FM / sgd

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/sgd/

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/sgd

Name	Size	Mode	Actions
adadelta_op.h	5852	0644	edit dl rm
adagrad_fused.h	16632	0644	edit dl rm
adagrad_op.h	17993	0644	edit dl rm
adam_op.h	26165	0644	edit dl rm
clip_tensor_op.h	1853	0644	edit dl rm
decay_adagrad_op.h	3215	0644	edit dl rm
fp16_momentum_sgd_op.h	2292	0644	edit dl rm
fp32_momentum_sgd_op.h	2015	0644	edit dl rm
ftrl_op.h	2219	0644	edit dl rm
gftrl_op.h	1028	0644	edit dl rm
iter_op.h	3379	0644	edit dl rm
lars_op.h	2479	0644	edit dl rm
learning_rate_adaption_op.h	1974	0644	edit dl rm
learning_rate_functors.h	15135	0644	edit dl rm
learning_rate_op.h	12696	0644	edit dl rm
math_lp.h	655	0644	edit dl rm
momentum_sgd_op.h	6035	0644	edit dl rm
rmsprop_op.h	1980	0644	edit dl rm
rowwise_adagrad_fused.h	29595	0644	edit dl rm
rowwise_counter.h	2083	0644	edit dl rm
storm_op.h	5915	0644	edit dl rm
weight_scale_op.h	2552	0644	edit dl rm
wngrad_op.h	7272	0644	edit dl rm
yellowfin_op.h	10284	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/sgd/clip_tensor_op.h (1853B)

#ifndef CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_
#define CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_

#include <vector>
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
#include "caffe2/utils/math.h"

namespace caffe2 {

template <typename Context>
class ClipTensorByScalingOp final : public Operator<Context> {
 public:
  USE_OPERATOR_CONTEXT_FUNCTIONS;

ClipTensorByScalingOp(const OperatorDef& operator_def, Workspace* ws)
      : Operator<Context>(operator_def, ws) {
    threshold_ = this->template GetSingleArgument<float>("threshold", 0.0);
    CAFFE_ENFORCE_GT(threshold_, 0, "Threshold must be greater than 0");
  }

bool RunOnDevice() override {
    const auto& input_tensor = Input(0);
    CAFFE_ENFORCE_GT(input_tensor.numel(), 0);
    const auto& val = Input(1);
    CAFFE_ENFORCE_EQ(val.numel(), 1);

const auto* input_tensor_data = input_tensor.template data<float>();
    const auto* val_data = val.template data<float>();

auto* clipped = Output(0, input_tensor.sizes(), at::dtype<float>());
    float* clipped_tensor_data = clipped->template mutable_data<float>();

if (InputSize() > 2) {
      const auto& additional_threshold = Input(2);
      CAFFE_ENFORCE_EQ(additional_threshold.numel(), 1);

threshold_ *= *(additional_threshold.template data<float>());
    }

if (*val_data > threshold_) {
      float ratio = threshold_ / *val_data;

math::Scale<float, float, Context>(
          clipped->numel(),
          ratio,
          input_tensor_data,
          clipped_tensor_data,
          &context_);
    } else {
      if (input_tensor_data != clipped_tensor_data) {
        clipped->CopyFrom(input_tensor, /*async*/ true);
      }
    }

return true;
  }

private:
  float threshold_;
};

} // namespace caffe2

#endif // CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_

#ifndef CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_ #define CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_ #include #include "caffe2/core/context.h" #include "caffe2/core/operator.h" #include "caffe2/core/tensor.h" #include "caffe2/utils/math.h" namespace caffe2 { template class ClipTensorByScalingOp final : public Operator { public: USE_OPERATOR_CONTEXT_FUNCTIONS; ClipTensorByScalingOp(const OperatorDef& operator_def, Workspace* ws) : Operator(operator_def, ws) { threshold_ = this->template GetSingleArgument("threshold", 0.0); CAFFE_ENFORCE_GT(threshold_, 0, "Threshold must be greater than 0"); } bool RunOnDevice() override { const auto& input_tensor = Input(0); CAFFE_ENFORCE_GT(input_tensor.numel(), 0); const auto& val = Input(1); CAFFE_ENFORCE_EQ(val.numel(), 1); const auto* input_tensor_data = input_tensor.template data(); const auto* val_data = val.template data(); auto* clipped = Output(0, input_tensor.sizes(), at::dtype()); float* clipped_tensor_data = clipped->template mutable_data(); if (InputSize() > 2) { const auto& additional_threshold = Input(2); CAFFE_ENFORCE_EQ(additional_threshold.numel(), 1); threshold_ *= *(additional_threshold.template data()); } if (*val_data > threshold_) { float ratio = threshold_ / *val_data; math::Scale( clipped->numel(), ratio, input_tensor_data, clipped_tensor_data, &context_); } else { if (input_tensor_data != clipped_tensor_data) { clipped->CopyFrom(input_tensor, /*async*/ true); } } return true; } private: float threshold_; }; } // namespace caffe2 #endif // CAFFE2_OPERATORS_CLIP_TENSOR_OP_H_