Added bilinear and nearest interp v2 oneDNN FP32 kernels (#32312)

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

@@ -43,8 +43,9 @@ void InterpolateMKLDNNPass::ApplyImpl(ir::Graph* graph) const {
 
   int found_count = 0;
   const std::vector<std::string> interpolate_op_types = {
-      "bilinear_interp", "nearest_interp", "trilinear_interp", "bicubic_interp",
-      "linear_interp"};
+      "bilinear_interp",  "nearest_interp", "trilinear_interp",
+      "bicubic_interp",   "linear_interp",  "bilinear_interp_v2",
+      "nearest_interp_v2"};
 
   for (const Node* node : graph->Nodes()) {
     if (node->IsOp() &&

paddle/fluid/framework/ir/placement_pass_base.cc

@@ -77,7 +77,8 @@ bool PlacementPassBase::IsDefaultOpTypes(const std::string& op_type) const {
     // the corresponding pass.
     const std::vector<std::string> not_default_op_types = {
         "bilinear_interp", "nearest_interp", "trilinear_interp",
-        "bicubic_interp", "linear_interp"};
+        "bicubic_interp",  "linear_interp",  "bilinear_interp_v2",
+        "linear_interp_v2"};
     bool is_interpolate_op =
         std::find(not_default_op_types.begin(), not_default_op_types.end(),
                   op_type) != not_default_op_types.end();

paddle/fluid/operators/interpolate_v2_op.cc

@@ -14,6 +14,9 @@
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
 
 namespace paddle {
 namespace operators {
@@ -359,13 +362,41 @@ class InterpolateV2Op : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
+    framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+    framework::LibraryType library = framework::LibraryType::kPlain;
+    auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+
+#ifdef PADDLE_WITH_MKLDNN
+    auto interp_method = ctx.Attr<std::string>("interp_method");
+    // TODO(danqing): support other interp_method
+    if (this->CanMKLDNNBeUsed(ctx, data_type) &&
+        (interp_method == "nearest" || interp_method == "bilinear")) {
+      layout = framework::DataLayout::kMKLDNN;
+      library = framework::LibraryType::kMKLDNN;
+    }
+#endif
+
+    return framework::OpKernelType(data_type, ctx.GetPlace(), layout, library);
   }
 
   framework::OpKernelType GetKernelTypeForVar(
       const std::string& var_name, const Tensor& tensor,
       const framework::OpKernelType& expected_kernel_type) const override {
+#ifdef PADDLE_WITH_MKLDNN
+    if ((expected_kernel_type.data_layout_ == framework::DataLayout::kMKLDNN) &&
+        (tensor.layout() != framework::DataLayout::kMKLDNN)) {
+      auto attrs = Attrs();
+      auto ar = paddle::framework::AttrReader(attrs);
+      const std::string data_format = ar.Get<std::string>("data_layout");
+      auto dl = framework::StringToDataLayout(data_format);
+      // Some models may have intentionally set "AnyLayout" for pool
+      // op. Treat this as NCHW (default data_format value)
+      if (dl != framework::DataLayout::kAnyLayout) {
+        return framework::OpKernelType(expected_kernel_type.data_type_,
+                                       tensor.place(), dl);
+      }
+    }
+#endif
     if (var_name == "SizeTensor" || var_name == "Scale") {
       return expected_kernel_type;
     }
@@ -436,6 +467,9 @@ class InterpolateV2OpMaker : public framework::OpProtoAndCheckerMaker {
                  "can be \'0\' for src_idx = scale*(dst_indx+0.5)-0.5 , "
                  "can be \'1\' for src_idx = scale*dst_index .")
         .SetDefault(1);
+    AddAttr<bool>("use_mkldnn",
+                  "(bool, default false) Only used in mkldnn kernel")
+        .SetDefault(false);
     AddComment(R"DOC(
           This operator samples input X to given output shape by using specified
           interpolation method, the interpolation methods can be \"nearest\"

paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc

@@ -33,7 +33,7 @@ class InterpolateMKLDNNHandler
     : public platform::MKLDNNHandlerT<T, dnnl::resampling_forward> {
  public:
   InterpolateMKLDNNHandler(const dnnl::algorithm algo,
-                           const paddle::platform::MKLDNNDeviceContext& dev_ctx,
+                           const platform::MKLDNNDeviceContext& dev_ctx,
                            const dnnl::engine engine, platform::Place cpu_place,
                            const Tensor* x, Tensor* z,
                            const std::string& uniq_name)
@@ -94,19 +94,32 @@ class InterpolateMKLDNNKernel : public framework::OpKernel<T> {
         out_dims = out_size_data;
       }
     } else {
-      float scale;
+      std::vector<float> scale;
+      scale.reserve(3);
       auto scale_tensor = ctx.Input<Tensor>("Scale");
       if (scale_tensor != nullptr) {
         auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
-        scale = scale_data[0];
+        scale.resize(3, scale_data[0]);
+        std::copy(scale_data.begin(), scale_data.end(), scale.begin());
       } else {
-        scale = ctx.Attr<float>("scale");
+        std::string op_type = ctx.Type();
+
+        if (op_type.find("v2") == std::string::npos) {  // v1
+          scale.push_back(ctx.Attr<float>("scale"));
+          scale.push_back(scale[0]);
+          scale.push_back(scale[0]);
+        } else {  // v2
+          std::vector<float> scale_attr = ctx.Attr<std::vector<float>>("scale");
+          scale.resize(3, scale_attr[0]);
+          std::copy(scale_attr.begin(), scale_attr.end(), scale.begin());
         }
-      if (scale > 0) {
+      }
+      if (scale[0] > 0.0f && scale[1] > 0.0f && scale[2] > 0.0f) {
+        int j = 0;
         std::vector<int64_t> in_dhw_vec = framework::vectorize(in_dhw_dims);
         std::transform(
             in_dhw_vec.begin(), in_dhw_vec.end(), out_dims.begin(),
-            [&](int64_t i) -> int { return static_cast<int>(i * scale); });
+            [&](int64_t i) -> int { return static_cast<int>(i * scale[j++]); });
       }
     }
 
@@ -172,3 +185,8 @@ REGISTER_OP_KERNEL(nearest_interp, MKLDNN, ::paddle::platform::CPUPlace,
                    ops::InterpolateMKLDNNKernel<float>);
 REGISTER_OP_KERNEL(bilinear_interp, MKLDNN, ::paddle::platform::CPUPlace,
                    ops::InterpolateMKLDNNKernel<float>);
+
+REGISTER_OP_KERNEL(nearest_interp_v2, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::InterpolateMKLDNNKernel<float>);
+REGISTER_OP_KERNEL(bilinear_interp_v2, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::InterpolateMKLDNNKernel<float>);

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

@@ -198,4 +198,6 @@ class TestBilinearNeighborInterpSame(TestBilinearInterpMKLDNNOp):
 
 
 if __name__ == "__main__":
+    from paddle import enable_static
+    enable_static()
     unittest.main()

python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_v2_mkldnn_op.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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import math
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid.tests.unittests.op_test import OpTest
+from paddle.fluid.tests.unittests.op_test import skip_check_grad_ci
+
+
+def bilinear_interp_mkldnn_np(input,
+                              out_h,
+                              out_w,
+                              out_size=None,
+                              actual_shape=None,
+                              data_layout='NCHW'):
+    """bilinear interpolation implement in shape [N, C, H, W]"""
+    if data_layout == "NHWC":
+        input = np.transpose(input, (0, 3, 1, 2))  # NHWC => NCHW
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    if actual_shape is not None:
+        out_h = actual_shape[0]
+        out_w = actual_shape[1]
+    batch_size, channel, in_h, in_w = input.shape
+
+    out = np.zeros((batch_size, channel, out_h, out_w))
+
+    for oh in range(out_h):
+        h0 = int(math.floor((oh + 0.5) * in_h / out_h - 0.5))
+        h1 = int(math.ceil((oh + 0.5) * in_h / out_h - 0.5))
+        h0 = max(h0, 0)
+        h1 = min(h1, in_h - 1)
+        Wh = (oh + 0.5) * in_h / out_h - 0.5 - h0
+        for ow in range(out_w):
+            w0 = int(math.floor((ow + 0.5) * in_w / out_w - 0.5))
+            w1 = int(math.ceil((ow + 0.5) * in_w / out_w - 0.5))
+            w0 = max(w0, 0)
+            w1 = min(w1, in_w - 1)
+            Ww = (ow + 0.5) * in_w / out_w - 0.5 - w0
+            input_h0_w0 = input[:, :, h0, w0]
+            input_h1_w0 = input[:, :, h1, w0]
+            input_h0_w1 = input[:, :, h0, w1]
+            input_h1_w1 = input[:, :, h1, w1]
+            out[:, :, oh, ow] = input_h0_w0 * (1 - Wh) * (
+                1 - Ww) + input_h1_w0 * Wh * (1 - Ww) + input_h0_w1 * (
+                    1 - Wh) * Ww + input_h1_w1 * Wh * Ww
+
+    if data_layout == "NHWC":
+        out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
+
+    return out.astype(input.dtype)
+
+
+@skip_check_grad_ci(reason="Haven not implement interpolate grad kernel.")
+class TestBilinearInterpMKLDNNOp(OpTest):
+    def init_test_case(self):
+        pass
+
+    def setUp(self):
+        self.op_type = "bilinear_interp_v2"
+        self.interp_method = 'bilinear'
+        self._cpu_only = True
+        self.use_mkldnn = True
+        self.input_shape = [1, 1, 2, 2]
+        self.data_layout = 'NCHW'
+        # priority: actual_shape > out_size > scale > out_h & out_w
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 2.0
+        self.out_size = None
+        self.actual_shape = None
+
+        self.init_test_case()
+
+        input_np = np.random.random(self.input_shape).astype("float32")
+        if self.data_layout == "NCHW":
+            in_h = self.input_shape[2]
+            in_w = self.input_shape[3]
+        else:
+            in_h = self.input_shape[1]
+            in_w = self.input_shape[2]
+
+        scale_h = 0
+        scale_w = 0
+
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                scale_h = float(self.scale)
+                scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_w = self.scale[0]
+                scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                scale_w = self.scale[1]
+                scale_h = self.scale[0]
+
+        if scale_h > 0 and scale_w > 0:
+            out_h = int(in_h * scale_h)
+            out_w = int(in_w * scale_w)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = bilinear_interp_mkldnn_np(input_np, out_h, out_w,
+                                              self.out_size, self.actual_shape,
+                                              self.data_layout)
+
+        if isinstance(self.scale, float):
+            self.scale = [self.scale, self.scale]
+
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        if self.actual_shape is not None:
+            self.inputs['OutSize'] = self.actual_shape
+        self.attrs = {
+            'interp_method': self.interp_method,
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'scale': self.scale,
+            'data_layout': self.data_layout,
+            'use_mkldnn': self.use_mkldnn
+        }
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output(check_dygraph=False)
+
+
+class TestBilinearInterpOpMKLDNNNHWC(TestBilinearInterpMKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 27
+        self.out_w = 49
+        self.scale = [2.0, 3.0]
+        self.data_layout = 'NHWC'
+
+
+class TestBilinearNeighborInterpMKLDNNCase2(TestBilinearInterpMKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+
+
+class TestBilinearNeighborInterpCase3(TestBilinearInterpMKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 128
+        self.scale = [0.1, 0.05]
+
+
+class TestBilinearNeighborInterpCase4(TestBilinearInterpMKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = [13.0, 15.0]
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
+class TestBilinearNeighborInterpCase5(TestBilinearInterpMKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.out_size = np.array([13, 13]).astype("int32")
+
+
+class TestBilinearNeighborInterpCase6(TestBilinearInterpMKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 1.0
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
+class TestBilinearNeighborInterpSame(TestBilinearInterpMKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [2, 3, 32, 64]
+        self.out_h = 32
+        self.out_w = 64
+        self.scale = 2.0
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
+if __name__ == "__main__":
+    from paddle import enable_static
+    enable_static()
+    unittest.main()

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

@@ -163,4 +163,6 @@ class TestNearestNeighborInterpSame(TestNearestInterpMKLDNNOp):
 
 
 if __name__ == "__main__":
+    from paddle import enable_static
+    enable_static()
     unittest.main()

python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_v2_mkldnn_op.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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid.tests.unittests.op_test import OpTest
+from paddle.fluid.tests.unittests.op_test import skip_check_grad_ci
+
+
+def nearest_neighbor_interp_mkldnn_np(X,
+                                      out_h,
+                                      out_w,
+                                      out_size=None,
+                                      actual_shape=None,
+                                      data_layout='NCHW'):
+    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
+    if data_layout == "NHWC":
+        X = np.transpose(X, (0, 3, 1, 2))  # NHWC => NCHW
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    if actual_shape is not None:
+        out_h = actual_shape[0]
+        out_w = actual_shape[1]
+
+    n, c, in_h, in_w = X.shape
+
+    fh = fw = 0.0
+    if (out_h > 1):
+        fh = out_h * 1.0 / in_h
+    if (out_w > 1):
+        fw = out_w * 1.0 / in_w
+
+    out = np.zeros((n, c, out_h, out_w))
+
+    for oh in range(out_h):
+        ih = int(round((oh + 0.5) / fh - 0.5))
+        for ow in range(out_w):
+            iw = int(round((ow + 0.5) / fw - 0.5))
+            out[:, :, oh, ow] = X[:, :, ih, iw]
+
+    if data_layout == "NHWC":
+        out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
+
+    return out.astype(X.dtype)
+
+
+@skip_check_grad_ci(reason="Haven not implement interpolate grad kernel.")
+class TestNearestInterpV2MKLDNNOp(OpTest):
+    def init_test_case(self):
+        pass
+
+    def setUp(self):
+        self.op_type = "nearest_interp_v2"
+        self.interp_method = 'nearest'
+        self._cpu_only = True
+        self.use_mkldnn = True
+        self.input_shape = [1, 1, 2, 2]
+        self.data_layout = 'NCHW'
+        # priority: actual_shape > out_size > scale > out_h & out_w
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = [2.0, 3.0]
+        self.out_size = None
+        self.actual_shape = None
+
+        self.init_test_case()
+
+        input_np = np.random.random(self.input_shape).astype("float32")
+        if self.data_layout == "NCHW":
+            in_h = self.input_shape[2]
+            in_w = self.input_shape[3]
+        else:
+            in_h = self.input_shape[1]
+            in_w = self.input_shape[2]
+
+        scale_h = 0
+        scale_w = 0
+
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                scale_h = float(self.scale)
+                scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_w = self.scale[0]
+                scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                scale_w = self.scale[1]
+                scale_h = self.scale[0]
+
+        if scale_h > 0 and scale_w > 0:
+            out_h = int(in_h * scale_h)
+            out_w = int(in_w * scale_w)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = nearest_neighbor_interp_mkldnn_np(
+            input_np, out_h, out_w, self.out_size, self.actual_shape,
+            self.data_layout)
+
+        if isinstance(self.scale, float):
+            self.scale = [self.scale]
+
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        if self.actual_shape is not None:
+            self.inputs['OutSize'] = self.actual_shape
+        self.attrs = {
+            'interp_method': self.interp_method,
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'scale': self.scale,
+            'data_layout': self.data_layout,
+            'use_mkldnn': self.use_mkldnn
+        }
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output(check_dygraph=False)
+
+
+class TestNearestInterpOpV2MKLDNNNHWC(TestNearestInterpV2MKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 27
+        self.out_w = 49
+        self.scale = [2.0, 3.0]
+        self.data_layout = 'NHWC'
+
+
+class TestNearestNeighborInterpV2MKLDNNCase2(TestNearestInterpV2MKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+
+
+class TestNearestNeighborInterpV2MKLDNNCase3(TestNearestInterpV2MKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 128
+        self.scale = [0.1, 0.05]
+
+
+class TestNearestNeighborInterpV2MKLDNNCase4(TestNearestInterpV2MKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = [13.0, 15.0]
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
+class TestNearestNeighborInterpV2MKLDNNSame(TestNearestInterpV2MKLDNNOp):
+    def init_test_case(self):
+        self.input_shape = [2, 3, 32, 64]
+        self.out_h = 32
+        self.out_w = 64
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
+if __name__ == "__main__":
+    from paddle import enable_static
+    enable_static()
+    unittest.main()

tools/static_mode_white_list.py

@@ -603,7 +603,9 @@ STATIC_MODE_TESTING_LIST = [
     'test_fc_mkldnn_op',
     'test_fc_bf16_mkldnn_op',
     'test_nearest_interp_mkldnn_op',
+    'test_nearest_interp_v2_mkldnn_op',
     'test_bilinear_interp_mkldnn_op',
+    'test_bilinear_interp_v2_mkldnn_op',
     'test_fusion_gru_int8_mkldnn_op',
     'test_fusion_gru_bf16_mkldnn_op',
     'test_fusion_gru_mkldnn_op',