add argmax and iou_similarity for kunlun (#35836)

* add argmax and iou_similarity for kunlun

* add argmax and iou_similarity for kunlun

* add argmax and iou_similarity for kunlun

cmake/external/xpu.cmake

@@ -35,7 +35,7 @@ ELSE ()
 ENDIF()
 
 SET(XPU_BASE_URL_WITHOUT_DATE "https://baidu-kunlun-product.cdn.bcebos.com/KL-SDK/klsdk-dev")
-SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20210909")
+SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20210917")
 SET(XPU_XRE_URL  "${XPU_BASE_URL}/${XPU_XRE_DIR_NAME}.tar.gz" CACHE STRING "" FORCE)
 SET(XPU_XDNN_URL "${XPU_BASE_URL}/${XPU_XDNN_DIR_NAME}.tar.gz" CACHE STRING "" FORCE)
 SET(XPU_XCCL_URL "${XPU_BASE_URL_WITHOUT_DATE}/20210623/${XPU_XCCL_DIR_NAME}.tar.gz" CACHE STRING "" FORCE)

paddle/fluid/operators/arg_max_op_xpu.cc

+/* 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 Licnse. */
+
+#ifdef PADDLE_WITH_XPU
+
+#include "paddle/fluid/operators/arg_min_max_op_base.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ArgMaxXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+    auto dtype = ctx.Attr<int>("dtype");
+    PADDLE_ENFORCE_EQ(
+        (dtype < 0 || dtype == 3), true,
+        platform::errors::InvalidArgument(
+            "The attribute of dtype in xpu argmin/argmax must be [%s], but "
+            "received [%s]",
+            paddle::framework::DataTypeToString(
+                framework::proto::VarType::INT64),
+            paddle::framework::DataTypeToString(
+                static_cast<framework::proto::VarType::Type>(dtype))));
+
+    out->template mutable_data<int64_t>(ctx.GetPlace());
+    auto axis = ctx.Attr<int64_t>("axis");
+    const bool& flatten = ctx.Attr<bool>("flatten");
+    framework::DDim x_dims;
+    if (flatten) {
+      x_dims = framework::make_ddim({x->numel()});
+      // if flatten, the axis just as 0
+      axis = 0;
+    } else {
+      x_dims = x->dims();
+      if (axis < 0) axis += x_dims.size();
+    }
+    auto xdims_vec = framework::vectorize<int>(x_dims);
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    int r = xpu::argmax(dev_ctx.x_context(), x->data<T>(), out->data<int64_t>(),
+                        xdims_vec, axis);
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External(
+                          "XPU argmax kernel return wrong value[%d %s].", r,
+                          XPUAPIErrorMsg[r]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    arg_max, ops::ArgMaxXPUKernel<paddle::platform::XPUDeviceContext, float>);
+
+#endif

paddle/fluid/operators/detection/CMakeLists.txt

@@ -17,8 +17,6 @@ endfunction()
 
 detection_library(bipartite_match_op SRCS bipartite_match_op.cc)
 detection_library(box_coder_op SRCS box_coder_op.cc box_coder_op.cu)
-detection_library(iou_similarity_op SRCS iou_similarity_op.cc
-iou_similarity_op.cu)
 detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
 detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
 detection_library(density_prior_box_op SRCS density_prior_box_op.cc density_prior_box_op.cu)
@@ -58,6 +56,12 @@ else()
   detection_library(collect_fpn_proposals_op SRCS collect_fpn_proposals_op.cc)
 endif()
 
+if(WITH_XPU)
+  detection_library(iou_similarity_op SRCS iou_similarity_op.cc iou_similarity_op_xpu.cc)
+else()
+  detection_library(iou_similarity_op SRCS iou_similarity_op.cc iou_similarity_op.cu)
+endif()
+
 detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op.cc roi_perspective_transform_op.cu)
 #Export local libraries to parent
 # set(DETECTION_LIBRARY ${LOCAL_DETECTION_LIBS} PARENT_SCOPE)

paddle/fluid/operators/detection/iou_similarity_op_xpu.cc

+/* Copyright (c) 2016 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. */
+
+#ifdef PADDLE_WITH_XPU
+
+#include "paddle/fluid/operators/detection/iou_similarity_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class XPUIOUSimilarityKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const framework::LoDTensor* in_x = ctx.Input<framework::LoDTensor>("X");
+    const framework::Tensor* in_y = ctx.Input<framework::Tensor>("Y");
+    bool normalized = ctx.Attr<bool>("box_normalized");
+    framework::LoDTensor* out = ctx.Output<framework::LoDTensor>("Out");
+
+    int x_n = in_x->dims()[0];
+    int y_n = in_y->dims()[0];
+    T eps = static_cast<T>(1e-10);
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    int r = xpu::iou_similarity(
+        dev_ctx.x_context(), in_x->data<T>(), in_y->data<T>(),
+        out->mutable_data<T>(ctx.GetPlace()), x_n, y_n, eps, normalized);
+    PADDLE_ENFORCE_EQ(
+        r, XPU_SUCCESS,
+        platform::errors::External(
+            "XPU iou_similarity kernel return wrong value[%d %s].", r,
+            XPUAPIErrorMsg[r]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+using XPU = paddle::platform::XPUDeviceContext;
+
+REGISTER_OP_XPU_KERNEL(iou_similarity, ops::XPUIOUSimilarityKernel<XPU, float>);
+
+#endif

paddle/fluid/platform/xpu/xpu1_op_list.h

@@ -318,7 +318,10 @@ XPUOpMap& get_kl1_ops() {
                      pOpKernelType(vartype::INT8, XPUPlace()),
                      pOpKernelType(vartype::UINT8, XPUPlace()),
                      pOpKernelType(vartype::FP32, XPUPlace())})},
-      {"momuntem", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})}
+      {"momuntem", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
+      {"iou_similarity",
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
+      {"arg_max", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})}
       // AddMore
   };
 

paddle/fluid/platform/xpu/xpu2_op_list.h

@@ -107,7 +107,9 @@ XPUOpMap& get_kl2_ops() {
       {"transpose2_grad",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                      pOpKernelType(vartype::FP16, XPUPlace())})},
-
+      {"iou_similarity",
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
+      {"arg_max", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})}
       // AddMore
   };
 

python/paddle/fluid/tests/unittests/xpu/test_arg_max_op_xpu.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 sys
+sys.path.append("..")
+from op_test import OpTest
+from op_test_xpu import XPUOpTest
+import paddle
+import paddle.fluid.core as core
+
+paddle.enable_static()
+
+
+class XPUBaseTestCase(XPUOpTest):
+    def initTestCase(self):
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 1
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.op_type = 'arg_max'
+        self.__class__.use_xpu = True
+        np.random.seed(2021)
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis, 'use_xpu': True}
+        if self.op_type == "arg_min":
+            self.outputs = {'Out': np.argmin(self.x, axis=self.axis)}
+        else:
+            self.outputs = {'Out': np.argmax(self.x, axis=self.axis)}
+
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+
+# test argmax, dtype: float32
+class TestArgMaxFloat32Case1(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMaxFloat32Case2(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case3(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMaxFloat32Case4(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 2
+
+
+class TestArgMaxFloat32Case5(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMaxFloat32Case6(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case7(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMaxFloat32Case8(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (1, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case9(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (2, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case10(XPUBaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxAPI(unittest.TestCase):
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_Xpu = True
+        self.place = [paddle.XPUPlace(0)]
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2021)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(numpy_input, axis=self.axis)
+            paddle_output = paddle.argmax(tensor_input, axis=self.axis)
+            self.assertEqual(
+                np.allclose(numpy_output, paddle_output.numpy()), True)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+class TestArgMaxAPI_2(unittest.TestCase):
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+        self.keep_dims = True
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_xpu = True
+        self.place = [paddle.XPUPlace(0)]
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2021)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(
+                numpy_input, axis=self.axis).reshape(1, 4, 5)
+            paddle_output = paddle.argmax(
+                tensor_input, axis=self.axis, keepdim=self.keep_dims)
+            self.assertEqual(
+                np.allclose(numpy_output, paddle_output.numpy()), True)
+            self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_iou_similarity_op_xpu.py

+#   Copyright (c) 2018 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 sys
+sys.path.append("..")
+
+import unittest
+import numpy as np
+import numpy.random as random
+import sys
+import math
+from op_test import OpTest
+from op_test_xpu import XPUOpTest
+import paddle
+
+paddle.enable_static()
+
+
+class TestXPUIOUSimilarityOp(XPUOpTest):
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def setUp(self):
+        self.op_type = "iou_similarity"
+        self.boxes1 = random.rand(2, 4).astype('float32')
+        self.boxes2 = random.rand(3, 4).astype('float32')
+        self.output = random.rand(2, 3).astype('float32')
+        self.box_normalized = False
+        # run python iou computation 
+        self._compute_iou()
+        self.inputs = {'X': self.boxes1, 'Y': self.boxes2}
+        self.attrs = {"box_normalized": self.box_normalized, 'use_xpu': True}
+        self.outputs = {'Out': self.output}
+
+    def _compute_iou(self, ):
+        for row in range(self.boxes1.shape[0]):
+            for col in range(self.boxes2.shape[0]):
+                xmin1, ymin1, xmax1, ymax1 = self.boxes1[row]
+                xmin2, ymin2, xmax2, ymax2 = self.boxes2[col]
+                if not self.box_normalized:
+                    area1 = (ymax1 - ymin1 + 1) * (xmax1 - xmin1 + 1)
+                    area2 = (ymax2 - ymin2 + 1) * (xmax2 - xmin2 + 1)
+                else:
+                    area1 = (ymax1 - ymin1) * (xmax1 - xmin1)
+                    area2 = (ymax2 - ymin2) * (xmax2 - xmin2)
+
+                inter_xmax = min(xmax1, xmax2)
+                inter_ymax = min(ymax1, ymax2)
+                inter_xmin = max(xmin1, xmin2)
+                inter_ymin = max(ymin1, ymin2)
+                inter_height = inter_ymax - inter_ymin
+                inter_width = inter_xmax - inter_xmin
+                if not self.box_normalized:
+                    inter_height += 1
+                    inter_width += 1
+                inter_height = max(inter_height, 0)
+                inter_width = max(inter_width, 0)
+                inter_area = inter_width * inter_height
+                union_area = area1 + area2 - inter_area
+                sim_score = inter_area / union_area
+                self.output[row, col] = sim_score
+
+
+class TestXPUIOUSimilarityOpWithLoD(TestXPUIOUSimilarityOp):
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place, check_dygraph=False)
+
+    def setUp(self):
+        super(TestXPUIOUSimilarityOpWithLoD, self).setUp()
+        self.boxes1_lod = [[1, 1]]
+        self.output_lod = [[1, 1]]
+        self.box_normalized = False
+        # run python iou computation 
+        self._compute_iou()
+        self.inputs = {'X': (self.boxes1, self.boxes1_lod), 'Y': self.boxes2}
+        self.attrs = {"box_normalized": self.box_normalized}
+        self.outputs = {'Out': (self.output, self.output_lod)}
+
+
+class TestXPUIOUSimilarityOpWithBoxNormalized(TestXPUIOUSimilarityOp):
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place, check_dygraph=False)
+
+    def setUp(self):
+        super(TestXPUIOUSimilarityOpWithBoxNormalized, self).setUp()
+        self.boxes1_lod = [[1, 1]]
+        self.output_lod = [[1, 1]]
+        self.box_normalized = True
+        # run python iou computation 
+        self._compute_iou()
+        self.inputs = {'X': (self.boxes1, self.boxes1_lod), 'Y': self.boxes2}
+        self.attrs = {"box_normalized": self.box_normalized}
+        self.outputs = {'Out': (self.output, self.output_lod)}
+
+
+if __name__ == '__main__':
+    unittest.main()