[NPU] Support npu kernel for flatten_contiguous_range op, test=develop (#34642)

* fix npu compile error, test=develop

* [NPU] Support npu kernel for flatten_contiguous_range op, test=develop

* [NPU] Support npu kernel for flatten_contiguous_range op, test=develop

* [NPU] Support npu kernel for flatten_contiguous_range op, test=develop

* [NPU] Support npu kernel for flatten_contiguous_range op, test=develop

* [NPU] Support npu kernel for flatten_contiguous_range op, test=develop

* [NPU] Support npu kernel for flatten_contiguous_range op, test=develop

* [NPU] Support npu kernel for flatten_contiguous_range op, test=develop

* Update flatten_op_npu.cc

* Update flatten_op_npu.cc
Co-authored-by: Nqili93 <qili93@qq.com>

paddle/fluid/operators/flatten_op_npu.cc

@@ -54,6 +54,30 @@ class Flatten2GradNPUKernel : public framework::OpKernel<T> {
   }
 };
 
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class FlattenContiguousRangeNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *X = ctx.Input<Tensor>("X");
+    auto *Out = ctx.Output<Tensor>("Out");
+    int start_axis = ctx.Attr<int>("start_axis");
+    int stop_axis = ctx.Attr<int>("stop_axis");
+
+    Out->mutable_data<T>(ctx.GetPlace());
+
+    const auto &runner =
+        NpuOpRunner("FlattenV2", {*X}, {*Out},
+                    {{"axis", static_cast<int32_t>(start_axis)},
+                     {"end_axis", static_cast<int32_t>(stop_axis)}});
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -71,3 +95,18 @@ REGISTER_OP_NPU_KERNEL(flatten2_grad, ops::Flatten2GradNPUKernel<float>,
                        ops::Flatten2GradNPUKernel<int>,
                        ops::Flatten2GradNPUKernel<int8_t>,
                        ops::Flatten2GradNPUKernel<int64_t>);
+
+REGISTER_OP_NPU_KERNEL(
+    flatten_contiguous_range,
+    ops::FlattenContiguousRangeNPUKernel<paddle::platform::NPUDeviceContext,
+                                         float>,
+    ops::FlattenContiguousRangeNPUKernel<paddle::platform::NPUDeviceContext,
+                                         double>,
+    ops::FlattenContiguousRangeNPUKernel<paddle::platform::NPUDeviceContext,
+                                         uint8_t>,
+    ops::FlattenContiguousRangeNPUKernel<paddle::platform::NPUDeviceContext,
+                                         int>,
+    ops::FlattenContiguousRangeNPUKernel<paddle::platform::NPUDeviceContext,
+                                         int8_t>,
+    ops::FlattenContiguousRangeNPUKernel<paddle::platform::NPUDeviceContext,
+                                         int64_t>);

paddle/fluid/operators/npu_op_runner.cc

@@ -33,6 +33,7 @@ static std::map<framework::proto::VarType::Type, aclDataType>
     DTYPE_2_ACL_DTYPE = {
         {framework::proto::VarType::BOOL, ACL_BOOL},
         {framework::proto::VarType::UINT8, ACL_UINT8},
+        {framework::proto::VarType::INT8, ACL_INT8},
         {framework::proto::VarType::INT16, ACL_INT16},
         {framework::proto::VarType::INT32, ACL_INT32},
         {framework::proto::VarType::INT64, ACL_INT64},

python/paddle/fluid/tests/unittests/npu/test_flatten_contiguous_range_op_npu.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 numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+
+
+class TestFlattenOp(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "flatten_contiguous_range"
+        self.place = paddle.NPUPlace(0)
+
+        self.start_axis = 0
+        self.stop_axis = -1
+        self.dtype = np.float64
+        self.init_test_case()
+        self.inputs = {"X": np.random.random(self.in_shape).astype(self.dtype)}
+        self.init_attrs()
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.new_shape),
+            "XShape": np.random.random(self.in_shape).astype("float32")
+        }
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, no_check_set=["XShape"])
+
+    def test_check_grad(self):
+        pass
+
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = -1
+        self.new_shape = (120)
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_1(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 1
+        self.stop_axis = 2
+        self.new_shape = (3, 10, 4)
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_2(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = 1
+        self.new_shape = (6, 5, 4)
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_3(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = 2
+        self.new_shape = (30, 4)
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_4(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = -2
+        self.stop_axis = -1
+        self.new_shape = (3, 2, 20)
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_5(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 2
+        self.stop_axis = 2
+        self.new_shape = (3, 2, 5, 4)
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOpSixDims(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 3, 2, 4, 4)
+        self.start_axis = 3
+        self.stop_axis = 5
+        self.new_shape = (3, 2, 3, 32)
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_Float32(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = 1
+        self.new_shape = (6, 5, 4)
+        self.dtype = np.float32
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_int(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = 1
+        self.new_shape = (6, 5, 4)
+        self.dtype = np.int
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_uint8(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = 1
+        self.new_shape = (6, 5, 4)
+        self.dtype = np.uint8
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_int8(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = 1
+        self.new_shape = (6, 5, 4)
+        self.dtype = np.int8
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlattenOp_int64(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = 1
+        self.new_shape = (6, 5, 4)
+        self.dtype = np.int64
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis
+        }
+
+
+class TestFlatten2OpError(unittest.TestCase):
+    def test_errors(self):
+        image_shape = (2, 3, 4, 4)
+        x = np.arange(image_shape[0] * image_shape[1] * image_shape[2] *
+                      image_shape[3]).reshape(image_shape) / 100.
+        x = x.astype('float32')
+
+        def test_ValueError1():
+            x_var = paddle.static.data(
+                name="x", shape=image_shape, dtype='float32')
+            out = paddle.flatten(x_var, start_axis=2, stop_axis=1)
+
+        self.assertRaises(ValueError, test_ValueError1)
+
+        def test_ValueError2():
+            x_var = paddle.static.data(
+                name="x", shape=image_shape, dtype='float32')
+            paddle.flatten(x_var, start_axis=10, stop_axis=1)
+
+        self.assertRaises(ValueError, test_ValueError2)
+
+        def test_ValueError3():
+            x_var = paddle.static.data(
+                name="x", shape=image_shape, dtype='float32')
+            paddle.flatten(x_var, start_axis=2, stop_axis=10)
+
+        self.assertRaises(ValueError, test_ValueError3)
+
+        def test_type():
+            # dtype must be float32, float64, int8, int32, int64, uint8.
+            x2 = np.arange(image_shape[0] * image_shape[1] * image_shape[2] *
+                           image_shape[3]).reshape(image_shape) / 100.
+            x2 = x2.astype('float16')
+            x2_var = paddle.fluid.data(
+                name='x2', shape=[3, 2, 4, 5], dtype='float16')
+            paddle.flatten(x2_var)
+
+        self.assertRaises(TypeError, test_type)
+
+        def test_InputError():
+            out = paddle.flatten(x)
+
+        self.assertRaises(ValueError, test_InputError)
+
+
+class TestStaticFlattenPythonAPI(unittest.TestCase):
+    def execute_api(self, x, start_axis=0, stop_axis=-1):
+        return paddle.flatten(x, start_axis, stop_axis)
+
+    def test_static_api(self):
+        paddle.enable_static()
+        np_x = np.random.rand(2, 3, 4, 4).astype('float32')
+
+        main_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, paddle.static.Program()):
+            x = paddle.static.data(
+                name="x", shape=[2, 3, 4, 4], dtype='float32')
+            out = self.execute_api(x, start_axis=-2, stop_axis=-1)
+
+        exe = paddle.static.Executor(place=paddle.NPUPlace(0))
+        fetch_out = exe.run(main_prog, feed={"x": np_x}, fetch_list=[out])
+        self.assertTrue((2, 3, 16) == fetch_out[0].shape)
+
+
+class TestStaticInplaceFlattenPythonAPI(TestStaticFlattenPythonAPI):
+    def execute_api(self, x, start_axis=0, stop_axis=-1):
+        return x.flatten_(start_axis, stop_axis)
+
+
+class TestFlattenPython(unittest.TestCase):
+    def test_python_api(self):
+        image_shape = (2, 3, 4, 4)
+        x = np.arange(image_shape[0] * image_shape[1] * image_shape[2] *
+                      image_shape[3]).reshape(image_shape) / 100.
+        x = x.astype('float32')
+
+        def test_InputError():
+            out = paddle.flatten(x)
+
+        self.assertRaises(ValueError, test_InputError)
+
+        def test_Negative():
+            paddle.disable_static(paddle.NPUPlace(0))
+            img = paddle.to_tensor(x)
+            out = paddle.flatten(img, start_axis=-2, stop_axis=-1)
+            return out.numpy().shape
+
+        res_shape = test_Negative()
+        self.assertTrue((2, 3, 16) == res_shape)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_flatten_contiguous_range_op.py

-#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#   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.