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https://github.com/nod-ai/SHARK-Studio.git
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2191fc8952
* Only xfail windows models in CI * downloader: make model updates more robust. * Separate baseline and native benchmarks in pytest. * Fix native benchmarks * Fix torchvision model utils.
547 lines
18 KiB
Python
547 lines
18 KiB
Python
import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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import tensorflow as tf
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import numpy as np
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BATCH_SIZE = 1
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################################## MHLO/TF models #########################################
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# TODO : Generate these lists or fetch model source from tank/tf/tf_model_list.csv
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keras_models = [
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"resnet50",
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"efficientnet_b0",
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"efficientnet_b7",
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"efficientnet-v2-s",
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]
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maskedlm_models = [
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"albert-base-v2",
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"bert-base-uncased",
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"bert-large-uncased",
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"camembert-base",
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"dbmdz/convbert-base-turkish-cased",
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"deberta-base",
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"distilbert-base-uncased",
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"google/electra-small-discriminator",
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"funnel-transformer/small",
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"microsoft/layoutlm-base-uncased",
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"longformer-base-4096",
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"google/mobilebert-uncased",
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"microsoft/mpnet-base",
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"google/rembert",
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"roberta-base",
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"tapas-base",
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"hf-internal-testing/tiny-random-flaubert",
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"xlm-roberta",
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]
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causallm_models = [
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"gpt2",
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]
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tfhf_models = [
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"microsoft/MiniLM-L12-H384-uncased",
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]
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tfhf_seq2seq_models = [
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"t5-base",
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"t5-large",
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]
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img_models = [
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"google/vit-base-patch16-224",
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"facebook/convnext-tiny-224",
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]
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def get_tf_model(name, import_args):
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if name in keras_models:
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return get_keras_model(name, import_args)
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elif name in maskedlm_models:
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return get_masked_lm_model(name, import_args)
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elif name in causallm_models:
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return get_causal_lm_model(name, import_args)
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elif name in tfhf_models:
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return get_TFhf_model(name, import_args)
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elif name in img_models:
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return get_causal_image_model(name, import_args)
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elif name in tfhf_seq2seq_models:
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return get_tfhf_seq2seq_model(name, import_args)
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else:
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raise Exception(
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"TF model not found! Please check that the modelname has been input correctly."
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)
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##################### Tensorflow Hugging Face Bert Models ###################################
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from transformers import (
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AutoModelForSequenceClassification,
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BertTokenizer,
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TFBertModel,
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)
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BERT_MAX_SEQUENCE_LENGTH = 128
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# Create a set of 2-dimensional inputs
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tf_bert_input = [
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tf.TensorSpec(
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shape=[BATCH_SIZE, BERT_MAX_SEQUENCE_LENGTH], dtype=tf.int32
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),
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tf.TensorSpec(
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shape=[BATCH_SIZE, BERT_MAX_SEQUENCE_LENGTH], dtype=tf.int32
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),
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tf.TensorSpec(
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shape=[BATCH_SIZE, BERT_MAX_SEQUENCE_LENGTH], dtype=tf.int32
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),
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]
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class TFHuggingFaceLanguage(tf.Module):
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def __init__(self, hf_model_name):
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super(TFHuggingFaceLanguage, self).__init__()
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# Create a BERT trainer with the created network.
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self.m = TFBertModel.from_pretrained(hf_model_name, from_pt=True)
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# Invoke the trainer model on the inputs. This causes the layer to be built.
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self.m.predict = lambda x, y, z: self.m.call(
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input_ids=x, attention_mask=y, token_type_ids=z, training=False
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)
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@tf.function(input_signature=tf_bert_input, jit_compile=True)
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def forward(self, input_ids, attention_mask, token_type_ids):
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return self.m.predict(input_ids, attention_mask, token_type_ids)
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def get_TFhf_model(name, import_args):
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model = TFHuggingFaceLanguage(name)
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tokenizer = BertTokenizer.from_pretrained(
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"microsoft/MiniLM-L12-H384-uncased"
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)
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text = "Replace me by any text you'd like."
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text = [text] * BATCH_SIZE
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encoded_input = tokenizer(
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text,
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padding="max_length",
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truncation=True,
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max_length=BERT_MAX_SEQUENCE_LENGTH,
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)
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test_input = [
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tf.reshape(
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tf.convert_to_tensor(encoded_input["input_ids"], dtype=tf.int32),
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[BATCH_SIZE, BERT_MAX_SEQUENCE_LENGTH],
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),
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tf.reshape(
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tf.convert_to_tensor(
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encoded_input["attention_mask"], dtype=tf.int32
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),
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[BATCH_SIZE, BERT_MAX_SEQUENCE_LENGTH],
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),
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tf.reshape(
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tf.convert_to_tensor(
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encoded_input["token_type_ids"], dtype=tf.int32
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),
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[BATCH_SIZE, BERT_MAX_SEQUENCE_LENGTH],
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),
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]
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actual_out = model.forward(*test_input)
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return model, test_input, actual_out
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# Utility function for comparing two tensors (tensorflow).
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def compare_tensors_tf(tf_tensor, numpy_tensor):
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# setting the absolute and relative tolerance
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rtol = 1e-02
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atol = 1e-03
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tf_to_numpy = tf_tensor.numpy()
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return np.allclose(tf_to_numpy, numpy_tensor, rtol, atol)
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# Tokenizer for language models
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def preprocess_input(
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model_name, max_length, text="This is just used to compile the model"
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):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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text = [text] * BATCH_SIZE
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inputs = tokenizer(
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text,
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return_tensors="tf",
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padding="max_length",
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truncation=True,
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max_length=max_length,
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)
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return inputs
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##################### Tensorflow Hugging Face Masked LM Models ###################################
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from transformers import TFAutoModelForMaskedLM, AutoTokenizer
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MASKED_LM_MAX_SEQUENCE_LENGTH = 128
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# Create a set of input signature.
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input_signature_maskedlm = [
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tf.TensorSpec(
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shape=[BATCH_SIZE, MASKED_LM_MAX_SEQUENCE_LENGTH], dtype=tf.int32
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),
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tf.TensorSpec(
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shape=[BATCH_SIZE, MASKED_LM_MAX_SEQUENCE_LENGTH], dtype=tf.int32
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),
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]
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# For supported models please see here:
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# https://huggingface.co/docs/transformers/model_doc/auto#transformers.TFAutoModelForMaskedLM
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class MaskedLM(tf.Module):
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def __init__(self, model_name):
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super(MaskedLM, self).__init__()
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self.m = TFAutoModelForMaskedLM.from_pretrained(
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model_name, output_attentions=False, num_labels=2
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)
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self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)[0]
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@tf.function(input_signature=input_signature_maskedlm, jit_compile=True)
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def forward(self, input_ids, attention_mask):
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return self.m.predict(input_ids, attention_mask)
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def get_masked_lm_model(
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hf_name, import_args, text="Hello, this is the default text."
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):
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model = MaskedLM(hf_name)
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encoded_input = preprocess_input(
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hf_name, MASKED_LM_MAX_SEQUENCE_LENGTH, text
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)
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test_input = (encoded_input["input_ids"], encoded_input["attention_mask"])
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actual_out = model.forward(*test_input)
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return model, test_input, actual_out
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##################### Tensorflow Hugging Face Causal LM Models ###################################
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from transformers import AutoConfig, TFAutoModelForCausalLM, TFGPT2Model
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CAUSAL_LM_MAX_SEQUENCE_LENGTH = 1024
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input_signature_causallm = [
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tf.TensorSpec(
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shape=[BATCH_SIZE, CAUSAL_LM_MAX_SEQUENCE_LENGTH], dtype=tf.int32
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),
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tf.TensorSpec(
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shape=[BATCH_SIZE, CAUSAL_LM_MAX_SEQUENCE_LENGTH], dtype=tf.int32
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),
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]
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# For supported models please see here:
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# https://huggingface.co/docs/transformers/model_doc/auto#transformers.TFAutoModelForCausalLM
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# For more background, see:
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# https://huggingface.co/blog/tf-xla-generate
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class CausalLM(tf.Module):
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def __init__(self, model_name):
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super(CausalLM, self).__init__()
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# Decoder-only models need left padding.
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self.tokenizer = AutoTokenizer.from_pretrained(
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model_name, padding_side="left", pad_token="</s>"
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)
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self.tokenization_kwargs = {
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"pad_to_multiple_of": CAUSAL_LM_MAX_SEQUENCE_LENGTH,
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"padding": True,
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"return_tensors": "tf",
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}
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self.model = TFGPT2Model.from_pretrained(model_name, return_dict=True)
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self.model.predict = lambda x, y: self.model(
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input_ids=x, attention_mask=y
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)[0]
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def preprocess_input(self, text):
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return self.tokenizer(text, **self.tokenization_kwargs)
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@tf.function(input_signature=input_signature_causallm, jit_compile=True)
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def forward(self, input_ids, attention_mask):
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return self.model.predict(input_ids, attention_mask)
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def get_causal_lm_model(
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hf_name, import_args, text="Hello, this is the default text."
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):
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model = CausalLM(hf_name)
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batched_text = [text] * BATCH_SIZE
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encoded_input = model.preprocess_input(batched_text)
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test_input = (encoded_input["input_ids"], encoded_input["attention_mask"])
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actual_out = model.forward(*test_input)
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return model, test_input, actual_out
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##################### TensorflowHugging Face Seq2SeqLM Models ###################################
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# We use a maximum sequence length of 512 since this is the default used in the T5 config.
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T5_MAX_SEQUENCE_LENGTH = 512
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input_signature_t5 = [
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tf.TensorSpec(
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shape=[BATCH_SIZE, T5_MAX_SEQUENCE_LENGTH],
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dtype=tf.int32,
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name="input_ids",
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),
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tf.TensorSpec(
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shape=[BATCH_SIZE, T5_MAX_SEQUENCE_LENGTH],
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dtype=tf.int32,
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name="attention_mask",
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),
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]
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class TFHFSeq2SeqLanguageModel(tf.Module):
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def __init__(self, model_name):
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super(TFHFSeq2SeqLanguageModel, self).__init__()
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from transformers import (
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AutoTokenizer,
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AutoConfig,
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TFAutoModelForSeq2SeqLM,
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TFT5Model,
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)
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self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.tokenization_kwargs = {
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"pad_to_multiple_of": T5_MAX_SEQUENCE_LENGTH,
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"padding": True,
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"return_tensors": "tf",
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}
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self.model = TFT5Model.from_pretrained(model_name, return_dict=True)
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self.model.predict = lambda x, y: self.model(x, decoder_input_ids=y)[0]
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def preprocess_input(self, text):
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return self.tokenizer(text, **self.tokenization_kwargs)
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@tf.function(input_signature=input_signature_t5, jit_compile=True)
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def forward(self, input_ids, decoder_input_ids):
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return self.model.predict(input_ids, decoder_input_ids)
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def get_tfhf_seq2seq_model(name, import_args):
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m = TFHFSeq2SeqLanguageModel(name)
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text = "Studies have been shown that owning a dog is good for you"
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batched_text = [text] * BATCH_SIZE
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encoded_input_ids = m.preprocess_input(batched_text).input_ids
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text = "Studies show that"
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batched_text = [text] * BATCH_SIZE
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decoder_input_ids = m.preprocess_input(batched_text).input_ids
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decoder_input_ids = m.model._shift_right(decoder_input_ids)
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test_input = (encoded_input_ids, decoder_input_ids)
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actual_out = m.forward(*test_input)
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return m, test_input, actual_out
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##################### TensorFlow Keras Resnet Models #########################################################
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# Static shape, including batch size (1).
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# Can be dynamic once dynamic shape support is ready.
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RESNET_INPUT_SHAPE = [BATCH_SIZE, 224, 224, 3]
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EFFICIENTNET_V2_S_INPUT_SHAPE = [BATCH_SIZE, 384, 384, 3]
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EFFICIENTNET_B0_INPUT_SHAPE = [BATCH_SIZE, 224, 224, 3]
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EFFICIENTNET_B7_INPUT_SHAPE = [BATCH_SIZE, 600, 600, 3]
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class ResNetModule(tf.Module):
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def __init__(self):
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super(ResNetModule, self).__init__()
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self.m = tf.keras.applications.resnet50.ResNet50(
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weights="imagenet",
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include_top=True,
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input_shape=tuple(RESNET_INPUT_SHAPE[1:]),
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)
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self.m.predict = lambda x: self.m.call(x, training=False)
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@tf.function(
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input_signature=[tf.TensorSpec(RESNET_INPUT_SHAPE, tf.float32)],
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jit_compile=True,
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)
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def forward(self, inputs):
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return self.m.predict(inputs)
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def input_shape(self):
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return RESNET_INPUT_SHAPE
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def preprocess_input(self, image):
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return tf.keras.applications.resnet50.preprocess_input(image)
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class EfficientNetB0Module(tf.Module):
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def __init__(self):
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super(EfficientNetB0Module, self).__init__()
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self.m = tf.keras.applications.efficientnet.EfficientNetB0(
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weights="imagenet",
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include_top=True,
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input_shape=tuple(EFFICIENTNET_B0_INPUT_SHAPE[1:]),
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)
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self.m.predict = lambda x: self.m.call(x, training=False)
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@tf.function(
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input_signature=[
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tf.TensorSpec(EFFICIENTNET_B0_INPUT_SHAPE, tf.float32)
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],
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jit_compile=True,
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)
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def forward(self, inputs):
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return self.m.predict(inputs)
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def input_shape(self):
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return EFFICIENTNET_B0_INPUT_SHAPE
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def preprocess_input(self, image):
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return tf.keras.applications.efficientnet.preprocess_input(image)
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class EfficientNetB7Module(tf.Module):
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def __init__(self):
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super(EfficientNetB7Module, self).__init__()
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self.m = tf.keras.applications.efficientnet.EfficientNetB7(
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weights="imagenet",
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include_top=True,
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input_shape=tuple(EFFICIENTNET_B7_INPUT_SHAPE[1:]),
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)
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self.m.predict = lambda x: self.m.call(x, training=False)
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@tf.function(
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input_signature=[
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tf.TensorSpec(EFFICIENTNET_B7_INPUT_SHAPE, tf.float32)
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],
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jit_compile=True,
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)
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def forward(self, inputs):
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return self.m.predict(inputs)
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def input_shape(self):
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return EFFICIENTNET_B7_INPUT_SHAPE
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def preprocess_input(self, image):
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return tf.keras.applications.efficientnet.preprocess_input(image)
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class EfficientNetV2SModule(tf.Module):
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def __init__(self):
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super(EfficientNetV2SModule, self).__init__()
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self.m = tf.keras.applications.efficientnet_v2.EfficientNetV2S(
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weights="imagenet",
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include_top=True,
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input_shape=tuple(EFFICIENTNET_V2_S_INPUT_SHAPE[1:]),
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)
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self.m.predict = lambda x: self.m.call(x, training=False)
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@tf.function(
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input_signature=[
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tf.TensorSpec(EFFICIENTNET_V2_S_INPUT_SHAPE, tf.float32)
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],
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jit_compile=True,
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)
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def forward(self, inputs):
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return self.m.predict(inputs)
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def input_shape(self):
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return EFFICIENTNET_V2_S_INPUT_SHAPE
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def preprocess_input(self, image):
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return tf.keras.applications.efficientnet_v2.preprocess_input(image)
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def load_image(path_to_image, width, height, channels):
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image = tf.io.read_file(path_to_image)
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image = tf.image.decode_image(image, channels=channels)
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image = tf.image.resize(image, (width, height))
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image = image[tf.newaxis, :]
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image = tf.tile(image, [BATCH_SIZE, 1, 1, 1])
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return image
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def get_keras_model(modelname, import_args):
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if modelname == "efficientnet-v2-s":
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model = EfficientNetV2SModule()
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elif modelname == "efficientnet_b0":
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model = EfficientNetB0Module()
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elif modelname == "efficientnet_b7":
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model = EfficientNetB7Module()
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else:
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model = ResNetModule()
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content_path = tf.keras.utils.get_file(
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"YellowLabradorLooking_new.jpg",
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"https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg",
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)
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input_shape = model.input_shape()
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content_image = load_image(
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content_path, input_shape[1], input_shape[2], input_shape[3]
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)
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input_tensor = model.preprocess_input(content_image)
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input_data = tf.expand_dims(input_tensor, 0)
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actual_out = model.forward(*input_data)
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return model, input_data, actual_out
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##################### Tensorflow Hugging Face Image Classification Models ###################################
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from transformers import TFAutoModelForImageClassification
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from transformers import ConvNextFeatureExtractor, ViTFeatureExtractor
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from transformers import BeitFeatureExtractor, AutoFeatureExtractor
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from PIL import Image
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import requests
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# Create a set of input signature.
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input_signature_img_cls = [
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tf.TensorSpec(shape=[BATCH_SIZE, 3, 224, 224], dtype=tf.float32),
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]
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class AutoModelImageClassfication(tf.Module):
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def __init__(self, model_name):
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super(AutoModelImageClassfication, self).__init__()
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self.m = TFAutoModelForImageClassification.from_pretrained(
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model_name, output_attentions=False
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)
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self.m.predict = lambda x: self.m(x)
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@tf.function(input_signature=input_signature_img_cls, jit_compile=True)
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def forward(self, inputs):
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return self.m.predict(inputs)
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fail_models = [
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"facebook/data2vec-vision-base-ft1k",
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"microsoft/swin-tiny-patch4-window7-224",
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]
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supported_models = [
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"facebook/convnext-tiny-224",
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"google/vit-base-patch16-224",
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]
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img_models_fe_dict = {
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"facebook/convnext-tiny-224": ConvNextFeatureExtractor,
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"facebook/data2vec-vision-base-ft1k": BeitFeatureExtractor,
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"microsoft/swin-tiny-patch4-window7-224": AutoFeatureExtractor,
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"google/vit-base-patch16-224": ViTFeatureExtractor,
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}
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def preprocess_input_image(model_name):
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# from datasets import load_dataset
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# dataset = load_dataset("huggingface/cats-image")
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# image1 = dataset["test"]["image"][0]
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# # print("image1: ", image1) # <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FA0B86BB6D0>
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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# <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FA0B86BB6D0>
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image = Image.open(requests.get(url, stream=True).raw)
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feature_extractor = img_models_fe_dict[model_name].from_pretrained(
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model_name
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)
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# inputs: {'pixel_values': <tf.Tensor: shape=(1, 3, 224, 224), dtype=float32, numpy=array([[[[]]]], dtype=float32)>}
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inputs = feature_extractor(images=image, return_tensors="tf")
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inputs["pixel_values"] = tf.tile(
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inputs["pixel_values"], [BATCH_SIZE, 1, 1, 1]
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)
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return [inputs[str(*inputs)]]
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def get_causal_image_model(hf_name, import_args):
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model = AutoModelImageClassfication(hf_name)
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test_input = preprocess_input_image(hf_name)
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# TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(1, 1000), dtype=float32, numpy=
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# array([[]], dtype=float32)>, hidden_states=None, attentions=None)
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actual_out = model.forward(*test_input)
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return model, test_input, actual_out
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