wehub-resource-sync
391 lines
14 KiB
Python
391 lines
14 KiB
Python
# Copyright (c) 2024 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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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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@classmethod
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def setUpClass(cls):
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device_id = paddle.distributed.ParallelEnv().dev_id
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paddle.set_device(f'gpu:{device_id}')
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assert paddle.distributed.is_available()
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pg = init_process_group()
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assert paddle.distributed.get_backend() == "NCCL"
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cls.pg = pg
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@classmethod
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def tearDownClass(cls):
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del cls.pg
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def test_allreduce_sum(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random(self.shape).astype(self.dtype)
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# rank 1
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y_np = np.random.random(self.shape).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name="x", shape=self.shape, dtype=self.dtype
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)
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y = paddle.static.data(
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name="y", shape=self.shape, dtype=self.dtype
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)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x)
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else:
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dist.all_reduce(y)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(x_np + y_np, x_out)
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else:
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np.testing.assert_array_equal(x_np + y_np, y_out)
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def test_allreduce_sum_with_0d_input(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random([]).astype(self.dtype)
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# rank 1
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y_np = np.random.random([]).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name="x", shape=[], dtype=self.dtype)
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y = paddle.static.data(name="y", shape=[], dtype=self.dtype)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x)
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else:
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dist.all_reduce(y)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(x_np + y_np, x_out)
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else:
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np.testing.assert_array_equal(x_np + y_np, y_out)
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def test_allreduce_max(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random(self.shape).astype(self.dtype)
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# rank 1
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y_np = np.random.random(self.shape).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name="x", shape=self.shape, dtype=self.dtype
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)
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y = paddle.static.data(
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name="y", shape=self.shape, dtype=self.dtype
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)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x, dist.ReduceOp.MAX)
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else:
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dist.all_reduce(y, dist.ReduceOp.MAX)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(np.maximum(x_np, y_np), x_out)
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else:
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np.testing.assert_array_equal(np.maximum(x_np, y_np), y_out)
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def test_allreduce_max_with_0d_input(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random([]).astype(self.dtype)
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# rank 1
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y_np = np.random.random([]).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name="x", shape=[], dtype=self.dtype)
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y = paddle.static.data(name="y", shape=[], dtype=self.dtype)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x, dist.ReduceOp.MAX)
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else:
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dist.all_reduce(y, dist.ReduceOp.MAX)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(np.maximum(x_np, y_np), x_out)
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else:
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np.testing.assert_array_equal(np.maximum(x_np, y_np), y_out)
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def test_allreduce_min(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random(self.shape).astype(self.dtype)
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# rank 1
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y_np = np.random.random(self.shape).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name="x", shape=self.shape, dtype=self.dtype
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)
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y = paddle.static.data(
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name="y", shape=self.shape, dtype=self.dtype
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)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x, dist.ReduceOp.MIN)
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else:
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dist.all_reduce(y, dist.ReduceOp.MIN)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(np.minimum(x_np, y_np), x_out)
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else:
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np.testing.assert_array_equal(np.minimum(x_np, y_np), y_out)
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def test_allreduce_min_with_0d_input(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random([]).astype(self.dtype)
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# rank 1
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y_np = np.random.random([]).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name="x", shape=[], dtype=self.dtype)
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y = paddle.static.data(name="y", shape=[], dtype=self.dtype)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x, dist.ReduceOp.MIN)
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else:
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dist.all_reduce(y, dist.ReduceOp.MIN)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(np.minimum(x_np, y_np), x_out)
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else:
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np.testing.assert_array_equal(np.minimum(x_np, y_np), y_out)
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def test_allreduce_prod(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random(self.shape).astype(self.dtype)
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# rank 1
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y_np = np.random.random(self.shape).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name="x", shape=self.shape, dtype=self.dtype
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)
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y = paddle.static.data(
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name="y", shape=self.shape, dtype=self.dtype
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)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x, dist.ReduceOp.PROD)
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else:
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dist.all_reduce(y, dist.ReduceOp.PROD)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(
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np.multiply(x_np, y_np), x_out
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)
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else:
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np.testing.assert_array_equal(
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np.multiply(x_np, y_np), y_out
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)
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def test_allreduce_prod_with_0d_input(self):
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pg = self.pg
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# rank 0
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x_np = np.random.random([]).astype(self.dtype)
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# rank 1
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y_np = np.random.random([]).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name="x", shape=[], dtype=self.dtype)
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y = paddle.static.data(name="y", shape=[], dtype=self.dtype)
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exe = paddle.static.Executor()
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if pg.rank() == 0:
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dist.all_reduce(x, dist.ReduceOp.PROD)
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else:
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dist.all_reduce(y, dist.ReduceOp.PROD)
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(x_out, y_out) = exe.run(
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main_program,
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feed={"x": x_np, "y": y_np},
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fetch_list=[x, y],
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)
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if pg.rank() == 0:
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np.testing.assert_array_equal(
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np.multiply(x_np, y_np), x_out
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)
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else:
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np.testing.assert_array_equal(
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np.multiply(x_np, y_np), y_out
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)
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def test_broadcast(self):
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# to_tensor dose not support float16 input
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if self.dtype == "float16":
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return
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pg = self.pg
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# rank 0
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x_np = np.random.random(self.shape).astype(self.dtype)
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# rank 1
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y_np = np.random.random(self.shape).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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if pg.rank() == 0:
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data = paddle.to_tensor(x_np)
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else:
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data = paddle.to_tensor(y_np)
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dist.broadcast(data, 1)
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exe = paddle.static.Executor()
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(data,) = exe.run(
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main_program,
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feed={},
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fetch_list=[data],
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)
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np.testing.assert_array_equal(y_np, data)
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def test_broadcast_with_0d_input(self):
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# to_tensor dose not support float16 input
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if self.dtype == "float16":
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return
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pg = self.pg
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# rank 0
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x_np = np.random.random([]).astype(self.dtype)
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# rank 1
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y_np = np.random.random([]).astype(self.dtype)
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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if pg.rank() == 0:
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data = paddle.to_tensor(x_np)
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else:
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data = paddle.to_tensor(y_np)
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dist.broadcast(data, 1)
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exe = paddle.static.Executor()
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(data,) = exe.run(
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main_program,
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feed={},
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fetch_list=[data],
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)
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np.testing.assert_array_equal(y_np, data)
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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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