wehub-resource-sync
363 lines
13 KiB
Python
363 lines
13 KiB
Python
# Copyright (c) 2020 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 errno
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import os
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import random
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import tempfile
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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 import base
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from paddle.base import core
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from paddle.base.core import AnalysisConfig, create_paddle_predictor
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class InferencePassTest(unittest.TestCase):
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def __init__(self, methodName='runTest'):
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paddle.enable_static()
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super().__init__(methodName)
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paddle.enable_static()
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with paddle.pir_utils.OldIrGuard():
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self.main_program = base.Program()
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self.startup_program = base.Program()
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self.feeds = None
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self.fetch_list = None
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self.enable_onednn = False
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self.enable_onednn_bfloat16 = False
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self.enable_trt = False
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self.enable_tensorrt_varseqlen = False
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self.trt_parameters = None
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self.dynamic_shape_params = None
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self.enable_lite = False
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self.lite_parameters = None
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self.temp_dir = tempfile.TemporaryDirectory()
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self.path = os.path.join(
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self.temp_dir.name, 'inference_pass', self.__class__.__name__
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)
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np.random.seed(1)
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random.seed(1)
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def _get_place(self):
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return {False, core.is_compiled_with_cuda()}
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def _save_models(
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self, dirname, feeded_var_names, target_vars, executor, program, scope
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):
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with base.scope_guard(scope):
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# save models as combined but sometimes params is null
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# To adapt to this situation, the path needs to be adjusted to the old version format.
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feeded_vars = []
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for var in program.list_vars():
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if var.name in feeded_var_names:
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feeded_vars.append(var)
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paddle.static.io.save_inference_model(
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dirname,
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feeded_vars,
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target_vars,
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executor,
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program=program,
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)
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# if the param save is null
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# replace model_path to old version
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param_file = dirname + ".pdiparams"
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if not os.path.exists(param_file):
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model_path = dirname + ".pdmodel"
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try:
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save_dirname = os.path.normpath(dirname)
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os.makedirs(save_dirname)
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except OSError as e:
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if e.errno != errno.EEXIST:
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raise
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model_path_old = os.path.join(save_dirname, "__model__")
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if not os.path.exists(model_path_old):
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os.rename(model_path, model_path_old)
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def _get_paddle_outs(self, executor, program, scope):
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'''
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Return PaddlePaddle outputs.
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'''
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with paddle.pir_utils.OldIrGuard():
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with base.scope_guard(scope):
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outs = executor.run(
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program=program,
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feed=self.feeds,
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fetch_list=self.fetch_list,
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return_numpy=False,
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)
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return outs
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def _get_inference_outs(self, config):
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'''
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Return AnalysisPredictor outputs.
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'''
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predictor = create_paddle_predictor(config)
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tensor_shapes = predictor.get_input_tensor_shape()
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names = predictor.get_input_names()
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for i, name in enumerate(names):
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shape = tensor_shapes[name]
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shape[0] = 1
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tensor = predictor.get_input_tensor(name)
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feed_data = list(self.feeds.values())[i]
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tensor.copy_from_cpu(np.array(feed_data))
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if type(feed_data) == base.DenseTensor:
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tensor.set_lod(feed_data.lod())
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predictor.zero_copy_run()
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output_names = predictor.get_output_names()
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outs = [
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predictor.get_output_tensor(out_name).copy_to_cpu()
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for out_name in output_names
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]
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return outs
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def _get_analysis_config(
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self, use_gpu=False, use_trt=False, use_onednn=False
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):
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'''
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Return a new object of AnalysisConfig.
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'''
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# To adapt to save_inference_model
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param_file = self.path + ".pdiparams"
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if not os.path.exists(param_file):
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config = AnalysisConfig(self.path)
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else:
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config = AnalysisConfig(
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self.path + ".pdmodel", self.path + ".pdiparams"
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)
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config.disable_gpu()
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config.disable_onednn()
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config.switch_specify_input_names(True)
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config.switch_ir_optim(True)
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config.switch_use_feed_fetch_ops(False)
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if use_gpu:
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config.enable_use_gpu(100, 0)
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if use_trt:
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config.enable_tensorrt_engine(
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self.trt_parameters.workspace_size,
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self.trt_parameters.max_batch_size,
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self.trt_parameters.min_subgraph_size,
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self.trt_parameters.precision,
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self.trt_parameters.use_static,
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self.trt_parameters.use_calib_mode,
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)
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if self.trt_parameters.use_inspector:
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config.enable_tensorrt_inspector(
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self.trt_parameters.inspector_serialize
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)
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self.assertTrue(
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config.tensorrt_inspector_enabled(),
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"The inspector option is not set correctly.",
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)
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if self.dynamic_shape_params:
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config.set_trt_dynamic_shape_info(
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self.dynamic_shape_params.min_input_shape,
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self.dynamic_shape_params.max_input_shape,
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self.dynamic_shape_params.optim_input_shape,
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self.dynamic_shape_params.disable_trt_plugin_fp16,
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)
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if self.enable_tensorrt_varseqlen:
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config.enable_tensorrt_varseqlen()
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elif use_onednn:
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config.enable_onednn()
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if self.enable_onednn_bfloat16:
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config.enable_onednn_bfloat16()
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print('config summary:', config.summary())
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return config
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def check_output(self, atol=1e-5):
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'''
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Check whether calculating on CPU and GPU, enable TensorRT
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or disable TensorRT, enable ONEDNN or disable ONEDNN
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are all the same.
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'''
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self.assertFalse(
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self.feeds is None, "The inputs of the model is None. "
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)
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use_gpu = self._get_place()
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for place_ in use_gpu:
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self.check_output_with_option(place_, atol)
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def check_output_with_option(
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self, use_gpu, atol=1e-5, flatten=False, quant=False, rtol=1e-5
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):
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'''
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Check whether calculating on CPU and GPU, enable TensorRT
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or disable TensorRT, enable ONEDNN or disable ONEDNN
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are all the same.
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'''
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place = base.CUDAPlace(0) if use_gpu else base.CPUPlace()
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executor = base.Executor(place)
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with paddle.pir_utils.OldIrGuard():
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scope = base.Scope()
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device = "GPU" if use_gpu else "CPU"
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with base.scope_guard(scope):
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executor.run(self.startup_program)
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self._save_models(
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self.path,
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list(self.feeds.keys()),
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self.fetch_list,
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executor,
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self.main_program,
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scope,
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)
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paddle_outs = self._get_paddle_outs(
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executor, self.main_program, scope
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)
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inference_outs = self._get_inference_outs(
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self._get_analysis_config(use_gpu=use_gpu)
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)
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# Check whether the results calculated on CPU and on GPU are the same.
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self.assertTrue(
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len(paddle_outs) == len(inference_outs),
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f"The number of outputs is different between inference and training forward at {device}",
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)
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for out, inference_out in zip(paddle_outs, inference_outs):
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paddle_out = np.array(out)
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if flatten:
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paddle_out = paddle_out.flatten()
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inference_out = inference_out.flatten()
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np.testing.assert_allclose(
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paddle_out,
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inference_out,
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rtol=1e-05,
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atol=atol,
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err_msg=f'Output has diff between inference and training forward at {device} ',
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)
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# Check whether the trt results and the GPU results are the same.
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if use_gpu and self.enable_trt:
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tensorrt_outputs = self._get_inference_outs(
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self._get_analysis_config(
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use_gpu=use_gpu, use_trt=self.enable_trt
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)
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)
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if self.trt_parameters.use_static:
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# deserialize
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tensorrt_outputs = self._get_inference_outs(
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self._get_analysis_config(
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use_gpu=use_gpu, use_trt=self.enable_trt
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)
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)
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self.assertTrue(
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len(tensorrt_outputs) == len(paddle_outs),
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"The number of outputs is different between GPU and TensorRT. ",
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)
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for paddle_out, tensorrt_output in zip(
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paddle_outs, tensorrt_outputs
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):
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paddle_out = np.array(paddle_out)
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if flatten:
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paddle_out = paddle_out.flatten()
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tensorrt_output = tensorrt_output.flatten()
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np.testing.assert_allclose(
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tensorrt_output,
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paddle_out,
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rtol=rtol,
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atol=atol,
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err_msg='Output has diff between GPU and TensorRT. ',
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)
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# Check whether the onednn results and the CPU results are the same.
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if (not use_gpu) and self.enable_onednn:
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onednn_outputs = self._get_inference_outs(
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self._get_analysis_config(
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use_gpu=use_gpu, use_onednn=self.enable_onednn
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)
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)
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self.assertTrue(
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len(paddle_outs) == len(onednn_outputs),
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"The number of outputs is different between CPU and ONEDNN. ",
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)
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if self.enable_onednn_bfloat16:
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atol = 0.01
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for paddle_out, onednn_output in zip(paddle_outs, onednn_outputs):
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np.testing.assert_allclose(
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np.array(paddle_out),
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onednn_output,
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rtol=1e-05,
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atol=atol,
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err_msg='Output has diff between CPU and ONEDNN. ',
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)
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class TensorRTParam:
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'''
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Prepare TensorRT subgraph engine parameters.
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'''
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def __init__(
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self,
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workspace_size,
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max_batch_size,
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min_subgraph_size,
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precision,
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use_static,
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use_calib_mode,
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use_inspector=False,
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inspector_serialize=False,
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):
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self.workspace_size = workspace_size
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self.max_batch_size = max_batch_size
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self.min_subgraph_size = min_subgraph_size
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self.precision = precision
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self.use_static = use_static
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self.use_calib_mode = use_calib_mode
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self.use_inspector = use_inspector
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self.inspector_serialize = inspector_serialize
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class DynamicShapeParam:
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'''
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Prepare TensorRT subgraph engine dynamic shape parameters.
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'''
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def __init__(
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self,
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min_input_shape,
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max_input_shape,
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optim_input_shape,
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disable_trt_plugin_fp16,
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):
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self.min_input_shape = min_input_shape
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self.max_input_shape = max_input_shape
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self.optim_input_shape = optim_input_shape
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self.disable_trt_plugin_fp16 = disable_trt_plugin_fp16
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class LiteParam:
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'''
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Prepare Lite subgraph engine parameters.
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'''
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def __init__(self, precision, passes_filter, ops_filter):
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self.precision = precision
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self.passes_filter = passes_filter
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self.ops_filter = ops_filter
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