forked from mindspore-Ecosystem/mindspore
124 lines
4.8 KiB
Python
124 lines
4.8 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import numpy as np
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import mindspore as ms
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from mindspore import context, Tensor, Parameter
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from mindspore.common.initializer import initializer
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from parallel.utils.utils import ParallelValidator
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class Net(Cell):
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def __init__(self, strategy1, strategy2, split_tuple, param_shape, mul_weight_shape):
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super().__init__()
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self.gatherv2 = P.Gather().shard(strategy1)
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self.gatherv2.add_prim_attr("manual_split", split_tuple)
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self.mul = P.Mul().shard(strategy2)
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self.param = Parameter(initializer("ones", param_shape, ms.float32), name="gather_param")
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self.mul_weight = Parameter(initializer("ones", mul_weight_shape, ms.float32), name="mul_weight")
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def construct(self, x, b):
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out = self.gatherv2(self.param, x, 0)
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out = self.mul(out, self.mul_weight)
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return out
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def compile_net(net, x):
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net.set_train()
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b = Tensor(np.ones([64, 8]), dtype=ms.float32)
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phase, _ = _cell_graph_executor.compile(net, x, b)
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context.reset_auto_parallel_context()
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return phase
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def test_field_split_dim_1x1():
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"""
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Feature: test field split
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Description: param dim is 1, indices dim is 1
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=1)
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strategy1 = ((8,), (8,))
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strategy2 = ((8,), (1,))
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split_tuple = (5, 6, 7, 8, 9, 10, 11, 8)
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param_shape = (64)
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mul_weight_shape = (1)
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x = Tensor(np.ones([16 // 8]), dtype=ms.int32)
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net = Net(strategy1, strategy2, split_tuple, param_shape, mul_weight_shape)
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phase = compile_net(net, x)
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validator = ParallelValidator(net, phase)
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# check layout, dev-matrix/tensor-map/slice_shape/field_size/uniform_split/opt_shard_group
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gather_param_layout = ([8], [0], [6], 0, False, '')
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assert validator.check_parameter_layout('gather_param', gather_param_layout)
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# check inputs
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sub_expect_inputs = ['TupleGetItem', 'value=5']
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assert validator.check_node_inputs_fuzzy_match('Sub-0', sub_expect_inputs)
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def test_field_split_dim_2x1():
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"""
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Feature: test field split
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Description: param dim is 2, indices dim is 1
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=2)
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strategy1 = ((4, 2), (4,))
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strategy2 = ((4, 2), (1,))
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split_tuple = (10, 20, 30, 4)
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param_shape = (64, 32)
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mul_weight_shape = (1)
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x = Tensor(np.ones([16 // 8]), dtype=ms.int32)
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net = Net(strategy1, strategy2, split_tuple, param_shape, mul_weight_shape)
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phase = compile_net(net, x)
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validator = ParallelValidator(net, phase)
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# check layout, dev-matrix/tensor-map/slice_shape/field_size/uniform_split/opt_shard_group
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gather_param_layout = ([4, 2], [1, 0], [20, 16], 0, False, '')
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assert validator.check_parameter_layout('gather_param', gather_param_layout)
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# check inputs
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sub_expect_inputs = ['Reshape', 'value=10']
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assert validator.check_node_inputs_fuzzy_match('Sub-0', sub_expect_inputs)
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def test_field_split_dim_3x1():
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"""
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Feature: test field split
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Description: param dim is 3, indices dim is 1
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=2)
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strategy1 = ((4, 2, 1), (4,))
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strategy2 = ((4, 2, 1), (1,))
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split_tuple = (10, 20, 30, 4)
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param_shape = (64, 32, 16)
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mul_weight_shape = (1)
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x = Tensor(np.ones([16 // 8]), dtype=ms.int32)
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net = Net(strategy1, strategy2, split_tuple, param_shape, mul_weight_shape)
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phase = compile_net(net, x)
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validator = ParallelValidator(net, phase)
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# check layout, dev-matrix/tensor-map/slice_shape/field_size/uniform_split/opt_shard_group
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gather_param_layout = ([4, 2], [1, 0, -1], [20, 16, 16], 0, False, '')
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assert validator.check_parameter_layout('gather_param', gather_param_layout)
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# check inputs
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sub_expect_inputs = ['Reshape', 'value=10']
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assert validator.check_node_inputs_fuzzy_match('Sub-0', sub_expect_inputs)
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