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Add more huggingface models in benchmark tools (#4986)
* checkin more huggingface models * review comments * review comments
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Ye Wang
GitHub
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3 changed files with 138 additions and 23 deletions
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@ -56,26 +56,7 @@ from onnx_exporter import create_onnxruntime_input, load_pretrained_model, expor
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logger = logging.getLogger('')
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# List of pretrained models: https://huggingface.co/transformers/pretrained_models.html
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# Pretrained model name to a tuple of input names, opset_version, use_external_data_format and optimization model type
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MODELS = {
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"bert-base-cased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"distilbert-base-uncased": (["input_ids", "attention_mask"], 11, False, "bert"),
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"roberta-base": (["input_ids", "attention_mask"], 11, False, "bert"),
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# No past state inputs for GPT models.
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"gpt2": (["input_ids"], 11, False, "gpt2"), # no past state inputs & outputs
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"gpt2-large": (["input_ids"], 11, True, "gpt2"), # Model>2GB. Need use_external_data_format=True to export it.
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"distilgpt2": (["input_ids"], 11, False, "gpt2"), # no past state inputs & outputs
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#"openai-gpt": (["input_ids"], 11, False, "gpt2"), # no past state inputs
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# Models uses Einsum, which need opset version 12 and PyTorch 1.5.0 or above.
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"albert-base-v2": (["input_ids"], 12, False, "bert"),
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#"xlnet-base-cased": (["input_ids"], 12, False, "bert"),
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#"xlm-mlm-en-2048": (["input_ids"], 11, True, "bert"),
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}
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from huggingface_models import MODELS
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cpu_count = psutil.cpu_count(logical=True)
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# Set OMP environment variable before importing onnxruntime or torch.
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@ -85,7 +66,6 @@ if "OMP_NUM_THREADS" not in os.environ:
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import torch
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from transformers import (AutoConfig, AutoTokenizer, AutoModel, GPT2Model)
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def run_onnxruntime(use_gpu, model_names, precision, batch_sizes, sequence_lengths, repeat_times, input_counts,
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optimize_onnx, validate_onnx, cache_dir, onnx_dir, verbose, overwrite, disable_ort_io_binding,
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use_raw_attention_mask, thread_num, model_fusion_statistics):
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@ -187,7 +167,9 @@ def run_pytorch(use_gpu, model_names, precision, batch_sizes, sequence_lengths,
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config = AutoConfig.from_pretrained(model_name, torchscript=torchscript, cache_dir=cache_dir)
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model = load_pretrained_model(model_name, config=config, cache_dir=cache_dir)
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tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
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max_input_size = tokenizer.max_model_input_sizes[model_name]
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max_input_size = tokenizer.max_model_input_sizes[model_name] if model_name in tokenizer.max_model_input_sizes else 1024
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logger.debug(f"Model {model}")
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logger.debug(f"Number of parameters {model.num_parameters()}")
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111
onnxruntime/python/tools/transformers/huggingface_models.py
Normal file
111
onnxruntime/python/tools/transformers/huggingface_models.py
Normal file
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@ -0,0 +1,111 @@
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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# List of pretrained models: https://huggingface.co/transformers/pretrained_models.html
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# Pretrained model name to a tuple of input names, opset_version, use_external_data_format and optimization model type
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MODELS = {
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# BERT
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"bert-base-uncased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-large-uncased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-cased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-large-cased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-multilingual-uncased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-multilingual-cased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-chinese": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-german-cased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-large-uncased-whole-word-masking": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-large-cased-whole-word-masking": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-large-uncased-whole-word-masking-finetuned-squad": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-large-cased-whole-word-masking-finetuned-squad": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-cased-finetuned-mrpc": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-german-dbmdz-cased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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"bert-base-german-dbmdz-uncased": (["input_ids", "attention_mask", "token_type_ids"], 11, False, "bert"),
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# todo: more models to add
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# GPT
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"openai-gpt": (["input_ids"], 11, False, "gpt2"), # no past state inputs
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# GPT-2
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"gpt2": (["input_ids"], 11, False, "gpt2"), # no past state inputs & outputs
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"gpt2-medium": (["input_ids"], 11, False, "gpt2"),
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"gpt2-large": (["input_ids"], 11, True, "gpt2"), # Model>2GB. Need use_external_data_format=True to export it. No past state inputs for GPT models.
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"gpt2-xl": (["input_ids"], 11, True, "gpt2"),
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"distilgpt2": (["input_ids"], 11, False, "gpt2"), # no past state inputs & outputs
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# Transformer-XL
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#"transfo-xl-wt103": (["input_ids"], 11, False, "bert"),
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# XLNet
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#"xlnet-base-cased": (["input_ids"], 12, False, "bert"), # Models uses Einsum, which need opset version 12 and PyTorch 1.5.0 or above.
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#"xlnet-large-cased": (["input_ids"], 12, False, "bert"), # Models uses Einsum, which need opset version 12 and PyTorch 1.5.0 or above.
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# XLM
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"xlm-mlm-en-2048": (["input_ids"], 11, True, "bert"),
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"xlm-mlm-ende-1024": (["input_ids"], 11, False, "bert"),
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"xlm-mlm-enfr-1024": (["input_ids"], 11, False, "bert"),
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"xlm-mlm-enro-1024": (["input_ids"], 11, False, "bert"),
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"xlm-mlm-xnli15-1024": (["input_ids"], 11, False, "bert"),
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"xlm-mlm-tlm-xnli15-1024": (["input_ids"], 11, False, "bert"),
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"xlm-clm-enfr-1024": (["input_ids"], 11, False, "bert"),
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"xlm-clm-ende-1024": (["input_ids"], 11, False, "bert"),
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"xlm-mlm-17-1280": (["input_ids"], 11, True, "bert"),
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"xlm-mlm-100-1280": (["input_ids"], 11, True, "bert"),
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# RoBERTa
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"roberta-base": (["input_ids", "attention_mask"], 11, False, "bert"),
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"roberta-large": (["input_ids", "attention_mask"], 11, False, "bert"),
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"roberta-large-mnli": (["input_ids", "attention_mask"], 11, False, "bert"),
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"distilroberta-base": (["input_ids", "attention_mask"], 11, False, "bert"),
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"roberta-base-openai-detector": (["input_ids", "attention_mask"], 11, False, "bert"),
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"roberta-large-openai-detector": (["input_ids", "attention_mask"], 11, False, "bert"),
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# DistilBERT
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"distilbert-base-uncased": (["input_ids", "attention_mask"], 11, False, "bert"),
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"distilbert-base-uncased-distilled-squad": (["input_ids", "attention_mask"], 11, False, "bert"),
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"distilbert-base-cased": (["input_ids", "attention_mask"], 11, False, "bert"),
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"distilbert-base-cased-distilled-squad": (["input_ids", "attention_mask"], 11, False, "bert"),
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"distilbert-base-german-cased": (["input_ids", "attention_mask"], 11, False, "bert"),
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"distilbert-base-multilingual-cased": (["input_ids", "attention_mask"], 11, False, "bert"),
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# CTRL
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"ctrl": (["input_ids"], 11, True, "bert"),
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# CamemBERT
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"camembert-base": (["input_ids"], 11, False, "bert"),
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# ALBERT
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# Models uses Einsum, which need opset version 12 and PyTorch 1.5.0 or above.
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"albert-base-v1": (["input_ids"], 12, False, "bert"),
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"albert-large-v1": (["input_ids"], 12, False, "bert"),
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"albert-xlarge-v1": (["input_ids"], 12, True, "bert"),
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"albert-xxlarge-v1": (["input_ids"], 12, True, "bert"),
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"albert-base-v2": (["input_ids"], 12, False, "bert"),
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"albert-large-v2": (["input_ids"], 12, False, "bert"),
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"albert-xlarge-v2": (["input_ids"], 12, True, "bert"),
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"albert-xxlarge-v2": (["input_ids"], 12, True, "bert"),
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# T5
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#"t5-small": (["input_ids"], 11, False, "bert"),
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#"t5-base": (["input_ids"], 11, False, "bert"),
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#"t5-large": (["input_ids"], 11, False, "bert"),
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#"t5-3b": (["input_ids"], 11, False, "bert"),
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#"t5-11b": (["input_ids"], 11, False, "bert"),
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# XLM-RoBERTa
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"xlm-roberta-base": (["input_ids"], 11, False, "bert"),
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"xlm-roberta-large": (["input_ids"], 11, True, "bert"),
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# FlauBERT
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"flaubert/flaubert_small_cased": (["input_ids"], 11, False, "bert"),
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"flaubert/flaubert_base_uncased": (["input_ids"], 11, False, "bert"),
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"flaubert/flaubert_base_cased": (["input_ids"], 11, False, "bert"),
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"flaubert/flaubert_large_cased": (["input_ids"], 11, False, "bert"),
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# Bart
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#"facebook/bart-large": (["input_ids"], 11, False, "bert"),
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#"facebook/bart-base": (["input_ids"], 11, False, "bert"),
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#"facebook/bart-large-mnli": (["input_ids"], 11, False, "bert"),
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#"facebook/bart-large-cnn": (["input_ids"], 11, False, "bert"),
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#"facebook/mbart-large-en-ro": (["input_ids"], 11, True, "bert"),
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# DialoGPT
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"microsoft/DialoGPT-small": (["input_ids"], 11, False, "gpt2"),
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"microsoft/DialoGPT-medium": (["input_ids"], 11, False, "gpt2"),
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"microsoft/DialoGPT-large": (["input_ids"], 11, True, "gpt2"),
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# Reformer
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#"google/reformer-enwik8": (["input_ids"], 11, False, "bert"),
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#"google/reformer-crime-and-punishment": (["input_ids"], 11, False, "bert"),
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# MarianMT
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#"Helsinki-NLP/opus-mt-ROMANCE-en": (["input_ids"], 12, False, "bert"),
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# Longformer
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#"allenai/longformer-base-4096": (["input_ids"], 12, False, "bert"),
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#"allenai/longformer-large-4096": (["input_ids"], 12, False, "bert"),
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}
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@ -15,6 +15,24 @@ from quantize_helper import QuantizeHelper
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logger = logging.getLogger(__name__)
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# Walkaround by replacing torch.triu using self-defined op
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# Since torch.triu cannot be exported to ONNX. See https://github.com/pytorch/pytorch/issues/32968
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torch_func = {"triu" : torch.triu}
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def triu_onnx(x, diagonal=0, out=None):
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assert out is None
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assert len(x.shape) == 2 and x.size(0) == x.size(1)
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torch_triu = torch_func["triu"]
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template = torch_triu(torch.ones((1024, 1024), dtype=torch.uint8), diagonal)
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mask = template[:x.size(0),:x.size(1)]
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return torch.where(mask.bool(), x, torch.zeros_like(x))
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def replace_torch_functions():
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torch.triu = triu_onnx
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def restore_torch_functions():
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torch.triu = torch_func["triu"]
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def create_onnxruntime_input(vocab_size, batch_size, sequence_length, input_names):
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input_ids = numpy.random.randint(low=0, high=vocab_size - 1, size=(batch_size, sequence_length), dtype=numpy.int64)
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@ -201,6 +219,7 @@ def export_onnx_model(model_name, opset_version, use_external_data_format, model
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dynamic_axes, output_names = build_dynamic_axes(example_inputs, example_outputs_flatten)
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replace_torch_functions()
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torch.onnx.export(model=model,
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args=tuple(example_inputs.values()),
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f=onnx_model_path,
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@ -211,6 +230,7 @@ def export_onnx_model(model_name, opset_version, use_external_data_format, model
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do_constant_folding=True,
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opset_version=opset_version,
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use_external_data_format=use_external_data_format)
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restore_torch_functions()
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else:
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logger.info(f"Skip export since model existed: {onnx_model_path}")
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@ -242,4 +262,6 @@ def export_onnx_model(model_name, opset_version, use_external_data_format, model
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use_external_data_format)
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optimize_onnx_model_by_ort(onnx_model_path, ort_model_path, use_gpu, overwrite, model_fusion_statistics)
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return onnx_model_path, is_valid_onnx_model, config.vocab_size, tokenizer.max_model_input_sizes[model_name]
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max_input_size = tokenizer.max_model_input_sizes[model_name] if model_name in tokenizer.max_model_input_sizes else 1024
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return onnx_model_path, is_valid_onnx_model, config.vocab_size, max_input_size
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