[NPU] Support npu kernel for amp_check_finite_and_unscale_npu op (#31457)

* Support npu kernel for amp_check_finite_and_unscale_npu op

* support EnforceNotMet exception

* fix exception bug

* modify python unittest

* precommit

* update c++ unittest

* fix review

* fix review

paddle/fluid/operators/amp/CMakeLists.txt

@@ -4,3 +4,7 @@ if(WITH_UNITY_BUILD)
     include(unity_build_rule.cmake)
 endif()
 register_operators()
+
+if(WITH_ASCEND_CL)
+    cc_test(check_finite_and_unscale_op_npu_test SRCS check_finite_and_unscale_op_npu_test.cc DEPS op_registry check_finite_and_unscale_op scope device_context enforce executor)
+endif()

paddle/fluid/operators/amp/check_finite_and_unscale_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/framework/tensor_util.h"
+#include "paddle/fluid/operators/amp/check_finite_and_unscale_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class CheckFiniteAndUnscaleNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    const auto xs = ctx.MultiInput<framework::Tensor>("X");
+    const auto* scale = ctx.Input<framework::Tensor>("Scale");
+    auto outs = ctx.MultiOutput<framework::Tensor>("Out");
+    auto* found_inf = ctx.Output<framework::Tensor>("FoundInfinite");
+
+    found_inf->mutable_data<bool>(ctx.GetPlace());
+
+    bool found_inf_data = false;
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    // step1: inverse scale(RealDiv)
+    Tensor const_tensor;
+    const_tensor.mutable_data<T>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<T>{static_cast<T>(1.0)}, ctx.device_context(),
+                     &const_tensor);
+
+    ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
+
+    // Inverse(1.0/scale)
+    Tensor* tmp_inverse_out = const_cast<Tensor*>(scale);
+    Tensor inverse_out(scale->type());
+    inverse_out.Resize(scale->dims());
+    inverse_out.mutable_data<T>(ctx.GetPlace());
+    auto runner_inverse =
+        NpuOpRunner("Div", {const_tensor, *scale}, {inverse_out}, {});
+    runner_inverse.Run(stream);
+    tmp_inverse_out = &inverse_out;
+
+    size_t x_size = xs.size();
+    for (size_t i = 0; i < x_size; ++i) {
+      found_inf_data = true;
+      const auto* x = xs[i];
+      auto* out = outs[i];
+      out->mutable_data<T>(ctx.GetPlace());
+
+      // step2: CheckNumerics
+      // CheckNumerics runs on the Ascend AI CPU, which delivers poor
+      // performance.
+      Tensor check_xout(x->type());
+      check_xout.Resize(x->dims());
+      check_xout.mutable_data<T>(ctx.GetPlace());
+      try {
+        auto runner_checknumerics =
+            NpuOpRunner("CheckNumerics", {*x}, {check_xout},
+                        {{"message", std::string("check_nan_and_inf")}});
+        runner_checknumerics.Run(stream);
+      } catch (platform::EnforceNotMet& exception) {
+        LOG(WARNING) << "[check_nan_and_inf] detected contains NaN or INF!!!";
+        found_inf_data = true;
+      } catch (...) {
+        LOG(WARNING) << "[check_nan_and_inf] detected contains NaN or INF!!!";
+        found_inf_data = true;
+      }
+
+      if (!found_inf_data) {
+        // MatMul
+        auto runner_matmul =
+            NpuOpRunner("Mul", {*x, *tmp_inverse_out}, {*out}, {});
+        runner_matmul.Run(stream);
+      } else {
+        // ZerosLike
+        auto runner_zeroslike = NpuOpRunner("ZerosLike", {*x}, {*out}, {});
+        runner_zeroslike.Run(stream);
+      }  // end if
+    }    // end for
+
+    // set found_inf to true
+    if (found_inf_data) {
+      Tensor found_inf_tensor;
+      found_inf_tensor.Resize({1});
+      bool* is_found_inf =
+          found_inf_tensor.mutable_data<bool>(paddle::platform::CPUPlace());
+      *is_found_inf = true;
+      framework::TensorCopySync(found_inf_tensor, ctx.GetPlace(), found_inf);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+REGISTER_OP_NPU_KERNEL(check_finite_and_unscale,
+                       ops::CheckFiniteAndUnscaleNPUKernel<float>,
+                       ops::CheckFiniteAndUnscaleNPUKernel<plat::float16>);

paddle/fluid/operators/amp/check_finite_and_unscale_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 <algorithm>
+#include <cstdlib>
+#include <memory>
+#include <random>
+#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/math/math_function.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace f = paddle::framework;
+namespace p = paddle::platform;
+namespace m = paddle::operators::math;
+
+using Tensor = paddle::framework::Tensor;
+
+USE_OP(check_finite_and_unscale);
+USE_OP_DEVICE_KERNEL(check_finite_and_unscale, NPU);
+
+struct InputVars {
+  std::string name;
+  f::LoDTensor *tensor;
+};
+
+template <typename T>
+void Compare(f::Scope *scope, const p::DeviceContext &ctx) {
+  const f::DDim dims = f::make_ddim({2, 2});
+  auto place = ctx.GetPlace();
+
+  // init input
+  std::vector<InputVars> input_names = {
+      {"x", scope->Var("x")->GetMutable<f::LoDTensor>()},
+      {"x1", scope->Var("x1")->GetMutable<f::LoDTensor>()}};
+
+  auto *scale = scope->Var("scale")->GetMutable<f::LoDTensor>();
+
+  // init output
+  auto *out = scope->Var("out")->GetMutable<f::LoDTensor>();
+  auto *out1 = scope->Var("out1")->GetMutable<f::LoDTensor>();
+  auto *found_inf = scope->Var("found_inf")->GetMutable<f::LoDTensor>();
+
+  // Initialize input data
+  const int num_inputs = input_names.size();
+  size_t numel = static_cast<size_t>(f::product(dims));
+
+  for (int i = 0; i < num_inputs; ++i) {
+    std::vector<T> init_xs;
+    for (size_t j = 0; j < numel; ++j) {
+      if (j == 0) {
+        init_xs.push_back(static_cast<T>(NAN));
+      } else {
+        init_xs.push_back(static_cast<T>(j + 1));
+      }
+    }
+    f::TensorFromVector(init_xs, ctx, input_names[i].tensor);
+    input_names[i].tensor->Resize(dims);
+  }
+
+  f::TensorFromVector(std::vector<T>{static_cast<T>(0.5)}, ctx, scale);
+
+  ctx.Wait();
+
+  // run
+  f::AttributeMap attrs;
+  auto op = f::OpRegistry::CreateOp(
+      "check_finite_and_unscale", {{"X", {"x", "x1"}}, {"Scale", {"scale"}}},
+      {{"Out", {"out", "out1"}}, {"FoundInfinite", {"found_inf"}}}, attrs);
+  op->Run(*scope, place);
+  ctx.Wait();
+
+  // out0
+  std::vector<T> out_vec;
+  f::TensorToVector(*out, ctx, &out_vec);
+  EXPECT_EQ(out_vec.size(), static_cast<size_t>(4));
+  for (size_t j = 0; j < out_vec.size(); ++j) {
+    VLOG(3) << "out_vec[" << j << "]:" << out_vec[j];
+  }
+
+  ctx.Wait();
+
+  // out0
+  std::vector<T> out1_vec;
+  f::TensorToVector(*out1, ctx, &out1_vec);
+  EXPECT_EQ(out1_vec.size(), static_cast<size_t>(4));
+  for (size_t j = 0; j < out1_vec.size(); ++j) {
+    VLOG(3) << "out1_vec[" << j << "]:" << out1_vec[j];
+  }
+
+  ctx.Wait();
+
+  // out found_inf
+  Tensor found_inf_tensor;
+  found_inf_tensor.Resize({1});
+  bool *is_finite_data =
+      found_inf_tensor.mutable_data<bool>(paddle::platform::CPUPlace());
+  f::TensorCopy(*found_inf, place, &found_inf_tensor);
+  EXPECT_FALSE(*is_finite_data);
+
+  ctx.Wait();
+}
+
+TEST(check_finite_and_unscale, NPU_fp32) {
+  f::Scope scope;
+  p::NPUDeviceContext ctx(p::NPUPlace(0));
+  Compare<float>(&scope, ctx);
+}
+
+TEST(check_finite_and_unscale, NPU_fp16) {
+  f::Scope scope;
+  p::NPUDeviceContext ctx(p::NPUPlace(0));
+  Compare<p::float16>(&scope, ctx);
+}

python/paddle/fluid/tests/unittests/npu/test_amp_check_finite_and_scale_op_npu.py

+#   Copyright (c) 2020 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.
+
+import unittest
+import numpy as np
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestCheckFiniteAndUnscaleOp(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "check_finite_and_unscale"
+        self.place = paddle.NPUPlace(0)
+        self.init_dtype()
+        x = np.random.random((1024, 1024)).astype(self.dtype)
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([0]),
+            'Out': [('out0', x / scale)],
+        }
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
+    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 TestCheckFiniteAndUnscaleOpWithNan(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "check_finite_and_unscale"
+        self.place = paddle.NPUPlace(0)
+        self.init_dtype()
+        x = np.random.random((1024, 1024)).astype(self.dtype)
+        x[128][128] = np.nan
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([1]),
+            'Out': [('out0', x)],
+        }
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        # When input contains nan, do not check the output, 
+        # since the output may be nondeterministic and will be discarded.
+        self.check_output_with_place(
+            self.place, check_dygraph=False, no_check_set=['Out'])
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestCheckFiniteAndUnscaleOpWithInf(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "check_finite_and_unscale"
+        self.place = paddle.NPUPlace(0)
+        self.init_dtype()
+        x = np.random.random((1024, 1024)).astype(self.dtype)
+        x[128][128] = np.inf
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([1]),
+            'Out': [('out0', x)],
+        }
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        # When input contains inf, do not check the output, 
+        # since the output may be nondeterministic and will be discarded.
+        self.check_output_with_place(
+            self.place, check_dygraph=False, no_check_set=['Out'])
+
+
+if __name__ == '__main__':
+    unittest.main()