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#ifndef CAFFE2_OPERATORS_GRU_UNIT_OP_H_
#define CAFFE2_OPERATORS_GRU_UNIT_OP_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
#include <c10/util/irange.h>
namespace caffe2 {
namespace detail {
template <typename T>
inline T sigmoid(T x) {
return 1.0f / (1.0f + exp(-x));
}
template <typename T>
inline T host_tanh(T x) {
return 2.0f * sigmoid(2.0f * x) - 1.0f;
}
template <typename T, typename Context>
void GRUUnit(
int N,
int D,
int t,
const T* H_prev,
const T* X,
const int32_t* seqLengths,
bool drop_states,
T* H,
Context* /*context*/) {
for (const auto n : c10::irange(N)) {
const bool valid = seqLengths == nullptr || t < seqLengths[n];
for (const auto d : c10::irange(D)) {
if (!valid) {
if (drop_states) {
H[d] = 0;
} else {
H[d] = H_prev[d];
}
} else {
const T update = X[1 * D + d];
const T output = X[2 * D + d];
T sigmoid_update = sigmoid(update);
H[d] = H_prev[d] * sigmoid_update +
host_tanh(output) * (1.0f - sigmoid_update);
}
}
H_prev += D;
X += 3 * D;
H += D;
}
}
template <typename T, typename Context>
void GRUUnitGradient(
int N,
int D,
int t,
const T* H_prev,
const T* X,
const int32_t* seqLengths,
const T* H,
const T* H_diff,
bool drop_states,
T* H_prev_diff,
T* X_diff,
Context* /*context*/) {
for (const auto n : c10::irange(N)) {
const bool valid = seqLengths == nullptr || t < seqLengths[n];
for (const auto d : c10::irange(D)) {
T* h_prev_diff = H_prev_diff + d;
T* reset_diff = X_diff + 0 * D + d;
T* update_diff = X_diff + 1 * D + d;
T* output_diff = X_diff + 2 * D + d;
if (!valid) {
if (drop_states) {
*h_prev_diff = 0;
} else {
*h_prev_diff = H_diff[d];
}
*reset_diff = 0;
*update_diff = 0;
*output_diff = 0;
} else {
// Calculate Gate Outputs
const T u = sigmoid(X[1 * D + d]);
const T o = host_tanh(X[2 * D + d]);
*h_prev_diff = H_diff[d] * u;
*reset_diff = 0; // 0 contribution to gradient from this operation
*update_diff = (H_diff[d] * H_prev[d] - H_diff[d] * o) * u * (1.0f - u);
*output_diff = H_diff[d] * (1.0f - u) * (1.0f - o * o);
}
}
H_prev += D;
X += 3 * D;
H += D;
H_diff += D;
X_diff += 3 * D;
H_prev_diff += D;
}
}
} // namespace detail
template <typename T, typename Context>
class GRUUnitOp : public Operator<Context> {
public:
template <class... Args>
explicit GRUUnitOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
drop_states_(
this->template GetSingleArgument<bool>("drop_states", false)),
sequence_lengths_(
this->template GetSingleArgument<bool>("sequence_lengths", true)) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
// handle potentially-missing sequence lengths input
const size_t TIMESTEP = SEQ_LENGTHS + (sequence_lengths_ ? 1 : 0);
// Extract N
const auto N = Input(HIDDEN_T_M_1).size(1);
// Gates: 1xNxG
const auto G = Input(GATES).size(2);
const auto D = Input(HIDDEN_T_M_1).size(2);
CAFFE_ENFORCE_EQ(3 * D, G);
const auto* H_prev = Input(HIDDEN_T_M_1).template data<T>();
const auto* X = Input(GATES).template data<T>();
const int32_t* seqLengths = nullptr;
if (sequence_lengths_) {
CAFFE_ENFORCE_EQ(Input(SEQ_LENGTHS).numel(), N);
seqLengths = Input(SEQ_LENGTHS).template data<int32_t>();
}
const auto t = static_cast<OperatorBase*>(this)
->Input<Tensor>(TIMESTEP, CPU)
.template data<int32_t>()[0];
Output(HIDDEN_T)->ResizeLike(Input(HIDDEN_T_M_1));
auto* H = Output(HIDDEN_T)->template mutable_data<T>();
detail::GRUUnit<T, Context>(
N, D, t, H_prev, X, seqLengths, drop_states_, H, &context_);
return true;
}
protected:
INPUT_TAGS(HIDDEN_T_M_1, GATES, SEQ_LENGTHS);
// additional input tags are determined dynamically based on whether
// sequence_lengths is present.
OUTPUT_TAGS(HIDDEN_T);
private:
bool drop_states_;
bool sequence_lengths_;
};
template <typename T, typename Context>
class GRUUnitGradientOp : public Operator<Context> {
public:
template <class... Args>
explicit GRUUnitGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
drop_states_(
this->template GetSingleArgument<bool>("drop_states", false)),
sequence_lengths_(
this->template GetSingleArgument<bool>("sequence_lengths", true)) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
// handle potentially-missing sequence lengths input
const size_t inputOffset = SEQ_LENGTHS + (sequence_lengths_ ? 1 : 0);
const size_t TIMESTEP = inputOffset;
const size_t HIDDEN_T = inputOffset + 1;
const size_t HIDDEN_T_GRAD = inputOffset + 2;
// Extract N
const auto N = Input(HIDDEN_T_M_1).size(1);
// Gates: 1xNxG
const auto G = Input(GATES).size(2);
const auto D = Input(HIDDEN_T_M_1).size(2);
CAFFE_ENFORCE_EQ(3 * D, G);
const auto* H_prev = Input(HIDDEN_T_M_1).template data<T>();
const auto* X = Input(GATES).template data<T>();
const auto t = static_cast<OperatorBase*>(this)
->Input<Tensor>(TIMESTEP, CPU)
.template data<int32_t>()[0];
const auto* H = Input(HIDDEN_T).template data<T>();
const auto* H_diff = Input(HIDDEN_T_GRAD).template data<T>();
const int32_t* seqLengths = nullptr;
if (sequence_lengths_) {
CAFFE_ENFORCE_EQ(Input(SEQ_LENGTHS).numel(), N);
seqLengths = Input(SEQ_LENGTHS).template data<int32_t>();
}
Output(HIDDEN_T_M_1_GRAD)->ResizeLike(Input(HIDDEN_T_M_1));
auto* H_prev_diff = Output(HIDDEN_T_M_1_GRAD)->template mutable_data<T>();
Output(GATES_GRAD)->ResizeLike(Input(GATES));
auto* X_diff = Output(GATES_GRAD)->template mutable_data<T>();
detail::GRUUnitGradient<T, Context>(
N,
D,
t,
H_prev,
X,
seqLengths,
H,
H_diff,
drop_states_,
H_prev_diff,
X_diff,
&context_);
return true;
}
protected:
INPUT_TAGS(HIDDEN_T_M_1, GATES, SEQ_LENGTHS);
OUTPUT_TAGS(HIDDEN_T_M_1_GRAD, GATES_GRAD);
private:
bool drop_states_;
bool sequence_lengths_;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_GRU_UNIT_OP_H_
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