wehub-resource-sync
277 lines
9.0 KiB
Python
277 lines
9.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import struct
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import sys
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import time
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import unittest
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import numpy as np
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import paddle
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from paddle.framework import core
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--fp32_model', type=str, default='', help='A path to a FP32 model.'
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)
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parser.add_argument(
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'--quant_model', type=str, default='', help='A path to a quant model.'
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)
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parser.add_argument('--infer_data', type=str, default='', help='Data file.')
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parser.add_argument(
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'--warmup_iter',
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type=int,
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default=1,
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help='Number of the first iterations to skip in performance statistics.',
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)
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parser.add_argument(
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'--acc_diff_threshold',
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type=float,
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default=0.01,
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help='Accepted accuracy difference threshold.',
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)
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parser.add_argument(
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'--num_threads', type=int, default=1, help='Number of threads.'
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)
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parser.add_argument(
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'--onednn_cache_capacity',
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type=int,
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default=0,
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help='Onednn cache capacity. The default value in Python API is 15, which can slow down int8 models. Default 0 means unlimited cache.',
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)
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test_args, args = parser.parse_known_args(namespace=unittest)
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return test_args, sys.argv[:1] + args
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class TestLstmModelPTQ(unittest.TestCase):
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def get_warmup_tensor(self, data_path, place):
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data = []
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with open(data_path, 'rb') as in_f:
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while True:
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plen = in_f.read(4)
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if plen is None or len(plen) != 4:
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break
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alllen = struct.unpack('i', plen)[0]
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label_len = alllen & 0xFFFF
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seq_len = (alllen >> 16) & 0xFFFF
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label = in_f.read(4 * label_len)
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label = np.frombuffer(label, dtype=np.int32).reshape(
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[len(label) // 4]
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)
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feat = in_f.read(4 * seq_len * 8)
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feat = np.frombuffer(feat, dtype=np.float32).reshape(
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[len(feat) // 4 // 8, 8]
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)
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lod_feat = [feat.shape[0]]
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minputs = paddle.base.create_lod_tensor(feat, [lod_feat], place)
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infer_data = core.PaddleTensor()
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infer_data.lod = minputs.lod()
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infer_data.data = core.PaddleBuf(np.array(minputs))
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infer_data.shape = minputs.shape()
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infer_data.dtype = core.PaddleDType.FLOAT32
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infer_label = core.PaddleTensor()
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infer_label.data = core.PaddleBuf(np.array(label))
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infer_label.shape = label.shape
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infer_label.dtype = core.PaddleDType.INT32
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data.append([infer_data, infer_label])
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warmup_data = data[:1]
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inputs = data[1:]
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return warmup_data, inputs
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def set_config(
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self,
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model_path,
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num_threads,
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onednn_cache_capacity,
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warmup_data=None,
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use_analysis=False,
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mode="fp32",
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):
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config = core.AnalysisConfig(model_path)
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config.set_cpu_math_library_num_threads(num_threads)
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if use_analysis:
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config.disable_gpu()
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config.switch_use_feed_fetch_ops(True)
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config.switch_ir_optim(True)
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config.enable_onednn()
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config.disable_onednn_fc_passes() # fc passes caused dnnl error
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config.pass_builder().insert_pass(5, "fc_lstm_fuse_pass")
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config.set_onednn_cache_capacity(onednn_cache_capacity)
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if mode == "ptq":
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config.enable_quantizer()
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config.quantizer_config().set_quant_data(warmup_data)
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config.quantizer_config().set_quant_batch_size(1)
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elif mode == "qat":
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config.enable_onednn_int8()
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return config
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def run_program(
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self,
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model_path,
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data_path,
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num_threads,
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onednn_cache_capacity,
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warmup_iter,
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use_analysis=False,
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mode="fp32",
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):
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place = paddle.CPUPlace()
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warmup_data, inputs = self.get_warmup_tensor(data_path, place)
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warmup_data = [item[0] for item in warmup_data]
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config = self.set_config(
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model_path,
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num_threads,
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onednn_cache_capacity,
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warmup_data,
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use_analysis,
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mode,
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)
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predictor = core.create_paddle_predictor(config)
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data = [item[0] for item in inputs]
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label = np.array([item[1] for item in inputs])
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all_hz_num = 0
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ok_hz_num = 0
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all_ctc_num = 0
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ok_ctc_num = 0
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dataset_size = len(data)
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start = time.time()
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for i in range(dataset_size):
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if i == warmup_iter:
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start = time.time()
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hz_out, ctc_out = predictor.run([data[i]])
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np_hz_out = np.array(hz_out.data.float_data()).reshape(-1)
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np_ctc_out = np.array(ctc_out.data.int64_data()).reshape(-1)
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out_hz_label = np.argmax(np_hz_out)
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this_label = label[i]
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this_label_data = np.array(this_label.data.int32_data()).reshape(-1)
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if this_label.shape[0] == 1:
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all_hz_num += 1
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best = this_label_data[0]
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if out_hz_label == best:
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ok_hz_num += 1
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if this_label_data[0] <= 6350:
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all_ctc_num += 1
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if (
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np_ctc_out.shape[0] == 1
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and np_ctc_out.all() == this_label_data.all()
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):
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ok_ctc_num += 1
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else:
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all_ctc_num += 1
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if (
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np_ctc_out.shape[0] == this_label.shape[0]
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and np_ctc_out.all() == this_label_data.all()
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):
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ok_ctc_num += 1
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if all_ctc_num > 1000 or all_hz_num > 1000:
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break
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end = time.time()
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fps = (dataset_size - warmup_iter) / (end - start)
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hx_acc = ok_hz_num / all_hz_num
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ctc_acc = ok_ctc_num / all_ctc_num
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return hx_acc, ctc_acc, fps
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def test_lstm_model(self):
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if not core.is_compiled_with_onednn():
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return
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fp32_model = test_case_args.fp32_model
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assert fp32_model, (
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'The FP32 model path cannot be empty. Please, use the --fp32_model option.'
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)
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quant_model = test_case_args.quant_model
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assert quant_model, (
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'The quant model path cannot be empty. Please, use the --quant_model option.'
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)
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infer_data = test_case_args.infer_data
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assert infer_data, (
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'The dataset path cannot be empty. Please, use the --infer_data option.'
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)
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num_threads = test_case_args.num_threads
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onednn_cache_capacity = test_case_args.onednn_cache_capacity
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warmup_iter = test_case_args.warmup_iter
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acc_diff_threshold = test_case_args.acc_diff_threshold
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(fp32_hx_acc, fp32_ctc_acc, fp32_fps) = self.run_program(
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fp32_model,
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infer_data,
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num_threads,
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onednn_cache_capacity,
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warmup_iter,
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False,
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mode="fp32",
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)
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(int8_hx_acc, int8_ctc_acc, int8_fps) = self.run_program(
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fp32_model,
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infer_data,
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num_threads,
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onednn_cache_capacity,
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warmup_iter,
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True,
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mode="ptq",
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)
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(quant_hx_acc, quant_ctc_acc, quant_fps) = self.run_program(
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quant_model,
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infer_data,
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num_threads,
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onednn_cache_capacity,
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warmup_iter,
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True,
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mode="qat",
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)
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print(
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f"FP32: fps {fp32_fps}, hx_acc {fp32_hx_acc}, ctc_acc {fp32_ctc_acc}"
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)
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print(
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f"PTQ_INT8: fps {int8_fps}, hx_acc {int8_hx_acc}, ctc_acc {int8_ctc_acc}"
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)
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print(
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f"QAT: fps {quant_fps}, hx_acc {quant_hx_acc}, ctc_acc {quant_ctc_acc}"
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)
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sys.stdout.flush()
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self.assertLess(fp32_hx_acc - int8_hx_acc, acc_diff_threshold)
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self.assertLess(fp32_ctc_acc - int8_ctc_acc, acc_diff_threshold)
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self.assertLess(fp32_hx_acc - quant_hx_acc, acc_diff_threshold)
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self.assertLess(fp32_ctc_acc - quant_ctc_acc, acc_diff_threshold)
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if __name__ == "__main__":
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global test_case_args
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test_case_args, remaining_args = parse_args()
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unittest.main(argv=remaining_args)
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