forked from mindspore-Ecosystem/mindspore
267 lines
10 KiB
Python
267 lines
10 KiB
Python
# Copyright 2022 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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""" test scatter update """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.ops import operations as P
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from mindspore import context
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from mindspore.common.api import _cell_graph_executor
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from parallel.utils.utils import ParallelValidator
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scatter_ops_dict = {"Add": P.ScatterAdd(), "Mul": P.ScatterMul(), "Div": P.ScatterDiv(),
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"Min": P.ScatterMin(), "Max": P.ScatterMax(), "Sub": P.ScatterSub()}
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self, input_shape, indices_shape, updates_shape, strategy1=None, strategy2=None, ops="Add"):
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super(Net, self).__init__()
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self.inputs = Parameter(Tensor(np.ones(input_shape).astype(np.float32)), "input")
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self.indices = Tensor(np.ones(indices_shape).astype(np.int32))
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self.updates = Tensor(np.ones(updates_shape).astype(np.float32))
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self.scatter_ops = scatter_ops_dict.get(ops)
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self.scatter_ops.shard(strategy1)
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self.add = P.TensorAdd().shard(strategy2)
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self.relu = P.ReLU()
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def construct(self, x):
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out = self.scatter_ops(self.inputs, self.indices, self.updates)
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out = self.add(x, out)
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out = self.relu(out)
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return out
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def compile_net(net, *inputs):
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net.set_auto_parallel()
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net.set_train()
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phase, _ = _cell_graph_executor.compile(net, *inputs)
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context.reset_auto_parallel_context()
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return phase
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def test_scatter_add_column_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter add column split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
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inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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input_shape = [32, 64, 128]
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indices_shape = [4, 8]
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updates_shape = [4, 8, 64, 128]
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strategy1 = ((1, 2, 4), (1, 1), (1, 1, 2, 4))
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strategy2 = ((1, 2, 4), (1, 2, 4))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2)
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compile_net(net, inputs)
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def test_scatter_min_column_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter add column split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
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inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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input_shape = [32, 64, 128]
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indices_shape = [4, 8]
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updates_shape = [4, 8, 64, 128]
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strategy1 = ((1, 2, 4), (1, 1), (1, 1, 2, 4))
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strategy2 = ((1, 2, 4), (1, 2, 4))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2, "Min")
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compile_net(net, inputs)
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def test_scatter_max_column_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter add column split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
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inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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input_shape = [32, 64, 128]
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indices_shape = [4, 8]
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updates_shape = [4, 8, 64, 128]
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strategy1 = ((1, 2, 4), (1, 1), (1, 1, 2, 4))
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strategy2 = ((1, 2, 4), (1, 2, 4))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2, "Max")
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compile_net(net, inputs)
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def test_scatter_add_row_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter add row split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
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inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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input_shape = [32, 64, 128]
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indices_shape = [4, 8]
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updates_shape = [4, 8, 64, 128]
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strategy1 = ((8, 1, 1), (1, 1), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1), (1, 1, 1))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2)
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phase = compile_net(net, inputs)
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validator = ParallelValidator(net, phase)
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# check sub_graph
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sub_graph = {
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'ScatterAdd-0': ['input', 'Minimum-0', 'Mul-0'],
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'Mul-0': ['_GetTensorSlice-1', 'Reshape-0'],
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}
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assert validator.check_graph_structure(sub_graph)
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def test_scatter_add_mix_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter add mix split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=24, full_batch=True)
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inputs = Tensor(np.ones([32 * 3, 64, 128]).astype(np.float32))
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input_shape = [32 * 3, 64, 128]
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indices_shape = [4, 8]
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updates_shape = [4, 8, 64, 128]
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strategy1 = ((3, 2, 4), (1, 1), (1, 1, 2, 4))
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strategy2 = ((1, 1, 1), (1, 1, 1))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2)
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compile_net(net, inputs)
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def test_scatter_mul_mix_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter mul mix split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=64, full_batch=True)
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inputs = Tensor(np.ones([32 * 4, 64]).astype(np.float32))
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input_shape = [32 * 4, 64]
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indices_shape = [4]
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updates_shape = [4, 64]
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strategy1 = ((8, 8), (1,), (1, 8))
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strategy2 = ((1, 1), (1, 1))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2, "Mul")
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phase = compile_net(net, inputs)
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validator = ParallelValidator(net, phase)
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# check sub_graph
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sub_graph = {
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'ScatterMul-0': ['input', 'Minimum-0', 'Add-0'],
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'Add-0': ['Mul-0', 'Sub-1'],
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}
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assert validator.check_graph_structure(sub_graph)
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def test_scatter_div_mix_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter div mix split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=64, full_batch=True)
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inputs = Tensor(np.ones([32 * 4, 64]).astype(np.float32))
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input_shape = [32 * 4, 64]
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indices_shape = [4, 4]
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updates_shape = [4, 4, 64]
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strategy1 = ((8, 8), (1, 1), (1, 1, 8))
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strategy2 = ((1, 1), (1, 1))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2, "Div")
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compile_net(net, inputs)
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def test_scatter_add_mix_split_auto_parallel_sharding_prop():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter add mix split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=24, full_batch=True,
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search_mode="sharding_propagation")
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inputs = Tensor(np.ones([32 * 3, 64, 128]).astype(np.float32))
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input_shape = [32 * 3, 64, 128]
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indices_shape = [4, 8]
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updates_shape = [4, 8, 64, 128]
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strategy1 = None
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strategy2 = ((3, 2, 4), (3, 2, 4))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2)
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compile_net(net, inputs)
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def test_scatter_mul_mix_split_auto_parallel():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter mul mix split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=64, full_batch=True)
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inputs = Tensor(np.ones([32 * 4, 64]).astype(np.float32))
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input_shape = [32 * 4, 64]
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indices_shape = [4]
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updates_shape = [4, 64]
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strategy1 = None
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strategy2 = None
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2, "Mul")
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compile_net(net, inputs)
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def test_scatter_div_mix_split_auto_parallel_rec():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter div mix split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=64, full_batch=True,
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search_mode="recursive_programming")
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inputs = Tensor(np.ones([32 * 4, 64]).astype(np.float32))
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input_shape = [32 * 4, 64]
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indices_shape = [4, 4]
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updates_shape = [4, 4, 64]
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strategy1 = None
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strategy2 = None
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2, "Div")
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compile_net(net, inputs)
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def test_scatter_sub_mix_split():
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"""
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Feature: test scatter ops auto parallel
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Description: test scatter sub mix split
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Expectation: compile success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=64, full_batch=True)
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inputs = Tensor(np.ones([32 * 4, 64]).astype(np.float32))
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input_shape = [32 * 4, 64]
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indices_shape = [4, 4]
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updates_shape = [4, 4, 64]
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strategy1 = ((8, 8), (1, 1), (1, 1, 8))
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strategy2 = ((1, 1), (1, 1))
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net = Net(input_shape, indices_shape, updates_shape, strategy1, strategy2, "Sub")
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compile_net(net, inputs)
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