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132 lines
4.3 KiB
132 lines
4.3 KiB
import towhee
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from towhee.dc2 import AutoPipes, AutoConfig
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from towhee import triton_client, ops
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import onnxruntime
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import numpy
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import torch
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from statistics import mean
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import time
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import argparse
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import os
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import re
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import warnings
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import logging
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from transformers import logging as t_logging
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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warnings.filterwarnings("ignore")
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t_logging.set_verbosity_error()
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', required=True, type=str)
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parser.add_argument('--pipe', action='store_true')
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parser.add_argument('--triton', action='store_true')
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parser.add_argument('--onnx', action='store_true')
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parser.add_argument('--atol', type=float, default=1e-3)
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parser.add_argument('--num', type=int, default=100)
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parser.add_argument('--device', type=int, default=-1)
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args = parser.parse_args()
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device = 'cuda:' + str(args.device) if args.device >= 0 else 'cpu'
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model_name = args.model
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# model_name = 'paraphrase-albert-small-v2'
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# model_name = 'all-MiniLM-L6-v2'
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# model_name = 'all-mpnet-base-v2'
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# model_name = 'distilbert-base-uncased'
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# p = (
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# pipe.input('text')
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# .map('text', 'vec', ops.sentence_embedding.transformers(model_name=model_name, device='cuda:3'))
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# .output('vec')
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# )
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conf = AutoConfig.load_config('sentence_embedding')
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conf.model = model_name
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conf.device = args.device
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p = AutoPipes.pipeline('sentence_embedding', conf)
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text = 'Hello, world.'
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out1 = p(text).get()[0]
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print('Pipe: OK')
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if args.num and args.pipe:
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qps = []
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for _ in range(10):
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start = time.time()
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# p([text] * args.num)
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p.batch([text] * args.num)
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end = time.time()
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q = args.num / (end - start)
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qps.append(q)
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print('Pipe qps:', mean(qps))
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if args.triton:
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client = triton_client.Client(url='localhost:8000')
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out2 = client(text)[0][0]
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print('Triton: OK')
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if numpy.allclose(out1, out2, atol=args.atol):
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print('Check accuracy: OK')
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else:
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max_diff = numpy.abs(out1 - out2).max()
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min_diff = numpy.abs(out1 - out2).min()
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mean_diff = numpy.abs(out1 - out2).mean()
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print(f'Check accuracy: atol is larger than {args.atol}.')
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print(f'Maximum absolute difference is {max_diff}.')
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print(f'Minimum absolute difference is {min_diff}.')
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print(f'Mean difference is {mean_diff}.')
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if args.num:
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qps = []
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for _ in range(10):
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start = time.time()
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client.batch([text] * args.num)
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end = time.time()
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q = args.num / (end - start)
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qps.append(q)
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print('Triton qps:', mean(qps))
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if args.onnx:
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op = ops.sentence_embedding.transformers(model_name=model_name, device='cpu').get_op()
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# if not os.path.exists('test.onnx'):
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op.save_model('onnx', 'test.onnx')
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sess = onnxruntime.InferenceSession('test.onnx',
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providers=['CUDAExecutionProvider'])
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inputs = op.tokenizer([text], padding=True, truncation=True, return_tensors='np')
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# inputs = {}
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# for k, v in tokens.items():
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# if k in op.onnx_config['inputs'].keys():
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# inputs[k] = v
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out3 = sess.run(None, input_feed=dict(inputs))
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torch_inputs = {}
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for k, v in inputs.items():
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torch_inputs[k] = torch.from_numpy(v)
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out3 = op.post_proc(torch.from_numpy(out3[0]), torch_inputs).cpu().detach().numpy()
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print('Onnx: OK')
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if numpy.allclose(out1, out3, atol=args.atol):
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print('Check accuracy: OK')
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else:
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max_diff = numpy.abs(out1 - out3).max()
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min_diff = numpy.abs(out1 - out3).min()
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mean_diff = numpy.abs(out1 - out3).mean()
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print(f'Check accuracy: atol is larger than {args.atol}.')
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print(f'Maximum absolute difference is {max_diff}.')
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print(f'Minimum absolute difference is {min_diff}.')
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print(f'Mean difference is {mean_diff}.')
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if args.num:
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qps = []
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for _ in range(10):
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start = time.time()
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for _ in range(args.num):
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tokens = op.tokenizer([text], padding=True, truncation=True, return_tensors='pt')
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outs = sess.run(None, input_feed=dict(inputs))
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op.post_proc(torch.from_numpy(outs[0]), torch_inputs)
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end = time.time()
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q = args.num / (end - start)
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qps.append(q)
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print('Onnx qps:', mean(qps))
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