forked from OSSInnovation/mindspore
back-to-fusedbatchnorm-operation-in-pynative-mode
This commit is contained in:
parent
1f4944fa15
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b16686964c
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@ -238,11 +238,16 @@ void AscendBackendIRFusionOptimization(const std::shared_ptr<session::KernelGrap
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}
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auto optimizer = std::make_shared<GraphOptimizer>();
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auto ir_fusion_pm = std::make_shared<PassManager>("ir_fusion_pm");
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ir_fusion_pm->AddPass(std::make_shared<BatchNormGradSplit>());
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ir_fusion_pm->AddPass(std::make_shared<LayerNormGradSplit>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormFusion>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormMixPrecisionFusion0>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormMixPrecisionFusion1>());
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if (context_ptr->execution_mode() == kPynativeMode) {
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ir_fusion_pm->AddPass(std::make_shared<BnSplit>());
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ir_fusion_pm->AddPass(std::make_shared<BnGradSplit>());
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} else {
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ir_fusion_pm->AddPass(std::make_shared<BatchNormGradSplit>());
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ir_fusion_pm->AddPass(std::make_shared<LayerNormGradSplit>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormFusion>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormMixPrecisionFusion0>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormMixPrecisionFusion1>());
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}
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ir_fusion_pm->AddPass(std::make_shared<InsertPadForNMSWithMask>());
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if (context_ptr->ir_fusion_flag()) {
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AddAscendBackendOptionalIRFusion(ir_fusion_pm.get());
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@ -282,11 +287,8 @@ void RunOpAscendBackendIRFusionOptimization(const std::shared_ptr<session::Kerne
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}
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auto optimizer = std::make_shared<GraphOptimizer>();
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auto ir_fusion_pm = std::make_shared<PassManager>("ir_fusion_pm");
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ir_fusion_pm->AddPass(std::make_shared<BatchNormGradSplit>());
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ir_fusion_pm->AddPass(std::make_shared<BnSplit>());
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ir_fusion_pm->AddPass(std::make_shared<LayerNormGradSplit>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormFusion>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormMixPrecisionFusion0>());
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ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormMixPrecisionFusion1>());
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ir_fusion_pm->AddPass(std::make_shared<TopKSplit>());
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ir_fusion_pm->AddPass(std::make_shared<AddnFission>());
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ir_fusion_pm->AddPass(std::make_shared<InsertPadForNMSWithMask>());
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@ -84,13 +84,14 @@ class _BatchNorm(Cell):
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self.dtype = P.DType()
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self.reshape = P.Reshape()
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self.is_ascend = context.get_context("device_target") == "Ascend"
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self.is_graph_mode = context.get_context("mode") == context.GRAPH_MODE
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self.momentum = 1.0 - momentum
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if context.get_context("enable_ge"):
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self.is_ge_backend = True
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else:
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self.is_ge_backend = False
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if self.is_ge_backend or self.is_ascend:
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if self.is_graph_mode and (self.is_ge_backend or self.is_ascend):
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self.bn_train = P.BatchNorm(is_training=True,
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epsilon=self.eps)
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else:
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@ -152,7 +153,7 @@ class _BatchNorm(Cell):
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if self.is_ge_backend and self.is_global:
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axes, re_shape = _shape_infer(F.shape(x), self.num_features)
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y = self._global_sync(x, axes, re_shape)
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elif self.is_ge_backend or self.is_ascend:
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elif self.is_graph_mode and (self.is_ge_backend or self.is_ascend):
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if self.is_global:
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axes, re_shape = _shape_infer(F.shape(x), self.num_features)
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y = self._global_sync(x, axes, re_shape)
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@ -157,4 +157,5 @@ def test_ascend_pynative_lenet():
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total_time = total_time + cost_time
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print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
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assert loss_output.asnumpy() < 0.1
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assert loss_output.asnumpy() < 0.004
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assert loss_output.asnumpy() > 0.003
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@ -0,0 +1,432 @@
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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 time
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import random
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.transforms.vision.c_transforms as vision
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import mindspore.nn as nn
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import mindspore.ops.functional as F
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from mindspore import Tensor
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from mindspore import context
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from mindspore import ParameterTuple
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from mindspore.ops import composite as CP
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.common.initializer import initializer
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from mindspore.nn.wrap.cell_wrapper import WithLossCell
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random.seed(1)
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np.random.seed(1)
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ds.config.set_seed(1)
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def weight_variable(shape):
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return initializer('XavierUniform', shape=shape, dtype=mstype.float32)
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def weight_variable_uniform(shape):
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return initializer('Uniform', shape=shape, dtype=mstype.float32)
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def weight_variable_0(shape):
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zeros = np.zeros(shape).astype(np.float32)
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return Tensor(zeros)
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def weight_variable_1(shape):
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ones = np.ones(shape).astype(np.float32)
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return Tensor(ones)
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def conv3x3(in_channels, out_channels, stride=1, padding=0):
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"""3x3 convolution """
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weight_shape = (out_channels, in_channels, 3, 3)
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weight = weight_variable(weight_shape)
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=3, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
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def conv1x1(in_channels, out_channels, stride=1, padding=0):
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"""1x1 convolution"""
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weight_shape = (out_channels, in_channels, 1, 1)
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weight = weight_variable(weight_shape)
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=1, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
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def conv7x7(in_channels, out_channels, stride=1, padding=0):
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"""1x1 convolution"""
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weight_shape = (out_channels, in_channels, 7, 7)
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weight = weight_variable(weight_shape)
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=7, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
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def bn_with_initialize(out_channels):
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shape = (out_channels)
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mean = weight_variable_0(shape)
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var = weight_variable_1(shape)
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beta = weight_variable_0(shape)
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gamma = weight_variable_uniform(shape)
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bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
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beta_init=beta, moving_mean_init=mean, moving_var_init=var)
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return bn
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def bn_with_initialize_last(out_channels):
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shape = (out_channels)
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mean = weight_variable_0(shape)
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var = weight_variable_1(shape)
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beta = weight_variable_0(shape)
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gamma = weight_variable_uniform(shape)
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bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
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beta_init=beta, moving_mean_init=mean, moving_var_init=var)
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return bn
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def fc_with_initialize(input_channels, out_channels):
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weight_shape = (out_channels, input_channels)
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weight = weight_variable(weight_shape)
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bias_shape = (out_channels)
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bias = weight_variable_uniform(bias_shape)
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return nn.Dense(input_channels, out_channels, weight, bias)
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class ResidualBlock(nn.Cell):
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expansion = 4
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def __init__(self,
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in_channels,
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out_channels,
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stride=1):
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super(ResidualBlock, self).__init__()
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out_chls = out_channels // self.expansion
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self.conv1 = conv1x1(in_channels, out_chls, stride=stride, padding=0)
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self.bn1 = bn_with_initialize(out_chls)
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self.conv2 = conv3x3(out_chls, out_chls, stride=1, padding=0)
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self.bn2 = bn_with_initialize(out_chls)
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self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
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self.bn3 = bn_with_initialize_last(out_channels)
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self.relu = P.ReLU()
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self.add = P.TensorAdd()
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def construct(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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out = self.add(out, identity)
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out = self.relu(out)
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return out
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class ResidualBlockWithDown(nn.Cell):
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expansion = 4
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def __init__(self,
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in_channels,
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out_channels,
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stride=1,
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down_sample=False):
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super(ResidualBlockWithDown, self).__init__()
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out_chls = out_channels // self.expansion
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self.conv1 = conv1x1(in_channels, out_chls, stride=stride, padding=0)
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self.bn1 = bn_with_initialize(out_chls)
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self.conv2 = conv3x3(out_chls, out_chls, stride=1, padding=0)
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self.bn2 = bn_with_initialize(out_chls)
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self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
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self.bn3 = bn_with_initialize_last(out_channels)
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self.relu = P.ReLU()
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self.downSample = down_sample
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self.conv_down_sample = conv1x1(in_channels, out_channels, stride=stride, padding=0)
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self.bn_down_sample = bn_with_initialize(out_channels)
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self.add = P.TensorAdd()
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def construct(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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identity = self.conv_down_sample(identity)
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identity = self.bn_down_sample(identity)
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out = self.add(out, identity)
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out = self.relu(out)
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return out
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class MakeLayer0(nn.Cell):
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def __init__(self, block, in_channels, out_channels, stride):
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super(MakeLayer0, self).__init__()
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self.a = ResidualBlockWithDown(in_channels, out_channels, stride=1, down_sample=True)
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self.b = block(out_channels, out_channels, stride=stride)
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self.c = block(out_channels, out_channels, stride=1)
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def construct(self, x):
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x = self.a(x)
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x = self.b(x)
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x = self.c(x)
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return x
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class MakeLayer1(nn.Cell):
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def __init__(self, block, in_channels, out_channels, stride):
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super(MakeLayer1, self).__init__()
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self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
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self.b = block(out_channels, out_channels, stride=1)
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self.c = block(out_channels, out_channels, stride=1)
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self.d = block(out_channels, out_channels, stride=1)
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def construct(self, x):
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x = self.a(x)
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x = self.b(x)
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x = self.c(x)
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x = self.d(x)
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return x
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class MakeLayer2(nn.Cell):
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def __init__(self, block, in_channels, out_channels, stride):
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super(MakeLayer2, self).__init__()
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self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
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self.b = block(out_channels, out_channels, stride=1)
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self.c = block(out_channels, out_channels, stride=1)
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self.d = block(out_channels, out_channels, stride=1)
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self.e = block(out_channels, out_channels, stride=1)
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self.f = block(out_channels, out_channels, stride=1)
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def construct(self, x):
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x = self.a(x)
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x = self.b(x)
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x = self.c(x)
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x = self.d(x)
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x = self.e(x)
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x = self.f(x)
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return x
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class MakeLayer3(nn.Cell):
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def __init__(self, block, in_channels, out_channels, stride):
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super(MakeLayer3, self).__init__()
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self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
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self.b = block(out_channels, out_channels, stride=1)
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self.c = block(out_channels, out_channels, stride=1)
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def construct(self, x):
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x = self.a(x)
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x = self.b(x)
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x = self.c(x)
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return x
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class ResNet(nn.Cell):
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def __init__(self, block, num_classes=100, batch_size=32):
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super(ResNet, self).__init__()
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self.batch_size = batch_size
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self.num_classes = num_classes
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self.conv1 = conv7x7(3, 64, stride=2, padding=0)
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self.bn1 = bn_with_initialize(64)
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self.relu = P.ReLU()
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self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="SAME")
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self.layer1 = MakeLayer0(block, in_channels=64, out_channels=256, stride=1)
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self.layer2 = MakeLayer1(block, in_channels=256, out_channels=512, stride=2)
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self.layer3 = MakeLayer2(block, in_channels=512, out_channels=1024, stride=2)
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self.layer4 = MakeLayer3(block, in_channels=1024, out_channels=2048, stride=2)
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self.pool = P.ReduceMean(keep_dims=True)
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self.squeeze = P.Squeeze(axis=(2, 3))
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self.fc = fc_with_initialize(512 * block.expansion, num_classes)
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def construct(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)[0]
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.pool(x, (2, 3))
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x = self.squeeze(x)
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x = self.fc(x)
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return x
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def resnet50(batch_size, num_classes):
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return ResNet(ResidualBlock, num_classes, batch_size)
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def create_dataset(repeat_num=1, training=True, batch_size=32):
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data_home = "/home/workspace/mindspore_dataset"
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data_dir = data_home + "/cifar-10-batches-bin"
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if not training:
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data_dir = data_home + "/cifar-10-verify-bin"
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data_set = ds.Cifar10Dataset(data_dir)
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resize_height = 224
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resize_width = 224
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rescale = 1.0 / 255.0
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shift = 0.0
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# define map operations
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random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
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random_horizontal_op = vision.RandomHorizontalFlip()
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# interpolation default BILINEAR
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resize_op = vision.Resize((resize_height, resize_width))
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rescale_op = vision.Rescale(rescale, shift)
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normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
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changeswap_op = vision.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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c_trans = []
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if training:
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c_trans = [random_crop_op, random_horizontal_op]
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c_trans += [resize_op, rescale_op, normalize_op,
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changeswap_op]
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# apply map operations on images
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data_set = data_set.map(input_columns="label", operations=type_cast_op)
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data_set = data_set.map(input_columns="image", operations=c_trans)
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# apply shuffle operations
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data_set = data_set.shuffle(buffer_size=1000)
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||||
|
||||
# apply batch operations
|
||||
data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
|
||||
|
||||
# apply repeat operations
|
||||
data_set = data_set.repeat(repeat_num)
|
||||
|
||||
return data_set
|
||||
|
||||
|
||||
class CrossEntropyLoss(nn.Cell):
|
||||
def __init__(self):
|
||||
super(CrossEntropyLoss, self).__init__()
|
||||
self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean()
|
||||
self.one_hot = P.OneHot()
|
||||
self.one = Tensor(1.0, mstype.float32)
|
||||
self.zero = Tensor(0.0, mstype.float32)
|
||||
|
||||
def construct(self, logits, label):
|
||||
label = self.one_hot(label, F.shape(logits)[1], self.one, self.zero)
|
||||
loss = self.cross_entropy(logits, label)[0]
|
||||
loss = self.mean(loss, (-1,))
|
||||
return loss
|
||||
|
||||
|
||||
class GradWrap(Cell):
|
||||
""" GradWrap definition """
|
||||
|
||||
def __init__(self, network):
|
||||
super(GradWrap, self).__init__()
|
||||
self.network = network
|
||||
self.weights = ParameterTuple(network.trainable_params())
|
||||
|
||||
def construct(self, x, label):
|
||||
weights = self.weights
|
||||
return CP.grad_by_list(self.network, weights)(x, label)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_pynative_resnet50():
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
|
||||
batch_size = 32
|
||||
num_classes = 10
|
||||
net = resnet50(batch_size, num_classes)
|
||||
criterion = CrossEntropyLoss()
|
||||
optimizer = Momentum(learning_rate=0.01, momentum=0.9,
|
||||
params=filter(lambda x: x.requires_grad, net.get_parameters()))
|
||||
|
||||
net_with_criterion = WithLossCell(net, criterion)
|
||||
net_with_criterion.set_grad()
|
||||
train_network = GradWrap(net_with_criterion)
|
||||
train_network.set_train()
|
||||
|
||||
step = 0
|
||||
max_step = 20
|
||||
data_set = create_dataset(repeat_num=1, training=True, batch_size=batch_size)
|
||||
for element in data_set.create_dict_iterator():
|
||||
step = step + 1
|
||||
if step > max_step:
|
||||
break
|
||||
start_time = time.time()
|
||||
input_data = Tensor(element["image"])
|
||||
input_label = Tensor(element["label"])
|
||||
loss_output = net_with_criterion(input_data, input_label)
|
||||
grads = train_network(input_data, input_label)
|
||||
optimizer(grads)
|
||||
end_time = time.time()
|
||||
cost_time = end_time - start_time
|
||||
print("======step: ", step, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
|
||||
if step > 1:
|
||||
assert cost_time < 0.5
|
||||
|
||||
Loading…
Reference in New Issue