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### 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: - https://github.com/huggingface/optimum/pull/872 - https://github.com/huggingface/optimum/pull/920 ### Motivation and Context This PR helps the following issues: - https://github.com/microsoft/onnxruntime/issues/15100 - https://github.com/microsoft/onnxruntime/issues/15235 - https://github.com/huggingface/optimum/issues/869 (work in progress) This PR can be used with the other currently merged Whisper PRs: - https://github.com/microsoft/onnxruntime/pull/15247 - https://github.com/microsoft/onnxruntime/pull/15339 - https://github.com/microsoft/onnxruntime/pull/15362 - https://github.com/microsoft/onnxruntime/pull/15365 - https://github.com/microsoft/onnxruntime/pull/15427 This PR uses changes from the following merged PRs: - https://github.com/microsoft/onnxruntime/pull/14198 - https://github.com/microsoft/onnxruntime/pull/14146 - https://github.com/microsoft/onnxruntime/pull/14201 - https://github.com/microsoft/onnxruntime/pull/14928 (this introduced the new multi-head attention spec) |
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| .. | ||
| external | ||
| patches | ||
| tensorboard | ||
| adjust_global_compile_flags.cmake | ||
| CMakeLists.txt | ||
| CMakeSettings.json | ||
| codeconv.runsettings | ||
| deps.txt | ||
| EnableVisualStudioCodeAnalysis.props | ||
| gdk_toolchain.cmake | ||
| Info.plist.in | ||
| libonnxruntime.pc.cmake.in | ||
| nuget_helpers.cmake | ||
| onnxruntime.cmake | ||
| onnxruntime_codegen_tvm.cmake | ||
| onnxruntime_common.cmake | ||
| onnxruntime_config.h.in | ||
| onnxruntime_csharp.cmake | ||
| onnxruntime_flatbuffers.cmake | ||
| onnxruntime_framework.cmake | ||
| onnxruntime_fuzz_test.cmake | ||
| onnxruntime_graph.cmake | ||
| onnxruntime_ios.toolchain.cmake | ||
| onnxruntime_java.cmake | ||
| onnxruntime_java_unittests.cmake | ||
| onnxruntime_kernel_explorer.cmake | ||
| onnxruntime_language_interop_ops.cmake | ||
| onnxruntime_mlas.cmake | ||
| onnxruntime_nodejs.cmake | ||
| onnxruntime_objectivec.cmake | ||
| onnxruntime_opschema_lib.cmake | ||
| onnxruntime_optimizer.cmake | ||
| onnxruntime_providers.cmake | ||
| onnxruntime_pyop.cmake | ||
| onnxruntime_python.cmake | ||
| onnxruntime_rocm_hipify.cmake | ||
| onnxruntime_session.cmake | ||
| onnxruntime_snpe_provider.cmake | ||
| onnxruntime_training.cmake | ||
| onnxruntime_unittests.cmake | ||
| onnxruntime_util.cmake | ||
| onnxruntime_webassembly.cmake | ||
| precompiled_header.cmake | ||
| Sdl.ruleset | ||
| set_winapi_family_desktop.h | ||
| target_delayload.cmake | ||
| uwp_stubs.h | ||
| wcos_rules_override.cmake | ||
| winml.cmake | ||
| winml_cppwinrt.cmake | ||
| winml_sdk_helpers.cmake | ||
| winml_unittests.cmake | ||