forked from mindspore-Ecosystem/mindspore
!8678 expand logsoftmax and grad, delete cast in softmax and fix layernorm compute dsl
From: @zengzitao Reviewed-by: @gaoxiong1,@ryanww Signed-off-by: @ryanww
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3b946d4eb2
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@ -29,3 +29,5 @@ from .maximum_grad import expand_maximumgrad
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from .minimum_grad import expand_minimumgrad
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from .dropout_grad import expand_dropoutgrad
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from .layernorm_grad import expand_layernormgrad
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from .logsoftmax import expand_logsoftmax
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from .logsoftmax_grad import expand_logsoftmaxgrad
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@ -18,7 +18,6 @@ from mindspore._extends.graph_kernel.model import model_builder as builder
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def expand_layernorm(expand_info):
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"""LayerNorm expander"""
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# get op info.
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input_desc_0 = expand_info['input_desc'][0]
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input_desc_1 = expand_info['input_desc'][1]
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@ -70,11 +69,8 @@ def expand_layernorm(expand_info):
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normalize_sub = graph_builder.emit('Sub', [input_x, mean])
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epsilon_v = graph_builder.value(input_x.dtype, epsilon, input_x.data_format)
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normalize_add = graph_builder.emit('TensorAdd', [variance, epsilon_v])
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normalize_log = graph_builder.emit('Log', [normalize_add])
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input_y = graph_builder.value(input_x.dtype, -0.5, input_x.data_format)
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normalize_log_mul = graph_builder.emit('Mul', [normalize_log, input_y])
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normalize_exp = graph_builder.emit('Exp', [normalize_log_mul])
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normalize_mul = graph_builder.emit('Mul', [normalize_sub, normalize_exp])
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normlize_rsqrt = graph_builder.emit('Rsqrt', [normalize_add])
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normalize_mul = graph_builder.emit('Mul', [normalize_sub, normlize_rsqrt])
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# Calculate scale and translate
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scale_mul = graph_builder.emit('Mul', [input_gamma, normalize_mul])
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@ -0,0 +1,49 @@
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# 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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"""generate json desc for LogSoftmax"""
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from mindspore._extends.graph_kernel.model import model_builder as builder
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def expand_logsoftmax(expand_info):
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"""LogSoftmax expander"""
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# get op info.
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input_desc = expand_info['input_desc'][0]
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attrs = expand_info['attr']
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axis = None
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for item in attrs:
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if 'axis' in item:
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axis = item['axis']
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graph_builder = builder.GraphBuilder()
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if isinstance(axis, int):
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axis = (axis,)
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# generate a graph.
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with graph_builder.graph_scope('main') as graph_scope:
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# create tensor input.
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input_x = graph_builder.tensor(input_desc['shape'], input_desc['data_type'], input_desc['format'])
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graph_scope.set_input(input_x)
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# cal logsoftmax.
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max_x = graph_builder.emit('ReduceMax', [input_x], attrs={'reduce_axis': axis, 'keep_dims': True})
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data_sub = graph_builder.emit('Sub', [input_x, max_x])
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data_exp = graph_builder.emit('Exp', [data_sub])
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data_expsum = graph_builder.emit('ReduceSum', [data_exp], attrs={'reduce_axis': axis, 'keep_dims': True})
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log_expsum = graph_builder.emit('Log', [data_expsum])
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result = graph_builder.emit('Sub', [data_sub, log_expsum])
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# set graph output.
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graph_scope.set_output(result)
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graph = graph_builder.get()[0]
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return graph
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@ -0,0 +1,50 @@
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# 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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"""generate json desc for LogSoftmaxGrad"""
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from mindspore._extends.graph_kernel.model import model_builder as builder
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def expand_logsoftmaxgrad(expand_info):
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"""LogSoftmaxGrad expander"""
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# get op info.
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input_desc_0 = expand_info['input_desc'][0]
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input_desc_1 = expand_info['input_desc'][1]
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attrs = expand_info['attr']
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axis = None
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for item in attrs:
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if 'axis' in item:
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axis = item['axis']
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graph_builder = builder.GraphBuilder()
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if isinstance(axis, int):
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axis = (axis,)
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# generate a graph.
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with graph_builder.graph_scope('main') as graph_scope:
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# create tensor input.
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input_logits = graph_builder.tensor(input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
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input_dy = graph_builder.tensor(input_desc_1['shape'], input_desc_1['data_type'], input_desc_1['format'])
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graph_scope.set_input(input_logits, input_dy)
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# cal logsoftmaxgrad.
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softmax = graph_builder.emit('Exp', [input_logits])
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dy_sum = graph_builder.emit('ReduceSum', [input_dy], attrs={'reduce_axis': axis, 'keep_dims': True})
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mul_result = graph_builder.emit('Mul', [softmax, dy_sum])
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result = graph_builder.emit('Sub', [input_dy, mul_result])
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# set graph output.
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graph_scope.set_output(result)
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graph = graph_builder.get()[0]
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return graph
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@ -18,7 +18,6 @@ from mindspore._extends.graph_kernel.model import model_builder as builder
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def expand_softmax(expand_info):
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"""Softmax expander"""
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# get op info.
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input_desc = expand_info['input_desc'][0]
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attrs = expand_info['attr']
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@ -33,13 +32,7 @@ def expand_softmax(expand_info):
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# create tensor input.
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input_x = graph_builder.tensor(input_desc['shape'], input_desc['data_type'], input_desc['format'])
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# cal softmax.
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if input_x.dtype == 'float32':
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input_x_cast = graph_builder.emit('Cast', [input_x], attrs={'dst_type': 'float16'})
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max_x = graph_builder.emit('ReduceMax', [input_x_cast], attrs={'reduce_axis': axis, 'keep_dims': True})
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max_x = graph_builder.emit('Cast', [max_x], attrs={'dst_type': 'float32'})
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else:
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max_x = graph_builder.emit('ReduceMax', [input_x], attrs={'reduce_axis': axis, 'keep_dims': True})
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max_x = graph_builder.emit('ReduceMax', [input_x], attrs={'reduce_axis': axis, 'keep_dims': True})
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data_sub = graph_builder.emit('Sub', [input_x, max_x])
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data_exp = graph_builder.emit('Exp', [data_sub])
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data_expsum = graph_builder.emit('ReduceSum', [data_exp], attrs={'reduce_axis': axis, 'keep_dims': True})
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@ -0,0 +1,125 @@
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# 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 pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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class LogSoftmax(nn.Cell):
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def __init__(self, axis=1):
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super(LogSoftmax, self).__init__()
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self.logsoftmax = P.LogSoftmax(axis)
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def construct(self, x):
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return self.logsoftmax(x)
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, input_data, sens):
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gout = self.grad(self.network)(input_data, sens)
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return gout
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def test_logsoftmax():
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x = np.array([[-0.08082921, -0.13706027, -0.4711177, -0.05606057],
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[-0.46082982, 1.1761844, -1.016654, -1.743829],
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[-1.5062045, 0.6910976, 0.4839723, 1.1502692]]).astype(np.float32)
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expect = np.array([[-1.2939762, -1.3502073, -1.6842647, -1.2692076],
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[-1.9445671, -0.3075528, -2.5003912, -3.2275662],
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[-3.452001, -1.2546989, -1.4618242, -0.79552734]]).astype(np.float32)
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logSoftmax = LogSoftmax()
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output = logSoftmax(Tensor(x))
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assert np.allclose(output.asnumpy(), expect)
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def test_logsoftmaxgrad():
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x = np.array([[-0.47705367, 0.48267725, -1.0453935, 1.574488, 0.20362134, 0.4435456, -0.23984082, -0.43684655,
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-0.7725506, 1.4481013],
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[1.1012247, 1.7069651, 0.55062026, 0.3361901, -1.1082426, -0.5001939, -0.3255393, -0.7972024,
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-0.27965206, -0.702805],
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[0.19450496, 0.87596166, 0.6467245, -1.044987, 0.5248943, -2.6166635, 1.6719198, 0.06600758,
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-0.4099178, 1.1861311],
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[1.1305193, -1.97308, 2.1047623, -1.5105937, 0.93052036, 1.2467804, 0.5310002, 0.7084912, -1.3681422,
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-0.9686862],
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[1.871408, 0.14219497, -0.41050452, -0.749807, 1.4900619, -1.8172716, -0.73839617, 0.17565694,
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-0.4553867, -1.5423119]]).astype(np.float32)
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dy = np.array([[1.516363, -0.15196544, 0.598733, 0.64357865, 0.16265012, -1.3521105, 0.22621834, 0.7168259,
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-0.6709239, 0.79757756],
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[-0.32457778, 1.2831115, 1.1211495, -0.02665559, 1.9170904, -1.3397789, 1.4124829, -1.4298155,
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0.758519, -0.25322974],
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[-0.24226122, -1.2555921, 0.6492511, -0.34847677, 0.19916506, 0.628554, -0.19658111, 0.44939864,
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-0.11677749, -1.2131723],
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[0.24267715, 0.28106326, 1.1075432, -0.29006946, 0.31335673, 0.8833154, 0.13152207, 1.5482179,
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0.29770762, -0.16246222],
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[0.02145994, 0.80424, -0.95061, 1.5875458, -0.00308682, 0.17964548, 0.49912593, 0.46977136,
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0.2151897, 0.30908248]]).astype(np.float32)
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expect = np.array([[1.4219905, -0.39837134, 0.5452743, -0.09062839, -0.02375537, -1.5890603, 0.10658137, 0.6185817,
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-0.7411523, 0.15054005],
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[-0.94926417, 0.13830578, 0.7609547, -0.31733334, 1.8485254, -1.4657221, 1.2625053, -1.523396,
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0.601499, -0.35607445],
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[-0.14447737, -1.0622973, 0.80294746, -0.32016528, 0.33523226, 0.63443416, 0.23186903,
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0.53539133, -0.0633494, -0.9495847],
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[-0.36894822, 0.253609, -0.5127511, -0.33366728, -0.18740037, 0.19628316, -0.20430653, 1.1471655,
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0.24743511, -0.23741922],
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[-1.2582518, 0.57718843, -1.0812542, 1.4944922, -0.8770549, 0.1476463, 0.40500447, 0.23499368,
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0.09027944, 0.26695627]]).astype(np.float32)
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net = LogSoftmax()
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dx = Grad(net)(Tensor(x), Tensor(dy))
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assert np.allclose(dx[0].asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_logsoftmax_gpu():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
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test_logsoftmax()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_logsoftmaxgrad_gpu():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
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test_logsoftmaxgrad()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_logsoftmax_asend():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
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test_logsoftmax()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_logsoftmaxgrad_asend():
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
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test_logsoftmaxgrad()
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