# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- import logging import os import sys from pathlib import Path from typing import Dict, Tuple, Union import numpy as np import torch from transformers import WhisperConfig, WhisperForConditionalGeneration, WhisperProcessor from whisper_decoder import WhisperDecoder, WhisperDecoderHelper, WhisperDecoderInit from whisper_encoder import WhisperEncoder, WhisperEncoderHelper from whisper_encoder_decoder_init import WhisperEncoderDecoderInit, WhisperEncoderDecoderInitHelper from onnxruntime import InferenceSession sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..")) from float16 import float_to_float16_max_diff # noqa: E402 from onnx_model import OnnxModel # noqa: E402 from optimizer import optimize_model # noqa: E402 logger = logging.getLogger(__name__) PRETRAINED_WHISPER_MODELS = [ "whisper-tiny", "whisper-tiny.en", "whisper-small", "whisper-small.en", "whisper-medium", "whisper-medium.en", "whisper-base", "whisper-base.en", "whisper-large", "whisper-large-v2", ] class WhisperHelper: @staticmethod def get_onnx_path( output_dir: str, model_name_or_path: str, suffix: str = "", new_folder: bool = False, ) -> str: """Build onnx path Args: output_dir (str): output directory model_name_or_path (str): pretrained model name, or path to the model checkpoint suffix (str, optional): suffix like "_encoder" or "_decoder_fp16" will be appended to file name. Defaults to None. new_folder (bool, optional): create a new directory for the model. Defaults to False. Returns: str: path of onnx model """ model_name = model_name_or_path if os.path.isdir(model_name_or_path): model_name = Path(model_name_or_path).parts[-1] else: model_name = model_name.split("/")[-1] model_name += suffix directory = os.path.join(output_dir, model_name) if new_folder else output_dir return os.path.join(directory, model_name + ".onnx") @staticmethod def load_model( model_name_or_path: str, cache_dir: str, device: torch.device, merge_encoder_and_decoder_init: bool = True, state_dict_path: str = "", ) -> Dict[str, torch.nn.Module]: """Load model given a pretrained name or path, then build models for ONNX conversion. Args: model_name_or_path (str): pretrained model name or path cache_dir (str): cache directory device (torch.device): device to run the model merge_encoder_and_decoder_init (bool, optional): Whether merge encoder and decoder initialization into one ONNX model. Defaults to True. Returns: Dict[str, torch.nn.Module]: mapping from name to modules for ONNX conversion. """ model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, cache_dir=cache_dir) if state_dict_path: model.load_state_dict(torch.load(state_dict_path), strict=False) decoder = WhisperDecoder(model, model.config) decoder.eval().to(device) if merge_encoder_and_decoder_init: encoder_decoder_init = WhisperEncoderDecoderInit( model, model, model.config, decoder_start_token_id=None, ) return {"encoder_decoder_init": encoder_decoder_init, "decoder": decoder} else: encoder = WhisperEncoder(model.model.encoder, model.config) encoder.eval().to(device) decoder_init = WhisperDecoderInit(model.decoder, model.config) decoder_init.eval().to(device) return { "encoder": encoder, "decoder": decoder, "decoder_init": decoder_init, } @staticmethod def export_onnx( model: Union[WhisperEncoder, WhisperDecoder, WhisperDecoderInit, WhisperEncoderDecoderInit], device: torch.device, onnx_model_path: str, verbose: bool = True, use_external_data_format: bool = False, use_decoder_input_ids: bool = True, use_int32_inputs: bool = False, ): if isinstance(model, WhisperEncoder): WhisperEncoderHelper.export_onnx( model, device, onnx_model_path, verbose, use_external_data_format, ) elif isinstance(model, WhisperEncoderDecoderInit): WhisperEncoderDecoderInitHelper.export_onnx( model, device, onnx_model_path, use_decoder_input_ids, verbose, use_external_data_format, use_int32_inputs, ) else: WhisperDecoderHelper.export_onnx( model, device, onnx_model_path, verbose, use_external_data_format, use_int32_inputs, ) @staticmethod def auto_mixed_precision( onnx_model: OnnxModel, op_block_list: Tuple[str] = ( "SimplifiedLayerNormalization", "SkipSimplifiedLayerNormalization", "Relu", "Add", ), ): """Convert model to mixed precision. It detects whether original model has fp16 precision weights, and set parameters for float16 conversion automatically. Args: onnx_model (OnnxModel): optimized ONNX model op_block_list (List[str], optional): . Defaults to ["SimplifiedLayerNormalization", "SkipSimplifiedLayerNormalization", "Relu", "Add"] Returns: parameters(dict): a dictionary of parameters used in float16 conversion """ op_full_set = set([node.op_type for node in onnx_model.nodes()]) fp32_op_set = set(op_block_list) fp16_op_set = op_full_set.difference(fp32_op_set) logger.info(f"fp32 op: {fp32_op_set} fp16 op: {fp16_op_set}") # logits is the first output logits_output_name = onnx_model.graph().output[0].name # We use the weight in last MatMul node to detect whether the model is stored with float16 weights from training. is_weight_fp16_precision = False output_name_to_node = onnx_model.output_name_to_node() assert logits_output_name in output_name_to_node node = output_name_to_node[logits_output_name] last_matmul_node = None if node.op_type == "MatMul": last_matmul_node = node logger.info(f"Found last MatMul node for logits: {node.name}") initializer = None for input in node.input: initializer = onnx_model.get_initializer(input) if initializer is not None: break # when the max difference of value after converting float to float16 is lower than a threshold (1e-6), # we can deduce that the weights are stored in float16 precision. max_diff = float_to_float16_max_diff(initializer) logger.debug(f"max diff of converting weights in last MatMul node {node.name}: {max_diff}") is_weight_fp16_precision = max_diff < 1e-6 else: logger.warning(f"Failed to find MatMul node for logits. Found {node.op_type} of node {node.name}") keep_io_types = [] node_block_list = [] if (not is_weight_fp16_precision) and (last_matmul_node is not None): # When original weight is float32 precision, keep logits and last MatMul in float32 could get better precision. keep_io_types = [logits_output_name] node_block_list = [last_matmul_node.name] parameters = { "keep_io_types": keep_io_types, "op_block_list": list(op_block_list), "node_block_list": node_block_list, "force_fp16_initializers": is_weight_fp16_precision, } logger.info(f"auto_mixed_precision parameters: {parameters}") onnx_model.convert_float_to_float16(use_symbolic_shape_infer=True, **parameters) return parameters @staticmethod def optimize_onnx( onnx_model_path: str, optimized_model_path: str, is_float16: bool, num_attention_heads: int, hidden_size: int, use_external_data_format: bool = False, auto_mixed_precision: bool = True, use_gpu: bool = False, provider: str = "cpu", ): """Optimize ONNX model with an option to convert it to use mixed precision.""" from fusion_options import FusionOptions optimization_options = FusionOptions("bart") optimization_options.use_multi_head_attention = True optimization_options.disable_multi_head_attention_bias = provider == "rocm" m = optimize_model( onnx_model_path, model_type="bart", num_heads=num_attention_heads, hidden_size=hidden_size, opt_level=2 if not use_external_data_format else None, optimization_options=optimization_options, use_gpu=use_gpu, only_onnxruntime=False, ) if is_float16: if auto_mixed_precision: WhisperHelper.auto_mixed_precision(m) else: m.convert_model_float32_to_float16(cast_input_output=False) m.save_model_to_file(optimized_model_path, use_external_data_format, all_tensors_to_one_file=True) @staticmethod def verify_onnx( model_name_or_path: str, ort_session: InferenceSession, device: torch.device, ): """Compare the result from PyTorch and ONNX Runtime to verify the ONNX model is good.""" pt_model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path).to(device) processor = WhisperProcessor.from_pretrained(model_name_or_path) config = WhisperConfig.from_pretrained(model_name_or_path) # Try to import `datasets` pip package try: from datasets import load_dataset except Exception as e: logger.error(f"An error occurred while importing `datasets`: {e}", exc_info=True) install_cmd = "pip install datasets" logger.warning(f"Could not import `datasets`. Attempting to install `datasets` via `{install_cmd}`.") os.system(install_cmd) from datasets import load_dataset # noqa: F811 ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") input_features = processor([ds[0]["audio"]["array"]], return_tensors="pt").input_features batch_size, max_length, min_length, num_beams, num_return_sequences = 1, 26, 0, 5, 1 length_penalty, repetition_penalty = 1.0, 1.0 inputs = { "input_features": input_features.to(device), "max_length": max_length, "min_length": min_length, "num_beams": num_beams, "num_return_sequences": num_return_sequences, "length_penalty": length_penalty, "repetition_penalty": repetition_penalty, "early_stopping": True, "use_cache": True, } pt_outputs = pt_model.generate(**inputs).detach().cpu().numpy() del inputs["early_stopping"] del inputs["use_cache"] ort_names = list(map(lambda entry: entry.name, ort_session.get_inputs())) ort_dtypes = list(map(lambda entry: entry.type, ort_session.get_inputs())) ort_to_np = { "tensor(float)": np.float32, "tensor(float16)": np.float16, "tensor(int64)": np.int64, "tensor(int32)": np.int32, "tensor(int8)": np.int8, "tensor(uint8)": np.uint8, } for name, dtype in zip(ort_names, ort_dtypes): if name == "input_features": inputs[name] = inputs[name].detach().cpu().numpy() elif name == "vocab_mask": inputs[name] = np.ones(config.vocab_size, dtype=ort_to_np[dtype]) elif name == "prefix_vocab_mask": inputs[name] = np.ones((batch_size, config.vocab_size), dtype=ort_to_np[dtype]) elif name == "decoder_input_ids": inputs[name] = np.array([[config.decoder_start_token_id, 50259, 50359, 50363]], dtype=ort_to_np[dtype]) elif name == "logits_processor": inputs[name] = np.array([1], dtype=ort_to_np[dtype]) else: inputs[name] = np.array([inputs[name]], dtype=ort_to_np[dtype]) ort_outputs = ort_session.run(None, inputs)[0][0] if pt_outputs.shape != ort_outputs.shape: logger.warning("PyTorch and ONNX Runtime outputs do not have the same shape") diff = pt_outputs - ort_outputs max_diff = max(diff.min(), diff.max(), key=abs) if max_diff > 0: # For ONNX Runtime INT8 model pt_expected_transcription = ( " Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel." ) pt_transcription = processor.batch_decode(pt_outputs, skip_special_tokens=True) ort_expected_transcription = ( " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." ) ort_transcription = processor.batch_decode(ort_outputs, skip_special_tokens=True) parity = ( pt_expected_transcription == pt_transcription[0] and ort_expected_transcription == ort_transcription[0] ) if parity: max_diff = 0 return max_diff