||
| max_new_tokens=args.max_new_tokens, | ||
| ignore_eos=args.ignore_eos, | ||
| use_flash_attention=args.use_flash_attention, | ||
| flash_attention_recompute=args.flash_attention_recompute, | ||
| ) | ||
| generate_texts = processor.batch_decode( | ||
| generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False | ||
| ) | ||
| end = time.perf_counter() | ||
| duration = end - start | ||
|
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| # Let's calculate the number of generated tokens | ||
| n_input_tokens = inputs["input_ids"].shape[1] | ||
| n_output_tokens = 0 | ||
| for i in range(generate_ids.shape[0]): | ||
| n_input_tokens = torch.sum(inputs["attention_mask"][i, :]).item() | ||
| # We have to subtract the number of input tokens as they are part of the returned sequence | ||
| n_output_tokens += len(generate_ids[i]) - n_input_tokens | ||
|
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| total_new_tokens_generated = args.n_iterations * n_output_tokens | ||
| throughput = total_new_tokens_generated / duration | ||
| logger.info(f"result = {generate_texts}") | ||
| logger.info( | ||
| f"time = {(end - start) * 1000 / args.n_iterations}ms, Throughput (including tokenization) = {throughput} tokens/second" | ||
| ) | ||
|
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| # Store results if necessary | ||
| if args.output_dir is not None: | ||
| output_dir = Path(args.output_dir) | ||
| output_dir.mkdir(parents=True, exist_ok=True) | ||
|
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| results = { | ||
| "throughput": throughput, | ||
| "output": generate_texts, | ||
| } | ||
| with (output_dir / "results.json").open("w", encoding="utf-8") as f: | ||
| json.dump(results, f, ensure_ascii=False, indent=4) | ||
|
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|
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| if __name__ == "__main__": | ||
| main() |
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@@ -112,6 +112,7 @@ | |
| "paligemma", | ||
| "idefics2", | ||
| "mllama", | ||
| "video_llava", | ||
| "minicpm3", | ||
| "baichuan", | ||
| "deepseek_v2", | ||
|
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||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1 @@ | ||
| from .modeling_video_llava import GaudiVideoLlavaForConditionalGeneration |
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