wehub-resource-sync
279 lines
12 KiB
Python
279 lines
12 KiB
Python
# Copyright (c) 2023 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 unittest
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import paddle
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import paddle.distributed as dist
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from paddle.distributed.auto_parallel.api import dtensor_from_local
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paddle.enable_static()
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BATCH_SIZE = 2
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SEQ_LEN = 4
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HIDDEN_SIZE = 8
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MP_SIZE = 2
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class TestBuildFakeProgram(unittest.TestCase):
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def test_build_api(self):
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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start_program = paddle.base.Program()
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with paddle.base.program_guard(main_program, start_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['mp'])
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input = paddle.static.data(
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name='input', shape=[BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE]
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)
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w0 = paddle.pir.core.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE, HIDDEN_SIZE],
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name="w0",
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initializer=paddle.nn.initializer.Uniform(),
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)
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# dense tensor could not access dist tensor attribute
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with self.assertRaises(ValueError):
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tmp = input._local_shape
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self.assertIsNone(input.dist_attr())
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self.assertIsNone(w0.dist_attr())
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dist_input = dtensor_from_local(input, mesh, [dist.Replicate()])
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dist_w0 = dtensor_from_local(w0, mesh, [dist.Replicate()])
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def test_build_replicated_program(self):
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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start_program = paddle.base.Program()
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with paddle.base.program_guard(main_program, start_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['mp'])
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input = paddle.static.data(
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name='input', shape=[BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE]
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)
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w0 = paddle.pir.core.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE, HIDDEN_SIZE],
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name="w0",
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initializer=paddle.nn.initializer.Uniform(),
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)
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self.assertTrue(input.is_dense_tensor_type())
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self.assertTrue(w0.is_dense_tensor_type())
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dist_input = dtensor_from_local(input, mesh, [dist.Replicate()])
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dist_w0 = dtensor_from_local(w0, mesh, [dist.Replicate()])
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dist_out = paddle.matmul(dist_input, dist_w0)
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self.assertTrue(dist_input.is_dist_dense_tensor_type())
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self.assertTrue(dist_w0.is_dist_dense_tensor_type())
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# check detail
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self.assertTrue(dist_input.shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertTrue(w0.shape == [HIDDEN_SIZE, HIDDEN_SIZE])
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self.assertTrue(dist_input.shape == dist_input._local_shape)
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self.assertTrue(w0.shape == dist_w0._local_shape)
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self.assertTrue(dist_input.dist_attr().dims_mapping == [-1, -1, -1])
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self.assertTrue(
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isinstance(
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dist_input.dist_attr().process_mesh,
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paddle.base.libpaddle.ProcessMesh,
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)
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)
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self.assertTrue(dist_input.dist_attr().process_mesh.shape == [2])
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self.assertTrue(
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dist_input.dist_attr().process_mesh.process_ids == [0, 1]
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)
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self.assertTrue(len(dist_input.dist_attr().partial_dims) == 0)
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self.assertTrue(dist_w0.dist_attr().dims_mapping == [-1, -1])
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self.assertTrue(
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isinstance(
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dist_w0.dist_attr().process_mesh,
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paddle.base.libpaddle.ProcessMesh,
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)
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)
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self.assertTrue(dist_w0.dist_attr().process_mesh.shape == [2])
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self.assertTrue(dist_w0.dist_attr().process_mesh.process_ids == [0, 1])
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self.assertTrue(len(dist_w0.dist_attr().partial_dims) == 0)
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# matmul out
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self.assertTrue(dist_out.shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertTrue(
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dist_out._local_shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE]
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)
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self.assertTrue(dist_out.dist_attr().dims_mapping == [-1, -1, -1])
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self.assertTrue(
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isinstance(
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dist_out.dist_attr().process_mesh,
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paddle.base.libpaddle.ProcessMesh,
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)
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)
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self.assertTrue(dist_out.dist_attr().process_mesh.shape == [2])
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self.assertTrue(dist_out.dist_attr().process_mesh.process_ids == [0, 1])
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self.assertTrue(len(dist_out.dist_attr().partial_dims) == 0)
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def test_build_col_parallel_program(self):
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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start_program = paddle.base.Program()
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with paddle.base.program_guard(main_program, start_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['mp'])
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input = paddle.static.data(
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name='input', shape=[BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE]
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)
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w0 = paddle.pir.core.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE, HIDDEN_SIZE // MP_SIZE],
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name="w0",
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initializer=paddle.nn.initializer.Uniform(),
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)
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self.assertTrue(input.is_dense_tensor_type())
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self.assertTrue(w0.is_dense_tensor_type())
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dist_input = dtensor_from_local(input, mesh, [dist.Replicate()])
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dist_w0 = dtensor_from_local(w0, mesh, [dist.Shard(1)])
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dist_out = paddle.matmul(dist_input, dist_w0)
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self.assertTrue(dist_input.is_dist_dense_tensor_type())
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self.assertTrue(dist_w0.is_dist_dense_tensor_type())
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# check detail
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self.assertTrue(dist_input.shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertTrue(dist_w0.shape == [HIDDEN_SIZE, HIDDEN_SIZE])
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self.assertTrue(dist_input.shape == dist_input._local_shape)
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self.assertTrue(
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dist_w0._local_shape == [HIDDEN_SIZE, HIDDEN_SIZE // MP_SIZE]
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)
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self.assertTrue(dist_input.dist_attr().dims_mapping == [-1, -1, -1])
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self.assertTrue(dist_w0.dist_attr().dims_mapping == [-1, 0])
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# matmul out
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self.assertTrue(dist_out.shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertTrue(
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dist_out._local_shape
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== [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE // MP_SIZE]
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)
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self.assertTrue(dist_out.dist_attr().dims_mapping == [-1, -1, 0])
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self.assertTrue(
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isinstance(
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dist_out.dist_attr().process_mesh,
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paddle.base.libpaddle.ProcessMesh,
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)
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)
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self.assertTrue(dist_out.dist_attr().process_mesh.shape == [2])
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self.assertTrue(dist_out.dist_attr().process_mesh.process_ids == [0, 1])
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self.assertTrue(len(dist_out.dist_attr().partial_dims) == 0)
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def test_build_row_parallel_program(self):
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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start_program = paddle.base.Program()
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with paddle.base.program_guard(main_program, start_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['mp'])
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input = paddle.static.data(
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name='input',
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shape=[BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE // MP_SIZE],
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)
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w0 = paddle.pir.core.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE // MP_SIZE, HIDDEN_SIZE],
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name="w0",
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initializer=paddle.nn.initializer.Uniform(),
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)
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self.assertTrue(input.is_dense_tensor_type())
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self.assertTrue(w0.is_dense_tensor_type())
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dist_input = dtensor_from_local(input, mesh, [dist.Shard(2)])
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dist_w0 = dtensor_from_local(w0, mesh, [dist.Shard(0)])
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dist_out = paddle.matmul(dist_input, dist_w0)
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self.assertTrue(dist_input.is_dist_dense_tensor_type())
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self.assertTrue(dist_w0.is_dist_dense_tensor_type())
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# check detail
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self.assertTrue(dist_input.shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertTrue(dist_w0.shape == [HIDDEN_SIZE, HIDDEN_SIZE])
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self.assertTrue(
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dist_input._local_shape
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== [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE // MP_SIZE]
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)
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self.assertTrue(
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dist_w0._local_shape == [HIDDEN_SIZE // MP_SIZE, HIDDEN_SIZE]
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)
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self.assertTrue(dist_input.dist_attr().dims_mapping == [-1, -1, 0])
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self.assertTrue(dist_w0.dist_attr().dims_mapping == [0, -1])
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# matmul out
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self.assertTrue(dist_out.shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertTrue(
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dist_out._local_shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE]
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)
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self.assertTrue(dist_out.dist_attr().dims_mapping == [-1, -1, -1])
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self.assertTrue(
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isinstance(
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dist_out.dist_attr().process_mesh,
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paddle.base.libpaddle.ProcessMesh,
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)
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)
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self.assertTrue(dist_out.dist_attr().process_mesh.shape == [2])
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self.assertTrue(dist_out.dist_attr().process_mesh.process_ids == [0, 1])
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self.assertTrue(dist_out.dist_attr().partial_dims == {0})
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def test_build_with_shard_tensor(self):
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with paddle.pir_utils.IrGuard():
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main_program = paddle.base.Program()
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start_program = paddle.base.Program()
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with paddle.base.program_guard(main_program, start_program):
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mesh = dist.ProcessMesh([0, 1], dim_names=['mp'])
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input = paddle.static.data(
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name='input',
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shape=[BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE],
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)
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w0 = paddle.pir.core.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE, HIDDEN_SIZE],
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name="w0",
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initializer=paddle.nn.initializer.Uniform(),
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)
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w1 = paddle.pir.core.create_parameter(
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dtype="float32",
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shape=[HIDDEN_SIZE, HIDDEN_SIZE],
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name="w0",
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initializer=paddle.nn.initializer.Uniform(),
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)
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self.assertTrue(input.is_dense_tensor_type())
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self.assertTrue(w0.is_dense_tensor_type())
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dist_input = dist.shard_tensor(input, mesh, [dist.Replicate()])
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dist_w0 = dist.shard_tensor(w0, mesh, [dist.Shard(0)])
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dist_w1 = dist.shard_tensor(w1, mesh, [dist.Shard(1)])
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self.assertTrue(dist_input.is_dist_dense_tensor_type())
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self.assertTrue(dist_w0.is_dist_dense_tensor_type())
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# check global shape
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self.assertTrue(dist_input.shape == [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE])
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self.assertTrue(dist_w0.shape == [HIDDEN_SIZE, HIDDEN_SIZE])
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self.assertTrue(dist_w1.shape == [HIDDEN_SIZE, HIDDEN_SIZE])
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# check local shape
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self.assertTrue(
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dist_input._local_shape == dist_input.shape
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) # replicated, local = global
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self.assertTrue(
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dist_w0._local_shape == [HIDDEN_SIZE // MP_SIZE, HIDDEN_SIZE]
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) # sharded, local != global, sharded by mesh size
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self.assertTrue(
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dist_w1._local_shape == [HIDDEN_SIZE, HIDDEN_SIZE // MP_SIZE]
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) # sharded, local != global, sharded by mesh size
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# TODO check Dtype, layout same as densetensor
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# TODO check dims_mapping & mesh as user annotated
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if __name__ == "__main__":
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unittest.main()
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