【NPU】Support npu op gelu and gelu_grad (#31530)

* Support npu op gelu and gelu_grad

* Support npu op gelu and gelu_grad

paddle/fluid/operators/CMakeLists.txt

@@ -179,3 +179,7 @@ if(WITH_UNITY_BUILD)
     # The specified link dependency needs to be displayed here.
     target_link_libraries(paddle_operators_unity ${OP_HEADER_DEPS} ${COMMON_OP_DEPS})
 endif()
+
+if(WITH_ASCEND_CL)
+cc_test(gelu_op_npu_test SRCS gelu_op_npu_test.cc DEPS op_registry gelu_op scope device_context enforce executor)
+endif()

paddle/fluid/operators/gelu_op_npu.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 License. */
+
+#include <memory>
+#include <string>
+
+#include "paddle/fluid/operators/gelu_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class GeluNPUKernel : 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");
+
+    auto place = ctx.GetPlace();
+
+    out->mutable_data<T>(place);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    auto runner = NpuOpRunner("Gelu", {*x}, {*out}, {});
+    runner.Run(stream);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class GeluGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    auto place = ctx.GetPlace();
+
+    dx->mutable_data<T>(place);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    Tensor out(x->type());
+    out.mutable_data<T>(x->dims(), place);
+    auto out_runner = NpuOpRunner("Gelu", {*x}, {out}, {});
+    out_runner.Run(stream);
+
+    auto dx_runner = NpuOpRunner("GeluGrad", {*dout, *x, out}, {*dx}, {});
+    dx_runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    gelu,
+    ops::GeluNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::GeluNPUKernel<paddle::platform::NPUDeviceContext,
+    paddle::platform::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    gelu_grad,
+    ops::GeluGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::GeluGradNPUKernel<paddle::platform::NPUDeviceContext,
+    paddle::platform::float16>);

paddle/fluid/operators/gelu_op_npu_test.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 License. */
+
+#ifndef _WIN32
+#include <unistd.h>
+#endif
+
+#include <string>
+#include <thread>  // NOLINT
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/operators/dropout_op.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/string/printf.h"
+
+namespace f = paddle::framework;
+namespace p = paddle::platform;
+namespace m = paddle::operators::math;
+
+USE_OP(gelu);
+USE_OP_DEVICE_KERNEL(gelu, NPU);
+
+template <typename T>
+void Compare(f::Scope* scope, const p::DeviceContext& ctx) {
+  // init
+  auto x = scope->Var("X");
+  auto tensor_x = x->GetMutable<f::LoDTensor>();
+
+  std::vector<T> init_x;
+  for (int64_t i = 0; i < 10 * 10; ++i) {
+    init_x.push_back(static_cast<T>(1.0));
+  }
+
+  TensorFromVector(init_x, ctx, tensor_x);
+  tensor_x->Resize({10, 10});
+
+  auto out = scope->Var("Out");
+  auto tensor_out = out->GetMutable<f::LoDTensor>();
+
+  f::AttributeMap attrs;
+
+  ctx.Wait();
+
+  // run
+  auto place = ctx.GetPlace();
+
+  auto op = f::OpRegistry::CreateOp("gelu", {{"X", {"X"}}},
+                                    {{"Out", {"Out"}}}, attrs);
+  op->Run(*scope, place);
+
+  ctx.Wait();
+
+  // eval time
+  struct timeval start, end;
+  gettimeofday(&start, NULL);
+
+  for (int i = 0; i < 100; i++) {
+    op->Run(*scope, place);
+  }
+
+  ctx.Wait();
+
+  gettimeofday(&end, NULL);
+  int micros = (((end.tv_sec - start.tv_sec) * 1000000) +
+                  end.tv_usec) - (start.tv_usec);
+  printf("used time: %d\n", micros / 100);
+
+  // eval value
+  std::vector<T> out_vec;
+  TensorToVector(*tensor_out, ctx, &out_vec);
+
+  float expected = 0.841192;
+  for (uint32_t i = 0; i < out_vec.size(); i++) {
+    EXPECT_FLOAT_EQ(out_vec[i], static_cast<T>(expected));
+  }
+}
+
+template <typename T>
+void CompareGrad(f::Scope* scope, const p::DeviceContext& ctx) {
+  auto dout = scope->Var("DOut");
+  auto tensor_dout = dout->GetMutable<f::LoDTensor>();
+
+  auto x = scope->Var("X");
+  auto tensor_x = x->GetMutable<f::LoDTensor>();
+
+  std::vector<T> init_dout;
+  for (int64_t i = 0; i < 10 * 10; ++i) {
+    init_dout.push_back(static_cast<T>(1.0));
+  }
+
+  std::vector<T> init_x;
+  for (int64_t i = 0; i < 10 * 10; ++i) {
+    init_x.push_back(static_cast<T>(1.0));
+  }
+
+  TensorFromVector(init_dout, ctx, tensor_dout);
+  tensor_dout->Resize({10, 10});
+  TensorFromVector(init_x, ctx, tensor_x);
+  tensor_x->Resize({10, 10});
+
+  auto dx = scope->Var("DX");
+  auto tensor_dx = dx->GetMutable<f::LoDTensor>();
+
+  f::AttributeMap attrs;
+
+  ctx.Wait();
+
+  // run
+  auto place = ctx.GetPlace();
+
+  auto op = f::OpRegistry::CreateOp("gelu_grad",
+    {{"Out@GRAD", {"DOut"}}, {"X", {"X"}}},
+    {{"X@GRAD", {"DX"}}}, attrs);
+  op->Run(*scope, place);
+
+  ctx.Wait();
+
+  // eval time
+  struct timeval start, end;
+  gettimeofday(&start, NULL);
+
+  for (int i = 0; i < 100; i++) {
+    op->Run(*scope, place);
+  }
+
+  ctx.Wait();
+
+  gettimeofday(&end, NULL);
+  int micros = (((end.tv_sec - start.tv_sec) * 1000000) +
+                  end.tv_usec) - (start.tv_usec);
+  printf("used time: %d\n", micros / 100);
+
+  // eval value
+  std::vector<T> dx_vec;
+  TensorToVector(*tensor_dx, ctx, &dx_vec);
+
+  float expected = 1.082964;
+  for (uint32_t i = 0; i < dx_vec.size(); i++) {
+    EXPECT_FLOAT_EQ(dx_vec[i], static_cast<T>(expected));
+  }
+}
+
+TEST(gelu, NPU_fp32) {
+    f::Scope scope;
+    p::NPUDeviceContext ctx(p::NPUPlace(0));
+    Compare<float>(&scope, ctx);
+}
+
+TEST(gelu_grad, NPU) {
+    f::Scope scope;
+    p::NPUDeviceContext ctx(p::NPUPlace(0));
+    CompareGrad<float>(&scope, ctx);
+}
+

python/paddle/fluid/tests/unittests/npu/test_gelu_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
+from scipy import special
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+SEED = 2021
+
+
+def np_gelu(x):
+    y = 0.5 * x * (1 + special.erf(x / np.sqrt(2)))
+    return y
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestGelu(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "gelu"
+        self.place = paddle.NPUPlace(0)
+
+        self.init_dtype()
+        np.random.seed(SEED)
+        x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
+        out = np_gelu(x)
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.attrs = {}
+        self.outputs = {'Out': out}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False, atol=1e-3)
+
+    # TODO(ascendrc): Add grad test
+    # def test_check_grad(self):
+    #     if self.dtype == np.float16:
+    #         return
+    #     self.check_grad(['X'], 'Out')
+    #
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestGeluFp16(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "gelu"
+        self.place = paddle.NPUPlace(0)
+
+        self.init_dtype()
+        np.random.seed(SEED)
+        x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
+        out = np_gelu(x)
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.attrs = {}
+        self.outputs = {'Out': out}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.__class__.no_need_check_grad = True
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False, atol=1e-3)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestGeluNet(unittest.TestCase):
+    def _test(self, run_npu=True):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+        np.random.seed(SEED)
+
+        a_np = np.random.random(size=(32, 32)).astype('float32')
+        b_np = np.random.random(size=(32, 32)).astype('float32')
+        label_np = np.random.randint(2, size=(32, 1)).astype('int64')
+
+        with paddle.static.program_guard(main_prog, startup_prog):
+            a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
+            b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
+            label = paddle.static.data(
+                name="label", shape=[32, 1], dtype='int64')
+
+            c = paddle.multiply(a, b)
+            d = fluid.layers.gelu(c)
+
+            fc_1 = fluid.layers.fc(input=d, size=128)
+            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
+
+            cost = fluid.layers.cross_entropy(input=prediction, label=label)
+            loss = fluid.layers.reduce_mean(cost)
+            sgd = fluid.optimizer.SGD(learning_rate=0.01)
+            sgd.minimize(loss)
+
+        if run_npu:
+            place = paddle.NPUPlace(0)
+        else:
+            place = paddle.CPUPlace()
+
+        exe = paddle.static.Executor(place)
+        exe.run(startup_prog)
+
+        print("Start run on {}".format(place))
+        for epoch in range(100):
+
+            pred_res, loss_res = exe.run(
+                main_prog,
+                feed={"a": a_np,
+                      "b": b_np,
+                      "label": label_np},
+                fetch_list=[prediction, loss])
+            if epoch % 10 == 0:
+                print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
+                    epoch, pred_res[0], loss_res))
+
+        return pred_res, loss_res
+
+    def test_npu(self):
+        cpu_pred, cpu_loss = self._test(False)
+        npu_pred, npu_loss = self._test(True)
+
+        self.assertTrue(np.allclose(npu_pred, cpu_pred, atol=1e-3))
+        self.assertTrue(np.allclose(npu_loss, cpu_loss, atol=1e-3))
+
+
+if __name__ == '__main__':
+    unittest.main()