forked from mindspore-Ecosystem/mindspore
650 lines
27 KiB
Python
650 lines
27 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 re
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import numpy as np
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import pytest
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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, TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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from parallel.utils.utils import ParallelValidator
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def setup_function():
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context.set_auto_parallel_context(dataset_strategy="full_batch")
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class Net(Cell):
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def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True,
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begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.strided_slice = P.StridedSlice(begin_mask=begin_mask,
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end_mask=end_mask,
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ellipsis_mask=ellipsis_mask, new_axis_mask=new_axis_mask,
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shrink_axis_mask=shrink_axis_mask).shard(strategy2)
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if is_parameter:
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self.weight = Parameter(weight, "w1")
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else:
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self.weight = weight
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self.mul2 = P.Mul()
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self.weight2 = Parameter(w2, "w2")
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self.begin = begin
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self.end = end
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self.strides = strides
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def construct(self, x, b):
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out = self.strided_slice(self.weight, self.begin, self.end, self.strides)
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out = self.mul(x, out)
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out = self.mul2(out, self.weight2)
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return out
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class Net2(Cell):
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def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None,
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begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.strided_slice = P.StridedSlice(begin_mask=begin_mask,
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end_mask=end_mask,
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ellipsis_mask=ellipsis_mask, new_axis_mask=new_axis_mask,
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shrink_axis_mask=shrink_axis_mask).shard(strategy2)
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self.weight2 = Parameter(weight2, "w2")
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self.begin = begin
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self.end = end
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self.strides = strides
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def construct(self, x, b):
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out = self.mul(x, self.weight2)
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out = self.strided_slice(out, self.begin, self.end, self.strides)
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return out
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class Net3(Cell):
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def __init__(self, begin, end, strides, strategy, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0,
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shrink_axis_mask=0):
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super().__init__()
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self.strided_slice = P.StridedSlice(begin_mask=begin_mask,
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end_mask=end_mask,
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ellipsis_mask=ellipsis_mask, new_axis_mask=new_axis_mask,
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shrink_axis_mask=shrink_axis_mask).shard(strategy)
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self.relu = P.ReLU()
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self.begin = begin
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self.end = end
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self.strides = strides
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def construct(self, x, b):
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out = self.strided_slice(x, self.begin, self.end, self.strides)
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out = self.relu(out)
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return out
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_x1 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
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_x2 = Tensor(np.ones([1, 64, 32, 32]), dtype=ms.float32)
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_x3 = Tensor(np.ones([64, 32]), dtype=ms.float32)
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_w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
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_w3 = Tensor(np.ones([1, 64, 32, 32]), dtype=ms.float32)
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_b1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
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_b2 = Tensor(np.ones([1, 64, 32, 32]), dtype=ms.float32)
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_x4 = Tensor(np.ones([2, 4, 8, 16]), dtype=ms.float32)
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def compile_net(net, _x1, _b1):
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_train()
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_cell_graph_executor.compile(train_net, _x1, _b1)
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context.reset_auto_parallel_context()
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def compile_net_utils(net: Cell, *inputs):
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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 compile_net_and_return_strategy(net: Cell, *inputs):
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net.set_train()
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_cell_graph_executor.compile(net, *inputs, phase='train')
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strategies = _cell_graph_executor._get_shard_strategy(net)
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context.reset_auto_parallel_context()
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return strategies
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def test_new_axis_mask():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: the strategy of input is (2, 4, 8, 16), new_axis_mask is 7
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Expectation: the strategy of output is (1, 1, 1, 2, 4, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=1024, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((2, 4, 8, 16),)
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net = Net3((0, 0, 0, 0), (2, 4, 8, 16), (1, 1, 1, 1), strategy, new_axis_mask=7)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[1, 1, 1, 2, 4, 8, 16]]
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def test_new_axis_mask_exceed_begin_len():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: the begin len is 2, new_axis_mask is 7, the mask part exceeding the original begin length is ignored.
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Expectation: the strategy of output is (1, 1, 2, 4, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=1024, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((2, 4, 8, 16),)
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net = Net3((0, 0), (2, 4), (1, 1), strategy, new_axis_mask=7)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[1, 1, 2, 4, 8, 16]]
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def test_new_axis_mask_exceed_begin_len1():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: the begin len is 2, new_axis_mask is 8, bit map is[0, 0, 1, 0, 0, 0, 0, 0]
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Expectation: the strategy of output is (2, 4, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=1024, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((2, 4, 8, 16),)
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net = Net3((0, 0), (2, 4), (1, 1), strategy, new_axis_mask=8)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[2, 4, 8, 16]]
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def test_new_axis_mask_no_fully_fetch():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: the strategy of input is (2, 4, 8, 16), new_axis_mask is 7, no fully fetch in begin[0]/[1]/[2]
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Expectation: the strategy of output is (1, 1, 1, 2, 4, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=1024, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((2, 4, 8, 16),)
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net = Net3((1, 1, 1, 0), (2, 4, 8, 16), (1, 1, 1, 1), strategy, new_axis_mask=7)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[1, 1, 1, 2, 4, 8, 16]]
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def test_new_axis_mask_out_of_range():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: the strategy of input is (2, 4, 8, 16), new_axis_mask is 20, bit map is[0, 0, 1, 0, 1, 0, 0, 0]
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Expectation: the strategy of output is (2, 4, 1, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=1024, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((2, 4, 8, 16),)
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net = Net3((0, 0, 0, 0), (2, 4, 8, 16), (1, 1, 1, 1), strategy, new_axis_mask=20)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[2, 4, 1, 8, 16]]
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def test_new_axis_mask_ignore_shrink_axis_mask():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: the strategy of input is (2, 4, 8, 16), new_axis_mask is 7, shrink_axis_mask is 7(it is ignored)
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Expectation: the strategy of output is (1, 1, 1, 2, 4, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=1024, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((2, 4, 8, 16),)
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net = Net3((0, 0, 0, 0), (2, 4, 8, 16), (1, 1, 1, 1), strategy, new_axis_mask=7, shrink_axis_mask=7)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[1, 1, 1, 2, 4, 8, 16]]
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def test_shrink_axis_mask():
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"""
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Features: test shrink axis mask, the input shape is (2, 4, 8, 16)
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Description: the strategy of input is (1, 1, 1, 16), shrink_axis_mask is 7
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Expectation: the strategy of output is (16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((1, 1, 1, 16),)
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net = Net3((0, 0, 0, 0), (2, 4, 8, 16), (1, 1, 1, 1), strategy, shrink_axis_mask=7)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[16]]
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def test_new_axis_mask_and_shrink_axis_mask():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: new_axis_mask is 6, shrink_axis_mask is 1, begin is (1, 1)
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Expectation: the strategy of output is (1, 4, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=512, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((1, 4, 8, 16),)
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net = Net3((1, 1), (2, 4), (1, 1), strategy, new_axis_mask=6, shrink_axis_mask=1)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[1, 4, 8, 16]]
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def test_new_axis_mask_and_shrink_axis_mask_and_begin_mask():
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"""
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Features: test new axis mask, the input shape is (2, 4, 8, 16)
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Description: new_axis_mask is 2, shrink_axis_mask is 1, begin is (1, 1, 3), begin_mask is 4
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Expectation: the strategy of output is (1, 4, 8, 16)
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=512, global_rank=0,
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search_mode="sharding_propagation")
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strategy = ((1, 4, 8, 16),)
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net = Net3((1, 1, 3), (2, 4, 8), (1, 1, 1), strategy, new_axis_mask=2, shrink_axis_mask=1, begin_mask=4)
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strategies = compile_net_and_return_strategy(net, _x4, _b1)
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for (k, v) in strategies.items():
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if re.search("ReLU", k) is not None:
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assert v == [[1, 4, 8, 16]]
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def test_stridedslice_no_fully_fetch_split_error():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((2, 2, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
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with pytest.raises(RuntimeError):
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compile_net(net, _x1, _b1)
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def test_stridedslice_strides_no_1_split_error():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice with strides no 1 split in semi auto parallel.
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Expectation: compile error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((1, 2, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2), strategy1, strategy2, is_parameter=True)
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with pytest.raises(RuntimeError):
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compile_net(net, _x1, _b1)
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def test_stridedslice_begin_size_smaller():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice with begin size is smaller in semi auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 1), (1, 4, 2))
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strategy2 = ((1, 4, 2),)
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net = Net(_w1, _w2, (0, 0), (128, 64), (1, 1), strategy1, strategy2, is_parameter=True)
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compile_net(net, _x1, _b1)
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def test_stridedslice_parameter():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice of parameter in semi auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 1), (1, 4, 2))
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strategy2 = ((1, 4, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
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compile_net(net, _x1, _b1)
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def test_stridedslice_begin_mask_no_0_split_parameter():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice with begin mask no 0 split in semi auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 1), (1, 4, 2))
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strategy2 = ((1, 4, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, begin_mask=1)
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compile_net(net, _x1, _b1)
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def test_stridedslice_end_mask_no_0_parameter():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice with end mask no 0 in semi auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 1), (1, 4, 2))
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strategy2 = ((1, 4, 2),)
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net = Net(_w1, _w2, (127, 0, 0), (128, 63, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
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begin_mask=1, end_mask=2)
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compile_net(net, _x1, _b1)
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def test_stridedslice_ellipsis_mask_no_0_parameter():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice with ellipsis mask no 0 in semi auto parallel.
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Expectation: compile runtime error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 1), (1, 4, 2))
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strategy2 = ((1, 4, 2),)
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net = Net(_w1, _w2, (127, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
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begin_mask=1, end_mask=2, ellipsis_mask=4)
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with pytest.raises(RuntimeError):
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compile_net(net, _x1, _b1)
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def test_stridedslice_new_axis_mask_no_0_parameter():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice with new axis mask no 0 in semi auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 2, 1), (1, 4, 2, 1))
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strategy2 = ((1, 1, 4),)
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net = Net(_w1, _w3, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
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new_axis_mask=1)
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compile_net(net, _x2, _b2)
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def test_stridedslice_shrink_axis_mask_no_0_parameter():
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"""
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Feature: distribute operator stridedslice in auto parallel mode.
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Description: test stridedslice with shrink axis mask no 0 in semi auto parallel.
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Expectation: compile done without error.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 2), (1, 2))
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strategy2 = ((1, 4, 1),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
|
|
shrink_axis_mask=1)
|
|
compile_net(net, _x3, _b1)
|
|
|
|
|
|
def test_stridedslice_shrink_axis_and_split():
|
|
"""
|
|
Feature: distribute operator stridedslice
|
|
Description: test stridedslice with shrink axis mask no 0 and split that dimension.
|
|
Expectation: runtime error
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 2), (1, 2))
|
|
strategy2 = ((2, 4, 1),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (256, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
|
|
shrink_axis_mask=1)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net, _x3, _b1)
|
|
|
|
|
|
def test_stridedslice_tensor():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice of tensor in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 4, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_begin_mask_no_0_tensor():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with begin mask no 0 in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 4, 2),)
|
|
net = Net(_w1, _w2, (127, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False, begin_mask=1)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_end_mask_no_0_tensor():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with end mask no 0 in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 4, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 63, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False, end_mask=2)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_new_axis_mask_no_0_tensor():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with new axis mask no 0 in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2, 1), (1, 4, 2, 1))
|
|
strategy2 = ((1, 1, 4),)
|
|
net = Net(_w1, _w3, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False,
|
|
new_axis_mask=1)
|
|
compile_net(net, _x2, _b2)
|
|
|
|
|
|
def test_stridedslice_shrink_axis_mask_no_0_tensor():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with shrink axis mask no 0 in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 2), (1, 2))
|
|
strategy2 = ((1, 4, 1),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False,
|
|
shrink_axis_mask=1)
|
|
compile_net(net, _x3, _b1)
|
|
|
|
|
|
def test_stridedslice_parameter_no_full_split():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with no full split in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 2, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_output():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice of output in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 8, 1),)
|
|
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_begin_mask_no_0_output():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with begin mask no 0 in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 8, 1),)
|
|
net = Net2(_w2, (61, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2, begin_mask=1)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_end_mask_no_0_output():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with end mask no 0 in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 8, 1),)
|
|
net = Net2(_w2, (0, 0, 0), (64, 63, 1), (1, 1, 1), strategy1, strategy2, end_mask=2)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_new_axis_mask_no_0_output():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with new axis mask no 0 in semi auto parallel.
|
|
Expectation: compile runtime error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((8, 1, 1),)
|
|
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2, new_axis_mask=1)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_shrink_axis_mask_no_0_output():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with shrink axis mask no 0 in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 8, 1),)
|
|
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2, shrink_axis_mask=1)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_output_no_full_split():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with no full split in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 4, 1),)
|
|
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_no_strategy():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with no strategy in semi auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = None
|
|
net = Net2(_w2, (0, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_begin_mask_no_0_no_strategy():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with begin mask no 0 in auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = None
|
|
net = Net2(_w2, (127, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2, begin_mask=1)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_auto_parallel():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice in auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
|
|
net = Net2(_w2, (0, 0, 0), (32, 64, 1), (1, 1, 1))
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
|
|
def test_stridedslice_begin_mask_no_0_auto_parallel():
|
|
"""
|
|
Feature: distribute operator stridedslice in auto parallel mode.
|
|
Description: test stridedslice with begin mask no 0 in auto parallel.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
|
|
net = Net2(_w2, (29, 0, 0), (32, 64, 1), (1, 1, 1), begin_mask=1)
|
|
compile_net(net, _x1, _b1)
|
|
|
|
|
|
def test_stridedslice_layout():
|
|
"""
|
|
Features: StridedSlice
|
|
Description: validate layout and structure
|
|
Expectation: No raise RuntimeError
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 4, 2),)
|
|
net = Net(_w1, _w2, (127, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
|
|
begin_mask=1, end_mask=2, ellipsis_mask=0)
|
|
phase = compile_net_utils(net, _x1, _b1)
|
|
validator = ParallelValidator(net, phase)
|
|
|
|
# check layout
|
|
features_expect_layout = ([4, 2], [-1, 1, 0], [256, 16, 16], 0, True, '')
|
|
assert validator.check_parameter_layout('w1', features_expect_layout)
|
|
|
|
# check attrs
|
|
roi_expect_attrs = {'begin_mask': 1, 'end_mask': 2, 'ellipsis_mask': 0}
|
|
assert validator.check_node_attrs('StridedSlice-1', roi_expect_attrs)
|
|
|
|
# check inputs
|
|
roi_expect_inputs = ['Load-0', 'out((127, 0, 0))', 'out((128, 64, 32))', 'out((1, 1, 1))']
|
|
assert validator.check_node_inputs('StridedSlice-1', roi_expect_inputs)
|
|
|
|
# check sub_graph
|
|
sub_graph = {
|
|
'StridedSlice-1': ['Load-0', 'out((127, 0, 0))', 'out((128, 64, 32))', 'out((1, 1, 1))'],
|
|
'Mul-0': ['Reshape-1', 'StridedSlice-1'],
|
|
'Split-1': ['AllGather-1'],
|
|
'Concat-1': ['MakeTuple-2']
|
|
}
|
|
assert validator.check_graph_structure(sub_graph)
|