Fix token_id values for whisper export #15362
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tianleiwu
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Apr 4, 2023
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I notice that whisper has the following configuration:
https://huggingface.co/openai/whisper-large/blob/27b5bb3f092ae34d8079fdc073b8195e6815dec7/config.json#L46
How to handle them in beam search?
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Good catch - I see they apply these values by setting logit values like this link to HF after obtaining logits. We could replicate this by adding attributes to the BeamSearch op and doing a similar transformation before processing logits - this could make a good PR & would be relevant to all transformer models it seems. |
tianleiwu
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Apr 5, 2023
yufenglee
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Apr 5, 2023
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Apr 19, 2023
### Description This PR contains fusion-level and kernel-level optimizations for [OpenAI's Whisper](https://github.com/openai/whisper). Some of the added optimizations include: - Pruning of duplicate/unnecessary inputs and outputs - Fusion support for Whisper models with or without these inputs/outputs (e.g. with these inputs/outputs if exporting with an older official Optimum version, without these inputs/outputs if exporting with Optimum from source) - Attention fusions - For Whisper's encoder and decoder - Modified symbolic shape inference for present output when no past input exists (for decoder) - Multi-head attention fusions - For Whisper's decoder and decoder with past - Packed MatMul for the 3 MatMuls excluded in multi-head attention fusion - Attention kernel changes - CPU: - Different Q and KV sequence lengths - Parallel memset for large sequence lengths - Convert broadcast add after MatMul of Q and K (add_qk) to element-wise add - Separate present key-value output into present key and present value (for multi-head attention spec) - CUDA: - Use memory efficient attention compute kernel with present state (for decoder) - Multi-head attention kernel changes - CPU: - Introduction of multi-head attention CPU kernel (previously did not exist) - Use AddBiasReshape instead of AddBiasTranspose when sequence length = 1 (for decoder with past) - Different Q, K, V input shapes - Pass past key and past value directly as key and value - CUDA: - Use memory efficient attention compute kernel with past and/or present state (for decoder with past) ### Usage To use the optimizations, run the ORT transformer optimizer script as follows: ``` $ cd onnxruntime/onnxruntime/python/tools/transformers/ $ python3 optimizer.py --input <filename>.onnx --output <filename>.onnx --model_type bart --num_heads <number of attention heads, depends on the size of the whisper model used> --hidden_size <attention hidden size, depends on the size of the whisper model used> --use_external_data_format --use_multi_head_attention ``` Once optimized, here's an example of how to run Whisper with [Hugging Face's Optimum](https://github.com/huggingface/optimum): ``` from transformers.onnx.utils import get_preprocessor from optimum.onnxruntime import ORTModelForSpeechSeq2Seq from optimum.pipelines import pipeline as ort_pipeline import whisper # Installed from OpenAI's repo - setup instructions at https://github.com/openai/whisper/ directory = './whisper_opt' # Where the optimized ONNX models are located model_name = 'openai/whisper-tiny' device = 'cpu' # Get pipeline processor = get_preprocessor(model_name) model = ORTModelForSpeechSeq2Seq.from_pretrained( directory, use_io_binding=(device == 'cuda'), provider='CPUExecutionProvider', ).to(device) pipe = ort_pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, device=(-1 if device == 'cpu' else 0), ) # Load audio file and run pipeline audio = whisper.load_audio('tests/jfk.flac') audio = whisper.pad_or_trim(audio) outputs = pipe([audio]) print(outputs) ``` Note: In order to use these changes with Optimum, it is recommended to use Optimum from source to have the following changes: - huggingface/optimum#872 - huggingface/optimum#920 ### Motivation and Context This PR helps the following issues: - #15100 - #15235 - huggingface/optimum#869 (work in progress) This PR can be used with the other currently merged Whisper PRs: - #15247 - #15339 - #15362 - #15365 - #15427 This PR uses changes from the following merged PRs: - #14198 - #14146 - #14201 - #14928 (this introduced the new multi-head attention spec)
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Description
The current ONNX export of Whisper utilizes hard-coded values for token_ids when configuring the BeamSearch node. This PR removes these literals and instead takes these values straight from the WhisperConfig.
Motivation and Context
Hard-coding these values can cause some parity issues when comparing to default PyTorch behavior - this change to take from WhisperConfig resolves these.