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#ifndef CAFFE2_OPERATORS_BATCH_GATHER_OPS_H_
#define CAFFE2_OPERATORS_BATCH_GATHER_OPS_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
// Reuse helper logic from GatherOp since BatchGather is the same with axis=1.
#include "caffe2/operators/gather_op.h"
namespace caffe2 {
template <class Context>
class BatchGatherOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit BatchGatherOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
OP_SINGLE_ARG(bool, "match_outer", match_outer_, false) {}
// virtual ~BatchGatherOp() noexcept {}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, this->template Input<Tensor>(INDICES, CPU));
}
template <typename TInd>
bool DoRunWithType() {
// BatchGather is a special-case of Gather with Axis = 1.
return gather_helper::gather_impl<TInd, Context>(
this, DATA, INDICES, 0, 1, false, match_outer_);
}
INPUT_TAGS(DATA, INDICES);
protected:
bool match_outer_;
};
template <class Context>
class BatchGatherGradientOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
// Constructor to receive axis in case it was passed for GatherOp gradient,
// use default of 1 for batch gather otherwise.
template <class... Args>
explicit BatchGatherGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
OP_SINGLE_ARG(int, "axis", axis_, 1),
OP_SINGLE_ARG(bool, "match_outer", match_outer_, false) {}
virtual ~BatchGatherGradientOp() noexcept {}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, this->template Input<Tensor>(INDICES, CPU));
}
template <typename TInd>
bool DoRunWithType() {
return DispatchHelper<
TensorTypes2<float, GenericTensorImplementation>,
TInd>::call(this, Input(DATA));
}
template <typename TInd, typename TData>
bool DoRunWithType2() {
auto& data = Input(DATA);
auto& indices = Input(INDICES);
auto& grad = Input(GRAD);
// ONNX allows negative axis to index from the back, valid range: [-r, r].
int axis = axis_;
bool match_outer = match_outer_;
if (axis < 0) {
axis = data.dim() + axis;
}
CAFFE_ENFORCE_GE(data.dim(), 2, "DATA should be at least 2-D");
// Outer dimensions of input data and gradient should be the same
// because they are preserved for gathers with axis > 0.
for (const auto acheck : c10::irange(axis)) {
CAFFE_ENFORCE_EQ(
data.size(acheck),
grad.size(acheck),
"batch gather outer dimensions should match");
}
auto* output = Output(0, data.sizes(), at::dtype<TData>());
TData* out_data = output->template mutable_data<TData>();
if (data.numel() <= 0) {
return true;
}
memset(out_data, 0, output->nbytes());
const TData* grad_data = grad.template data<TData>();
const TInd* idxs = indices.template data<TInd>();
auto outer_dims_product = data.size_to_dim(axis);
auto batch_size = data.size_from_dim(axis);
auto block_size = data.size_from_dim(axis + 1);
auto N = indices.numel();
auto idx_inner_dims_product = indices.size_from_dim(axis);
if (match_outer) {
CAFFE_ENFORCE_GE(axis, 1, "Axis should be at least 1");
for (const auto i : c10::irange(axis)) {
CAFFE_ENFORCE_EQ(
data.size(i),
indices.size(i),
"INDICES must have the same outer dims as DATA (before dim AXIS)");
}
N = idx_inner_dims_product;
}
auto gathered_grad_batch_size = N * block_size;
// Check indexing bounds.
auto src_indexing_axis_dim = data.dim(axis);
gather_helper::check_indexarray_range<TInd>(
idxs, N, src_indexing_axis_dim, false);
for (const auto batch : c10::irange(outer_dims_product)) {
auto grad_batch_base = grad_data + batch * gathered_grad_batch_size;
auto out_batch_base = out_data + batch * batch_size;
for (const auto i : c10::irange(N)) {
auto idx = idxs[i];
if (match_outer) {
idx = idxs[batch * idx_inner_dims_product + i];
}
if (idx < 0) {
idx = idx + src_indexing_axis_dim;
}
if (block_size == 1) {
out_batch_base[idx] += grad_batch_base[i];
} else {
math::Add(
block_size,
out_batch_base + idx * block_size,
grad_batch_base + i * block_size,
out_batch_base + idx * block_size,
&context_);
}
}
}
return true;
}
template <typename TInd>
bool DoRunWithOtherType2() {
CAFFE_THROW(
"BatchGatherGradient is not implemented on tensor of type ",
Input(DATA).meta().name(),
"consider adding it as a type in the DispatchHelper list or "
"implementing a generic version (which won't work for "
"duplicated indices though)");
}
INPUT_TAGS(DATA, INDICES, GRAD);
protected:
int axis_;
bool match_outer_;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_BATCH_GATHER_OPS_H_
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