wehub-resource-sync
262 lines
8.9 KiB
Python
262 lines
8.9 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 sys
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import unittest
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import numpy as np
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import paddle
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import paddle.distributed as dist
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def init_process_group(strategy=None):
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nranks = paddle.distributed.ParallelEnv().nranks
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rank = dist.ParallelEnv().local_rank
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is_master = True if rank == 0 else False
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pg_group = dist.init_parallel_env()
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return pg_group.process_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_bkcl(self):
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device_id = paddle.distributed.ParallelEnv().dev_id
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paddle.set_device(f'xpu:{device_id}')
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pg = init_process_group()
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sys.stdout.write(
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f"rank {pg.rank()}: size {pg.size()} name {pg.name()}\n"
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)
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sys.stdout.write(f"rank {pg.rank()}: test new group api ok\n")
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# test allreduce sum
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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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sum_result = tensor_x + tensor_y
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if pg.rank() == 0:
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task = dist.all_reduce(tensor_x)
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np.testing.assert_array_equal(tensor_x, sum_result)
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else:
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task = dist.all_reduce(tensor_y)
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np.testing.assert_array_equal(tensor_y, sum_result)
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sys.stdout.write(f"rank {pg.rank()}: test allreduce sum api ok\n")
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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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# XPU don't support event query by now, so just use sync op here
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task = dist.broadcast(tensor_x, 0)
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paddle.device.xpu.synchronize()
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np.testing.assert_array_equal(broadcast_result, tensor_x)
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else:
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task = dist.broadcast(tensor_y, 0)
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paddle.device.xpu.synchronize()
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np.testing.assert_array_equal(broadcast_result, tensor_y)
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sys.stdout.write(f"rank {pg.rank()}: test broadcast api ok\n")
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# test barrier
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# rank 0
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if pg.rank() == 0:
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pg.barrier(device_id)
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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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sys.stdout.write(f"rank {pg.rank()}: test barrier api ok\n")
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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_x, tensor_out)
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task.wait()
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paddle.device.xpu.synchronize()
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# rank 1
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else:
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tensor_out_list = [
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paddle.empty_like(tensor_x),
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paddle.empty_like(tensor_x),
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]
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task = dist.all_gather(tensor_out_list, tensor_y)
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paddle.device.xpu.synchronize()
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tensor_out = paddle.concat(tensor_out_list)
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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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np.testing.assert_array_equal(tensor_x, out_1)
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np.testing.assert_array_equal(tensor_y, out_2)
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sys.stdout.write(f"rank {pg.rank()}: test allgather api ok\n")
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if pg.rank() == 0:
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task = pg.all_gather(tensor_x, tensor_out)
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task.wait()
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paddle.device.xpu.synchronize()
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# rank 1
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else:
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tensor_out_list = []
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task = dist.all_gather(tensor_out_list, tensor_y)
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paddle.device.xpu.synchronize()
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tensor_out = paddle.concat(tensor_out_list)
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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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np.testing.assert_array_equal(tensor_x, out_1)
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np.testing.assert_array_equal(tensor_y, out_2)
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sys.stdout.write(f"rank {pg.rank()}: test allgather api2 ok\n")
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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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old_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 = dist.reduce(tensor_x, 0, sync_op=True)
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paddle.device.xpu.synchronize()
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# rank 1
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else:
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task = dist.reduce(tensor_y, 0, sync_op=False)
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task.wait()
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paddle.device.xpu.synchronize()
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if pg.rank() == 0:
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np.testing.assert_array_equal(tensor_x, sum_result)
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np.testing.assert_array_equal(tensor_y, old_tensor_y)
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sys.stdout.write(f"rank {pg.rank()}: test reduce sum api ok\n")
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# test reduce_scatter
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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(in_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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need_result = tensor_x + tensor_y
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need_result0 = paddle.slice(need_result, [0], [0], [self.shape[0]])
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need_result1 = paddle.slice(
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need_result, [0], [self.shape[0]], [in_shape[0]]
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)
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out = np.random.random(self.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 = dist.reduce_scatter(tensor_out, tensor_x, sync_op=True)
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else:
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task = dist.reduce_scatter(tensor_out, tensor_y, sync_op=False)
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task.wait()
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paddle.device.xpu.synchronize()
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if pg.rank() == 0:
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np.testing.assert_array_equal(need_result0, tensor_out)
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else:
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np.testing.assert_array_equal(need_result1, tensor_out)
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sys.stdout.write(f"rank {pg.rank()}: test reduce_scatter sum api ok\n")
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# test send async api
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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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if pg.rank() == 0:
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task = dist.send(tensor_x, 1, sync_op=False)
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task.wait()
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else:
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task = dist.recv(tensor_y, 0, sync_op=False)
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task.wait()
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np.testing.assert_array_equal(tensor_y, tensor_x)
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# test send sync api
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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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if pg.rank() == 0:
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task = dist.send(tensor_x, 1, sync_op=True)
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else:
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task = dist.recv(tensor_y, 0, sync_op=True)
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np.testing.assert_array_equal(tensor_y, tensor_x)
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# test send 0-d tensor
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# rank 0
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x = np.random.uniform(-1, 1, []).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.array(0.2022).astype(self.dtype)
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tensor_y = paddle.to_tensor(y)
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if pg.rank() == 0:
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task = dist.send(tensor_x, 1, sync_op=True)
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else:
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task = dist.recv(tensor_y, 0, sync_op=True)
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assert np.array_equal(tensor_y, tensor_x) and tensor_y.shape == []
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sys.stdout.write(f"rank {pg.rank()}: test send api ok\n")
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class TestProcessGroupFp16(TestProcessGroupFp32):
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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 = "float16"
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self.shape = (4, 20, 20)
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if __name__ == "__main__":
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unittest.main()
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