forked from mindspore-Ecosystem/mindspore
246 lines
10 KiB
Python
246 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 pytest
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import mindspore.nn as nn
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from mindspore import Tensor, Model, 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_nd_ops_map = {"Add": P.ScatterNdAdd(), "Update": P.ScatterNdUpdate(), "Sub": P.ScatterNdSub()}
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tensor_scatter_ops_map = {"Add": P.TensorScatterAdd(), "Update": P.TensorScatterUpdate(),
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"Sub": P.TensorScatterSub(), "Mul": P.TensorScatterMul(), "Div": P.TensorScatterDiv()}
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# The shape of input: [A, B, C, D], the strategy of input: (a, b, c, d)
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# The shape of indices: [Q, W, 2], the strategy of indices: (1, 1, 1)
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# here the 2 respect to the size of [A, B]
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# The shape of updates: [Q, W, C, D], the strategy of updates: (1, 1, c, d)
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# The shape of output: [A, B, C, D], the strategy of output: (a, b, c, d)
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self, strategy1=None, strategy2=None, ops_type="Add"):
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super(Net, self).__init__()
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self.inputs = Parameter(Tensor(np.ones([32, 64, 128]).astype(np.float32)), "input")
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self.indices = Tensor(np.ones([4, 2]).astype(np.int32))
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self.updates = Tensor(np.ones([4, 128]).astype(np.float32))
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self.scatter_ops = scatter_nd_ops_map.get(ops_type).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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class Net1(nn.Cell):
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"""Net definition"""
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def __init__(self, strategy1=None, strategy2=None, ops_type="Add"):
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super(Net1, self).__init__()
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self.inputs = Parameter(Tensor(np.ones([32, 64, 128]).astype(np.float32)), "input")
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self.indices = Tensor(np.ones([4, 3]).astype(np.int32))
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self.updates = Tensor(np.ones([4]).astype(np.float32))
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self.scatter_ops = scatter_nd_ops_map.get(ops_type).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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class Net2(nn.Cell):
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"""Net definition"""
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def __init__(self, strategy1=None, strategy2=None, ops_type="Add"):
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super(Net2, self).__init__()
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self.indices = Tensor(np.ones([4, 3]).astype(np.int32))
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self.updates = Tensor(np.ones([4]).astype(np.float32))
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self.scatter_ops = tensor_scatter_ops_map.get(ops_type).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, inputs, x):
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inputs = self.relu(inputs)
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out = self.scatter_ops(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(False)
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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_nd_add():
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"""
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Feature: distribute operator scatter_nd_add in auto parallel.
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Description: scatter_nd_add net with sharding updating strategy in semi auto parallel.
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Expectation: assert ok.
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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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strategy1 = ((1, 2, 4), (1, 1), (1, 4))
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strategy2 = ((1, 2, 4), (1, 2, 4))
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net = Net(strategy1, strategy2)
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phase = compile_net(net, inputs)
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validator = ParallelValidator(net, phase)
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# check layout
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inputs_expect_layout = ([2, 4], [-1, 1, 0], [32, 32, 32], 0, True, '')
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assert validator.check_parameter_layout('input', inputs_expect_layout)
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def test_scatter_nd_wrong_strategy():
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"""
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Feature: distribute operator scatter_nd_add in auto parallel.
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Description: scatter_nd_add net with wrong strategy in semi auto parallel.
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Expectation: raise runtime error.
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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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strategy1 = ((1, 2, 4), (1, 1), (1, 2))
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strategy2 = ((1, 2, 4), (1, 2, 4))
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net = Net(strategy1, strategy2)
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model = Model(net)
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with pytest.raises(RuntimeError):
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model.predict(inputs)
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context.reset_auto_parallel_context()
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def test_scatter_nd_sub():
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"""
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Feature: distribute operator scatter_nd_sub in auto parallel.
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Description: scatter_nd_sub net with sharding input gather dims in semi auto parallel.
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Expectation: assert ok.
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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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strategy1 = ((2, 2, 2), (1, 1), (1,))
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strategy2 = ((2, 2, 2), (2, 2, 2))
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net = Net1(strategy1, strategy2, ops_type="Sub")
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phase = compile_net(net, inputs)
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validator = ParallelValidator(net, phase)
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# check layout
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inputs_expect_layout = ([2, 2, 2], [2, 1, 0], [16, 32, 64], 0, True, '')
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assert validator.check_parameter_layout('input', inputs_expect_layout)
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# check sub_graph
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sub_graph = {
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'ScatterNdSub-0': ['input', 'Sub-0', 'Mul-2'],
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'FloorDiv-0': ['_GetTensorSlice-0', 'Reshape-0'],
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}
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assert validator.check_graph_structure(sub_graph)
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def test_scatter_nd_update():
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"""
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Feature: distribute operator scatter_nd_update in auto parallel.
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Description: scatter_nd_update net with sharding input gather dims and tables in semi auto parallel.
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Expectation: assert ok.
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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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strategy1 = ((2, 2, 2), (1, 1), (1, 2))
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strategy2 = ((2, 2, 2), (2, 2, 2))
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net = Net(strategy1, strategy2, ops_type="Update")
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model = Model(net)
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with pytest.raises(RuntimeError):
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model.predict(inputs)
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context.reset_auto_parallel_context()
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def test_tensor_scatter_add():
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"""
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Feature: distribute operator tensor_scatter_add in auto parallel.
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Description: tensor_scatter_update net with sharding input gather dims and tables in semi auto parallel.
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Expectation: assert ok.
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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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input1 = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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input2 = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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strategy1 = ((2, 2, 2), (1, 1), (1,))
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strategy2 = ((1, 2, 2), (1, 2, 2))
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net = Net2(strategy1, strategy2, ops_type="Add")
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phase = compile_net(net, input1, input2)
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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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'TensorScatterAdd-0': ['Reshape-1', 'Sub-0', 'Mul-2'],
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'Equal-0': ['Sub-1', 'Minimum-0'],
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'AllGather-2': ['TensorScatterAdd-0']
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}
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assert validator.check_graph_structure(sub_graph)
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def test_tensor_scatter_mul():
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"""
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Feature: distribute operator tensor_scatter_mul in auto parallel.
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Description: tensor_scatter_update net with sharding input gather dims and tables in semi auto parallel.
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Expectation: assert ok.
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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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input1 = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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input2 = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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strategy1 = ((2, 2, 2), (1, 1), (1,))
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strategy2 = ((1, 2, 2), (1, 2, 2))
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net = Net2(strategy1, strategy2, ops_type="Mul")
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phase = compile_net(net, input1, input2)
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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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'TensorScatterMul-0': ['Reshape-1', 'Sub-0', 'Add-0'],
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'Equal-0': ['Sub-1', 'Minimum-0'],
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'AllGather-2': ['TensorScatterMul-0']
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}
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assert validator.check_graph_structure(sub_graph)
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def test_tensor_scatter_mul_auto_parallel():
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"""
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Feature: distribute operator tensor_scatter_mul in auto parallel.
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Description: tensor_scatter_update net with sharding input gather dims and tables in auto parallel.
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Expectation: assert ok.
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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=8, full_batch=True)
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input1 = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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input2 = Tensor(np.ones([32, 64, 128]).astype(np.float32))
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strategy1 = ((2, 2, 2), (1, 1), (1,))
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strategy2 = None
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net = Net2(strategy1, strategy2, ops_type="Mul")
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phase = compile_net(net, input1, input2)
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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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'TensorScatterMul-0': ['ReLU-0', 'Sub-0', 'Add-0'],
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'Equal-0': ['Sub-1', 'Minimum-0']
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}
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assert validator.check_graph_structure(sub_graph)
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