chore: import upstream snapshot with attribution

python/paddle/utils/install_check.py

@@ -0,0 +1,302 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+
+import numpy as np
+
+import paddle
+
+__all__ = []
+
+
+def _simple_network():
+    """
+    Define a simple network composed by a single linear layer.
+    """
+    input = paddle.static.data(
+        name="input", shape=[None, 2, 2], dtype="float32"
+    )
+    weight = paddle.create_parameter(
+        shape=[2, 3],
+        dtype="float32",
+        attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(0.1)),
+    )
+    bias = paddle.create_parameter(shape=[3], dtype="float32")
+    linear_out = paddle.nn.functional.linear(x=input, weight=weight, bias=bias)
+    out = paddle.tensor.sum(linear_out)
+    return input, out, weight
+
+
+def _prepare_data():
+    """
+    Prepare feeding data for simple network. The shape is [1, 2, 2].
+
+    """
+    # Prepare the feeding data.
+    np_input_single = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+    return np_input_single.reshape(1, 2, 2)
+
+
+def _is_cuda_available():
+    """
+    Check whether CUDA is available.
+    """
+    try:
+        assert len(paddle.static.cuda_places()) > 0
+        return True
+    except Exception as e:
+        logging.warning(
+            "You are using GPU version PaddlePaddle, but there is no GPU "
+            "detected on your machine. Maybe CUDA devices is not set properly."
+            f"\n Original Error is {e}"
+        )
+        return False
+
+
+def _is_xpu_available():
+    """
+    Check whether XPU is available.
+    """
+    try:
+        assert len(paddle.static.xpu_places()) > 0
+        return True
+    except Exception as e:
+        logging.warning(
+            "You are using XPU version PaddlePaddle, but there is no XPU "
+            "detected on your machine. Maybe XPU devices is not set properly."
+            f"\n Original Error is {e}"
+        )
+        return False
+
+
+def _run_dygraph_single(use_cuda, use_xpu, use_custom, custom_device_name):
+    """
+    Testing the simple network in dygraph mode using one CPU/GPU/XPU.
+
+    Args:
+        use_cuda (bool): Whether running with CUDA.
+        use_xpu (bool): Whether running with XPU.
+    """
+    paddle.disable_static()
+    if use_cuda:
+        paddle.set_device('gpu')
+    elif use_xpu:
+        paddle.set_device('xpu')
+    elif use_custom:
+        paddle.set_device(custom_device_name)
+    else:
+        paddle.set_device('cpu')
+    weight_attr = paddle.ParamAttr(
+        name="weight", initializer=paddle.nn.initializer.Constant(value=0.5)
+    )
+    bias_attr = paddle.ParamAttr(
+        name="bias", initializer=paddle.nn.initializer.Constant(value=1.0)
+    )
+    linear = paddle.nn.Linear(
+        2, 4, weight_attr=weight_attr, bias_attr=bias_attr
+    )
+    input_np = _prepare_data()
+    input_tensor = paddle.to_tensor(input_np)
+    linear_out = linear(input_tensor)
+    out = paddle.tensor.sum(linear_out)
+    out.backward()
+    opt = paddle.optimizer.Adam(
+        learning_rate=0.001, parameters=linear.parameters()
+    )
+    opt.step()
+
+
+def _run_static_single(use_cuda, use_xpu, use_custom, custom_device_name):
+    """
+    Testing the simple network with executor running directly, using one CPU/GPU/XPU.
+
+    Args:
+        use_cuda (bool): Whether running with CUDA.
+        use_xpu (bool): Whether running with XPU.
+    """
+    paddle.enable_static()
+    with paddle.static.scope_guard(paddle.static.Scope()):
+        train_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        startup_prog.random_seed = 1
+        with paddle.static.program_guard(train_prog, startup_prog):
+            input, out, weight = _simple_network()
+            param_grads = paddle.static.append_backward(
+                out, parameter_list=[weight]
+            )[0]
+
+        if use_cuda:
+            place = paddle.CUDAPlace(0)
+        elif use_xpu:
+            place = paddle.XPUPlace(0)
+        elif use_custom:
+            place = paddle.CustomPlace(custom_device_name, 0)
+        else:
+            place = paddle.CPUPlace()
+
+        exe = paddle.static.Executor(place)
+        exe.run(startup_prog)
+        exe.run(
+            train_prog,
+            feed={input.name: _prepare_data()},
+            fetch_list=[out, param_grads[1]],
+        )
+    paddle.disable_static()
+
+
+def train_for_run_parallel():
+    """
+    train script for parallel training check
+    """
+
+    # to avoid cyclic import
+    class LinearNet(paddle.nn.Layer):
+        """
+        simple fc network for parallel training check
+        """
+
+        def __init__(self):
+            super().__init__()
+            self._linear1 = paddle.nn.Linear(10, 10)
+            self._linear2 = paddle.nn.Linear(10, 1)
+
+        def forward(self, x):
+            """
+            forward
+            """
+            return self._linear2(self._linear1(x))
+
+    paddle.distributed.init_parallel_env()
+
+    layer = LinearNet()
+    dp_layer = paddle.DataParallel(layer)
+
+    loss_fn = paddle.nn.MSELoss()
+    adam = paddle.optimizer.Adam(
+        learning_rate=0.001, parameters=dp_layer.parameters()
+    )
+
+    inputs = paddle.randn([10, 10], 'float32')
+    outputs = dp_layer(inputs)
+    labels = paddle.randn([10, 1], 'float32')
+    loss = loss_fn(outputs, labels)
+
+    loss.backward()
+    adam.step()
+    adam.clear_grad()
+
+
+def _run_parallel(device_list):
+    """
+    Testing the simple network in data parallel mode, using multiple CPU/GPU.
+
+    Args:
+        use_cuda (bool): Whether running with CUDA.
+        use_xpu (bool): Whether running with XPU.
+        device_list (int): The specified devices.
+    """
+    paddle.distributed.spawn(train_for_run_parallel, nprocs=len(device_list))
+
+
+def run_check() -> None:
+    """
+    Check whether PaddlePaddle is installed correctly and running successfully
+    on your system.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+
+            >>> # doctest: +SKIP('the output will change in different run')
+            >>> paddle.utils.run_check()
+            Running verify PaddlePaddle program ...
+            I0818 15:35:08.335391 30540 program_interpreter.cc:173] New Executor is Running.
+            I0818 15:35:08.398319 30540 interpreter_util.cc:529] Standalone Executor is Used.
+            PaddlePaddle works well on 1 CPU.
+            PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.
+    """
+
+    print("Running verify PaddlePaddle program ... ")
+
+    use_cuda = False
+    use_xpu = False
+    use_custom = False
+    custom_device_name = None
+
+    if paddle.is_compiled_with_cuda():
+        use_cuda = _is_cuda_available()
+    elif paddle.is_compiled_with_xpu():
+        use_xpu = _is_xpu_available()
+    elif len(paddle.framework.core.get_all_custom_device_type()) > 0:
+        use_custom = True
+        if len(paddle.framework.core.get_all_custom_device_type()) > 1:
+            logging.warning(
+                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]}."
+            )
+
+    if use_cuda:
+        device_str = "GPU"
+        device_list = paddle.static.cuda_places()
+    elif use_xpu:
+        device_str = "XPU"
+        device_list = paddle.static.xpu_places()
+    elif use_custom:
+        device_str = paddle.framework.core.get_all_custom_device_type()[0]
+        custom_device_name = device_str
+        device_list = list(
+            range(
+                paddle.framework.core.get_custom_device_count(
+                    custom_device_name
+                )
+            )
+        )
+    else:
+        device_str = "CPU"
+        device_list = paddle.static.cpu_places(device_count=1)
+    device_count = len(device_list)
+
+    _run_static_single(use_cuda, use_xpu, use_custom, custom_device_name)
+    _run_dygraph_single(use_cuda, use_xpu, use_custom, custom_device_name)
+    print(f"PaddlePaddle works well on 1 {device_str}.")
+
+    try:
+        if len(device_list) > 1:
+            if use_custom:
+                import os
+
+                os.environ['PADDLE_DISTRI_BACKEND'] = "xccl"
+            _run_parallel(device_list)
+            print(f"PaddlePaddle works well on {device_count} {device_str}s.")
+        print(
+            "PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now."
+        )
+    except Exception as e:
+        logging.warning(
+            f"PaddlePaddle meets some problem with {device_count} {device_str}s. This may be caused by:"
+            "\n 1. There is not enough GPUs visible on your system"
+            "\n 2. Some GPUs are occupied by other process now"
+            "\n 3. NVIDIA-NCCL2 is not installed correctly on your system. Please follow instruction on https://github.com/NVIDIA/nccl-tests "
+            "\n to test your NCCL, or reinstall it following https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html"
+        )
+
+        logging.warning(f"\n Original Error is: {e}")
+        print(
+            f"PaddlePaddle is installed successfully ONLY for single {device_str}! "
+            "Let's start deep learning with PaddlePaddle now."
+        )
+        raise e