mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-02-19 11:56:43 -05:00
3514822cac
* Add ONNX env var flags for venv setup. * Setup arguments for ONNX benchmarking via pytest. * Enable ONNX benchmarking on MiniLM via pytest (experimental) * Fix sequence lengths to 128 for TF model creation and fix issue with benchmarks. * Disable CI CPU benchmarks on A100, change some default args. * add xfails for roberta TF model tests on GPU.
282 lines
9.2 KiB
Python
282 lines
9.2 KiB
Python
import tensorflow as tf
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import numpy as np
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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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visible_default = tf.config.list_physical_devices("GPU")
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try:
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tf.config.set_visible_devices([], "GPU")
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visible_devices = tf.config.get_visible_devices()
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for device in visible_devices:
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assert device.device_type != "GPU"
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except:
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# Invalid device or cannot modify virtual devices once initialized.
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pass
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BATCH_SIZE = 1
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MAX_SEQUENCE_LENGTH = 128
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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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]
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maskedlm_models = [
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"albert-base-v2",
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"bert-base-uncased",
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"camembert-base",
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"convbert-base-turkish-cased",
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"deberta-base",
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"distilbert-base-uncased",
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"electra-small-discriminator",
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"funnel-transformer",
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"layoutlm-base-uncased",
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"longformer-base-4096",
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"mobilebert-uncased",
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"mpnet-base",
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"rembert",
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"roberta-base",
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"tapas-base",
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"tiny-random-flaubert",
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"xlm-roberta",
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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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def get_tf_model(name):
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if name in keras_models:
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return get_keras_model(name)
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elif name in maskedlm_models:
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return get_causal_lm_model(name)
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elif name in tfhf_models:
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return get_TFhf_model(name)
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else:
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return get_causal_image_model(name)
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##################### Tensorflow Hugging Face LM Models ###################################
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# Create a set of 2-dimensional inputs
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tf_bert_input = [
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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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)
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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):
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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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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=MAX_SEQUENCE_LENGTH,
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)
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for key in encoded_input:
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encoded_input[key] = tf.expand_dims(
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tf.convert_to_tensor(encoded_input[key]), 0
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)
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test_input = (
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encoded_input["input_ids"],
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encoded_input["attention_mask"],
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encoded_input["token_type_ids"],
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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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##################### Tensorflow Hugging Face Masked LM Models ###################################
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from transformers import TFAutoModelForMaskedLM, AutoTokenizer
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import tensorflow as tf
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# Create a set of input signature.
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input_signature_maskedlm = [
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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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.TFAutoModelForCasualLM
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def preprocess_input(
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model_name, 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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inputs = tokenizer(
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text,
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padding="max_length",
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return_tensors="tf",
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truncation=True,
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max_length=MAX_SEQUENCE_LENGTH,
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)
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return inputs
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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)
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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_causal_lm_model(hf_name, text="Hello, this is the default text."):
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model = MaskedLM(hf_name)
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encoded_input = preprocess_input(hf_name, 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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##################### 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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INPUT_SHAPE = [1, 224, 224, 3]
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tf_model = tf.keras.applications.resnet50.ResNet50(
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weights="imagenet", include_top=True, input_shape=tuple(INPUT_SHAPE[1:])
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)
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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_model
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self.m.predict = lambda x: self.m.call(x, training=False)
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@tf.function(input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])
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def forward(self, inputs):
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return self.m.predict(inputs)
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def load_image(path_to_image):
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image = tf.io.read_file(path_to_image)
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image = tf.image.decode_image(image, channels=3)
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image = tf.image.resize(image, (224, 224))
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image = image[tf.newaxis, :]
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return image
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def get_keras_model(modelname):
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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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content_image = load_image(content_path)
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input_tensor = tf.keras.applications.resnet50.preprocess_input(
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content_image
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)
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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=[1, 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)
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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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return [inputs[str(*inputs)]]
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def get_causal_image_model(hf_name):
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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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