[cherry-pick] Add support for asymetric padding in MKLDNN pool, conv and conv_transpose (#21072)

* Add asymetric padding support for mkldnn pooling
test=develop

* Add asymetric padding support for mkldnn conv
test=develop

* Add asymetric padding support for mkldnn conv_transpose
test=develop

paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc

@@ -171,8 +171,20 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     float fuse_beta = ctx.Attr<float>("fuse_beta");
     bool fuse_residual_conn = ctx.Attr<bool>("fuse_residual_connection");
     int groups = ctx.Attr<int>("groups");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
     bool is_conv3d = strides.size() == 3U;
 
+    auto input_dims = input->dims();
+    auto data_dims = framework::slice_ddim(input_dims, 2, input_dims.size());
+    auto filter_dims = filter->dims();
+    auto filter_data_dims =
+        framework::slice_ddim(filter_dims, 2, filter_dims.size());
+
+    auto ksize = framework::vectorize<int>(filter_data_dims);
+
+    UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                             data_dims, strides, ksize);
+
     PADDLE_ENFORCE(
         is_conv3d
             ? dilations.size() == 3 && dilations[0] == 1 && dilations[1] == 1 &&
@@ -435,12 +447,25 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
       std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
       std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+      std::string padding_algorithm =
+          ctx.Attr<std::string>("padding_algorithm");
 
       bool is_conv3d = strides.size() == 3U;
 
       PADDLE_ENFORCE_NE(is_conv3d, true,
                         "int8 does not support conv3d currently");
 
+      auto input_dims = input->dims();
+      auto data_dims = framework::slice_ddim(input_dims, 2, input_dims.size());
+      auto filter_dims = filter->dims();
+      auto filter_data_dims =
+          framework::slice_ddim(filter_dims, 2, filter_dims.size());
+
+      auto ksize = framework::vectorize<int>(filter_data_dims);
+
+      UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                               data_dims, strides, ksize);
+
       int groups = ctx.Attr<int>("groups");
       auto weights_tz = paddle::framework::vectorize<int>(filter->dims());
       int g = std::max(groups, 1);
@@ -696,6 +721,7 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
     int groups = ctx.Attr<int>("groups");
 
     bool is_conv3d = strides.size() == 3U;
@@ -705,6 +731,17 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     T* input_grad_data = nullptr;
     T* filter_grad_data = nullptr;
 
+    auto input_dims = input->dims();
+    auto data_dims = framework::slice_ddim(input_dims, 2, input_dims.size());
+    auto filter_dims = filter->dims();
+    auto filter_data_dims =
+        framework::slice_ddim(filter_dims, 2, filter_dims.size());
+
+    auto ksize = framework::vectorize<int>(filter_data_dims);
+
+    UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                             data_dims, strides, ksize);
+
     auto src_tz = paddle::framework::vectorize<int>(input->dims());
     auto weights_tz = paddle::framework::vectorize<int>(filter->dims());
     int g = std::max(groups, 1);
@@ -766,10 +803,13 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     PADDLE_ENFORCE_NE(conv_pd, nullptr,
                       "Fail to find conv_pd in device context");
 
+    auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);
+
     // create backward convolution weights primitive descriptor
     auto conv_bwd_weights_desc = mkldnn::convolution_backward_weights::desc(
         mkldnn::convolution_direct, src_md, diff_weights_md, diff_dst_md,
-        strides, paddings, paddings, mkldnn::padding_kind::zero);
+        strides, mkldnn_paddings[0], mkldnn_paddings[1],
+        mkldnn::padding_kind::zero);
     auto conv_bwd_weights_pd =
         std::make_shared<mkldnn::convolution_backward_weights::primitive_desc>(
             conv_bwd_weights_desc, mkldnn_engine, *conv_pd);
@@ -777,7 +817,8 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     // create backward convolution data primitive descriptor
     auto conv_bwd_data_desc = mkldnn::convolution_backward_data::desc(
         mkldnn::convolution_direct, diff_src_md, weights_md, diff_dst_md,
-        strides, paddings, paddings, mkldnn::padding_kind::zero);
+        strides, mkldnn_paddings[0], mkldnn_paddings[1],
+        mkldnn::padding_kind::zero);
     auto conv_bwd_data_pd =
         std::make_shared<mkldnn::convolution_backward_data::primitive_desc>(
             conv_bwd_data_desc, mkldnn_engine, *conv_pd);

paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc

@@ -16,6 +16,7 @@
 #include "paddle/fluid/framework/data_layout_transform.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/malloc.h"
+#include "paddle/fluid/operators/conv_op.h"
 #include "paddle/fluid/platform/mkldnn_reuse.h"
 
 namespace paddle {
@@ -74,6 +75,18 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
     int groups = ctx.Attr<int>("groups");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
+
+    auto input_dims = input->dims();
+    auto data_dims = framework::slice_ddim(input_dims, 2, input_dims.size());
+    auto filter_dims = filter->dims();
+    auto filter_data_dims =
+        framework::slice_ddim(filter_dims, 2, filter_dims.size());
+
+    auto ksize = framework::vectorize<int>(filter_data_dims);
+
+    UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                             data_dims, strides, ksize);
 
     PADDLE_ENFORCE(
         dilations.size() == 2 && dilations[0] == 1 && dilations[1] == 1,

paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc

@@ -50,20 +50,26 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    bool global_pooling = ctx.Attr<bool>("global_pooling");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
 
-    if (ctx.Attr<bool>("global_pooling")) {
-      for (size_t i = 0; i < ksize.size(); ++i) {
-        paddings[i] = 0;
-        ksize[i] = static_cast<int>(input->dims()[i + 2]);
-      }
+    // Only 2D pooling is supported now
+    PADDLE_ENFORCE_EQ(ksize.size(), 2, "ksize must be 2D, i.e. 2D pooling");
+    PADDLE_ENFORCE_EQ(pooling_type == "max" || pooling_type == "avg", true,
+                      "pooling_type must be 'max' or 'avg'");
+    PADDLE_ENFORCE_EQ(input->dims().size(), 4,
+                      "Input dim must be with 4, i.e. NCHW");
+
+    auto input_dims = input->dims();
+    framework::DDim data_dims =
+        framework::slice_ddim(input_dims, 2, input_dims.size());
+
+    if (global_pooling) {
+      UpdateKsize(&ksize, data_dims);
     }
 
-    // Only 2D pooling is supported now
-    PADDLE_ENFORCE(ksize.size() == 2, "ksize must be 2D, i.e. 2D pooling");
-    PADDLE_ENFORCE(pooling_type == "max" || pooling_type == "avg",
-                   "pooling_type must be 'max' or 'avg'");
-    PADDLE_ENFORCE(input->dims().size() == 4,
-                   "Input dim must be with 4, i.e. NCHW");
+    UpdatePadding(&paddings, global_pooling, 0, padding_algorithm, data_dims,
+                  strides, ksize);
 
     auto src_tz = paddle::framework::vectorize<int>(input->dims());
     auto dst_tz = paddle::framework::vectorize<int>(output->dims());
@@ -81,6 +87,7 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 
     std::shared_ptr<mkldnn::pooling_forward> pool_p;
     std::shared_ptr<mkldnn::memory> workspace_memory;
+
     if ((is_test == false) && (pooling_type == "max")) {
       // Training
       workspace_memory = handler.AcquireWorkspaceMemory();
@@ -129,14 +136,20 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    bool global_pooling = ctx.Attr<bool>("global_pooling");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
 
-    if (ctx.Attr<bool>("global_pooling")) {
-      for (size_t i = 0; i < ksize.size(); ++i) {
-        paddings[i] = 0;
-        ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
-      }
+    auto in_x_dims = in_x->dims();
+    framework::DDim data_dims =
+        framework::slice_ddim(in_x_dims, 2, in_x_dims.size());
+
+    if (global_pooling) {
+      UpdateKsize(&ksize, data_dims);
     }
 
+    UpdatePadding(&paddings, global_pooling, 0, padding_algorithm, data_dims,
+                  strides, ksize);
+
     auto& dev_ctx =
         ctx.template device_context<platform::MKLDNNDeviceContext>();
 
@@ -162,6 +175,7 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
 
     std::shared_ptr<mkldnn::pooling_backward> pool_bwd_p;
     std::shared_ptr<mkldnn::memory> workspace_memory;
+
     if (pooling_type == "max") {
       // Max - pooling needs Workspace
       workspace_memory = handler.AcquireWorkspaceMemory();

paddle/fluid/platform/mkldnn_helper.h

@@ -211,5 +211,27 @@ inline std::string CreateKey(ArgTypes&&... args) {
   return key;
 }
 
+inline std::vector<std::vector<int>> ToMkldnnPadding(
+    const std::vector<int>& paddings) {
+  if (paddings.size() == 6) {
+    int padding_front = paddings[0];
+    int padding_back = paddings[1];
+    int padding_top = paddings[2];
+    int padding_bottom = paddings[3];
+    int padding_left = paddings[4];
+    int padding_right = paddings[5];
+
+    return {{padding_front, padding_top, padding_left},
+            {padding_back, padding_bottom, padding_right}};
+  } else {
+    int padding_top = paddings[0];
+    int padding_bottom = paddings[1];
+    int padding_left = paddings[2];
+    int padding_right = paddings[3];
+
+    return {{padding_top, padding_left}, {padding_bottom, padding_right}};
+  }
+}
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/mkldnn_reuse.h

@@ -540,13 +540,12 @@ class PoolingMKLDNNHandler : public MKLDNNHandlerT<T, mkldnn::pooling_forward,
     auto dst_md =
         platform::MKLDNNMemDesc(dst_dims, dt, MKLDNNMemoryFormat::any);
 
-    std::vector<int> padding_left_top(paddings);
-    std::vector<int> padding_right_bottom(paddings);
+    auto mkldnn_paddings = ToMkldnnPadding(paddings);
+
     if (ceil_mode) {
       CorrectOutputSize(src_dims, dst_dims, ksize, paddings, strides,
-                        padding_right_bottom);
+                        mkldnn_paddings[1]);
     }
-
     this->AcquireForwardPrimitiveDescriptor(
         is_test ? mkldnn::prop_kind::forward_inference
                 : mkldnn::prop_kind::forward_training,
@@ -555,7 +554,7 @@ class PoolingMKLDNNHandler : public MKLDNNHandlerT<T, mkldnn::pooling_forward,
             : (exclude_padding
                    ? mkldnn::algorithm::pooling_avg_exclude_padding
                    : mkldnn::algorithm::pooling_avg_include_padding),
-        src_md, dst_md, strides, ksize, padding_left_top, padding_right_bottom,
+        src_md, dst_md, strides, ksize, mkldnn_paddings[0], mkldnn_paddings[1],
         mkldnn::padding_kind::zero);
   }
 
@@ -578,14 +577,16 @@ class PoolingMKLDNNHandler : public MKLDNNHandlerT<T, mkldnn::pooling_forward,
         mkldnn::memory::desc(diff_src_dims, platform::MKLDNNGetDataType<T>(),
                              MKLDNNMemoryFormat::any);
 
+    auto mkldnn_paddings = ToMkldnnPadding(paddings);
+
     this->AcquireBackwardPrimitiveDescriptor(
         pooling_type == "max"
             ? mkldnn::algorithm::pooling_max
             : (exclude_padding
                    ? mkldnn::algorithm::pooling_avg_exclude_padding
                    : mkldnn::algorithm::pooling_avg_include_padding),
-        diff_src_md, diff_dst_md, strides, ksize, paddings, paddings,
-        mkldnn::padding_kind::zero);
+        diff_src_md, diff_dst_md, strides, ksize, mkldnn_paddings[0],
+        mkldnn_paddings[1], mkldnn::padding_kind::zero);
   }
 
   std::shared_ptr<mkldnn::memory> AcquireWorkspaceMemory(void) {
@@ -1035,17 +1036,19 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
           dev_ctx_.GetBlob(key_conv_pd));
       if (conv_pd_ == nullptr) {
         mkldnn::memory::dims stride_dims = strides;
-        mkldnn::memory::dims padding_dims = paddings;
+
+        auto mkldnn_paddings = ToMkldnnPadding(paddings);
 
         auto conv_desc =
-            bias ? typename forward_t::desc(
-                       fwd_prop_kind, convolutional_algorithm<forward_t>::T,
-                       src, weights, *bias, dst, stride_dims, padding_dims,
-                       padding_dims, mkldnn::padding_kind::zero)
-                 : typename forward_t::desc(
-                       fwd_prop_kind, convolutional_algorithm<forward_t>::T,
-                       src, weights, dst, stride_dims, padding_dims,
-                       padding_dims, mkldnn::padding_kind::zero);
+            bias
+                ? typename forward_t::desc(
+                      fwd_prop_kind, convolutional_algorithm<forward_t>::T, src,
+                      weights, *bias, dst, stride_dims, mkldnn_paddings[0],
+                      mkldnn_paddings[1], mkldnn::padding_kind::zero)
+                : typename forward_t::desc(
+                      fwd_prop_kind, convolutional_algorithm<forward_t>::T, src,
+                      weights, dst, stride_dims, mkldnn_paddings[0],
+                      mkldnn_paddings[1], mkldnn::padding_kind::zero);
 
         mkldnn::primitive_attr conv_attr =
             CreatePostOps(fuse_activation, fuse_alpha, fuse_beta,

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py

@@ -343,5 +343,27 @@ create_test_int8_class(TestWithGroup)
 create_test_int8_class(TestWith1x1)
 create_test_int8_class(TestWithInput1x1Filter1x1)
 
+
+class TestConv2dOp_AsyPadding_INT_MKLDNN(TestConv2dInt8Op):
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_paddings(self):
+        self.pad = [0, 0, 1, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestConv2dOp_Same_INT_MKLDNN(TestConv2dOp_AsyPadding_INT_MKLDNN):
+    def init_paddings(self):
+        self.pad = [0, 0]
+        self.padding_algorithm = "SAME"
+
+
+class TestConv2dOp_Valid_INT_MKLDNN(TestConv2dOp_AsyPadding_INT_MKLDNN):
+    def init_paddings(self):
+        self.pad = [1, 1]
+        self.padding_algorithm = "VALID"
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py

@@ -19,7 +19,7 @@ import numpy as np
 
 import paddle.fluid.core as core
 from paddle.fluid.tests.unittests.op_test import OpTest
-from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp
+from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestConv2dOp_v2
 
 
 def conv2d_bias_naive(out, bias):
@@ -176,5 +176,26 @@ class TestWithInput1x1Filter1x1(TestConv2dMKLDNNOp):
         self.groups = 3
 
 
+class TestConv2dOp_AsyPadding_MKLDNN(TestConv2dOp_v2):
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_paddings(self):
+        self.pad = [0, 0, 1, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestConv2dOp_Same_MKLDNN(TestConv2dOp_AsyPadding_MKLDNN):
+    def init_paddings(self):
+        self.pad = [0, 0]
+        self.padding_algorithm = "SAME"
+
+
+class TestConv2dOp_Valid_MKLDNN(TestConv2dOp_AsyPadding_MKLDNN):
+    def init_paddings(self):
+        self.pad = [1, 1]
+        self.padding_algorithm = "VALID"
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py

@@ -105,3 +105,24 @@ class TestMKLDNNWithStride(TestConv2dTransposeMKLDNNOp):
         self.pad = [1, 1]
         self.stride = [2, 2]
         self.input_size = [2, 3, 6, 6]  # NCHW
+
+
+class TestMKLDNNWithAsymPad(TestConv2dTransposeMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dTransposeMKLDNNOp.init_test_case(self)
+        self.pad = [0, 0, 1, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestMKLDNNWithSamePad(TestConv2dTransposeMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dTransposeMKLDNNOp.init_test_case(self)
+        self.pad = [0, 0]
+        self.padding_algorithm = "SAME"
+
+
+class TestMKLDNNWithValidPad(TestConv2dTransposeMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dTransposeMKLDNNOp.init_test_case(self)
+        self.pad = [1, 1]
+        self.padding_algorithm = "VALID"

python/paddle/fluid/tests/unittests/mkldnn/test_conv3d_mkldnn_op.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 import unittest
 
-from paddle.fluid.tests.unittests.test_conv3d_op import TestConv3dOp, TestCase1, TestWithGroup1, TestWithGroup2, TestWith1x1, TestWithInput1x1Filter1x1
+from paddle.fluid.tests.unittests.test_conv3d_op import TestConv3dOp, TestCase1, TestWithGroup1, TestWithGroup2, TestWith1x1, TestWithInput1x1Filter1x1, TestConv3dOp_2
 
 
 class TestMKLDNN(TestConv3dOp):
@@ -55,5 +55,27 @@ class TestMKLDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
         self.data_format = "NCHW"
 
 
+class TestConv3dOp_AsyPadding_MKLDNN(TestConv3dOp):
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+        self.data_format = "NCHW"
+
+    def init_paddings(self):
+        self.pad = [1, 0, 1, 0, 0, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestConv3dOp_Same_MKLDNN(TestConv3dOp_AsyPadding_MKLDNN):
+    def init_paddings(self):
+        self.pad = [0, 0, 0]
+        self.padding_algorithm = "SAME"
+
+
+class TestConv3dOp_Valid_MKLDNN(TestConv3dOp_AsyPadding_MKLDNN):
+    def init_paddings(self):
+        self.pad = [1, 1, 1]
+        self.padding_algorithm = "VALID"
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 import unittest
-from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
+from paddle.fluid.tests.unittests.test_pool2d_op import *
 
 
 def create_test_mkldnn_use_ceil_class(parent):
@@ -53,5 +53,93 @@ create_test_mkldnn_class(TestCase3)
 create_test_mkldnn_class(TestCase4)
 create_test_mkldnn_class(TestCase5)
 
+
+class TestAsymPad(TestPool2D_Op):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+
+    def init_paddings(self):
+        self.paddings = [1, 0, 1, 0]
+
+    def init_pool_type(self):
+        self.pool_type = "avg"
+        self.pool2D_forward_naive = avg_pool2D_forward_naive
+
+    def init_global_pool(self):
+        self.global_pool = False
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_global_pool(self):
+        self.global_pool = False
+
+
+class TestAsymPadCase1(TestAsymPad):
+    def init_paddings(self):
+        self.paddings = [1, 1, 0, 0]
+
+
+class TestAsymPadCase2(TestAsymPad):
+    def init_paddings(self):
+        self.paddings = [1, 0, 1, 2]
+
+
+class TestAsymPadCase3(TestAsymPad):
+    def init_paddings(self):
+        self.paddings = [1, 2, 1, 2]
+
+
+class TestAsymPadCase4(TestAsymPad):
+    def init_paddings(self):
+        self.paddings = [1, 0, 1, 2]
+
+
+class TestAsymPadCase5(TestAsymPad):
+    def init_paddings(self):
+        self.paddings = [2, 2, 1, 2]
+
+
+class TestAsymPadMaxCase1(TestAsymPadCase1):
+    def init_pool_type(self):
+        self.pool_type = "max"
+
+
+class TestAsymPadMaxCase2(TestAsymPadCase2):
+    def init_pool_type(self):
+        self.pool_type = "max"
+
+
+class TestAsymPadMaxCase3(TestAsymPadCase3):
+    def init_pool_type(self):
+        self.pool_type = "max"
+
+
+class TestAsymPadMaxCase4(TestAsymPadCase4):
+    def init_pool_type(self):
+        self.pool_type = "max"
+
+
+class TestAsymPadMaxCase5(TestAsymPadCase5):
+    def init_pool_type(self):
+        self.pool_type = "max"
+
+
+class TestAsymPadSame(TestAsymPad):
+    def init_paddings(self):
+        self.paddings = [0, 0]
+        self.padding_algorithm = "SAME"
+
+
+class TestAsymPadValid(TestAsymPad):
+    def init_paddings(self):
+        self.paddings = [0, 0, 0, 0]
+        self.padding_algorithm = "VALID"
+
+
 if __name__ == '__main__':
     unittest.main()