wehub-resource-sync
236 lines
8.3 KiB
Python
236 lines
8.3 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 random
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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.base import core
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def init_process_group(strategy=None):
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nranks = paddle.distributed.ParallelEnv().nranks
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rank = paddle.distributed.ParallelEnv().local_rank
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is_master = True if rank == 0 else False
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store = paddle.base.core.TCPStore("127.0.0.1", 6173, is_master, nranks)
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pg_group = core.ProcessGroupCustom.create(
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store,
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paddle.distributed.ParallelEnv().device_type,
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rank,
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nranks,
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)
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return pg_group
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class TestProcessGroupFp32(unittest.TestCase):
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def setUp(self):
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paddle.seed(2022)
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random.seed(2022)
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np.random.seed(2022)
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self.config()
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def config(self):
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self.dtype = "float32"
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self.shape = (2, 10, 5)
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def test_create_process_group_xccl(self):
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device_id = paddle.distributed.ParallelEnv().dev_id
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paddle.set_device(f'custom_cpu:{device_id}')
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pg = init_process_group()
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x = np.random.random(self.shape).astype(self.dtype)
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tensor_x = paddle.to_tensor(x)
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y = np.random.random(self.shape).astype(self.dtype)
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tensor_y = paddle.to_tensor(y)
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sum_result = tensor_x + tensor_y
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if pg.rank() == 0:
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task = pg.all_reduce(tensor_x, core.ReduceOp.SUM, sync_op=True)
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task.wait()
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# assert np.array_equal(tensor_x, sum_result)
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else:
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task = pg.all_reduce(tensor_y, core.ReduceOp.SUM, sync_op=True)
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task.wait()
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# assert np.array_equal(tensor_y, sum_result)
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print("test allreduce sum api ok", flush=True)
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x = np.random.random(self.shape).astype(self.dtype)
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tensor_x = paddle.to_tensor(x)
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y = np.random.random(self.shape).astype(self.dtype)
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tensor_y = paddle.to_tensor(y)
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max_result = paddle.maximum(tensor_x, tensor_y)
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if pg.rank() == 0:
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task = pg.all_reduce(tensor_x, core.ReduceOp.MAX, sync_op=True)
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task.wait()
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# assert np.array_equal(tensor_x, max_result)
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else:
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task = pg.all_reduce(tensor_y, core.ReduceOp.MAX, sync_op=True)
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task.wait()
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# assert np.array_equal(tensor_y, max_result)
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print("test allreduce max api ok", flush=True)
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# test broadcast
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# rank 0
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x = np.random.random(self.shape).astype(self.dtype)
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tensor_x = paddle.to_tensor(x)
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# rank 1
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y = np.random.random(self.shape).astype(self.dtype)
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tensor_y = paddle.to_tensor(y)
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broadcast_result = paddle.assign(tensor_x)
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if pg.rank() == 0:
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task = pg.broadcast(tensor_x, 0, sync_op=True)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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assert task.is_completed()
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# assert np.array_equal(broadcast_result, tensor_x)
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else:
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task = pg.broadcast(tensor_y, 0, sync_op=True)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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assert task.is_completed()
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# assert np.array_equal(broadcast_result, tensor_y)
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print("test broadcast api ok", flush=True)
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# test barrier
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# rank 0
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if pg.rank() == 0:
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task = pg.barrier(device_id)
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task.wait()
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# rank 1
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else:
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task = pg.barrier(device_id)
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task.wait()
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print("test barrier api ok\n", flush=True)
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return
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# test allgather
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# rank 0
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x = np.random.random(self.shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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out_shape = list(self.shape)
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out_shape[0] *= 2
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out = np.random.random(out_shape).astype(self.dtype)
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tensor_out = paddle.to_tensor(out)
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if pg.rank() == 0:
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task = pg.all_gather(tensor_out, tensor_x, sync_op=True)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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# rank 1
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else:
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task = pg.all_gather(tensor_out, tensor_y, sync_op=True)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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out_1 = paddle.slice(tensor_out, [0], [0], [out_shape[0] // 2])
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out_2 = paddle.slice(
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tensor_out, [0], [out_shape[0] // 2], [out_shape[0]]
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)
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# assert np.array_equal(tensor_x, out_1)
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# assert np.array_equal(tensor_y, out_2)
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print("test allgather api ok\n", flush=True)
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# test alltoall
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# rank 0
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x = np.random.random(self.shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.dtype)
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out1 = np.random.random(self.shape).astype(self.dtype)
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out2 = np.random.random(self.shape).astype(self.dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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tensor_out1 = paddle.to_tensor(out1)
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tensor_out2 = paddle.to_tensor(out2)
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raw_tensor_x_2 = paddle.slice(
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tensor_x, [0], [self.shape[0] // 2], [self.shape[0]]
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)
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raw_tensor_y_1 = paddle.slice(tensor_y, [0], [0], [self.shape[0] // 2])
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if pg.rank() == 0:
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task = pg.alltoall(tensor_out1, tensor_x)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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# rank 1
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else:
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task = pg.alltoall(tensor_out2, tensor_y)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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out1_2 = paddle.slice(
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tensor_out1, [0], [self.shape[0] // 2], [self.shape[0]]
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)
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out2_1 = paddle.slice(tensor_out2, [0], [0], [self.shape[0] // 2])
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# if pg.rank() == 0:
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# assert np.array_equal(out1_2.numpy(), raw_tensor_y_1.numpy())
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# else:
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# assert np.array_equal(out2_1, raw_tensor_x_2)
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print("test alltoall api ok\n", flush=True)
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# test Reduce
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# rank 0
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x = np.random.random(self.shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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sum_result = tensor_x + tensor_y
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if pg.rank() == 0:
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task = pg.reduce(tensor_x, 0)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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# rank 1
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else:
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task = pg.reduce(tensor_y, 0)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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# if pg.rank() == 0:
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# assert np.array_equal(tensor_x, sum_result)
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print("test reduce sum api ok\n", flush=True)
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# test Scatter
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# rank 0
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in_shape = list(self.shape)
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in_shape[0] *= 2
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x = np.random.random(in_shape).astype(self.dtype)
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y = np.random.random(self.shape).astype(self.dtype)
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tensor_x = paddle.to_tensor(x)
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tensor_y = paddle.to_tensor(y)
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if pg.rank() == 0:
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task = pg.scatter(tensor_x, tensor_y, 0)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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# rank 1
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else:
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task = pg.scatter(tensor_x, tensor_y, 0)
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task.wait()
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# paddle.base.core._custom_device_synchronize("custom_cpu", -1)
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out1 = paddle.slice(tensor_x, [0], [0], [self.shape[0]])
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out2 = paddle.slice(tensor_x, [0], [self.shape[0]], [self.shape[0] * 2])
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# if pg.rank() == 0:
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# assert np.array_equal(tensor_y, out1)
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# else:
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# assert np.array_equal(tensor_y, out2)
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print("test scatter api ok\n", flush=True)
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if __name__ == "__main__":
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unittest.main()
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