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188 lines
6.9 KiB
188 lines
6.9 KiB
# 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
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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from mindspore.common.python_pass_register import registe_pass, PyPassManager
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from mindspore.common.api import _generate_pip_args
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from mindspore._c_expression import generate_key, Executor_
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from mindspore.common.graph_pattern import IsIn, IsPrimTypeOf, CallWith, IsNot, AnyPattern, NewTensor
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context.set_context(mode=context.GRAPH_MODE)
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def get_func_graph(obj, *args, phase="validate"):
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args_names, args_list = _generate_pip_args(obj, *args)
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dic = dict(zip(args_names, args_list))
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key = generate_key(phase, dic)
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phase_prefix = str(key[1])
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if phase == 'export':
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phase = phase + '.' + phase_prefix + '.' + str(obj.create_time)
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else:
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phase = phase_prefix + phase + '.' + str(obj.create_time)
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_executor = Executor_.get_instance()
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_executor.compile(obj, args_list, phase, False)
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return _executor.get_func_graph(phase)
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def test_softmax_relu():
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"""
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Use python pass to transform from Softmax to ReLU.
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"""
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inputs = Tensor(np.ones([42]), mindspore.float16)
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softmax_model = nn.Softmax()
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@registe_pass(run_only_once=True)
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def softmax_relu_pass():
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x = AnyPattern()
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softmax_pattern = IsPrimTypeOf(P.Softmax())
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pattern = CallWith(softmax_pattern, inputs=[x])
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relu_pattern = IsPrimTypeOf(P.ReLU(), should_replace=False)
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target = CallWith(relu_pattern, inputs=[x])
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return pattern, target
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transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(2)
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ppm = PyPassManager()
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ppm.unregiste(softmax_relu_pass)
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assert "ReLU" in transformed_repr
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assert "Softmax" not in transformed_repr
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def test_isin_pattern():
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"""
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Test IsIn pattern which expresses the IsIn/OneOf semantics.
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"""
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inputs = Tensor(np.ones([42]), mindspore.float16)
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softmax_model = nn.Softmax()
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@registe_pass(run_only_once=True)
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def softmax_relu_pass():
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x = AnyPattern()
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softmax_pattern = IsPrimTypeOf(P.Softmax())
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call_softmax = CallWith(softmax_pattern, inputs=[x])
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relu_pattern = IsPrimTypeOf(P.ReLU())
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call_relu = CallWith(relu_pattern, inputs=[x])
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pattern = IsIn([call_softmax, call_relu])
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relu6_pattern = IsPrimTypeOf(P.ReLU6(), should_replace=False)
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target = CallWith(relu6_pattern, inputs=[x])
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return pattern, target
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transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(2)
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ppm = PyPassManager()
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ppm.unregiste(softmax_relu_pass)
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assert "ReLU6" in transformed_repr
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assert "Softmax" not in transformed_repr
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def test_isnot_pattern_0():
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"""
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Test IsNot pattern which expresses the IsNot semantics.
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Case: IsNot pass failed to match
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"""
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class ConvBN(nn.Cell):
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def __init__(self):
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super(ConvBN, self).__init__()
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self.conv = P.Conv2D(32, 3)
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self.conv_weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)
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self.scale = Tensor(np.ones([32]), mindspore.float32)
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self.bias = Tensor(np.ones([32]), mindspore.float32)
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self.mean = Tensor(np.ones([32]), mindspore.float32)
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self.variance = Tensor(np.ones([32]), mindspore.float32)
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self.bn = P.BatchNorm()
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def construct(self, x):
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x = self.conv(x, self.conv_weight)
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x = self.bn(x, self.scale, self.bias, self.mean, self.variance)
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return x
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inputs = Tensor(np.random.normal(0, 1, (10, 32, 32, 32)), mindspore.float32)
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conv_bn_model = ConvBN()
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@registe_pass(run_only_once=True)
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def single_bn_pass():
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"""
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Sub a BN which does NOT take Conv as inputs to ReLU6.
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"""
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conv2d_prim = IsPrimTypeOf("Conv2D")
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conv2d = CallWith(conv2d_prim)
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pattern_0 = IsNot(conv2d)
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pattern = CallWith(P.BatchNorm(), inputs=[pattern_0])
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target = CallWith(P.ReLU6(), inputs=[pattern_0])
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return pattern, target
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@registe_pass(run_only_once=True)
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def bn_pass():
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"""
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Sub a BN to Softmax.
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"""
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bn = P.BatchNorm()
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pattern = CallWith(bn)
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softmax = P.Softmax()
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target = CallWith(softmax, should_replace=False)
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return pattern, target
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transformed_repr = get_func_graph(conv_bn_model, inputs).get_return().expanded_str(5)
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ppm = PyPassManager()
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ppm.unregiste(single_bn_pass)
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ppm.unregiste(bn_pass)
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assert "ReLU6" not in transformed_repr
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assert "Softmax" in transformed_repr
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def test_isnot_pattern_1():
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"""
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Test IsNot pattern which expresses the IsNot semantics.
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Case: IsNot pattern matches with the graph
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"""
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inputs = Tensor(np.ones([42]), mindspore.float16)
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softmax_model = nn.Softmax()
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@registe_pass(run_only_once=True)
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def single_bn_pass():
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"""
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Sub a BN which does NOT take MatMul as inputs to ReLU6.
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"""
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matmul = IsPrimTypeOf("MatMul")
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pattern_0 = IsNot(matmul)
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softmax = P.Softmax()
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pattern = CallWith(softmax, inputs=[pattern_0])
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relu6 = P.ReLU6()
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target = CallWith(relu6, inputs=[pattern_0], should_replace=False)
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return pattern, target
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transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
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ppm = PyPassManager()
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ppm.unregiste(single_bn_pass)
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assert "ReLU6" in transformed_repr
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assert "Softmax" not in transformed_repr
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def test_newtensor_pattern():
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inputs = Tensor(np.ones([42]), mindspore.float16)
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softmax_model = nn.Softmax()
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@registe_pass(run_only_once=True)
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def softmax_addn_pass():
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x = AnyPattern()
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softmax = P.Softmax()
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pattern = CallWith(softmax, inputs=[x])
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weight_tensor = Tensor(np.zeros([42]), mindspore.float16)
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new_weight = NewTensor(weight_tensor)
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addn_ops = P.AddN()
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target = CallWith(addn_ops, inputs=[x, new_weight], should_replace=False)
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return pattern, target
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transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(2)
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ppm = PyPassManager()
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ppm.unregiste(softmax_addn_pass)
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assert "AddN" in transformed_repr
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assert "Softmax" not in transformed_repr
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