# ------------------------------------------------------------------------- # 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 numpy import os import torch from pathlib import Path from transformers import AutoConfig, AutoTokenizer, AutoModel, LxmertConfig, TransfoXLConfig from benchmark_helper import create_onnxruntime_session, Precision from gpt2_helper import GPT2ModelNoPastState, PRETRAINED_GPT2_MODELS, TFGPT2ModelNoPastState from quantize_helper import QuantizeHelper from huggingface_models import MODEL_CLASSES os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' logger = logging.getLogger(__name__) # Walkaround by replacing torch.triu using self-defined op # Since torch.triu cannot be exported to ONNX. See https://github.com/pytorch/pytorch/issues/32968 torch_func = {"triu": torch.triu} def triu_onnx(x, diagonal=0, out=None): assert out is None assert len(x.shape) == 2 and x.size(0) == x.size(1) torch_triu = torch_func["triu"] template = torch_triu(torch.ones((1024, 1024), dtype=torch.uint8), diagonal) mask = template[:x.size(0), :x.size(1)] return torch.where(mask.bool(), x, torch.zeros_like(x)) def replace_torch_functions(): torch.triu = triu_onnx def restore_torch_functions(): torch.triu = torch_func["triu"] def create_onnxruntime_input(vocab_size, batch_size, sequence_length, input_names, config, data_type=numpy.int64): input_ids = numpy.random.randint(low=0, high=vocab_size - 1, size=(batch_size, sequence_length), dtype=data_type) inputs = {'input_ids': input_ids} if "attention_mask" in input_names: attention_mask = numpy.ones([batch_size, sequence_length], dtype=data_type) inputs['attention_mask'] = attention_mask if "token_type_ids" in input_names: segment_ids = numpy.zeros([batch_size, sequence_length], dtype=data_type) inputs['token_type_ids'] = segment_ids if config.is_encoder_decoder: inputs['decoder_input_ids'] = input_ids if isinstance(config, LxmertConfig): inputs["visual_feats"] = numpy.random.randn(1, 1, config.visual_feat_dim).astype(numpy.float32) inputs["visual_pos"] = numpy.random.randn(1, 1, config.visual_pos_dim).astype(numpy.float32) if isinstance(config, TransfoXLConfig): inputs["tf_transfo_xl_model/transformer/pos_emb/einsum/Einsum/inputs_1:0"] = numpy.zeros([config.hidden_size], dtype=numpy.float32) return inputs def filter_inputs(inputs, input_names): remaining_model_inputs = {} for input_name in input_names: if input_name in inputs: remaining_model_inputs[input_name] = inputs[input_name] return remaining_model_inputs def flatten(inputs): return [[flatten(i) for i in inputs] if isinstance(inputs, (list, tuple)) else inputs] def update_flatten_list(inputs, res_list): for i in inputs: res_list.append(i) if not isinstance(i, (list, tuple)) else update_flatten_list(i, res_list) return res_list def build_dynamic_axes(example_inputs, outputs_flatten): sequence_length = example_inputs["input_ids"].shape[-1] dynamic_axes = {key: {0: 'batch_size', 1: 'seq_len'} for key in example_inputs.keys()} output_names = ['output_' + str(i + 1) for i in range(len(outputs_flatten))] for i, output_name in enumerate(output_names): dynamic_axes[output_name] = {0: 'batch_size'} dims = outputs_flatten[i].shape for j, dim in enumerate(dims): if dim == sequence_length: dynamic_axes[output_name].update({j: 'seq_len'}) return dynamic_axes, output_names def validate_onnx_model(onnx_model_path, example_inputs, example_outputs_flatten, use_gpu, fp16, output_names=None): test_session = create_onnxruntime_session(onnx_model_path, use_gpu, enable_all_optimization=False) if test_session is None: logger.error(f"{onnx_model_path} is an invalid ONNX model") return False logger.info(f"{onnx_model_path} is a valid ONNX model") # Compare the inference result with PyTorch or Tensorflow example_ort_inputs = {k: t.numpy() for k, t in example_inputs.items()} example_ort_outputs = test_session.run(output_names, example_ort_inputs) if len(example_outputs_flatten) != len(example_ort_outputs): logger.error( f"Number of output tensors expected {len(example_outputs_flatten)}, got {len(example_ort_outputs)}") return False for i in range(len(example_outputs_flatten)): abs_diff = numpy.amax(numpy.abs(example_ort_outputs[i] - example_outputs_flatten[i].cpu().numpy())) if abs_diff > 1e-4: logger.info(f"Max absolute diff={abs_diff} for output tensor {i}") rtol = 5e-02 if fp16 else 1e-4 atol = 1e-01 if fp16 else 1e-4 if not numpy.allclose(example_ort_outputs[i], example_outputs_flatten[i].cpu().numpy(), rtol=rtol, atol=atol): logger.error(f"Output tensor {i} is not close: rtol={rtol}, atol={atol}") return False logger.info(f"inference result of onnxruntime is validated on {onnx_model_path}") return True def get_onnx_file_path(onnx_dir: str, model_name: str, input_count: int, optimized_by_script: bool, use_gpu: bool, precision: Precision, optimized_by_onnxruntime: bool, use_external_data: bool): from re import sub normalized_model_name = sub(r'[^a-zA-Z0-9_]', '_', model_name) if not optimized_by_script: filename = f"{normalized_model_name}_{input_count}" else: device = "gpu" if use_gpu else "cpu" filename = f"{normalized_model_name}_{input_count}_{precision}_{device}" if optimized_by_onnxruntime: filename += f"_ort" directory = onnx_dir # ONNXRuntime will not write external data so the raw and optimized models shall be in same directory. if use_external_data and not optimized_by_onnxruntime: directory = os.path.join(onnx_dir, filename) if not os.path.exists(directory): os.makedirs(directory) return os.path.join(directory, f"{filename}.onnx") def add_filename_suffix(file_path: str, suffix: str) -> str: """ Append a suffix at the filename (before the extension). Args: path: pathlib.Path The actual path object we would like to add a suffix suffix: The suffix to add Returns: path with suffix appended at the end of the filename and before extension """ path = Path(file_path) return str(path.parent.joinpath(path.stem + suffix).with_suffix(path.suffix)) def optimize_onnx_model_by_ort(onnx_model_path, ort_model_path, use_gpu, overwrite, model_fusion_statistics): if overwrite or not os.path.exists(ort_model_path): Path(ort_model_path).parent.mkdir(parents=True, exist_ok=True) from optimizer import optimize_by_onnxruntime, get_fusion_statistics # Use onnxruntime to optimize model, which will be saved to *_ort.onnx opt_model = optimize_by_onnxruntime(onnx_model_path, use_gpu=use_gpu, optimized_model_path=ort_model_path, opt_level=99) model_fusion_statistics[ort_model_path] = get_fusion_statistics(ort_model_path) else: logger.info(f"Skip optimization since model existed: {ort_model_path}") def optimize_onnx_model(model_name, onnx_model_path, optimized_model_path, model_type, num_attention_heads, hidden_size, use_gpu, precision, use_raw_attention_mask, overwrite, model_fusion_statistics, use_external_data_format): if overwrite or not os.path.exists(optimized_model_path): Path(optimized_model_path).parent.mkdir(parents=True, exist_ok=True) from optimizer import optimize_model from onnx_model_bert import BertOptimizationOptions optimization_options = BertOptimizationOptions(model_type) optimization_options.use_raw_attention_mask(use_raw_attention_mask) if Precision.FLOAT16 == precision: optimization_options.enable_gelu_approximation = True if Precision.INT8 == precision: optimization_options.enable_embed_layer_norm = False # Use script to optimize model. # Use opt_level <= 1 for models to be converted to fp16, because some fused op (like FusedGemm) has only fp32 and no fp16. # It is better to be conservative so we use opt_level=0 here, in case MemcpyFromHost is added to the graph by OnnxRuntime. opt_model = optimize_model(onnx_model_path, model_type, num_heads=num_attention_heads, hidden_size=hidden_size, opt_level=0, optimization_options=optimization_options, use_gpu=use_gpu, only_onnxruntime=False) if model_type == 'bert_keras' or model_type == "bert_tf": opt_model.use_dynamic_axes() model_fusion_statistics[optimized_model_path] = opt_model.get_fused_operator_statistics() if Precision.FLOAT16 == precision: opt_model.convert_model_float32_to_float16() opt_model.save_model_to_file(optimized_model_path, use_external_data_format) else: logger.info(f"Skip optimization since model existed: {optimized_model_path}") def modelclass_dispatcher(model_name, custom_model_class): if (custom_model_class != None): if (custom_model_class in MODEL_CLASSES): return custom_model_class else: raise Exception("Valid model class: " + ' '.join(MODEL_CLASSES)) if model_name in PRETRAINED_GPT2_MODELS: return "GPT2ModelNoPastState" import re if (re.search('-squad$', model_name) != None): return "AutoModelForQuestionAnswering" elif (re.search('-mprc$', model_name) != None): return "AutoModelForSequenceClassification" elif (re.search('gpt2', model_name) != None): return "AutoModelWithLMHead" return "AutoModel" def load_pretrained_model(model_name, config, cache_dir, custom_model_class, is_tf_model=False): model_class_name = modelclass_dispatcher(model_name, custom_model_class) if model_class_name == "GPT2ModelNoPastState": if is_tf_model: return TFGPT2ModelNoPastState.from_pretrained(model_name, config=config, cache_dir=cache_dir) else: return GPT2ModelNoPastState.from_pretrained(model_name, config=config, cache_dir=cache_dir) if is_tf_model: model_class_name = 'TF' + model_class_name transformers_module = __import__("transformers", fromlist=[model_class_name]) model_class = getattr(transformers_module, model_class_name) return model_class.from_pretrained(model_name, config=config, cache_dir=cache_dir) def load_pt_model(model_name, model_class, cache_dir): config = AutoConfig.from_pretrained(model_name, cache_dir=cache_dir) if hasattr(config, 'return_dict'): config.return_dict = False model = load_pretrained_model(model_name, config=config, cache_dir=cache_dir, custom_model_class=model_class) return config, model def load_tf_model(model_name, model_class, cache_dir): config = AutoConfig.from_pretrained(model_name, cache_dir=cache_dir) model = load_pretrained_model(model_name, config=config, cache_dir=cache_dir, custom_model_class=model_class, is_tf_model=True) return config, model # For test only def load_pt_model_from_tf(model_name): # Note that we could get pt model from tf, but model source and its structure in this case is different from directly using # load_pt_model() and load_tf_model() even with the same name. Therefore it should not be used for comparing with them from convert_tf_models_to_pytorch import tf2pt_pipeline config, model = tf2pt_pipeline(model_name) return config, model def validate_and_optimize_onnx(model_name, use_external_data_format, model_type, onnx_dir, input_names, use_gpu, precision, optimize_onnx, validate_onnx, use_raw_attention_mask, overwrite, config, model_fusion_statistics, onnx_model_path, example_inputs, example_outputs_flatten, output_names=None): is_valid_onnx_model = True if validate_onnx: is_valid_onnx_model = validate_onnx_model(onnx_model_path, example_inputs, example_outputs_flatten, use_gpu, False, output_names) if optimize_onnx or precision == Precision.FLOAT16 or precision == Precision.INT8: # Use script (optimizer.py) to optimize optimized_model_path = get_onnx_file_path(onnx_dir, model_name, len(input_names), True, use_gpu, precision, False, use_external_data_format) optimize_onnx_model(model_name, onnx_model_path, optimized_model_path, model_type, config.num_attention_heads, config.hidden_size, use_gpu, precision, use_raw_attention_mask, overwrite, model_fusion_statistics, use_external_data_format) onnx_model_path = optimized_model_path if validate_onnx: is_valid_onnx_model = validate_onnx_model(onnx_model_path, example_inputs, example_outputs_flatten, use_gpu, precision == Precision.FLOAT16, output_names) if precision == Precision.INT8: logger.info(f"Quantizing model: {onnx_model_path}") QuantizeHelper.quantize_onnx_model(onnx_model_path, onnx_model_path, use_external_data_format) logger.info(f"Finished quantizing model: {onnx_model_path}") else: # Use OnnxRuntime to optimize if is_valid_onnx_model: ort_model_path = add_filename_suffix(onnx_model_path, '_ort') optimize_onnx_model_by_ort(onnx_model_path, ort_model_path, use_gpu, overwrite, model_fusion_statistics) return onnx_model_path, is_valid_onnx_model, config.vocab_size def export_onnx_model_from_pt(model_name, opset_version, use_external_data_format, model_type, model_class, cache_dir, onnx_dir, input_names, use_gpu, precision, optimize_onnx, validate_onnx, use_raw_attention_mask, overwrite, model_fusion_statistics): config, model = load_pt_model(model_name, model_class, cache_dir) # config, model = load_pt_model_from_tf(model_name) model.cpu() tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir) max_input_size = tokenizer.max_model_input_sizes[ model_name] if model_name in tokenizer.max_model_input_sizes else 1024 example_inputs = tokenizer.encode_plus("This is a sample input", return_tensors="pt") example_inputs = filter_inputs(example_inputs, input_names) example_outputs = model(**example_inputs) assert isinstance(example_outputs, (list, tuple)), f"type of output is not list or tuple: {type(example_outputs)}" # Flatten is needed for gpt2 and distilgpt2. example_outputs_flatten = flatten(example_outputs) example_outputs_flatten = update_flatten_list(example_outputs_flatten, []) onnx_model_path = get_onnx_file_path(onnx_dir, model_name, len(input_names), False, use_gpu, precision, False, use_external_data_format) if overwrite or not os.path.exists(onnx_model_path): logger.info("Exporting ONNX model to {}".format(onnx_model_path)) Path(onnx_model_path).parent.mkdir(parents=True, exist_ok=True) dynamic_axes, output_names = build_dynamic_axes(example_inputs, example_outputs_flatten) replace_torch_functions() torch.onnx.export(model=model, args=tuple(example_inputs.values()), f=onnx_model_path, input_names=list(example_inputs.keys()), output_names=output_names, example_outputs=example_outputs, dynamic_axes=dynamic_axes, do_constant_folding=True, opset_version=opset_version, use_external_data_format=use_external_data_format) restore_torch_functions() else: logger.info(f"Skip export since model existed: {onnx_model_path}") onnx_model_file, is_valid_onnx_model, vocab_size = validate_and_optimize_onnx( model_name, use_external_data_format, model_type, onnx_dir, input_names, use_gpu, precision, optimize_onnx, validate_onnx, use_raw_attention_mask, overwrite, config, model_fusion_statistics, onnx_model_path, example_inputs, example_outputs_flatten) return onnx_model_file, is_valid_onnx_model, vocab_size, max_input_size def export_onnx_model_from_tf(model_name, opset_version, use_external_data_format, model_type, model_class, cache_dir, onnx_dir, input_names, use_gpu, precision, optimize_onnx, validate_onnx, use_raw_attention_mask, overwrite, model_fusion_statistics): # Use CPU to export import tensorflow as tf tf.config.set_visible_devices([], 'GPU') tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir) # Fix "Using pad_token, but it is not set yet" error. if tokenizer.pad_token is None: tokenizer.add_special_tokens({'pad_token': '[PAD]'}) max_input_size = tokenizer.max_model_input_sizes[ model_name] if model_name in tokenizer.max_model_input_sizes else 1024 config, model = load_tf_model(model_name, model_class, cache_dir) model.resize_token_embeddings(len(tokenizer)) example_inputs = tokenizer.encode_plus("This is a sample input", return_tensors="tf", max_length=max_input_size, padding="max_length", truncation=True) example_inputs = filter_inputs(example_inputs, input_names) if config.is_encoder_decoder: example_inputs["decoder_input_ids"] = tokenizer.encode_plus("This is a sample input", return_tensors="tf", max_length=max_input_size, padding="max_length", truncation=True).input_ids if model_name == "unc-nlp/lxmert-base-uncased": example_inputs["visual_feats"] = tf.random.normal([1, 1, config.visual_feat_dim]) example_inputs["visual_pos"] = tf.random.normal([1, 1, config.visual_pos_dim]) try: # Use no past state for these models if config.use_cache: config.use_cache = False except: pass example_outputs = model(example_inputs, training=False) output_names = None # For xlnet models, only compare the last_hidden_state output. if model_name == "xlnet-base-cased" or model_name == "xlnet-large-cased": output_names = ["last_hidden_state"] example_outputs = example_outputs["last_hidden_state"] # Flatten is needed for gpt2 and distilgpt2. Output name sorting is needed for tf2onnx outputs to match onnx outputs. from tensorflow.python.util import nest example_outputs_flatten = nest.flatten(example_outputs) onnx_model_path = get_onnx_file_path(onnx_dir, model_name, len(input_names), False, use_gpu, precision, False, use_external_data_format) tf_internal_model_path = onnx_model_path[:-5] if use_external_data_format else onnx_model_path if overwrite or not os.path.exists(tf_internal_model_path): logger.info("Exporting ONNX model to {}".format(onnx_model_path)) if not use_external_data_format: Path(tf_internal_model_path).parent.mkdir(parents=True, exist_ok=True) import tf2onnx, zipfile tf2onnx.logging.set_level(tf2onnx.logging.ERROR) specs = [] for name, value in example_inputs.items(): dims = [None] * len(value.shape) specs.append(tf.TensorSpec(tuple(dims), value.dtype, name=name)) _, _ = tf2onnx.convert.from_keras(model, input_signature=tuple(specs), opset=opset_version, large_model=use_external_data_format, output_path=tf_internal_model_path) if use_external_data_format: # need to unpack the zip for run_onnxruntime() with zipfile.ZipFile(tf_internal_model_path, 'r') as z: z.extractall(os.path.dirname(tf_internal_model_path)) tf_internal_model_path = os.path.join(os.path.dirname(tf_internal_model_path), "__MODEL_PROTO.onnx") if os.path.exists(onnx_model_path): os.remove(onnx_model_path) os.rename(tf_internal_model_path, onnx_model_path) else: logger.info(f"Skip export since model existed: {onnx_model_path}") model_type = model_type + '_tf' onnx_model_file, is_valid_onnx_model, vocab_size = validate_and_optimize_onnx( model_name, use_external_data_format, model_type, onnx_dir, input_names, use_gpu, precision, optimize_onnx, validate_onnx, use_raw_attention_mask, overwrite, config, model_fusion_statistics, onnx_model_path, example_inputs, example_outputs_flatten, output_names) return onnx_model_file, is_valid_onnx_model, vocab_size, max_input_size