wehub-resource-sync
116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
# Copyright (c) 2022 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 os
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import sys
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import tempfile
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import numpy as np
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import paddle
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from paddle.distributed import fleet
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from paddle.inference import Config, PrecisionType, create_predictor
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def run(reduce_type, precision):
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fleet.init(is_collective=True)
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paddle.enable_static()
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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block = main_program.blocks[0]
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with paddle.static.program_guard(main_program, startup_program):
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data = paddle.static.data(name='data', shape=[3, 4], dtype='float32')
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c_data = block.create_var(
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shape=data.shape,
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dtype=data.dtype,
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type=data.type,
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lod_level=data.lod_level,
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persistable=False,
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is_data=False,
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initializer=paddle.nn.initializer.Constant(value=1.0),
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)
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block.append_op(
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type='all_reduce',
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inputs={'x': data},
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outputs={'out': c_data},
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attrs={
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'ring_id': 0,
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'reduce_type': reduce_type,
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},
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)
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out = paddle.static.nn.fc(
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x=c_data,
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size=1,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.5)
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),
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)
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mean = paddle.mean(out)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_program)
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nranks = 2
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current_endpoint = "127.0.0.1:600" + str(fleet.worker_index())
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trainer_endpoints = ["127.0.0.1:6000", "127.0.0.1:6001"]
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with tempfile.TemporaryDirectory(prefix="allreduce_") as tmpdir:
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paddle.static.save_inference_model(
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os.path.join(tmpdir, "model"),
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[data],
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[mean],
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exe,
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program=main_program,
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)
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config = Config(
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os.path.join(tmpdir, "model.pdmodel"),
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os.path.join(tmpdir, "model.pdiparams"),
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)
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config.enable_memory_optim()
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config.enable_use_gpu(1000, fleet.worker_index())
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config.enable_tensorrt_engine(
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workspace_size=1 << 30,
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max_batch_size=1,
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min_subgraph_size=1,
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precision_mode=(
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PrecisionType.Half
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if precision == "fp16"
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else PrecisionType.Int8
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),
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use_static=False,
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use_calib_mode=False,
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)
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config.set_trt_dynamic_shape_info(
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{"data": [3, 4]}, {"data": [3, 4]}, {"data": [3, 4]}
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)
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predictor = create_predictor(config)
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input_names = predictor.get_input_names()
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input_tensor = predictor.get_input_handle("data")
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input_tensor.reshape([3, 4])
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input_tensor.copy_from_cpu(np.ones([3, 4]).astype(np.float32))
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predictor.run()
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output_names = predictor.get_output_names()
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output_handle = predictor.get_output_handle(output_names[0])
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output_data = output_handle.copy_to_cpu() # numpy.ndarray类型
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print(f"c_allreduce_out={output_data[0]}")
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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# This script just be called by test_trt_convert_c_allreduce.py
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sys.exit(0)
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reduce_type = sys.argv[1]
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precision = sys.argv[2]
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run(reduce_type, precision)
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