Add ResNetUnit Python API (#35426)

paddle/fluid/framework/ir/memory_optimize_pass/inplace_addto_op_pass.cc

@@ -179,7 +179,8 @@ void InplaceAddToOpPass::Run(Graph *graph) const {
         out_var_ptr->GeneratedOp());
 
     // NOTE(zhiqiu): currently, only conv2d_grad supports addto strategy
-    if (right_generated_op->Name() != "conv2d_grad") {
+    if (right_generated_op->Name() != "conv2d_grad" &&
+        right_generated_op->Name() != "resnet_unit_grad") {
       continue;
     }
 
@@ -224,11 +225,13 @@ static bool IsValidConv2DGradDataGradNode(const Node &node) {
   if (node.inputs.empty()) return false;
   auto *generated_op = node.inputs[0];
   auto *op_desc = generated_op->Op();
-  if (op_desc == nullptr || op_desc->Type() != "conv2d_grad") {
+  if (op_desc == nullptr || (op_desc->Type() != "conv2d_grad" &&
+                             op_desc->Type() != "resnet_unit_grad")) {
     return false;
   }
   const auto &outputs = op_desc->Outputs();
-  auto iter = outputs.find(GradVarName("Input"));
+  std::string grad_var_name = op_desc->Type() == "conv2d_grad" ? "Input" : "X";
+  auto iter = outputs.find(GradVarName(grad_var_name));
   return iter != outputs.end() && !iter->second.empty() &&
          iter->second[0] == node.Name() &&
          !op_desc->GetAttrIfExists<bool>("use_addto");

paddle/fluid/operators/fused/resnet_unit_op.cc

@@ -232,6 +232,7 @@ class ResNetUnitOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool, default false) Set to true for inference only, false "
                   "for training. Some layers may run faster when this is true.")
         .SetDefault(false);
+    AddAttr<bool>("use_addto", "").SetDefault(false);
     AddAttr<std::string>("act_type", "The activation type to be fused.")
         .SetDefault("relu");
     AddComment(R"DOC(

paddle/fluid/operators/fused/resnet_unit_op.cu

@@ -55,7 +55,7 @@ class ResNetUnitKernel : public framework::OpKernel<T> {
     int padding = ctx.Attr<int>("padding");
     int stride = ctx.Attr<int>("stride");
     int stride_z = ctx.Attr<int>("stride_z");
-    int dilate = ctx.Attr<int>("dilate");
+    int dilation = ctx.Attr<int>("dilation");
     int group = ctx.Attr<int>("group");
     double eps = static_cast<double>(ctx.Attr<float>("epsilon"));
     double momentum = static_cast<double>(ctx.Attr<float>("momentum"));
@@ -87,7 +87,7 @@ class ResNetUnitKernel : public framework::OpKernel<T> {
     sum_x.Resize(param_dims);
     sum_of_squares_x.Resize(param_dims);
     CudnnNormConvolution<T> conv_x_op(dev_ctx, input_x_shape, filter_x_shape,
-                                      output_shape, padding, stride, dilate,
+                                      output_shape, padding, stride, dilation,
                                       group);
     conv_x_op.Forward(dev_ctx, *input_x, *filter_x, conv_out_x, &sum_x,
                       &sum_of_squares_x);
@@ -129,8 +129,8 @@ class ResNetUnitKernel : public framework::OpKernel<T> {
       sum_z.Resize(param_dims);
       sum_of_squares_z.Resize(param_dims);
       CudnnNormConvolution<T> conv_z_op(dev_ctx, input_z_shape, filter_z_shape,
-                                        output_shape, padding, stride_z, dilate,
-                                        group);
+                                        output_shape, padding, stride_z,
+                                        dilation, group);
       conv_z_op.Forward(dev_ctx, *input_z, *filter_z, conv_out_z, &sum_z,
                         &sum_of_squares_z);
 
@@ -189,7 +189,7 @@ class ResNetUnitGradKernel : public framework::OpKernel<T> {
     int padding = ctx.Attr<int>("padding");
     int stride = ctx.Attr<int>("stride");
     int stride_z = ctx.Attr<int>("stride_z");
-    int dilate = ctx.Attr<int>("dilate");
+    int dilation = ctx.Attr<int>("dilation");
     int group = ctx.Attr<int>("group");
     double eps = static_cast<double>(ctx.Attr<float>("epsilon"));
     double momentum = static_cast<double>(ctx.Attr<float>("momentum"));
@@ -263,7 +263,7 @@ class ResNetUnitGradKernel : public framework::OpKernel<T> {
       auto filter_z_shape = framework::vectorize<int>(filter_z->dims());
       CudnnNormConvolutionGrad<T> conv_z_op(dev_ctx, z_shape, filter_z_shape,
                                             output_shape, padding, stride_z,
-                                            dilate, group);
+                                            dilation, group);
       conv_z_op.Backward(dev_ctx, *z, *filter_z, conv_out_z_grad, z_grad,
                          filter_z_grad);
     } else {
@@ -278,11 +278,12 @@ class ResNetUnitGradKernel : public framework::OpKernel<T> {
     }
 
     // 2. Backward of Conv for x, get x_grad and filter_x_grad
+    bool use_addto = ctx.Attr<bool>("use_addto");
     CudnnNormConvolutionGrad<T> conv_x_op(dev_ctx, x_shape, filter_x_shape,
-                                          output_shape, padding, stride, dilate,
-                                          group);
+                                          output_shape, padding, stride,
+                                          dilation, group);
     conv_x_op.Backward(dev_ctx, *x, *filter_x, conv_out_x_grad, x_grad,
-                       filter_x_grad);
+                       filter_x_grad, use_addto);
   }
 };
 

python/paddle/incubate/operators/__init__.py

@@ -14,3 +14,4 @@
 
 from .softmax_mask_fuse_upper_triangle import softmax_mask_fuse_upper_triangle  # noqa: F401
 from .softmax_mask_fuse import softmax_mask_fuse  # noqa: F401
+from .resnet_unit import ResNetUnit  #noqa: F401

python/paddle/incubate/operators/resnet_unit.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import collections
+import itertools
+import six
+import math
+import sys
+import warnings
+from functools import partial, reduce
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+from paddle import framework
+from paddle.device import get_device, get_cudnn_version
+from paddle.nn import initializer as I
+from paddle.nn import Layer, LayerList
+from paddle.fluid.layers import utils
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.layers.utils import map_structure, flatten, pack_sequence_as
+from paddle.fluid.data_feeder import convert_dtype
+from paddle.fluid.param_attr import ParamAttr
+from paddle import _C_ops
+__all__ = ['resnet_unit', 'ResNetUnit']
+
+
+def resnet_unit(x, filter_x, scale_x, bias_x, mean_x, var_x, z, filter_z,
+                scale_z, bias_z, mean_z, var_z, stride, stride_z, padding,
+                dilation, groups, momentum, eps, data_format, fuse_add,
+                has_shortcut, use_global_stats, is_test, act):
+
+    helper = LayerHelper('resnet_unit', **locals())
+    bn_param_dtype = fluid.core.VarDesc.VarType.FP32
+    bit_mask_dtype = fluid.core.VarDesc.VarType.INT32
+    out = helper.create_variable_for_type_inference(x.dtype)
+    bit_mask = helper.create_variable_for_type_inference(
+        dtype=bit_mask_dtype, stop_gradient=True)
+    # intermediate_out for x
+    conv_x = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=True)
+    saved_mean_x = helper.create_variable_for_type_inference(
+        dtype=bn_param_dtype, stop_gradient=True)
+    saved_invstd_x = helper.create_variable_for_type_inference(
+        dtype=bn_param_dtype, stop_gradient=True)
+    running_mean_x = mean_x
+    running_var_x = var_x
+    # intermediate_out for z
+    conv_z = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=True)
+    saved_mean_z = helper.create_variable_for_type_inference(
+        dtype=bn_param_dtype, stop_gradient=True)
+    saved_invstd_z = helper.create_variable_for_type_inference(
+        dtype=bn_param_dtype, stop_gradient=True)
+    running_mean_z = helper.create_variable_for_type_inference(
+        dtype=bn_param_dtype, stop_gradient=True) if mean_z is None else mean_z
+    running_var_z = helper.create_variable_for_type_inference(
+        dtype=bn_param_dtype, stop_gradient=True) if var_z is None else var_z
+
+    inputs = {
+        'X': x,
+        'FilterX': filter_x,
+        'ScaleX': scale_x,
+        'BiasX': bias_x,
+        'MeanX': mean_x,
+        'VarX': var_x,
+        'Z': z,
+        'FilterZ': filter_z,
+        'ScaleZ': scale_z,
+        'BiasZ': bias_z,
+        'MeanZ': mean_z,
+        'VarZ': var_z
+    }
+
+    attrs = {
+        'stride': stride,
+        'stride_z': stride_z,
+        'padding': padding,
+        'dilation': dilation,
+        'group': groups,
+        'momentum': momentum,
+        'epsilon': eps,
+        'data_format': data_format,
+        'fuse_add': fuse_add,
+        'has_shortcut': has_shortcut,
+        'use_global_stats': use_global_stats,
+        'is_test': is_test,
+        'act_type': act
+    }
+
+    outputs = {
+        'Y': out,
+        'BitMask': bit_mask,
+        'ConvX': conv_x,
+        'SavedMeanX': saved_mean_x,
+        'SavedInvstdX': saved_invstd_x,
+        'RunningMeanX': running_mean_x,
+        'RunningVarX': running_var_x,
+        'ConvZ': conv_z,
+        'SavedMeanZ': saved_mean_z,
+        'SavedInvstdZ': saved_invstd_z,
+        'RunningMeanZ': running_mean_z,
+        'RunningVarZ': running_var_z,
+    }
+
+    helper.append_op(
+        type='resnet_unit', inputs=inputs, outputs=outputs, attrs=attrs)
+
+    return out
+
+
+class ResNetUnit(Layer):
+    r"""
+    ******Temporary version******.
+    ResNetUnit is designed for optimize the performence by using cudnnv8 API.
+    """
+
+    def __init__(self,
+                 num_channels_x,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 momentum=0.9,
+                 eps=1e-5,
+                 data_format='NHWC',
+                 act='relu',
+                 fuse_add=False,
+                 has_shortcut=False,
+                 use_global_stats=False,
+                 is_test=False,
+                 filter_x_attr=None,
+                 scale_x_attr=None,
+                 bias_x_attr=None,
+                 moving_mean_x_name=None,
+                 moving_var_x_name=None,
+                 num_channels_z=1,
+                 stride_z=1,
+                 filter_z_attr=None,
+                 scale_z_attr=None,
+                 bias_z_attr=None,
+                 moving_mean_z_name=None,
+                 moving_var_z_name=None):
+        super(ResNetUnit, self).__init__()
+        self._stride = stride
+        self._stride_z = stride_z
+        self._dilation = 1
+        self._kernel_size = utils.convert_to_list(filter_size, 2, 'kernel_size')
+        self._padding = (filter_size - 1) // 2
+        self._groups = 1
+        self._momentum = momentum
+        self._eps = eps
+        self._data_format = data_format
+        self._act = act
+        self._fuse_add = fuse_add
+        self._has_shortcut = has_shortcut
+        self._use_global_stats = use_global_stats
+        self._is_test = is_test
+
+        # check format
+        valid_format = {'NHWC'}
+        if data_format not in valid_format:
+            raise ValueError(
+                "conv_format must be one of {}, but got conv_format='{}'".
+                format(valid_format, data_format))
+
+        def _get_default_param_initializer(channels):
+            filter_elem_num = np.prod(self._kernel_size) * channels
+            std = (2.0 / filter_elem_num)**0.5
+            return I.Normal(0.0, std)
+
+        # initial filter
+        bn_param_dtype = fluid.core.VarDesc.VarType.FP32
+        bn_param_shape = [1, 1, 1, num_filters]
+        filter_x_shape = [num_filters, filter_size, filter_size, num_channels_x]
+        filter_z_shape = [num_filters, filter_size, filter_size, num_channels_z]
+
+        self.filter_x = self.create_parameter(
+            shape=filter_x_shape,
+            attr=filter_x_attr,
+            default_initializer=_get_default_param_initializer(num_channels_x))
+        self.scale_x = self.create_parameter(
+            shape=bn_param_shape,
+            attr=scale_x_attr,
+            dtype=bn_param_dtype,
+            default_initializer=I.Constant(1.0))
+        self.bias_x = self.create_parameter(
+            shape=bn_param_shape,
+            attr=bias_x_attr,
+            dtype=bn_param_dtype,
+            is_bias=True)
+        self.mean_x = self.create_parameter(
+            attr=ParamAttr(
+                name=moving_mean_x_name,
+                initializer=I.Constant(0.0),
+                trainable=False),
+            shape=bn_param_shape,
+            dtype=bn_param_dtype)
+        self.mean_x.stop_gradient = True
+        self.var_x = self.create_parameter(
+            attr=ParamAttr(
+                name=moving_var_x_name,
+                initializer=I.Constant(1.0),
+                trainable=False),
+            shape=bn_param_shape,
+            dtype=bn_param_dtype)
+        self.var_x.stop_gradient = True
+        if has_shortcut:
+            self.filter_z = self.create_parameter(
+                shape=filter_z_shape,
+                attr=filter_z_attr,
+                default_initializer=_get_default_param_initializer(
+                    num_channels_z))
+            self.scale_z = self.create_parameter(
+                shape=bn_param_shape,
+                attr=scale_z_attr,
+                dtype=bn_param_dtype,
+                default_initializer=I.Constant(1.0))
+            self.bias_z = self.create_parameter(
+                shape=bn_param_shape,
+                attr=bias_z_attr,
+                dtype=bn_param_dtype,
+                is_bias=True)
+            self.mean_z = self.create_parameter(
+                attr=ParamAttr(
+                    name=moving_mean_z_name,
+                    initializer=I.Constant(0.0),
+                    trainable=False),
+                shape=bn_param_shape,
+                dtype=bn_param_dtype)
+            self.mean_z.stop_gradient = True
+            self.var_z = self.create_parameter(
+                attr=ParamAttr(
+                    name=moving_var_z_name,
+                    initializer=I.Constant(1.0),
+                    trainable=False),
+                shape=bn_param_shape,
+                dtype=bn_param_dtype)
+            self.var_z.stop_gradient = True
+        else:
+            self.filter_z = None
+            self.scale_z = None
+            self.bias_z = None
+            self.mean_z = None
+            self.var_z = None
+
+    def forward(self, x, z=None):
+        if self._fuse_add and z is None:
+            raise ValueError("z can not be None")
+
+        out = resnet_unit(
+            x, self.filter_x, self.scale_x, self.bias_x, self.mean_x,
+            self.var_x, z, self.filter_z, self.scale_z, self.bias_z,
+            self.mean_z, self.var_z, self._stride, self._stride_z,
+            self._padding, self._dilation, self._groups, self._momentum,
+            self._eps, self._data_format, self._fuse_add, self._has_shortcut,
+            self._use_global_stats, self._is_test, self._act)
+        return out