[NPU] Support softmax npu kernel (#31564)

paddle/fluid/operators/CMakeLists.txt

@@ -187,4 +187,5 @@ 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)
+cc_test(top_k_op_npu_test SRCS top_k_op_npu_test.cc DEPS op_registry top_k_op scope device_context enforce executor)
 endif()

paddle/fluid/operators/softmax_op.cc

@@ -79,11 +79,13 @@ class SoftmaxOp : public framework::OperatorWithKernel {
 #endif
 
     auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+#ifndef PADDLE_WITH_ASCEND_CL
     if (input_data_type == framework::proto::VarType::FP16) {
       PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
                         platform::errors::InvalidArgument(
                             "float16 can only be used on GPU place"));
     }
+#endif
 
     return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
                                    library_);

paddle/fluid/operators/softmax_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/npu_op_runner.h"
+#include "paddle/fluid/operators/softmax_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class SoftmaxNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in = ctx.Input<framework::LoDTensor>("X");
+    auto axis = ctx.Attr<int>("axis");
+    std::vector<int> axes;
+    axes.push_back(axis);
+    framework::NPUAttributeMap attr_input = {{"axes", axes}};
+
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+    out->mutable_data<T>(ctx.GetPlace());
+
+    auto runner = NpuOpRunner("SoftmaxV2", {*in}, {*out}, attr_input);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class SoftmaxGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* out = ctx.Input<framework::LoDTensor>("Out");
+    auto* dOut = ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
+
+    auto* dX = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    auto dims = dX->dims();
+    const int rank = dims.size();
+    const int axis = CanonicalAxis(ctx.Attr<int>("axis"), rank);
+    int64_t first_dim = 1;
+    int64_t sec_dim = 1;
+    for (int i = 0; i < axis; i++) {
+      first_dim *= dims[i];
+    }
+    for (int i = axis; i < rank; i++) {
+      sec_dim *= dims[i];
+    }
+
+    Tensor tmp_out;
+    tmp_out.ShareDataWith(*out).Resize({first_dim, sec_dim});
+
+    Tensor tmp_dOut;
+    tmp_dOut.ShareDataWith(*dOut).Resize({first_dim, sec_dim});
+
+
+    dX->Resize(framework::make_ddim({first_dim, sec_dim}));
+    dX->mutable_data<T>(ctx.GetPlace());
+
+    framework::NPUAttributeMap attr_input = {};
+    auto runner = NpuOpRunner(std::string("SoftmaxGrad"), {tmp_out, tmp_dOut},
+                              {*dX}, attr_input);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+
+    dX->Resize(dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_NPU_KERNEL(
+    softmax, ops::SoftmaxNPUKernel<plat::NPUDeviceContext, float>,
+    ops::SoftmaxNPUKernel<plat::NPUDeviceContext, double>,
+    ops::SoftmaxNPUKernel<plat::NPUDeviceContext, plat::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    softmax_grad, ops::SoftmaxGradNPUKernel<plat::NPUDeviceContext, float>,
+    ops::SoftmaxGradNPUKernel<plat::NPUDeviceContext, double>,
+    ops::SoftmaxGradNPUKernel<plat::NPUDeviceContext,
+                              paddle::platform::float16>);

paddle/fluid/operators/softmax_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"
+#include "paddle/fluid/framework/tensor_util.h"
+
+
+namespace f = paddle::framework;
+namespace p = paddle::platform;
+namespace m = paddle::operators::math;
+
+USE_OP(softmax);
+USE_OP_DEVICE_KERNEL(softmax, 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;
+  for (int i = 3; i < 9; ++i) {
+    init.push_back(static_cast<T>(i));
+  }
+
+  TensorFromVector(init, ctx, tensor_x);
+  tensor_x->Resize({2, 3});
+
+  ctx.Wait();
+
+  auto place = ctx.GetPlace();
+  auto out = scope->Var("Out");
+  auto tensor_out = out->GetMutable<f::LoDTensor>();
+  tensor_out->Resize({2, 3});
+  tensor_out->mutable_data<T>(place);  // allocate
+
+  // run
+  int axis = 1;
+  f::AttributeMap attrs = {
+            {"axis", axis},
+            {"use_cudnn", false},
+            {"use_mkldnn", false},
+            {"mkldnn_data_type", std::string("float32")},
+            {"is_test", false}, };
+
+  auto op =
+      f::OpRegistry::CreateOp("softmax", {{"X", {"X"}}},
+                              {{"Out", {"Out"}}}, attrs);
+
+  op->Run(*scope, place);
+  ctx.Wait();
+
+  std::vector<T> out_vec;
+  TensorToVector(*tensor_out, ctx, &out_vec);
+
+  for (int i = 0; i < static_cast<int>(out_vec.size()); ++i) {
+       VLOG(3) << "out_vec[" << i << "] : "<< out_vec[i];
+  }
+
+  ctx.Wait();
+
+  EXPECT_EQ((uint32_t)out_vec.size(), (uint32_t)(6));
+}
+
+
+template <typename T>
+void CompareGrad(f::Scope* scope, const p::DeviceContext& ctx) {
+  // init
+  auto out = scope->Var("Out");
+  auto tensor_out = out->GetMutable<f::LoDTensor>();
+
+  std::vector<T> out_init;
+
+  out_init.push_back(static_cast<T>(0.6670));
+  out_init.push_back(static_cast<T>(0.5888));
+  out_init.push_back(static_cast<T>(0.4543));
+  out_init.push_back(static_cast<T>(0.3330));
+  out_init.push_back(static_cast<T>(0.4112));
+  out_init.push_back(static_cast<T>(0.5457));
+
+  TensorFromVector(out_init, ctx, tensor_out);
+  tensor_out->Resize({2, 3});
+
+  ctx.Wait();
+
+  auto dout = scope->Var("DOut");
+  auto tensor_dout = dout->GetMutable<f::LoDTensor>();
+
+  std::vector<T> dout_init;
+  for (int i = 0; i < 6; ++i) {
+    dout_init.push_back(static_cast<T>(1.0));
+  }
+
+  TensorFromVector(dout_init, ctx, tensor_dout);
+  tensor_dout->Resize({2, 3});
+
+  ctx.Wait();
+
+  auto dx = scope->Var("DX");
+  auto tensor_dx = dx->GetMutable<f::LoDTensor>();
+
+  ctx.Wait();
+
+  // run
+  f::AttributeMap attrs;
+  attrs = {
+      {"name", std::string("softmax_grad")},
+      {"axis", static_cast<int>(0)},
+            {"use_cudnn", false},
+            {"use_mkldnn", false},
+            {"mkldnn_data_type", std::string("float32")},
+            {"is_test", false},
+            {"data_format", std::string("AnyLayout")}, };
+  auto op =
+      f::OpRegistry::CreateOp("softmax_grad",
+                              {{"Out", {"Out"}},
+                               {"Out@GRAD", {"DOut"}}},
+                              {{"X@GRAD", {"DX"}}}, attrs);
+
+  auto place = ctx.GetPlace();
+  op->Run(*scope, place);
+  ctx.Wait();
+
+  EXPECT_EQ((uint32_t)tensor_dx->dims()[0], (uint32_t)(2));
+  EXPECT_EQ((uint32_t)tensor_dx->dims()[1], (uint32_t)(3));
+
+  ctx.Wait();
+
+  std::vector<float> out_vec;
+  TensorToVector(*tensor_dx, ctx, &out_vec);
+
+  ctx.Wait();
+
+  EXPECT_EQ((uint32_t)out_vec.size(), (uint32_t)(6));
+  EXPECT_NEAR((float)out_vec[0], (float)(-0.4737), 0.1);
+  EXPECT_NEAR((float)out_vec[1], (float)(-0.4181), 0.1);
+  EXPECT_NEAR((float)out_vec[2], (float)(-0.3226), 0.1);
+  EXPECT_NEAR((float)out_vec[3], (float)(-0.0965), 0.1);
+  EXPECT_NEAR((float)out_vec[4], (float)(-0.1192), 0.1);
+  EXPECT_NEAR((float)out_vec[5], (float)(-0.1582), 0.1);
+}
+
+TEST(softmax, NPU_fp32) {
+  f::Scope scope;
+  p::NPUDeviceContext ctx(p::NPUPlace(0));
+  Compare<float>(&scope, ctx);
+}
+
+TEST(softmax_grad, NPU_fp32) {
+  f::Scope scope;
+  p::NPUDeviceContext ctx(p::NPUPlace(0));
+  CompareGrad<float>(&scope, ctx);
+}

python/paddle/fluid/tests/unittests/npu/test_softmax_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
+from paddle.fluid import core
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestSoftmax(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.place = paddle.NPUPlace(0)
+        self.op_type = "softmax"
+        self.init_dtype()
+
+        x = np.random.random([3, 3]).astype(self.dtype)
+        np_out = np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)
+        self.inputs = {'X': x}
+
+        self.attrs = {}
+        self.outputs = {'Out': np_out}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.__class__.no_need_check_grad = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestSoftmaxNet(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=(4, 32)).astype('float32')
+        b_np = np.random.random(size=(4, 32)).astype('float32')
+        label_np = np.random.randint(2, size=(4, 1)).astype('int64')
+
+        with paddle.static.program_guard(main_prog, startup_prog):
+            a = paddle.static.data(name="a", shape=[4, 32], dtype='float32')
+            b = paddle.static.data(name="b", shape=[4, 32], dtype='float32')
+            label = paddle.static.data(
+                name="label", shape=[4, 1], dtype='int64')
+
+            c = paddle.multiply(a, b)
+            d = paddle.sqrt(c)
+
+            # 4 x 128
+            fc_1 = fluid.layers.fc(input=d, size=128)
+            # 4 x 2
+            prediction = fluid.layers.fc(input=fc_1, size=2)
+
+            # 4 x 2
+            prob = fluid.layers.softmax(prediction, axis=1)
+
+            cost = fluid.layers.cross_entropy(input=prob, label=label)
+            loss = fluid.layers.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, rtol=1e-2))
+        self.assertTrue(np.allclose(npu_loss, cpu_loss, rtol=1e-2))
+
+
+if __name__ == '__main__':
+    unittest.main()