[oneDNN] Conv dilation support (#27914)

* conv dilated mkldnn support: forward and backward pass

* add mkldnn conv_transpose dilation UT
test=develop

* remove unnecessary PADDLE_ENFORCE

* add int8 and bf16 dilated conv UT

* update according to reviews

paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc

@@ -84,19 +84,6 @@ void ConvBiasFusePass::ApplyImpl(ir::Graph* graph) const {
       VLOG(3) << "do not perform " + type() + "+bias fuse";
       return;
     }
-    if (conv->Op()->HasAttr("dilations")) {
-      auto dilations =
-          BOOST_GET_CONST(std::vector<int>, conv->Op()->GetAttr("dilations"));
-      for (const auto& d : dilations) {
-        if (d != 1) {
-          LOG(WARNING)
-              << "dilation conv not supported in MKLDNN, fuse not apply "
-              << "and set conv attribute use_mkldnn = false";
-          conv->Op()->SetAttr("use_mkldnn", false);
-          return;
-        }
-      }
-    }
 
     auto* eltwise_bias_tensor =
         scope->FindVar(eltwise_bias->Name())->GetMutable<LoDTensor>();

paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc

@@ -193,13 +193,8 @@ class ConvMKLDNNHandlerT
                                data_dims, strides, ksize);
       const bool is_conv3d = strides.size() == 3U;
 
-      PADDLE_ENFORCE_EQ(
-          is_conv3d
-              ? dilations.size() == 3 && dilations[0] == 1 &&
-                    dilations[1] == 1 && dilations[2] == 1
-              : dilations.size() == 2 && dilations[0] == 1 && dilations[1] == 1,
-          true, platform::errors::Unimplemented(
-                    "Dilation in oneDNN convolution is not implemented yet"));
+      std::transform(dilations.begin(), dilations.end(), dilations.begin(),
+                     [](int64_t i) { return i - 1; });
 
       const auto src_tz = paddle::framework::vectorize(input->dims());
 
@@ -210,6 +205,7 @@ class ConvMKLDNNHandlerT
 
       const mkldnn::memory::dims stride_dims = strides;
       const auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);
+      const mkldnn::memory::dims dilations_dims = dilations;
 
       /* create memory descriptor for convolution without specified format
        * ('any') which lets a primitive (convolution in this case) choose
@@ -256,13 +252,13 @@ class ConvMKLDNNHandlerT
 
         this->AcquireForwardPrimitiveDescriptor(
             conv_attr, fwd_prop_kind, dnnl::algorithm::convolution_direct,
-            src_md, weights_md, bias_md, dst_md, stride_dims,
+            src_md, weights_md, bias_md, dst_md, stride_dims, dilations_dims,
             mkldnn_paddings[0], mkldnn_paddings[1]);
       } else {
         this->AcquireForwardPrimitiveDescriptor(
             conv_attr, fwd_prop_kind, dnnl::algorithm::convolution_direct,
-            src_md, weights_md, dst_md, stride_dims, mkldnn_paddings[0],
-            mkldnn_paddings[1]);
+            src_md, weights_md, dst_md, stride_dims, dilations_dims,
+            mkldnn_paddings[0], mkldnn_paddings[1]);
       }
     }
   }
@@ -619,9 +615,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       bool is_conv3d = strides.size() == 3U;
 
       PADDLE_ENFORCE_NE(is_conv3d, true,
-                        platform::errors::InvalidArgument(
-                            "int8 does not support conv3d currently, should "
-                            "set param is_conv3d as False"));
+                        platform::errors::Unimplemented(
+                            "int8 does not support conv3d currently"));
 
       auto input_dims = input->dims();
       auto data_dims = framework::slice_ddim(input_dims, 2, input_dims.size());
@@ -641,13 +636,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       GetWeightsTz(weights_tz, g);
       auto dst_tz = paddle::framework::vectorize(output->dims());
 
-      PADDLE_ENFORCE_EQ(
-          is_conv3d
-              ? dilations.size() == 3 && dilations[0] == 1 &&
-                    dilations[1] == 1 && dilations[2] == 1
-              : dilations.size() == 2 && dilations[0] == 1 && dilations[1] == 1,
-          true, platform::errors::Unimplemented(
-                    "dilation in convolution is not implemented yet"));
+      std::transform(dilations.begin(), dilations.end(), dilations.begin(),
+                     [](int64_t i) { return i - 1; });
 
       const K* filter_data = filter->data<K>();
       auto scale_in_data = ctx.Attr<float>("Scale_in");
@@ -710,13 +700,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
         auto bias_md = platform::MKLDNNMemDesc(bias_tz, memory::data_type::s32,
                                                MKLDNNMemoryFormat::x);
         conv_pd = handler->AcquireConvolutionPrimitiveDescriptor(
-            src_md, weights_md, bias_md, dst_md, strides, paddings,
+            src_md, weights_md, bias_md, dst_md, strides, dilations, paddings,
             mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta,
             fuse_residual_conn, propagation, output_shift_scale, sum_scale);
       } else {
         conv_pd = handler->AcquireConvolutionPrimitiveDescriptor(
-            src_md, weights_md, boost::none, dst_md, strides, paddings,
-            mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta,
+            src_md, weights_md, boost::none, dst_md, strides, dilations,
+            paddings, mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta,
             fuse_residual_conn, propagation, output_shift_scale, sum_scale);
       }
 
@@ -1019,11 +1009,14 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
                           "Fail to find conv_pd in device context"));
 
     auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);
-
+    std::transform(dilations.begin(), dilations.end(), dilations.begin(),
+                   [](int64_t i) { return i - 1; });
+    const mkldnn::memory::dims dilations_dims = dilations;
     // create backward convolution weights primitive descriptor
     auto conv_bwd_weights_desc = mkldnn::convolution_backward_weights::desc(
         mkldnn::algorithm::convolution_direct, src_md, diff_weights_md,
-        diff_dst_md, strides, mkldnn_paddings[0], mkldnn_paddings[1]);
+        diff_dst_md, strides, dilations_dims, mkldnn_paddings[0],
+        mkldnn_paddings[1]);
 
     auto conv_bwd_weights_pd =
         std::make_shared<mkldnn::convolution_backward_weights::primitive_desc>(
@@ -1032,7 +1025,8 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     // create backward convolution data primitive descriptor
     auto conv_bwd_data_desc = mkldnn::convolution_backward_data::desc(
         mkldnn::algorithm::convolution_direct, diff_src_md, weights_md,
-        diff_dst_md, strides, mkldnn_paddings[0], mkldnn_paddings[1]);
+        diff_dst_md, strides, dilations_dims, mkldnn_paddings[0],
+        mkldnn_paddings[1]);
 
     auto conv_bwd_data_pd =
         std::make_shared<mkldnn::convolution_backward_data::primitive_desc>(

paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc

@@ -104,6 +104,11 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     int groups = ctx.Attr<int>("groups");
     std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
 
+    PADDLE_ENFORCE_EQ(
+        strides.size(), 2,
+        platform::errors::Unimplemented(
+            "Now we only support 2d oneDNN convolution transpose op"));
+
     auto input_dims = input->dims();
     auto data_dims = framework::slice_ddim(input_dims, 2, input_dims.size());
     auto filter_dims = filter->dims();
@@ -115,10 +120,8 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
                              data_dims, strides, ksize);
 
-    PADDLE_ENFORCE(
-        dilations.size() == 2 && dilations[0] == 1 && dilations[1] == 1,
-        platform::errors::Unimplemented(
-            "dilation in convolution is not implemented yet"));
+    std::transform(dilations.begin(), dilations.end(), dilations.begin(),
+                   [](int64_t i) { return i - 1; });
 
     const T* input_data = input->data<T>();
     const T* filter_data = filter->data<T>();
@@ -210,11 +213,12 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       auto bias_md = platform::MKLDNNMemDesc(
           bias_tz, platform::MKLDNNGetDataType<T>(), MKLDNNMemoryFormat::x);
       conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
-          src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine,
-          fuse_activation, fuse_alpha, fuse_beta, false, fwd_prop_kind);
+          src_md, weights_md, bias_md, dst_md, strides, dilations, paddings,
+          mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta, false,
+          fwd_prop_kind);
     } else {
       conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
-          src_md, weights_md, boost::none, dst_md, strides, paddings,
+          src_md, weights_md, boost::none, dst_md, strides, dilations, paddings,
           mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta, false,
           fwd_prop_kind);
     }

paddle/fluid/platform/mkldnn_reuse.h

@@ -1330,6 +1330,7 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
       const mkldnn::memory::desc& src, const mkldnn::memory::desc& weights,
       boost::optional<const mkldnn::memory::desc&> bias,
       const mkldnn::memory::desc& dst, const std::vector<int64_t>& strides,
+      const std::vector<int64_t>& dilations,
       const std::vector<int64_t>& paddings, const mkldnn::engine& engine,
       const std::string& fuse_activation, float fuse_alpha, float fuse_beta,
       const bool fuse_residual_conn, mkldnn::prop_kind fwd_prop_kind,
@@ -1352,18 +1353,18 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
           dev_ctx_.GetBlob(key_conv_pd));
       if (conv_pd_ == nullptr) {
         mkldnn::memory::dims stride_dims = strides;
-
+        mkldnn::memory::dims dilations_dims = dilations;
         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,
+                       src, weights, *bias, dst, stride_dims, dilations_dims,
                        mkldnn_paddings[0], mkldnn_paddings[1])
                  : typename forward_t::desc(
                        fwd_prop_kind, convolutional_algorithm<forward_t>::T,
-                       src, weights, dst, stride_dims, mkldnn_paddings[0],
-                       mkldnn_paddings[1]);
+                       src, weights, dst, stride_dims, dilations_dims,
+                       mkldnn_paddings[0], mkldnn_paddings[1]);
 
         mkldnn::primitive_attr conv_attr =
             CreatePostOps(fuse_activation, fuse_alpha, fuse_beta,

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py

@@ -23,12 +23,6 @@ from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16
 from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2dOp
 
 
-def conv2d_forward_refer(input, filter, group, conv_param):
-    out, in_n, out_h, out_w, out_c = conv2d_forward_naive(input, filter, group,
-                                                          conv_param)
-    return out
-
-
 def conv2d_residual_naive(out, residual):
     assert out.shape == residual.shape
     out = np.add(out, residual)
@@ -176,6 +170,21 @@ class TestWithStride(TestConv2dBf16Op):
         self.input_type = np.uint16
 
 
+class TestWithDilations(TestConv2dBf16Op):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [2, 2]
+        self.input_size = [2, 3, 10, 10]
+        self.input_residual_size = [2, 6, 8, 8]
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+    def init_data_type(self):
+        self.input_type = np.uint16
+
+
 class TestWith1x1ForceFP32Output(TestConv2dBf16Op):
     def init_test_case(self):
         self.pad = [0, 0]

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

@@ -228,6 +228,22 @@ class TestWithStride(TestConv2dInt8Op):
         self.scale_in_eltwise = 0.5
 
 
+class TestWithDilations(TestConv2dInt8Op):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [2, 2]
+        self.input_size = [2, 3, 10, 10]
+        self.input_residual_size = [2, 6, 8, 8]
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+        self.scale_in = 1.0
+        self.scale_out = 0.8
+        self.scale_weights = [10.0]
+        self.scale_in_eltwise = 0.5
+
+
 class TestWith1x1(TestConv2dInt8Op):
     def init_test_case(self):
         self.pad = [0, 0]
@@ -343,6 +359,7 @@ def create_test_int8_class(parent):
 create_test_int8_class(TestConv2dInt8Op)
 create_test_int8_class(TestWithPad)
 create_test_int8_class(TestWithStride)
+create_test_int8_class(TestWithDilations)
 create_test_int8_class(TestWithGroup)
 create_test_int8_class(TestWith1x1)
 create_test_int8_class(TestWithInput1x1Filter1x1)

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

@@ -215,5 +215,22 @@ class TestConv2dOp_AsyPadding_NHWC_MKLDNN(TestConv2dOp_Valid_NHWC_MKLDNN):
         self.padding_algorithm = "EXPLICIT"
 
 
+class TestMKLDNNDilations(TestConv2dMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dMKLDNNOp.init_test_case(self)
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [12, f_c, 3, 3]
+
+    def init_dilation(self):
+        self.dilations = [2, 2]
+
+    def init_group(self):
+        self.groups = 3
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -136,3 +136,17 @@ class TestMKLDNNWithValidPad_NHWC(TestMKLDNNWithValidPad):
         self.data_format = "NHWC"
         N, C, H, W = self.input_size
         self.input_size = [N, H, W, C]
+
+
+class TestConv2dTransposeMKLDNNWithDilationsExplicitPad(
+        TestConv2dTransposeMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dTransposeMKLDNNOp.init_test_case(self)
+        self.stride = [2, 1]
+        self.dilations = [1, 2]
+        self.groups = 1
+        self.input_size = [4, 3, 8, 7]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 4, 3]
+        self.pad = [1, 3, 2, 1]
+        self.padding_algorithm = "EXPLICIT"