Optimize conv performance (#28766)

* optimize conv performance

python/paddle/nn/functional/conv.py

@@ -95,6 +95,68 @@ def _update_padding_nd(padding, channel_last, num_dims):
     return padding, padding_algorithm
 
 
+def _conv_nd(x,
+             weight,
+             bias=None,
+             stride=1,
+             padding=0,
+             padding_algorithm=None,
+             dilation=1,
+             groups=1,
+             data_format="NCHW",
+             channel_dim=1,
+             op_type="conv2d",
+             use_cudnn=True,
+             use_mkldnn=False,
+             name=None):
+
+    if in_dygraph_mode():
+        attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
+                 'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn',
+                 use_mkldnn, 'fuse_relu_before_depthwise_conv', False,
+                 "padding_algorithm", padding_algorithm, "data_format",
+                 data_format)
+        pre_bias = getattr(core.ops, op_type)(x, weight, *attrs)
+        if bias is not None:
+            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+        else:
+            out = pre_bias
+    else:
+        inputs = {'Input': [x], 'Filter': [weight]}
+        attrs = {
+            'strides': stride,
+            'paddings': padding,
+            'dilations': dilation,
+            'groups': groups,
+            'use_cudnn': use_cudnn,
+            'use_mkldnn': use_mkldnn,
+            'fuse_relu_before_depthwise_conv': False,
+            "padding_algorithm": padding_algorithm,
+            "data_format": data_format
+        }
+        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                                 op_type)
+        helper = LayerHelper(op_type, **locals())
+        dtype = helper.input_dtype(input_param_name='x')
+        pre_bias = helper.create_variable_for_type_inference(dtype)
+        outputs = {"Output": [pre_bias]}
+        helper.append_op(
+            type=op_type, inputs=inputs, outputs=outputs, attrs=attrs)
+        if bias is not None:
+            out = helper.create_variable_for_type_inference(dtype)
+            helper.append_op(
+                type='elementwise_add',
+                inputs={'X': [pre_bias],
+                        'Y': [bias]},
+                outputs={'Out': [out]},
+                attrs={'axis': channel_dim,
+                       'use_mkldnn': use_mkldnn})
+        else:
+            out = pre_bias
+
+    return out
+
+
 def conv1d(x,
            weight,
            bias=None,
@@ -472,12 +534,13 @@ def conv2d(x,
             "received: the number of filters is {}, the shape of weight is {}"
             ", the groups is {}".format(num_filters, weight.shape, groups))
 
-    # use_cudnn = True if core.is_compiled_with_cuda() else False
     cudnn_version = get_cudnn_version()
 
     use_cudnn = True if (core.is_compiled_with_cuda() and
                          cudnn_version is not None) else False
 
+    use_mkldnn = core.globals()["FLAGS_use_mkldnn"]
+
     # update attrs
     padding, padding_algorithm = _update_padding_nd(padding, channel_last, 2)
     stride = utils.convert_to_list(stride, 2, 'stride')
@@ -489,56 +552,9 @@ def conv2d(x,
         l_type = 'depthwise_conv2d'
         use_cudnn = False
 
-    inputs = {'Input': [x], 'Filter': [weight]}
-    attrs = {
-        'strides': stride,
-        'paddings': padding,
-        'dilations': dilation,
-        'groups': groups,
-        'use_cudnn': use_cudnn,
-        'use_mkldnn': False,
-        'fuse_relu_before_depthwise_conv': False,
-        "padding_algorithm": padding_algorithm,
-        "data_format": data_format
-    }
-
-    if in_dygraph_mode():
-        attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
-                 'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn', False,
-                 'fuse_relu_before_depthwise_conv', False, "padding_algorithm",
-                 padding_algorithm, "data_format", data_format)
-        pre_bias = getattr(core.ops, l_type)(x, weight, *attrs)
-        if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
-        else:
-            out = pre_bias
-    else:
-        inputs = {'Input': [x], 'Filter': [weight]}
-        attrs = {
-            'strides': stride,
-            'paddings': padding,
-            'dilations': dilation,
-            'groups': groups,
-            'use_cudnn': use_cudnn,
-            'use_mkldnn': False,
-            'fuse_relu_before_depthwise_conv': False,
-            "padding_algorithm": padding_algorithm,
-            "data_format": data_format
-        }
-        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
-                                 'conv2d')
-        helper = LayerHelper(l_type, **locals())
-        dtype = helper.input_dtype(input_param_name='x')
-        pre_bias = helper.create_variable_for_type_inference(dtype)
-        outputs = {"Output": [pre_bias]}
-        helper.append_op(
-            type=l_type, inputs=inputs, outputs=outputs, attrs=attrs)
-        if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
-        else:
-            out = pre_bias
-
-    return out
+    return _conv_nd(x, weight, bias, stride, padding, padding_algorithm,
+                    dilation, groups, data_format, channel_dim, l_type,
+                    use_cudnn, use_mkldnn, name)
 
 
 def conv1d_transpose(x,
@@ -1201,44 +1217,9 @@ def conv3d(x,
     dilation = utils.convert_to_list(dilation, 3, 'dilation')
     op_type = "conv3d"
 
-    if in_dygraph_mode():
-        attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
-                 'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn', False,
-                 "padding_algorithm", padding_algorithm, "data_format",
-                 data_format)
-        pre_bias = getattr(core.ops, op_type)(x, weight, *attrs)
-        if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
-        else:
-            out = pre_bias
-    else:
-        inputs = {'Input': [x], 'Filter': [weight]}
-        attrs = {
-            'strides': stride,
-            'paddings': padding,
-            'dilations': dilation,
-            'groups': groups,
-            'use_cudnn': use_cudnn,
-            'use_mkldnn': False,
-            "padding_algorithm": padding_algorithm,
-            "data_format": data_format
-        }
-        helper = LayerHelper(op_type, **locals())
-        dtype = helper.input_dtype(input_param_name='x')
-        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
-                                 'conv3d')
-
-        pre_bias = helper.create_variable_for_type_inference(dtype)
-        outputs = {"Output": [pre_bias]}
-
-        helper.append_op(
-            type=op_type, inputs=inputs, outputs=outputs, attrs=attrs)
-        if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
-        else:
-            out = pre_bias
-
-    return out
+    return _conv_nd(x, weight, bias, stride, padding, padding_algorithm,
+                    dilation, groups, data_format, channel_dim, op_type,
+                    use_cudnn, False, name)
 
 
 def conv3d_transpose(x,

python/paddle/nn/layer/conv.py

@@ -25,6 +25,8 @@ __all__ = [
 
 import numpy as np
 
+from ...fluid import core
+from ...device import get_cudnn_version
 from ...fluid.dygraph import layers
 from ...fluid.initializer import Normal
 from .. import functional as F
@@ -83,6 +85,13 @@ class _ConvNd(layers.Layer):
                 "when padding_mode in ['reflect', 'replicate', 'circular'], type of padding must be int"
             )
 
+        channel_last = (data_format == "NHWC") or (data_format == "NDHWC") or (
+            data_format == "NLC")
+        if channel_last:
+            self._channel_dim = len(data_format) - 1
+        else:
+            self._channel_dim = 1
+
         self._stride = utils.convert_to_list(stride, dims, 'stride')
         self._dilation = utils.convert_to_list(dilation, dims, 'dilation')
         self._kernel_size = utils.convert_to_list(kernel_size, dims,
@@ -90,10 +99,15 @@ class _ConvNd(layers.Layer):
         self._padding = padding
         self._padding_mode = padding_mode
         self.output_padding = output_padding
+        if dims != 1:
+            self._padding, self._padding_algorithm = _update_padding_nd(
+                padding, channel_last, dims)
 
         if transposed:
             filter_shape = [self._in_channels, out_channels // groups
                             ] + self._kernel_size
+            self._padding, self._padding_algorithm = _update_padding_nd(
+                padding, channel_last, dims)
         else:
             if in_channels % groups != 0:
                 raise ValueError("in_channels must be divisible by groups.")
@@ -104,6 +118,8 @@ class _ConvNd(layers.Layer):
                 self._reversed_padding_repeated_twice = _reverse_repeat_list(
                     _paired_padding, 2)
 
+                self._padding, _ = _update_padding_nd(0, channel_last, dims)
+
             filter_shape = [out_channels, in_channels // groups
                             ] + self._kernel_size
 
@@ -112,6 +128,17 @@ class _ConvNd(layers.Layer):
         self.bias = self.create_parameter(
             attr=self._bias_attr, shape=[self._out_channels], is_bias=True)
 
+        cudnn_version = get_cudnn_version()
+
+        self._use_cudnn = True if (core.is_compiled_with_cuda() and
+                                   cudnn_version is not None) else False
+
+        self._op_type = "conv" + str(dims) + 'd'
+        if dims == 2 and (in_channels == groups and in_channels != 1 and
+                          out_channels % in_channels == 0):
+            self.op_type = 'depthwise_conv2d'
+            self._use_cudnn = False
+
 
 class Conv1D(_ConvNd):
     """
@@ -581,24 +608,20 @@ class Conv2D(_ConvNd):
                       self._reversed_padding_repeated_twice,
                       mode=self._padding_mode,
                       data_format=self._data_format)
-            return F.conv2d(
-                x,
-                self.weight,
-                bias=self.bias,
-                stride=self._stride,
-                dilation=self._dilation,
-                groups=self._groups,
-                data_format=self._data_format)
-
-        out = F.conv2d(
+
+        out = F.conv._conv_nd(
             x,
             self.weight,
             bias=self.bias,
-            padding=self._padding,
             stride=self._stride,
+            padding=self._padding,
+            padding_algorithm=self._padding_algorithm,
             dilation=self._dilation,
             groups=self._groups,
-            data_format=self._data_format)
+            data_format=self._data_format,
+            channel_dim=self._channel_dim,
+            op_type=self._op_type,
+            use_cudnn=self._use_cudnn)
         return out
 
 
@@ -902,24 +925,20 @@ class Conv3D(_ConvNd):
                       self._reversed_padding_repeated_twice,
                       mode=self._padding_mode,
                       data_format=self._data_format)
-            return F.conv3d(
-                x,
-                self.weight,
-                bias=self.bias,
-                stride=self._stride,
-                dilation=self._dilation,
-                groups=self._groups,
-                data_format=self._data_format)
-
-        out = F.conv3d(
+
+        out = F.conv._conv_nd(
             x,
             self.weight,
             bias=self.bias,
-            padding=self._padding,
             stride=self._stride,
+            padding=self._padding,
+            padding_algorithm=self._padding_algorithm,
             dilation=self._dilation,
             groups=self._groups,
-            data_format=self._data_format)
+            data_format=self._data_format,
+            channel_dim=self._channel_dim,
+            op_type=self._op_type,
+            use_cudnn=self._use_cudnn)
         return out