Add sqrt, exp, round, log functions

2025-10-06 00:57:33 +08:00 · 2022-11-23 07:23:05 +00:00
parent 5ce0fd29f8
commit 1b32381201
4 changed files with 254 additions and 0 deletions
--- a/fastdeploy/function/math.cc
+++ b/fastdeploy/function/math.cc
@@ -0,0 +1,64 @@
+// Copyright (c) 2022 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 "fastdeploy/function/math.h"
+#include "fastdeploy/function/eigen.h"
+#include "fastdeploy/function/math_functor.h"
+
+namespace fastdeploy {
+namespace function {
+
+#define DEFINE_ACTIVATION_KERNEL(name, functor_class)                          \
+  template <typename T> void name##Kernel(const FDTensor& x, FDTensor* out) {  \
+    functor_class<T> functor;                                                  \
+    ActivationImpl<T, functor_class<T>>(x, out, functor);                      \
+  }
+
+template <typename T, typename Functor>
+void ActivationImpl(const FDTensor& X, FDTensor* Out, const Functor& functor) {
+  FDASSERT(Out != nullptr, "Output Out should not be nullptr");
+  auto x = EigenVector<T>::Flatten(X);
+  Out->Allocate(X.Shape(), X.Dtype());
+  auto out = EigenVector<T>::Flatten(*Out);
+  const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
+  functor(dev, x, out);
+}
+
+DEFINE_ACTIVATION_KERNEL(Sqrt, SqrtFunctor)
+DEFINE_ACTIVATION_KERNEL(Log, LogFunctor)
+DEFINE_ACTIVATION_KERNEL(Round, RoundFunctor)
+DEFINE_ACTIVATION_KERNEL(Exp, ExpFunctor)
+
+void Sqrt(const FDTensor& x, FDTensor* out) {
+  FD_VISIT_FLOAT_TYPES(x.dtype, "SqrtKernel",
+                       ([&] { SqrtKernel<data_t>(x, out); }));
+}
+
+void Log(const FDTensor& x, FDTensor* out) {
+  FD_VISIT_FLOAT_TYPES(x.dtype, "LogKernel",
+                       ([&] { LogKernel<data_t>(x, out); }));
+}
+
+void Round(const FDTensor& x, FDTensor* out) {
+  FD_VISIT_FLOAT_TYPES(x.dtype, "RoundKernel",
+                       ([&] { RoundKernel<data_t>(x, out); }));
+}
+
+void Exp(const FDTensor& x, FDTensor* out) {
+  FD_VISIT_FLOAT_TYPES(x.dtype, "ExpKernel",
+                       ([&] { ExpKernel<data_t>(x, out); }));
+}
+
+}  // namespace function
+}  // namespace fastdeploy
--- a/fastdeploy/function/math.h
+++ b/fastdeploy/function/math.h
@@ -0,0 +1,47 @@
+// Copyright (c) 2022 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.
+
+#pragma once
+
+#include "fastdeploy/core/fd_tensor.h"
+
+namespace fastdeploy {
+namespace function {
+
+/** Calculates the sqrt of the given input Tensor, element-wise. Only for float type FDTensor
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+*/
+FASTDEPLOY_DECL void Sqrt(const FDTensor& x, FDTensor* out);
+
+/** Calculates the natural log of the given input Tensor, element-wise. Only for float type FDTensor
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+*/
+FASTDEPLOY_DECL void Log(const FDTensor& x, FDTensor* out);
+
+/** Rounds the values in the input to the nearest integer value, element-wise. Only for float type FDTensor
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+*/
+FASTDEPLOY_DECL void Round(const FDTensor& x, FDTensor* out);
+
+/** Computes exp of x element-wise with a natural number e as the base, element-wise. Only for float type FDTensor
+    @param x The input tensor.
+    @param out The output tensor which stores the result.
+*/
+FASTDEPLOY_DECL void Exp(const FDTensor& x, FDTensor* out);
+
+}  // namespace function
+}  // namespace fastdeploy
--- a/fastdeploy/function/math_functor.h
+++ b/fastdeploy/function/math_functor.h
@@ -0,0 +1,56 @@
+// Copyright (c) 2022 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.
+
+#pragma once
+
+#include "fastdeploy/function/eigen.h"
+
+namespace fastdeploy {
+namespace function {
+
+// log(x) = natural logarithm of x
+template <typename T> struct LogFunctor {
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) const {
+    out.device(d) = x.log();
+  }
+};
+
+// exp functor
+// exp(x) = e^x
+template <typename T> struct ExpFunctor {
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) const {
+    out.device(d) = x.exp();
+  }
+};
+
+// round(x) = [x]
+template <typename T> struct RoundFunctor {
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) const {
+    out.device(d) = x.round();
+  }
+};
+
+// sqrt(x) = x^(1/2)
+template <typename T> struct SqrtFunctor {
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) const {
+    out.device(d) = x.sqrt();
+  }
+};
+
+}  // namespace function
+}  // namespace fastdeploy
--- a/tests/function/test_math.cc
+++ b/tests/function/test_math.cc
@@ -0,0 +1,87 @@
+// Copyright (c) 2022 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 "fastdeploy/core/fd_tensor.h"
+#include "fastdeploy/function/math.h"
+#include "glog/logging.h"
+#include "gtest_utils.h"
+#include "gtest/gtest.h"
+#include <array>
+#include <vector>
+
+namespace fastdeploy {
+namespace function {
+
+std::vector<float> CreateTestData() {
+  // Shape: [2, 3, 4]
+  std::vector<float> x_data = {
+      0.8428625,  0.6461913, 0.13740455, 0.11430702, 0.659926,  0.535816,
+      0.7429162,  0.8456049, 0.21228176, 0.29970083, 0.8621713, 0.40894133,
+      0.12684688, 0.1566195, 0.42884097, 0.8476526,  0.2458633, 0.669046,
+      0.87888306, 0.6762589, 0.666453,   0.32523027, 0.4139388, 0.8341406};
+  return x_data;
+}
+
+TEST(fastdeploy, exp_sqrt_round_log) {
+  CheckShape check_shape;
+  CheckData check_data;
+  FDTensor x, y;
+  auto test_data = CreateTestData();
+  x.SetExternalData({2, 3, 4}, FDDataType::FP32, test_data.data());
+
+  // Test Sqrt function
+  Sqrt(x, &y);
+  std::vector<float> sqrt_result = {
+      0.918075, 0.80386,  0.370681, 0.338093, 0.812358, 0.731995,
+      0.861926, 0.919568, 0.46074,  0.547449, 0.928532, 0.639485,
+      0.356156, 0.395752, 0.654859, 0.920681, 0.495846, 0.817952,
+      0.937488, 0.82235,  0.816366, 0.57029,  0.643381, 0.913313};
+  check_shape(y.shape, {2, 3, 4});
+  check_data(reinterpret_cast<const float*>(y.Data()), sqrt_result.data(),
+             sqrt_result.size());
+
+  // Test Exp function
+  Exp(x, &y);
+  std::vector<float> exp_result = {
+      2.323007, 1.908259, 1.147292, 1.121096, 1.934649, 1.708842,
+      2.102057, 2.329386, 1.236496, 1.349455, 2.368297, 1.505223,
+      1.135243, 1.169551, 1.535477, 2.334161, 1.278725, 1.952374,
+      2.408208, 1.966507, 1.947318, 1.384349, 1.512764, 2.302834};
+  check_shape(y.shape, {2, 3, 4});
+  check_data(reinterpret_cast<const float*>(y.Data()), exp_result.data(),
+             exp_result.size());
+
+  // Test Round function
+  Round(x, &y);
+  std::vector<float> round_result = {1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0,
+                                     0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0,
+                                     0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0};
+  check_shape(y.shape, {2, 3, 4});
+  check_data(reinterpret_cast<const float*>(y.Data()), round_result.data(),
+             round_result.size());
+
+  // Test Log function
+  Log(x, &y);
+  std::vector<float> log_result = {
+      -0.170951, -0.43666,  -1.984826, -2.168867, -0.415628, -0.623964,
+      -0.297172, -0.167703, -1.549841, -1.204971, -0.148301, -0.894184,
+      -2.064775, -1.853936, -0.846669, -0.165284, -1.40298,  -0.401902,
+      -0.129103, -0.391179, -0.405786, -1.123222, -0.882037, -0.181353};
+  check_shape(y.shape, {2, 3, 4});
+  check_data(reinterpret_cast<const float*>(y.Data()), log_result.data(),
+             log_result.size());
+}
+
+}  // namespace function
+}  // namespace fastdeploy