add relu, sigmoid and log grad computing units.

modified file format

modified file format

change the header file in activation_fp16_grad.h

change the year of Copyright info and added LiteKernelCreator.

mindspore/lite/nnacl/fp16_grad/activation_grad.c

@@ -0,0 +1,72 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 <math.h>
+#include "nnacl/op_base.h"
+#include "nnacl/fp16_grad/activation_grad.h"
+#include "nnacl/errorcode.h"
+
+int Fp16ReluGrad(const float16_t *src0, const float16_t *src1, size_t length, float16_t *dst) {
+  int i = 0;
+#ifdef ENABLE_NEON
+  float16x8_t zero_4 = vdupq_n_f16(0);
+  for (; i < length - 4; i += 4) {
+    float16x8_t src0_4 = vld1q_f16(src0 + i);
+    float16x8_t src1_4 = vld1q_f16(src1 + i);
+    uint16x8_t mask_4 = vcgtq_f16(src1_4, zero_4);
+    float16x8_t dst_4 = vbslq_f16(mask_4, src0_4, zero_4);
+    vst1q_f16(dst + i, dst_4);
+  }
+#endif
+  for (; i < length; i++) {
+    dst[i] = (src1[i] > 0.0f) ? src0[i] : 0.0f;
+  }
+  return NNACL_OK;
+}
+
+int Fp16SigmoidGrad(const float16_t *src0, const float16_t *src1, size_t length, float16_t *dst) {
+  int i = 0;
+#ifdef ENABLE_NEON
+  float16x8_t one_4 = vdupq_n_f16(1);
+  for (; i < length - 4; i += 4) {
+    float16x8_t src0_4 = vld1q_f16(src0 + i);
+    float16x8_t src1_4 = vld1q_f16(src1 + i);
+    float16x8_t dst_4 = vmulq_f16(src0_4, vmulq_f16(src1_4, vsubq_f16(one_4, src1_4)));
+    vst1q_f16(dst + i, dst_4);
+  }
+#endif
+  for (; i < length; i++) {
+    dst[i] = src0[i] * (src1[i] * (1.0f - src1[i]));
+  }
+  return NNACL_OK;
+}
+
+int Fp16LogGrad(const float16_t *src0, const float16_t *src1, size_t length, float16_t *dst) {
+  int i = 0;
+#ifdef ENABLE_NEON
+  float16x8_t log_10 = vdupq_n_f16(log(10));
+  for (; i < length - 4; i += 4) {
+    float16x8_t src0_4 = vld1q_f16(src0 + i);
+    float16x8_t src1_4 = vld1q_f16(src1 + i);
+    float16x8_t dst_4 = vmulq_f16(src0_4, vrecpeq_f16(vmulq_f16(src1_4, log_10)));
+    vst1q_f16(dst + i, dst_4);
+  }
+#endif
+  for (; i < length; i++) {
+    dst[i] = src0[i] * 1.0f / (src1[i] * log(10));
+  }
+  return NNACL_OK;
+}

mindspore/lite/nnacl/fp16_grad/activation_grad.h

@@ -0,0 +1,43 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 MINDSPORE_LITE_NNACL_FP16_GRAD_ACTIVATION_GRAD_H_
+#define MINDSPORE_LITE_NNACL_FP16_GRAD_ACTIVATION_GRAD_H_
+
+#ifdef ENABLE_NEON
+#include <arm_neon.h>
+#endif
+#include <math.h>
+#include "nnacl/op_base.h"
+#include "mindspore/lite/nnacl/int8/fixed_point.h"
+
+typedef struct ActivationGradParameterFp16 {
+  OpParameter op_parameter;
+  int type_;
+  float alpha_;
+} ActivationGradParameterFp16;
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+int Fp16ReluGrad(const float16_t *src0, const float16_t *src1, size_t length, float16_t *dst);
+int Fp16SigmoidGrad(const float16_t *src0, const float16_t *src1, size_t length, float16_t *dst);
+int Fp16LogGrad(const float16_t *src0, const float16_t *src1, size_t length, float16_t *dst);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif  // MINDSPORE_LITE_NNACL_FP16_GRAD_ACTIVATION_GRAD_H_

mindspore/lite/nnacl/optimize/CMakeLists.txt

@@ -17,6 +17,11 @@ list(APPEND SDOT_FILES ${SDOT_SRC})
 list(APPEND FP16_FILES ${FP16_C_SRC})
 list(APPEND FP16_FILES ${FP16_NEON_SRC})
 
+if(SUPPORT_TRAIN)
+    file(GLOB FP16_TRAIN_SRC ${NNACL_DIR}/fp16_grad/*.c)
+    list(APPEND FP16_FILES ${FP16_TRAIN_SRC})
+endif()
+
 string(REPLACE "-fvisibility=hidden" "-fvisibility=default" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
 set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -march=armv8.2-a+dotprod+fp16")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=armv8.2-a+dotprod+fp16")

mindspore/lite/schema/ops.fbs

@@ -99,7 +99,8 @@ enum ActivationGradType : byte {
     HSIGMOID = 13,
     THRESHOLDRELU = 14,
     LINEAR = 15,
-    UNKNOWN = 16
+    UNKNOWN = 16,
+    LOG = 17
 }
 enum ReduceType : byte {
     REDUCE_MAX = 0,

mindspore/lite/src/runtime/kernel/arm/CMakeLists.txt

@@ -9,6 +9,7 @@ file(GLOB KERNEL_SRC
 list(REMOVE_ITEM KERNEL_SRC ${CMAKE_CURRENT_SOURCE_DIR}/int8/opt_op_handler.cc)
 
 if(SUPPORT_TRAIN)
+    file(GLOB TRAIN_KERNEL_SRC ${CMAKE_CURRENT_SOURCE_DIR}/fp16_grad/*.cc)
     file(GLOB TRAIN_KERNEL_SRC ${CMAKE_CURRENT_SOURCE_DIR}/fp32_grad/*.cc)
     set(KERNEL_SRC ${KERNEL_SRC} ${TRAIN_KERNEL_SRC})
 endif()
@@ -19,6 +20,9 @@ add_dependencies(cpu_kernel_mid fbs_src)
 if(PLATFORM_ARM64)
     if(ENABLE_FP16)
         file(GLOB FP16_KERNEL_SRC ${CMAKE_CURRENT_SOURCE_DIR}/fp16/*.cc)
+        if(SUPPORT_TRAIN)
+            file(GLOB FP16_KERNEL_SRC ${CMAKE_CURRENT_SOURCE_DIR}/fp16_grad/*.cc)
+        endif()
         add_library(cpu_fp16_kernel_mid OBJECT ${FP16_KERNEL_SRC})
         add_dependencies(cpu_fp16_kernel_mid fbs_src)
     endif()

mindspore/lite/src/runtime/kernel/arm/fp16_grad/activation_fp16_grad.cc

@@ -0,0 +1,93 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 "src/runtime/kernel/arm/fp16_grad/activation_fp16_grad.h"
+#include "nnacl/fp16_grad/activation_grad.h"
+#include "schema/model_generated.h"
+#include "src/kernel_registry.h"
+#include "src/runtime/runtime_api.h"
+#include "include/errorcode.h"
+
+using mindspore::kernel::KERNEL_ARCH::kCPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+using mindspore::schema::ActivationType_RELU;
+using mindspore::schema::ActivationType_SIGMOID;
+using mindspore::schema::PrimitiveType_ActivationGrad;
+
+namespace mindspore::kernel {
+int ActivationGradCPUKernelFp16::Init() {
+  if (in_tensors_.size() != 2) {
+    MS_LOG(ERROR) << "ActivationGrad should have 2 input tensors";
+    return RET_ERROR;
+  }
+  return RET_OK;
+}
+
+int ActivationGradCPUKernelFp16::ReSize() { return RET_OK; }
+
+int ActivationGradCPUKernelFp16::DoActivation(int task_id) {
+  auto yt_addr = reinterpret_cast<float16_t *>(in_tensors_.at(0)->MutableData());
+  auto input_addr = reinterpret_cast<float16_t *>(in_tensors_.at(1)->MutableData());
+  auto output_addr = reinterpret_cast<float16_t *>(out_tensors_.at(0)->MutableData());
+  int length = in_tensors_.at(0)->ElementsNum();
+
+  int stride = UP_DIV(length, thread_count_);
+  int count = MSMIN(stride, length - stride * task_id);
+  int start = stride * task_id;
+
+  auto error_code = RET_OK;
+
+  if (param_act_grad_->type_ == schema::ActivationGradType_RELU) {
+    error_code = Fp16ReluGrad(yt_addr + start, input_addr + start, count, output_addr + start);
+  } else if (param_act_grad_->type_ == schema::ActivationGradType_SIGMOID) {
+    // Sigmoid gets the input tensors in reverse order!
+    error_code = Fp16SigmoidGrad(input_addr + start, yt_addr + start, count, output_addr + start);
+  } else if (param_act_grad_->type_ == schema::ActivationGradType_LOG) {
+    error_code = Fp16LogGrad(yt_addr + start, input_addr + start, count, output_addr + start);
+  } else {
+    MS_LOG(ERROR) << "Activation type error";
+    return RET_ERROR;
+  }
+  if (error_code != RET_OK) {
+    return RET_ERROR;
+  }
+  return RET_OK;
+}
+
+int ActivationGradRunFp16(void *cdata, int task_id) {
+  MS_ASSERT(cdata != nullptr);
+  auto activationGrad_kernel = reinterpret_cast<ActivationGradCPUKernelFp16 *>(cdata);
+  auto error_code = activationGrad_kernel->DoActivation(task_id);
+  if (error_code != RET_OK) {
+    MS_LOG(ERROR) << "ActivationGradRun error task_id[" << task_id << "] error_code[" << error_code << "]";
+    return RET_ERROR;
+  }
+  return RET_OK;
+}
+
+int ActivationGradCPUKernelFp16::Run() {
+  int error_code = ParallelLaunch(this->context_->thread_pool_, ActivationGradRunFp16, this, thread_count_);
+  if (error_code != RET_OK) {
+    MS_LOG(ERROR) << "Activation Grad function error error_code[" << error_code << "]";
+    return RET_ERROR;
+  }
+  return RET_OK;
+}
+
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_ActivationGrad, LiteKernelCreator<ActivationGradCPUKernelFp16>)
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp16_grad/activation_fp16_grad.h

@@ -0,0 +1,46 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 MINDSPORE_ACTIVATION_FP16_GRAD_H
+#define MINDSPORE_ACTIVATION_FP16_GRAD_H
+
+#include <vector>
+#include "src/lite_kernel.h"
+#include "nnacl/fp16_grad/activation_grad.h"
+
+namespace mindspore::kernel {
+class ActivationGradCPUKernelFp16 : public LiteKernel {
+ public:
+  explicit ActivationGradCPUKernelFp16(OpParameter *param, const std::vector<lite::Tensor *> &inputs,
+                                       const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
+                                       const mindspore::lite::PrimitiveC *primitive)
+      : LiteKernel(param, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {
+    param_act_grad_ = reinterpret_cast<ActivationGradParameterFp16 *>(param);
+  }
+  ~ActivationGradCPUKernelFp16() override = default;
+
+  int Init() override;
+  int ReSize() override;
+  int Run() override;
+  int DoActivation(int task_id);
+
+ private:
+  ActivationGradParameterFp16 *param_act_grad_;
+  int thread_count_;
+};
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_ACTIVATION_FP16_GRAD_H

mindspore/lite/test/CMakeLists.txt

@@ -323,6 +323,12 @@ if(ENABLE_FP16)
             )
 endif()
 
+if(SUPPORT_TRAIN)
+    file(GLOB_RECURSE TEST_CASE_KERNEL_FP16_SRC_GRAD
+        ${TEST_DIR}/ut/src/runtime/kernel/arm/fp6_grad/*.cc)
+    list(APPEND TEST_SRC ${TEST_CASE_KERNEL_FP16_SRC_GRAD})
+endif()
+
 add_executable(lite-test ${TEST_SRC})
 add_dependencies(lite-test fbs_src)
 target_link_libraries(lite-test dl mindspore::gtest)

mindspore/lite/test/ut/src/runtime/kernel/arm/fp16_grad/activation_grad_fp16_test.cc

@@ -0,0 +1,199 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 <iostream>
+#include <vector>
+#ifdef ENABLE_NEON
+#include <arm_neon.h>
+#endif
+#include "src/common/log_adapter.h"
+#include "common/common_test.h"
+#include "src/common/file_utils.h"
+#include "nnacl/fp16_grad/activation_grad.h"
+
+namespace mindspore {
+class TestActGradFp16 : public mindspore::CommonTest {
+ public:
+  TestActGradFp16() {}
+  float error_bound = 1e-3;
+};
+
+TEST_F(TestActGradFp16, ReluGradFp16) {
+  size_t output_data_size = 50;
+  size_t input_size;
+  std::string input_path = "./test_data/activationGrad/relu_y_50.bin";
+  auto input_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
+  ASSERT_NE(input_data, nullptr);
+  EXPECT_EQ(input_size, output_data_size * sizeof(float));
+
+  std::string yt_path = "./test_data/activationGrad/relu_yt_50.bin";
+  auto yt_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(yt_path.c_str(), &input_size));
+  ASSERT_NE(yt_data, nullptr);
+  EXPECT_EQ(input_size, output_data_size * sizeof(float));
+
+  std::string output_path = "./test_data/activationGrad/relu_out_50.bin";
+  auto ref_data = reinterpret_cast<const float *>(mindspore::lite::ReadFile(output_path.c_str(), &input_size));
+  ASSERT_NE(ref_data, nullptr);
+  EXPECT_EQ(input_size, output_data_size * sizeof(float));
+
+  auto yt_buf = new float16_t[output_data_size];
+  auto input_buf = new float16_t[output_data_size];
+  auto output_buf = new float16_t[output_data_size];
+
+  std::cout << "======yt_buf======" << std::endl;
+  for (int i = 0; i < output_data_size; i++) {
+    yt_buf[i] = (float16_t)yt_data[i];
+    input_buf[i] = (float16_t)input_data[i];
+  }
+
+  Fp16ReluGrad(yt_buf, input_buf, 50, output_buf);
+
+  int res = 0;
+  float error = 0;
+  std::cout << "======Compare with reference data======" << std::endl;
+  for (int i = 0; i < output_data_size; i++) {
+    float diff = std::fabs(static_cast<float>(output_buf[i]) - ref_data[i]);
+    if (diff > 0.00001) {
+      error += diff;
+    }
+  }
+  error /= static_cast<float>(output_data_size);
+  if (error > error_bound) {
+    printf("error=%f while error_bound=%f\n", error, error_bound);
+    res = 1;
+  }
+
+  EXPECT_EQ(res, 0);
+
+  delete[] output_buf;
+  delete[] yt_buf;
+  delete[] input_buf;
+  delete[] ref_data;
+  delete[] yt_data;
+  delete[] input_data;
+
+  MS_LOG(INFO) << "ReluGradFp16 passed";
+}
+
+TEST_F(TestActGradFp16, SigmoidGradFp16) {
+  size_t output_data_size = 50;
+  size_t input_size;
+  std::string input_path = "./test_data/activationGrad/sigmoid_y_50.bin";
+  auto input_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
+  ASSERT_NE(input_data, nullptr);
+
+  std::string yt_path = "./test_data/activationGrad/sigmoid_yt_50.bin";
+  auto yt_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(yt_path.c_str(), &input_size));
+  ASSERT_NE(yt_data, nullptr);
+
+  std::string output_path = "./test_data/activationGrad/sigmoid_out_50.bin";
+  auto ref_data = reinterpret_cast<const float *>(mindspore::lite::ReadFile(output_path.c_str(), &input_size));
+  ASSERT_NE(ref_data, nullptr);
+  EXPECT_EQ(input_size, output_data_size * sizeof(float));
+
+  auto yt_buf = new float16_t[output_data_size];
+  auto input_buf = new float16_t[output_data_size];
+  auto output_buf = new float16_t[output_data_size];
+
+  std::cout << "======yt_buf======" << std::endl;
+  for (int i = 0; i < output_data_size; i++) {
+    yt_buf[i] = (float16_t)yt_data[i];
+    input_buf[i] = (float16_t)input_data[i];
+  }
+
+  Fp16SigmoidGrad(yt_buf, input_buf, 50, output_buf);
+
+  int res = 0;
+  float error = 0;
+  std::cout << "======Compare with reference data======" << std::endl;
+  for (int i = 0; i < output_data_size; i++) {
+    float diff = std::fabs(static_cast<float>(output_buf[i]) - ref_data[i]);
+    if (diff > 0.00001) {
+      error += diff;
+    }
+  }
+  error /= static_cast<float>(output_data_size);
+  if (error > error_bound) {
+    printf("error=%f while error_bound=%f\n", error, error_bound);
+    res = 1;
+  }
+
+  EXPECT_EQ(res, 0);
+
+  delete[] output_buf;
+  delete[] yt_buf;
+  delete[] input_buf;
+  delete[] ref_data;
+  delete[] yt_data;
+  delete[] input_data;
+
+  MS_LOG(INFO) << "SigmoidGradFp16 passed";
+}
+
+TEST_F(TestActGradFp16, LogGradFp16) {
+  size_t output_data_size = 50;
+  size_t input_size;
+  std::string input_path = "./test_data/activationGrad/log_x_50.bin";
+  auto input_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
+  ASSERT_NE(input_data, nullptr);
+
+  std::string yt_path = "./test_data/activationGrad/log_yt_50.bin";
+  auto yt_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(yt_path.c_str(), &input_size));
+  ASSERT_NE(yt_data, nullptr);
+
+  std::string output_path = "./test_data/activationGrad/log_out_50.bin";
+  auto ref_data = reinterpret_cast<const float *>(mindspore::lite::ReadFile(output_path.c_str(), &input_size));
+  ASSERT_NE(ref_data, nullptr);
+  EXPECT_EQ(input_size, output_data_size * sizeof(float));
+
+  auto yt_buf = new float16_t[output_data_size];
+  auto input_buf = new float16_t[output_data_size];
+  auto output_buf = new float16_t[output_data_size];
+
+  for (int i = 0; i < output_data_size; i++) {
+    yt_buf[i] = (float16_t)yt_data[i];
+    input_buf[i] = (float16_t)input_data[i];
+  }
+
+  Fp16LogGrad(yt_buf, input_buf, 50, output_buf);
+
+  int res = 0;
+  float error = 0;
+  std::cout << "======Compare with reference data======" << std::endl;
+  for (int i = 0; i < output_data_size; i++) {
+    float diff = std::fabs(static_cast<float>(output_buf[i]) - ref_data[i]);
+    if (diff > 0.00001) {
+      error += diff;
+    }
+  }
+  error /= static_cast<float>(output_data_size);
+  if (error > error_bound) {
+    printf("error%f while error_bound=%f\n", error, error_bound);
+    res = 1;
+  }
+
+  EXPECT_EQ(res, 0);
+
+  delete[] output_buf;
+  delete[] yt_buf;
+  delete[] input_buf;
+  delete[] ref_data;
+  delete[] yt_data;
+  delete[] input_data;
+
+  MS_LOG(INFO) << "LogGradFp16 passed";
+}
+
+}  // namespace mindspore

mindspore/lite/test/ut/src/runtime/kernel/arm/test_data/activationGrad/log_out_50.bin

@@ -0,0 +1 @@
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