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adagrad_op.h
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adagrad_op.h
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#pragma once
#include "caffe2/core/operator.h"
#include "caffe2/perfkernels/adagrad.h"
namespace caffe2 {
template <typename Context>
void adagrad_update(
int N,
const float* w,
const float* g,
const float* h,
float* nw,
float* nh,
float epsilon,
float decay,
const float* lr,
Context* /*context*/) {
return adagrad_update(N, w, g, h, nw, nh, epsilon, decay, lr[0]);
}
template <typename Context>
void adagrad_update_output_effective_lr(
int N,
const float* paramIn,
const float* gradIn,
const float* momentIn,
float* paramOut,
float* momentOut,
float* effectiveLROut,
float epsilon,
float decay,
const float* lr,
Context* /*context*/) {
for (auto i = 0; i < N; ++i) {
float grad = gradIn[i];
float moment = momentOut[i] = decay * momentIn[i] + grad * grad;
float effective_lr = effectiveLROut[i] =
lr[0] / (std::sqrt(moment) + epsilon);
paramOut[i] = paramIn[i] + effective_lr * grad;
}
}
template <typename Context>
void adagrad_update_output_effective_lr_and_update(
int N,
const float* paramIn,
const float* gradIn,
const float* momentIn,
float* paramOut,
float* momentOut,
float* effectiveLROut,
float* updateOut,
float epsilon,
float decay,
const float* lr,
Context* /*context*/) {
for (auto i = 0; i < N; ++i) {
float grad = gradIn[i];
float moment = momentOut[i] = decay * momentIn[i] + grad * grad;
float effective_lr = effectiveLROut[i] =
lr[0] / (std::sqrt(moment) + epsilon);
float update = updateOut[i] = effective_lr * grad;
paramOut[i] = paramIn[i] + update;
}
}
template <typename T, class Context>
class AdagradOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
AdagradOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
epsilon_(this->template GetSingleArgument<T>("epsilon", 1e-5f)),
decay_(this->template GetSingleArgument<T>("decay", 1.0f)) {}
bool RunOnDevice() override {
CAFFE_ENFORCE_EQ(
Input(GRAD).numel(),
Input(MOMENT_1).numel(),
"PARAM size: ",
Input(PARAM).numel(),
", GRAD size: ",
Input(GRAD).numel(),
", MOMENT_1 size: ",
Input(MOMENT_1).numel(),
", LR size: ",
Input(LR).numel());
CAFFE_ENFORCE_EQ(Input(GRAD).numel(), Input(PARAM).numel());
Output(OUTPUT_PARAM)->ResizeLike(Input(PARAM));
Output(OUTPUT_MOMENT_1)->ResizeLike(Input(MOMENT_1));
if (OutputSize() == 2) {
adagrad_update<Context>(
Input(GRAD).numel(),
Input(PARAM).template data<T>(),
Input(GRAD).template data<T>(),
Input(MOMENT_1).template data<T>(),
Output(OUTPUT_PARAM)->template mutable_data<T>(),
Output(OUTPUT_MOMENT_1)->template mutable_data<T>(),
epsilon_,
decay_,
Input(LR).template data<T>(),
&context_);
} else if (OutputSize() == 3) {
Output(OUTPUT_EFFECTIVE_LR)->ResizeLike(Input(GRAD));
adagrad_update_output_effective_lr<Context>(
Input(GRAD).numel(),
Input(PARAM).template data<T>(),
Input(GRAD).template data<T>(),
Input(MOMENT_1).template data<T>(),
Output(OUTPUT_PARAM)->template mutable_data<T>(),
Output(OUTPUT_MOMENT_1)->template mutable_data<T>(),
Output(OUTPUT_EFFECTIVE_LR)->template mutable_data<T>(),
epsilon_,
decay_,
Input(LR).template data<T>(),
&context_);
} else {
Output(OUTPUT_EFFECTIVE_LR)->ResizeLike(Input(GRAD));
Output(OUTPUT_UPDATE)->ResizeLike(Input(GRAD));
adagrad_update_output_effective_lr_and_update<Context>(
Input(GRAD).numel(),
Input(PARAM).template data<T>(),
Input(GRAD).template data<T>(),
Input(MOMENT_1).template data<T>(),
Output(OUTPUT_PARAM)->template mutable_data<T>(),
Output(OUTPUT_MOMENT_1)->template mutable_data<T>(),
Output(OUTPUT_EFFECTIVE_LR)->template mutable_data<T>(),
Output(OUTPUT_UPDATE)->template mutable_data<T>(),
epsilon_,
decay_,
Input(LR).template data<T>(),
&context_);
}
return true;
}
protected:
T epsilon_;
T decay_;
INPUT_TAGS(PARAM, MOMENT_1, GRAD, LR);
OUTPUT_TAGS(
OUTPUT_PARAM,
OUTPUT_MOMENT_1,
OUTPUT_EFFECTIVE_LR,
OUTPUT_UPDATE);
};
template <typename T, class Context>
class SparseAdagradOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
SparseAdagradOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
epsilon_(this->template GetSingleArgument<float>("epsilon", 1e-5f)) {}
bool RunOnDevice() override {
// Enforce shapes
CAFFE_ENFORCE_EQ(Input(PARAM).numel(), Input(MOMENT_1).numel());
CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
CAFFE_ENFORCE_EQ(
Input(PARAM).size_from_dim(1),
Input(GRAD).size_from_dim(Input(INDICES).dim()));
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(INDICES));
}
template <typename SIndex>
bool DoRunWithType() {
const auto* lr = Input(LR).template data<T>();
const auto* indices = Input(INDICES).template data<SIndex>();
const auto* gradIn = Input(GRAD).template data<T>();
const auto* paramIn = Input(PARAM).template data<T>();
const auto* momentIn = Input(MOMENT_1).template data<T>();
auto* paramOut = Output(OUTPUT_PARAM)->template mutable_data<T>();
auto* momentOut = Output(OUTPUT_MOMENT_1)->template mutable_data<T>();
auto n = Input(INDICES).numel();
if (n == 0) {
return true;
}
auto block_size = Input(GRAD).numel() / n;
for (auto i = 0; i < n; ++i) {
auto idx = indices[i];
if (block_size == 1) {
float gi = gradIn[i];
float hi = momentOut[idx] = momentIn[idx] + gi * gi;
paramOut[idx] = paramIn[idx] + lr[0] * gi / (std::sqrt(hi) + epsilon_);
} else {
auto offsetI = i * block_size;
auto offsetIdx = idx * block_size;
#ifndef NDEBUG
CAFFE_ENFORCE_GE(
Input(PARAM).numel(),
block_size + offsetIdx,
this->debug_def().input(PARAM),
", out of bound, idx:",
idx,
" for input i:",
i,
" and block size:",
block_size);
CAFFE_ENFORCE_GE(
Input(GRAD).numel(),
block_size + offsetI,
this->debug_def().input(GRAD),
", out of bound idx, idx:",
idx,
" for input i:",
i);
#endif
adagrad_update(
block_size,
paramIn + offsetIdx,
gradIn + offsetI,
momentIn + offsetIdx,
paramOut + offsetIdx,
momentOut + offsetIdx,
epsilon_,
1.0f,
lr,
&context_);
}
}
return true;
}
protected:
T epsilon_;
INPUT_TAGS(PARAM, MOMENT_1, INDICES, GRAD, LR);
OUTPUT_TAGS(OUTPUT_PARAM, OUTPUT_MOMENT_1);
};
template <typename T, class Context>
class RowWiseSparseAdagradOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
RowWiseSparseAdagradOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
epsilon_(this->template GetSingleArgument<float>("epsilon", 1e-5f)) {}
bool RunOnDevice() override {
// Enforce shapes
CAFFE_ENFORCE_EQ(Input(PARAM).sizes()[0], Input(MOMENT_1).numel());
CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
CAFFE_ENFORCE_EQ(
Input(PARAM).size_from_dim(1),
Input(GRAD).size_from_dim(Input(INDICES).dim()));
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(INDICES));
}
template <typename SIndex>
bool DoRunWithType() {
const auto* lr = Input(LR).template data<T>();
const auto* indices = Input(INDICES).template data<SIndex>();
const auto* gradIn = Input(GRAD).template data<T>();
const auto* paramIn = Input(PARAM).template data<T>();
const auto* momentIn = Input(MOMENT_1).template data<T>();
auto* paramOut = Output(OUTPUT_PARAM)->template mutable_data<T>();
auto* momentOut = Output(OUTPUT_MOMENT_1)->template mutable_data<T>();
auto n = Input(INDICES).numel();
if (n == 0) {
return true;
}
auto block_size = Input(GRAD).numel() / n;
for (auto i = 0; i < n; ++i) {
auto idx = indices[i];
if (block_size == 1) {
float gi = gradIn[i];
float hi = momentOut[idx] = momentIn[idx] + gi * gi;
paramOut[idx] = paramIn[idx] + lr[0] * gi / (std::sqrt(hi) + epsilon_);
} else {
auto offsetI = i * block_size;
auto offsetIdx = idx * block_size;
#ifndef NDEBUG
CAFFE_ENFORCE_GE(
Input(PARAM).numel(),
block_size + offsetIdx,
this->debug_def().input(PARAM),
", out of bound, idx:",
idx,
" for input i:",
i,
" and block size:",
block_size);
CAFFE_ENFORCE_GE(
Input(GRAD).numel(),
block_size + offsetI,
this->debug_def().input(GRAD),
", out of bound idx, idx:",
idx,
" for input i:",
i);
#endif
const float* w = paramIn + offsetIdx;
const float* g = gradIn + offsetI;
const float* h = momentIn + idx;
float* nw = paramOut + offsetIdx;
float* nh = momentOut + idx;
float hs = 0.;
for (auto j = 0; j < block_size; ++j) {
float gj = g[j];
hs += gj * gj;
}
float hi = nh[0] = h[0] + hs / block_size;
float step = lr[0] / (std::sqrt(hi) + epsilon_);
for (auto j = 0; j < block_size; ++j) {
nw[j] = w[j] + g[j] * step;
}
}
}
return true;
}
protected:
T epsilon_;
INPUT_TAGS(PARAM, MOMENT_1, INDICES, GRAD, LR);
OUTPUT_TAGS(OUTPUT_PARAM, OUTPUT_MOMENT_1);
};
}