wehub-resource-sync
303 lines
9.5 KiB
Python
303 lines
9.5 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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from __future__ import annotations
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import logging
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import numpy as np
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import paddle
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__all__ = []
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def _simple_network():
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"""
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Define a simple network composed by a single linear layer.
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"""
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input = paddle.static.data(
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name="input", shape=[None, 2, 2], dtype="float32"
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)
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weight = paddle.create_parameter(
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shape=[2, 3],
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dtype="float32",
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attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(0.1)),
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)
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bias = paddle.create_parameter(shape=[3], dtype="float32")
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linear_out = paddle.nn.functional.linear(x=input, weight=weight, bias=bias)
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out = paddle.tensor.sum(linear_out)
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return input, out, weight
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def _prepare_data():
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"""
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Prepare feeding data for simple network. The shape is [1, 2, 2].
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"""
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# Prepare the feeding data.
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np_input_single = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
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return np_input_single.reshape(1, 2, 2)
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def _is_cuda_available():
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"""
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Check whether CUDA is available.
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"""
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try:
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assert len(paddle.static.cuda_places()) > 0
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return True
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except Exception as e:
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logging.warning(
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"You are using GPU version PaddlePaddle, but there is no GPU "
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"detected on your machine. Maybe CUDA devices is not set properly."
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f"\n Original Error is {e}"
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)
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return False
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def _is_xpu_available():
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"""
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Check whether XPU is available.
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"""
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try:
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assert len(paddle.static.xpu_places()) > 0
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return True
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except Exception as e:
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logging.warning(
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"You are using XPU version PaddlePaddle, but there is no XPU "
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"detected on your machine. Maybe XPU devices is not set properly."
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f"\n Original Error is {e}"
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)
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return False
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def _run_dygraph_single(use_cuda, use_xpu, use_custom, custom_device_name):
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"""
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Testing the simple network in dygraph mode using one CPU/GPU/XPU.
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Args:
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use_cuda (bool): Whether running with CUDA.
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use_xpu (bool): Whether running with XPU.
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"""
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paddle.disable_static()
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if use_cuda:
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paddle.set_device('gpu')
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elif use_xpu:
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paddle.set_device('xpu')
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elif use_custom:
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paddle.set_device(custom_device_name)
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else:
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paddle.set_device('cpu')
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weight_attr = paddle.ParamAttr(
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name="weight", initializer=paddle.nn.initializer.Constant(value=0.5)
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)
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bias_attr = paddle.ParamAttr(
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name="bias", initializer=paddle.nn.initializer.Constant(value=1.0)
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)
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linear = paddle.nn.Linear(
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2, 4, weight_attr=weight_attr, bias_attr=bias_attr
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)
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input_np = _prepare_data()
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input_tensor = paddle.to_tensor(input_np)
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linear_out = linear(input_tensor)
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out = paddle.tensor.sum(linear_out)
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out.backward()
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opt = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=linear.parameters()
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)
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opt.step()
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def _run_static_single(use_cuda, use_xpu, use_custom, custom_device_name):
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"""
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Testing the simple network with executor running directly, using one CPU/GPU/XPU.
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Args:
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use_cuda (bool): Whether running with CUDA.
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use_xpu (bool): Whether running with XPU.
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"""
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paddle.enable_static()
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with paddle.static.scope_guard(paddle.static.Scope()):
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train_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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startup_prog.random_seed = 1
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with paddle.static.program_guard(train_prog, startup_prog):
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input, out, weight = _simple_network()
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param_grads = paddle.static.append_backward(
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out, parameter_list=[weight]
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)[0]
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if use_cuda:
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place = paddle.CUDAPlace(0)
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elif use_xpu:
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place = paddle.XPUPlace(0)
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elif use_custom:
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place = paddle.CustomPlace(custom_device_name, 0)
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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exe.run(
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train_prog,
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feed={input.name: _prepare_data()},
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fetch_list=[out, param_grads[1]],
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)
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paddle.disable_static()
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def train_for_run_parallel():
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"""
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train script for parallel training check
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"""
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# to avoid cyclic import
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class LinearNet(paddle.nn.Layer):
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"""
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simple fc network for parallel training check
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"""
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def __init__(self):
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super().__init__()
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self._linear1 = paddle.nn.Linear(10, 10)
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self._linear2 = paddle.nn.Linear(10, 1)
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def forward(self, x):
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"""
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forward
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"""
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return self._linear2(self._linear1(x))
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paddle.distributed.init_parallel_env()
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layer = LinearNet()
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dp_layer = paddle.DataParallel(layer)
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loss_fn = paddle.nn.MSELoss()
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adam = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=dp_layer.parameters()
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)
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inputs = paddle.randn([10, 10], 'float32')
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outputs = dp_layer(inputs)
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labels = paddle.randn([10, 1], 'float32')
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loss = loss_fn(outputs, labels)
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loss.backward()
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adam.step()
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adam.clear_grad()
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def _run_parallel(device_list):
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"""
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Testing the simple network in data parallel mode, using multiple CPU/GPU.
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Args:
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use_cuda (bool): Whether running with CUDA.
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use_xpu (bool): Whether running with XPU.
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device_list (int): The specified devices.
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"""
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paddle.distributed.spawn(train_for_run_parallel, nprocs=len(device_list))
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def run_check() -> None:
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"""
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Check whether PaddlePaddle is installed correctly and running successfully
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on your system.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> # doctest: +SKIP('the output will change in different run')
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>>> paddle.utils.run_check()
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Running verify PaddlePaddle program ...
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I0818 15:35:08.335391 30540 program_interpreter.cc:173] New Executor is Running.
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I0818 15:35:08.398319 30540 interpreter_util.cc:529] Standalone Executor is Used.
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PaddlePaddle works well on 1 CPU.
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PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.
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"""
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print("Running verify PaddlePaddle program ... ")
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use_cuda = False
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use_xpu = False
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use_custom = False
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custom_device_name = None
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if paddle.is_compiled_with_cuda():
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use_cuda = _is_cuda_available()
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elif paddle.is_compiled_with_xpu():
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use_xpu = _is_xpu_available()
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elif len(paddle.framework.core.get_all_custom_device_type()) > 0:
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use_custom = True
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if len(paddle.framework.core.get_all_custom_device_type()) > 1:
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logging.warning(
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f"More than one kind of custom devices detected, but run check would only be executed on {paddle.framework.core.get_all_custom_device_type()[0]}."
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)
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if use_cuda:
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device_str = "GPU"
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device_list = paddle.static.cuda_places()
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elif use_xpu:
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device_str = "XPU"
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device_list = paddle.static.xpu_places()
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elif use_custom:
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device_str = paddle.framework.core.get_all_custom_device_type()[0]
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custom_device_name = device_str
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device_list = list(
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range(
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paddle.framework.core.get_custom_device_count(
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custom_device_name
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)
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)
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)
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else:
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device_str = "CPU"
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device_list = paddle.static.cpu_places(device_count=1)
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device_count = len(device_list)
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_run_static_single(use_cuda, use_xpu, use_custom, custom_device_name)
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_run_dygraph_single(use_cuda, use_xpu, use_custom, custom_device_name)
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print(f"PaddlePaddle works well on 1 {device_str}.")
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try:
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if len(device_list) > 1:
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if use_custom:
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import os
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os.environ['PADDLE_DISTRI_BACKEND'] = "xccl"
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_run_parallel(device_list)
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print(f"PaddlePaddle works well on {device_count} {device_str}s.")
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print(
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"PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now."
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)
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except Exception as e:
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logging.warning(
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f"PaddlePaddle meets some problem with {device_count} {device_str}s. This may be caused by:"
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"\n 1. There is not enough GPUs visible on your system"
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"\n 2. Some GPUs are occupied by other process now"
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"\n 3. NVIDIA-NCCL2 is not installed correctly on your system. Please follow instruction on https://github.com/NVIDIA/nccl-tests "
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"\n to test your NCCL, or reinstall it following https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html"
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)
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logging.warning(f"\n Original Error is: {e}")
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print(
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f"PaddlePaddle is installed successfully ONLY for single {device_str}! "
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"Let's start deep learning with PaddlePaddle now."
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)
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raise e
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