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EmbeddingBag.cpp
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#include <ATen/native/EmbeddingBag.h>
#include <ATen/ATen.h>
#include <ATen/NativeFunctions.h>
#include <ATen/Parallel.h>
#include <ATen/TensorUtils.h>
#include <ATen/native/CPUBlas.h>
#include <c10/util/irange.h>
#ifdef USE_FBGEMM
#include <fbgemm/Fbgemm.h>
#else
#include <caffe2/perfkernels/embedding_lookup_idx.h>
#endif
#include <algorithm>
#include <cstring>
#include <iostream>
#include <memory>
#include <sstream>
#include <tuple>
#include <vector>
namespace {
const int MODE_SUM = 0;
const int MODE_MEAN = 1;
const int MODE_MAX = 2;
}
namespace at {
namespace native {
template<typename scalar_t>
scalar_t dot_impl(int64_t n, scalar_t *x, int64_t incx, scalar_t *y, int64_t incy);
static void make_offset2bag(const Tensor &offsets, Tensor& offset2bag) {
offset2bag.index_add_(
0, offsets, at::ones_like(offsets, LEGACY_CONTIGUOUS_MEMORY_FORMAT)); // offset2bag = [1 0 1 0 1]
offset2bag[0] -= 1; // offset2bag = [0 0 1 0 1]
offset2bag = offset2bag.cumsum(0, offset2bag.scalar_type()); // offset2bag = [0 0 1 1 2]
}
namespace {
std::pair<Tensor, Tensor> promoteIndicesAndOffsets(
const Tensor& indices,
const Tensor& offsets) {
const auto commonType =
promoteTypes(offsets.scalar_type(), indices.scalar_type());
return {
indices.scalar_type() == commonType ? indices
: indices.toType(commonType),
offsets.scalar_type() == commonType ? offsets
: offsets.toType(commonType)};
}
// Determines if we can use a fast implementation for index_select_add, which
// is only applicable if special conditions are met
template<typename index_t>
bool is_fast_path_index_select(const Tensor& src, Tensor& output, index_t padding_idx) {
return src.scalar_type() == kFloat && src.strides()[1] == 1 && output.strides()[1] == 1 && padding_idx < static_cast<index_t>(0);
}
// Determines if we can use a fast implementation for index_select_scale_add,
// which is only applicable if special conditions are met
template<typename index_t>
bool is_fast_path_index_select_scale(const Tensor& src, const Tensor& scale, Tensor& output, index_t padding_idx) {
return src.scalar_type() == kFloat && src.strides()[1] == 1 && output.strides()[1] == 1 && scale.strides()[0] == 1 && padding_idx < static_cast<index_t>(0);
}
template<typename index_t>
bool is_fast_path(const Tensor& src, const c10::optional<Tensor>& scale, Tensor& output, index_t padding_idx) {
return (scale.has_value() && scale.value().defined()) ?
is_fast_path_index_select_scale(src, scale.value(), output, padding_idx) :
is_fast_path_index_select(src, output, padding_idx);
}
// This function combines index_select (using select_indices as the index) and
// index_add (using add_indices as the index), without creating an intermediary
// tensor to hold the selected embeddings
template<typename data_t, typename index_t>
typename std::enable_if<!std::is_same<data_t, float>::value, void>::type
index_select_add(const Tensor &select_indices,
const Tensor &add_indices,
const Tensor &src,
Tensor &output,
const Tensor& /*offsets*/,
bool /*include_last_offset*/,
Tensor &bag_size,
index_t padding_idx) {
TORCH_CHECK(select_indices.numel() == add_indices.numel());
auto* add_indices_data = add_indices.data_ptr<index_t>();
auto* select_indices_data = select_indices.data_ptr<index_t>();
auto* src_data = src.data_ptr<data_t>();
auto* output_data = output.data_ptr<data_t>();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
index_t* bag_size_data;
if (bag_size.defined()) {
bag_size_data = bag_size.data_ptr<index_t>();
}
auto numel = add_indices.numel();
int64_t ddim = src.sizes()[1];
auto src_stride0 = src.strides()[0];
auto src_stride1 = src.strides()[1];
auto output_stride0 = output.strides()[0];
auto output_stride1 = output.strides()[1];
for (const auto i : c10::irange(numel)) {
// We can skip indices equal to padding_idx so they are not included in
// the reduction
if (select_indices_data[i] != padding_idx) {
at::native::cpublas::axpy<data_t>(ddim, 1,
src_data + src_stride0 * select_indices_data[i], src_stride1,
output_data + output_stride0 * add_indices_data[i], output_stride1);
} else if (bag_size.defined()) {
// Decrement bag_size to reflect that the index is padded
// NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
bag_size_data[add_indices_data[i]]--;
}
}
}
namespace {
template <typename index_t>
void fbgemm_spmdm_report_error_(
int64_t output_size,
int index_size,
int64_t N,
const index_t* offsets,
const index_t* indices) {
for (int m = 0; m < output_size; ++m) {
for (index_t i = offsets[m]; i < offsets[m + 1]; ++i) {
TORCH_CHECK(i < index_size);
index_t idx = indices[i];
TORCH_CHECK(
0 <= idx && idx < N,
"Index ",
i,
" is out of bounds: ",
idx,
", range 0 to ",
N);
}
}
TORCH_CHECK(
offsets[output_size] == index_size,
"Yout input seems to be incorrect: the last offset value should be "
"the size of the indices tensor, but it appears not.");
}
} // namespace
template<typename data_t, typename index_t>
typename std::enable_if<std::is_same<data_t, float>::value, void>::type
index_select_add(const Tensor &select_indices,
const Tensor &add_indices,
const Tensor &src,
Tensor &output,
const Tensor& offsets,
bool include_last_offset,
Tensor &bag_size,
index_t padding_idx) {
int64_t ddim = src.sizes()[1];
auto* select_indices_data = select_indices.data_ptr<index_t>();
auto* output_data = output.data_ptr<float>();
if (is_fast_path_index_select(src, output, padding_idx)) {
auto src_contig = src.contiguous();
auto* src_data = src_contig.data_ptr<float>();
int64_t output_size = offsets.numel() - 1;
auto* offsets_data = offsets.data_ptr<index_t>();
std::vector<index_t> offsets_include_last;
if (include_last_offset) {
output_size = offsets.numel() - 1;
} else {
output_size = offsets.numel();
offsets_include_last.resize(offsets.numel() + 1);
if (offsets.numel() > 0) {
std::memcpy(
offsets_include_last.data(),
offsets.data_ptr<index_t>(),
sizeof(index_t) * offsets.numel());
}
offsets_include_last[offsets.numel()] = select_indices.numel();
offsets_data = offsets_include_last.data();
}
#ifdef USE_FBGEMM
auto kernel_fp32_index_t =
fbgemm::GenerateEmbeddingSpMDM<float, index_t, index_t>(
/* block_size */ddim,
/* has_weight */false,
/* normalize_by_lengths */false,
/* prefetch */16,
/* is_weight_positional */false,
/* use_offsets */true
);
#endif
at::parallel_for(
0, output_size, 1, [&](index_t start_idx, index_t end_idx) {
#ifdef USE_FBGEMM
bool success = kernel_fp32_index_t(
/* output_size */end_idx - start_idx,
/* index_size */offsets_data[end_idx] - offsets_data[start_idx],
/* data_size */src.sizes()[0],
/* input */src_data,
/* indices */select_indices_data + offsets_data[start_idx],
/* offsets_or_lengths */offsets_data + start_idx,
/* weights */nullptr,
/* output */output_data + start_idx * ddim);
if (!success) {
fbgemm_spmdm_report_error_(
end_idx - start_idx,
offsets_data[end_idx] - offsets_data[start_idx],
src.sizes()[0],
offsets_data + start_idx,
select_indices_data + offsets_data[start_idx]);
}
#else
caffe2::EmbeddingLookupIdx(
/*block_size=*/ddim,
/*output_size=*/end_idx - start_idx,
/*index_size=*/offsets_data[end_idx] - offsets_data[start_idx],
/*data_size=*/src.sizes()[0],
/*input=*/src_data,
/*indices=*/select_indices_data + offsets_data[start_idx],
/*offsets=*/offsets_data + start_idx,
/*weights=*/nullptr,
/*scale_bias=*/nullptr,
/*normalize_by_lengths=*/false,
/*out=*/output_data + start_idx * ddim);
#endif
});
} else {
AT_ASSERT(select_indices.numel() == add_indices.numel());
auto* src_data = src.data_ptr<float>();
auto* add_indices_data = add_indices.data_ptr<index_t>();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
index_t* bag_size_data;
if (bag_size.defined()) {
bag_size_data = bag_size.data_ptr<index_t>();
}
auto src_stride0 = src.strides()[0];
auto src_stride1 = src.strides()[1];
auto output_stride0 = output.strides()[0];
auto output_stride1 = output.strides()[1];
auto numel = add_indices.numel();
for (const auto i : c10::irange(numel)) {
// We can skip indices equal to padding_idx so they are not included in
// the reduction
if (select_indices_data[i] != padding_idx) {
at::native::cpublas::axpy<float>(
ddim,
1,
src_data + src_stride0 * select_indices_data[i],
src_stride1,
output_data + output_stride0 * add_indices_data[i],
output_stride1);
} else if (bag_size.defined()) {
// Decrement bag_size to reflect that the index is padded
// NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
bag_size_data[add_indices_data[i]]--;
}
}
}
}
// This function fuses the following three fns:
// index_select (using select_indices as the index)
// mul (scaling by per_sample_weights)
// index_add (using add_indices as the index)
template<typename data_t, typename index_t>
static typename std::enable_if<!std::is_same<data_t, float>::value, void>::type
index_select_scale_add(const Tensor &select_indices,
const Tensor &add_indices,
const Tensor &scale,
const Tensor &src,
Tensor &output,
const Tensor& /*offsets*/,
bool /*include_last_offset*/,
Tensor &bag_size,
index_t padding_idx) {
AT_ASSERT(select_indices.numel() == add_indices.numel());
auto* add_indices_data = add_indices.data_ptr<index_t>();
auto* select_indices_data = select_indices.data_ptr<index_t>();
auto* src_data = src.data_ptr<data_t>();
auto* output_data = output.data_ptr<data_t>();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
index_t* bag_size_data;
if (bag_size.defined()) {
bag_size_data = bag_size.data_ptr<index_t>();
}
auto numel = add_indices.numel();
int64_t ddim = src.sizes()[1];
auto src_stride0 = src.strides()[0];
auto src_stride1 = src.strides()[1];
auto output_stride0 = output.strides()[0];
auto output_stride1 = output.strides()[1];
auto* scale_data = scale.data_ptr<data_t>();
auto scale_stride = scale.strides()[0];
for (const auto i : c10::irange(numel)) {
// We can skip indices equal to padding_idx so they are not included in
// the reduction
if (select_indices_data[i] != padding_idx) {
auto* src_base = src_data + src_stride0 * select_indices_data[i];
auto* output_base = output_data + output_stride0 * add_indices_data[i];
auto scale = scale_data[i * scale_stride];
for (const auto j : c10::irange(ddim)) {
output_base[j * output_stride1] += src_base[j * src_stride1] * scale;
}
} else if (bag_size.defined()) {
// Decrement bag_size to reflect that the index is padded
// NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
bag_size_data[add_indices_data[i]]--;
}
}
}
template<typename data_t, typename index_t>
typename std::enable_if<std::is_same<data_t, float>::value, void>::type
index_select_scale_add(const Tensor &select_indices,
const Tensor &add_indices,
const Tensor &scale,
const Tensor &src,
Tensor &output,
const Tensor& offsets,
bool include_last_offset,
Tensor &bag_size,
index_t padding_idx) {
int64_t ddim = src.sizes()[1];
auto* scale_data = scale.data_ptr<float>();
auto* select_indices_data = select_indices.data_ptr<index_t>();
auto* output_data = output.data_ptr<float>();
if (is_fast_path_index_select_scale(src, scale, output, padding_idx)) {
auto src_contig = src.contiguous();
auto* src_data = src_contig.data_ptr<float>();
int64_t output_size = offsets.numel() - 1;
auto* offsets_data = offsets.data_ptr<index_t>();
std::vector<index_t> offsets_include_last;
if (include_last_offset) {
output_size = offsets.numel() - 1;
} else {
output_size = offsets.numel();
offsets_include_last.resize(offsets.numel() + 1);
std::memcpy(
offsets_include_last.data(),
offsets.data_ptr<index_t>(),
sizeof(index_t) * offsets.numel());
offsets_include_last[offsets.numel()] = select_indices.numel();
offsets_data = offsets_include_last.data();
}
#ifdef USE_FBGEMM
auto kernel_fp32_index_t =
fbgemm::GenerateEmbeddingSpMDM<float, index_t, index_t>(
/* block_size */ddim,
/* has_weight */true,
/* normalize_by_lengths */false,
/* prefetch */16,
/* is_weight_positional */false,
/* use_offsets */true
);
#endif
at::parallel_for(
0, output_size, 1, [&](index_t start_idx, index_t end_idx) {
#ifdef USE_FBGEMM
bool success = kernel_fp32_index_t(
/* output_size */end_idx - start_idx,
/* index_size */offsets_data[end_idx] - offsets_data[start_idx],
/* data_size */src.sizes()[0],
/* input */src_data,
/* indices */select_indices_data + offsets_data[start_idx],
/* offsets_or_lengths */offsets_data + start_idx,
/* weights */scale_data + offsets_data[start_idx],
/* output */output_data + start_idx * ddim);
if (!success) {
fbgemm_spmdm_report_error_(
end_idx - start_idx,
offsets_data[end_idx] - offsets_data[start_idx],
src.sizes()[0],
offsets_data + start_idx,
select_indices_data + offsets_data[start_idx]);
}
#else
caffe2::EmbeddingLookupIdx(
/*block_size=*/ddim,
/*output_size=*/end_idx - start_idx,
/*index_size=*/offsets_data[end_idx] - offsets_data[start_idx],
/*data_size=*/src.sizes()[0],
/*input=*/src_data,
/*indices=*/select_indices_data + offsets_data[start_idx],
/*offsets=*/offsets_data + start_idx,
/*weights=*/scale_data + offsets_data[start_idx],
/*scale_bias=*/nullptr,
/*normalize_by_lengths=*/false,
/*out=*/output_data + start_idx * ddim);
#endif
});
} else {
AT_ASSERT(select_indices.numel() == add_indices.numel());
auto* src_data = src.data_ptr<float>();
auto* add_indices_data = add_indices.data_ptr<index_t>();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
index_t* bag_size_data;
if (bag_size.defined()) {
bag_size_data = bag_size.data_ptr<index_t>();
}
auto src_stride0 = src.strides()[0];
auto src_stride1 = src.strides()[1];
auto output_stride0 = output.strides()[0];
auto output_stride1 = output.strides()[1];
auto scale_stride = scale.strides()[0];
auto numel = add_indices.numel();
for (const auto i : c10::irange(numel)) {
// We can skip indices equal to padding_idx so they are not included in
// the reduction
if (select_indices_data[i] != padding_idx) {
auto* src_base = src_data + src_stride0 * select_indices_data[i];
auto* output_base = output_data + output_stride0 * add_indices_data[i];
auto scale = scale_data[i * scale_stride];
for (const auto j : c10::irange(ddim)) {
output_base[j * output_stride1] += src_base[j * src_stride1] * scale;
}
} else if (bag_size.defined()) {
// Decrement bag_size to reflect that the index is padded
// NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
bag_size_data[add_indices_data[i]]--;
}
}
}
}
} // namespace
void check_arguments(
const Tensor& weight,
const Tensor& indices,
const Tensor& offsets,
const int64_t mode,
const c10::optional<Tensor>& per_sample_weights,
bool include_last_offset) {
auto indices_arg = TensorArg(indices, "indices", 1);
checkScalarTypes("embedding_bag", indices_arg, {kLong, kInt});
auto offsets_arg = TensorArg(offsets, "offsets", 1);
checkScalarTypes("embedding_bag", offsets_arg, {kLong, kInt});
checkSameType("embedding_bag", indices_arg, offsets_arg);
auto weight_arg = TensorArg(weight, "weight", 1);
checkScalarTypes("embedding_bag", weight_arg, {kFloat, kDouble});
AT_DISPATCH_INDEX_TYPES(offsets.scalar_type(), "_embedding_bag_cpu_impl", [&]() {
if (offsets.sizes()[0] > 0) {
index_t offset_0 = offsets.data_ptr<index_t>()[0];
index_t offset_n = offsets.data_ptr<index_t>()[offsets.sizes()[0]-1];
TORCH_CHECK(offset_0 == 0, "offsets[0] has to be 0, i.e., the first sequence "
"in the mini-batch has to start from position 0. "
"However, got ", offsets[0]);
TORCH_CHECK(offset_n <= indices.sizes()[0], "offsets[-1] can not "
"be greater than input's length ", indices.sizes()[0], " but got offsets[-1] of ",
offset_n);
}
});
if (per_sample_weights.has_value() && per_sample_weights.value().defined()) {
TORCH_CHECK(mode == MODE_SUM,
"embedding_bag: per_sample_weights only supported with mode='sum'");
auto per_input_weights_arg = TensorArg(
per_sample_weights.value(),"per_sample_weights", 1);
checkSameType("embedding_bag", weight_arg, per_input_weights_arg);
TORCH_CHECK(per_sample_weights.value().dim() == 1);
TORCH_CHECK(per_sample_weights.value().numel() == indices.numel());
}
if (include_last_offset) {
TORCH_CHECK(
offsets.sizes()[0] >= 1,
"include_last_offset: number of offset should be at least 1");
}
}
void make_bag_size_out(
Tensor& bag_size_out,
const Tensor& offsets,
const Tensor& indices,
const int64_t mode,
const bool include_last_offset,
const bool requires_grad) {
if (requires_grad || mode == MODE_MEAN || mode == MODE_MAX) {
auto num_bags = offsets.size(0) - (include_last_offset ? 1 : 0);
bag_size_out = at::zeros({num_bags}, offsets.options());
// Compute this for MODE_MEAN and MODE_MAX (latter needed for backwards)
if (num_bags != 1) {
bag_size_out.slice(0, 0, bag_size_out.sizes()[0] - 1, 1) =
offsets.slice(0, 1, num_bags, 1) -
offsets.slice(0, 0, num_bags - 1, 1);
}
if (num_bags > 0) {
bag_size_out[-1] = indices.sizes()[0] - offsets[num_bags - 1];
}
}
}
void make_max_indices_out(
Tensor& max_indices_out,
const Tensor& weight,
const Tensor& indices,
const Tensor& offsets,
const Tensor& bag_size,
const int64_t mode,
bool include_last_offset) {
int64_t numBags = offsets.sizes()[0];
if (mode == MODE_MAX) {
if (include_last_offset) {
TORCH_CHECK(
numBags >= 1, "include_last_offset: numBags should be at least 1");
numBags -= 1;
}
at::native::resize_(max_indices_out, {numBags, weight.sizes()[1]}, c10::nullopt);
at::native::zero_(max_indices_out);
} else {
at::native::resize_(max_indices_out, bag_size.sizes(), c10::nullopt);
}
}
void make_offset2bag_out(
Tensor& offset2bag,
Tensor& output,
const Tensor& weight,
const Tensor& indices,
const Tensor& offsets,
const int64_t mode,
const c10::optional<Tensor>& per_sample_weights,
const int64_t padding_idx) {
// To save compute, if we are going to go down the fast path case for the 'sum'
// mode, we skip calculating offset2bag, since it is not going to be used.
bool fast_path_sum = is_fast_path(weight, per_sample_weights, output, padding_idx);
if (mode == MODE_MEAN || mode == MODE_MAX || !fast_path_sum) {
at::native::resize_(offset2bag, {indices.sizes()[0] + 1}, c10::nullopt);
at::native::zero_(offset2bag);
}
if (mode == MODE_MEAN || mode == MODE_MAX || !fast_path_sum) {
make_offset2bag(offsets, offset2bag);
at::native::resize_(offset2bag, {indices.sizes()[0]}, c10::nullopt);
// only initialize output in slow path
at::native::zero_(output);
}
}
static Tensor make_bag_size(
const Tensor& offsets,
const Tensor& indices,
const int64_t mode,
const bool include_last_offset,
const bool requires_grad) {
Tensor bag_size = at::empty(offsets.sizes(), offsets.options());
make_bag_size_out(bag_size, offsets, indices, mode, include_last_offset, requires_grad);
return bag_size;
}
static Tensor make_max_indices(
const Tensor& weight,
const Tensor& indices,
const Tensor& offsets,
const Tensor& bag_size,
const int64_t mode,
bool include_last_offset) {
Tensor max_indices = at::empty(bag_size.sizes(), offsets.options());
make_max_indices_out(max_indices, weight, indices, offsets, bag_size, mode, include_last_offset);
return max_indices;
}
static Tensor make_offset2bag(
Tensor& output,
const Tensor& weight,
const Tensor& indices,
const Tensor& offsets,
const int64_t mode,
const c10::optional<Tensor>& per_sample_weights,
const int64_t padding_idx) {
Tensor offset2bag = at::empty({0}, offsets.options());
make_offset2bag_out(offset2bag, output, weight, indices, offsets, mode, per_sample_weights, padding_idx);
return offset2bag;
}
static Tensor apply_bag_size(
const int64_t mode,
Tensor &output,
const Tensor &bag_size) {
if (mode == MODE_MEAN) {
auto bag_size_ = at::max(bag_size, at::ones_like(bag_size, LEGACY_CONTIGUOUS_MEMORY_FORMAT))
.to(output.options())
.unsqueeze(1)
.expand_as(output);
output /= bag_size_;
}
return output;
}
static Tensor apply_bag_size_backward(
const int64_t mode,
Tensor &output,
const Tensor &offset2bag,
const Tensor &bag_size) {
if (mode == MODE_MEAN) {
auto inv_bag_size_ = (1 / bag_size.to(output.options()))
.unsqueeze(1)
.index_select(0, offset2bag);
output *= inv_bag_size_;
}
return output;
}
template <typename scalar_t>
void embedding_bag_cpu_max_out(
Tensor& max_indices,
const Tensor& weight,
const Tensor& indices,
const Tensor& offset2bag,
const Tensor& output,
bool include_last_offset,
Tensor& bag_size,
int64_t padding_idx) {
int64_t numIndices = indices.numel();
int64_t featureSize = weight.sizes()[1];
AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_bag_cpu_max_out", [&] {
auto* indices_data = indices.data_ptr<index_t>();
auto* offset2bag_data = offset2bag.data_ptr<index_t>();
auto* max_indices_data = max_indices.data_ptr<index_t>();
auto max_indices_stride = max_indices.strides()[0];
auto* weight_data = weight.data_ptr<scalar_t>();
auto* output_data = output.data_ptr<scalar_t>();
auto* bag_size_data = bag_size.data_ptr<index_t>();
auto weight_stride0 = weight.strides()[0];
auto weight_stride1 = weight.strides()[1];
auto output_stride = output.strides()[0];
int64_t numBags = bag_size.size(0);
std::vector<bool> bag_empty(numBags, true);
for (const auto i : c10::irange(numIndices)) {
auto bag = offset2bag_data[i];
auto word_idx = indices_data[i];
if (word_idx != static_cast<index_t>(padding_idx)) {
bool is_first_for_bag = bag_empty[bag];
for (const auto dim : c10::irange(featureSize)) {
auto& current_item = output_data[output_stride * bag + dim];
auto weight_item =
weight_data[weight_stride0 * word_idx + dim * weight_stride1];
if (is_first_for_bag || (weight_item > current_item)) {
current_item = weight_item;
max_indices_data[max_indices_stride * bag + dim] = word_idx;
}
}
if (is_first_for_bag) {
bag_empty[bag] = false;
}
} else {
// Decrement bag_size to reflect that the index is padded
bag_size_data[bag]--;
}
}
});
}
void _embedding_bag_cpu_impl_out(Tensor& output, Tensor& offset2bag,
Tensor& bag_size, Tensor& max_indices,
const Tensor &weight, const Tensor &indices,
const Tensor &offsets, const int64_t mode,
const c10::optional<Tensor>& per_sample_weights,
bool include_last_offset, int64_t padding_idx) {
if (mode == MODE_MEAN || mode == MODE_SUM) {
AT_DISPATCH_FLOATING_TYPES(weight.scalar_type(), "embedding_bag_no_grad_cpu_out",
[&indices, &offset2bag, &per_sample_weights, &weight, &output, &offsets, &include_last_offset, &mode, &bag_size, &padding_idx]() {
AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_bag_no_grad_cpu_out",
[&indices, &offset2bag, &per_sample_weights, &weight, &output, &offsets, &include_last_offset, &mode, &bag_size, &padding_idx]() {
if (per_sample_weights.has_value() && per_sample_weights.value().defined()) {
TORCH_INTERNAL_ASSERT(mode == MODE_SUM);
index_select_scale_add<scalar_t, index_t>(
indices, offset2bag, per_sample_weights.value(), weight, output, offsets, include_last_offset, bag_size, padding_idx);
} else {
index_select_add<scalar_t, index_t>(indices, offset2bag, weight, output, offsets, include_last_offset, bag_size, padding_idx);
}
});
});
apply_bag_size(mode, output, bag_size);
if (mode == MODE_SUM) {
// make bag_size output deterministic
at::native::zero_(bag_size);
}
max_indices.copy_(bag_size);
} else { // MODE_MAX
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
weight.scalar_type(), "embedding_bag_cpu_max_out", [&]() {
embedding_bag_cpu_max_out<scalar_t>(
max_indices, weight, indices, offset2bag, output, include_last_offset, bag_size, padding_idx);
}
);
}
}
// Assumes all input tensors except for `weight` are contiguous.
// See NOTE [ embedding_bag Native Functions ] in native_functions.yaml for details
std::tuple<Tensor, Tensor, Tensor, Tensor> _embedding_bag_cpu_impl(
const Tensor& weight,
const Tensor& indices_,
const Tensor& offsets_,
const int64_t mode,
const Tensor& per_sample_weights,
bool include_last_offset,
int64_t padding_idx,
bool requires_grad) {
Tensor indices, offsets;
std::tie(indices, offsets) = promoteIndicesAndOffsets(indices_, offsets_);
check_arguments(weight, indices, offsets, mode, per_sample_weights, include_last_offset);
Tensor output = at::empty(
{include_last_offset ? offsets.sizes()[0] - 1 : offsets.sizes()[0],
weight.sizes()[1]},
weight.options());
Tensor offset2bag = make_offset2bag(output, weight, indices, offsets, mode, per_sample_weights, padding_idx);
Tensor bag_size = make_bag_size(offsets, indices, mode, include_last_offset, requires_grad);
Tensor max_indices = make_max_indices(weight, indices, offsets, bag_size, mode, include_last_offset);
_embedding_bag_cpu_impl_out(output, offset2bag,
bag_size, max_indices,
weight, indices, offsets,
mode, per_sample_weights,
include_last_offset, padding_idx);
return std::make_tuple(std::move(output), std::move(offset2bag), std::move(bag_size), std::move(max_indices));
}
// embedding_bag wrapper to enforce contiguity in tensors other than `weight`.
// This is created to save extra `.contiguous()` call in backward.
// See NOTE [ embedding_bag Native Functions ] in native_functions.yaml for details
std::tuple<Tensor, Tensor, Tensor, Tensor>
embedding_bag(const Tensor &weight, const Tensor &indices,
const Tensor &offsets, const bool scale_grad_by_freq,
const int64_t mode, bool sparse, const c10::optional<Tensor>& per_sample_weights_opt,
bool include_last_offset, c10::optional<int64_t> padding_idx_opt) {
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> per_sample_weights_maybe_owned = at::borrow_from_optional_tensor(per_sample_weights_opt);
const Tensor& per_sample_weights = *per_sample_weights_maybe_owned;
int64_t padding_idx = -1;
if (padding_idx_opt.has_value()) {
auto num_embeddings = weight.size(0);
padding_idx = padding_idx_opt.value();
TORCH_CHECK(
(padding_idx >= -num_embeddings) && (padding_idx < num_embeddings),
"padding_idx must be within the number of embeddings, -", num_embeddings,
" through ", num_embeddings - 1, ", but got ", padding_idx);
padding_idx = maybe_wrap_dim(padding_idx, weight.size(0));
}
std::tuple<Tensor, Tensor, Tensor, Tensor> out;
if (!weight.requires_grad()) {
out = at::_embedding_bag_forward_only(
weight, indices.contiguous(), offsets.contiguous(), scale_grad_by_freq,
mode, sparse, per_sample_weights, include_last_offset, padding_idx);
} else {
out = at::_embedding_bag(
weight, indices.contiguous(), offsets.contiguous(), scale_grad_by_freq,
mode, sparse, per_sample_weights, include_last_offset, padding_idx);
}
return out;
};
std::tuple<Tensor, Tensor, Tensor, Tensor>
embedding_bag(const Tensor &weight, const Tensor &indices,
const Tensor &offsets, const bool scale_grad_by_freq,
const int64_t mode, bool sparse, const c10::optional<Tensor>& per_sample_weights_opt,
bool include_last_offset) {
return at::native::embedding_bag(weight, indices, offsets, scale_grad_by_freq,
mode, sparse, per_sample_weights_opt, include_last_offset, c10::nullopt);
}
// Assumes all input tensors except for `weight` are contiguous.
// See NOTE [ embedding_bag Native Functions ] in native_functions.yaml for details
std::tuple<Tensor, Tensor, Tensor, Tensor>
_embedding_bag_forward_only_cpu(const Tensor &weight, const Tensor &indices,
const Tensor &offsets, const bool scale_grad_by_freq,
const int64_t mode, bool sparse, const c10::optional<Tensor>& per_sample_weights_opt, bool include_last_offset,
int64_t padding_idx) {
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> per_sample_weights_maybe_owned = at::borrow_from_optional_tensor(per_sample_weights_opt);
const Tensor& per_sample_weights = *per_sample_weights_maybe_owned;
std::ignore = scale_grad_by_freq;
std::ignore = sparse;
return _embedding_bag_cpu_impl(
weight,
indices,
offsets,
mode,
per_sample_weights,
include_last_offset,
padding_idx,
/*requires_grad=*/false);
}
// Assumes all input tensors except for `weight` are contiguous.
// See NOTE [ embedding_bag Native Functions ] in native_functions.yaml for details
std::tuple<Tensor, Tensor, Tensor, Tensor>
_embedding_bag_cpu(const Tensor &weight, const Tensor &indices,
const Tensor &offsets, const bool scale_grad_by_freq,
const int64_t mode, bool sparse, const c10::optional<Tensor>& per_sample_weights_opt, bool include_last_offset,
int64_t padding_idx) {
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> per_sample_weights_maybe_owned = at::borrow_from_optional_tensor(per_sample_weights_opt);
const Tensor& per_sample_weights = *per_sample_weights_maybe_owned;
std::ignore = scale_grad_by_freq;
std::ignore = sparse;
return _embedding_bag_cpu_impl(
weight,
indices,
offsets,
mode,
per_sample_weights,
include_last_offset,
padding_idx,
/*requires_grad=*/true);
}
// Assumes all input tensors are contiguous.
// See NOTE [ embedding_bag Native Functions ] in native_functions.yaml for details
Tensor _embedding_bag_backward(const Tensor &grad, const Tensor &indices_,
const Tensor &offsets_,
const Tensor &offset2bag,
const Tensor &bag_size_,
const Tensor &max_indices_,
int64_t num_weights,
bool scale_grad_by_freq, int64_t mode,
bool sparse, const c10::optional<Tensor>& per_sample_weights_opt,
int64_t padding_idx) {
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> per_sample_weights_maybe_owned = at::borrow_from_optional_tensor(per_sample_weights_opt);
const Tensor& per_sample_weights = *per_sample_weights_maybe_owned;
Tensor indices, offsets;
std::tie(indices, offsets) = promoteIndicesAndOffsets(indices_, offsets_);
auto indices_arg = TensorArg(indices, "indices", 1);
checkScalarTypes("embedding_bag", indices_arg, {kLong, kInt});
checkContiguous("embedding_bag", indices_arg);
auto offsets_arg = TensorArg(offsets, "offsets", 1);
checkScalarTypes("embedding_bag", offsets_arg, {kLong, kInt});
checkSameType("embedding_bag", indices_arg, offsets_arg);
checkContiguous("embedding_bag", offsets_arg);
Tensor offset2bag_;
if (indices.numel() != 0 && offset2bag.numel() == 0) {
offset2bag_ = at::zeros(
{indices.sizes()[0] + 1}, offsets.options()); // offset2bag = [0 0 0 0 0]
make_offset2bag(offsets, offset2bag_);
offset2bag_.resize_({indices.sizes()[0]});
} else {
auto offset2bag_arg = TensorArg(offset2bag, "offset2bag", 1);
checkScalarTypes("embedding_bag", offset2bag_arg, {kLong, kInt});
checkContiguous("embedding_bag", offset2bag_arg);
offset2bag_ = offset2bag;
}
if (sparse) {
return at::_embedding_bag_sparse_backward(
grad, indices, offsets, offset2bag_, bag_size_, num_weights,
scale_grad_by_freq, mode, per_sample_weights, padding_idx);
} else {
return at::_embedding_bag_dense_backward(
grad, indices, offset2bag_, bag_size_, max_indices_, num_weights,
scale_grad_by_freq, mode, per_sample_weights, padding_idx);
}
}
static Tensor _embedding_bag_dense_backward_cpu_max(
const Tensor& grad,
const Tensor& bag_size,
const Tensor& max_indices,
int64_t num_weights) {
AT_ASSERT(max_indices.defined());
auto index_grad_weight =
at::zeros({num_weights, grad.sizes()[1]}, grad.options());
auto nonempty_max_indices = max_indices.index_select(0, bag_size.nonzero().view(-1));
auto nonempty_grad = grad.index_select(0, bag_size.nonzero().view(-1));
for (int64_t dim = 0; dim < grad.sizes()[1]; dim++) {
index_grad_weight.select(1, dim).index_add_(
0, nonempty_max_indices.select(1, dim), nonempty_grad.select(1, dim));
}
return index_grad_weight;
}
template<typename index_t>
static std::vector<index_t> compute_counts(
int64_t num_weights,
index_t* indices_data,
int64_t indices_length) {
std::vector<index_t> counts(num_weights, 0);
for (const auto i : c10::irange(indices_length)) {
counts[indices_data[i]]++;
}
return counts;
}
// counts_uniq stores the index of the NEXT unique element
// of the (sorted) indices vector.
//
// For example:
// indices: [0, 0, 0, 1, 3, 3, 4]
// counts: [3, 1, 0, 2, 1, 0]
// counts_uniq: [3, 4, 6, 7]
//
// The unique indices can be found at index 0, 3, 4, 6.
template<typename index_t>
static std::vector<index_t> compute_counts_uniq(
int64_t num_weights,
index_t* indices_data,
int64_t indices_length,
const std::vector<index_t>& counts) {
std::vector<index_t> counts_uniq;
counts_uniq.reserve(num_weights);
int64_t o = 0;
for (int64_t i = 0; i < indices_length; i += counts[indices_data[i]]) {
counts_uniq.push_back(counts[indices_data[i]]);
if (o > 0) {
counts_uniq[o] += counts_uniq[o - 1];
}
o++;
}
return counts_uniq;
}
template <typename scalar_t>
void _embedding_bag_dense_backward_cpu_sum_mean(
const Tensor& grad,
const Tensor& indices_,
const Tensor& offset2bag__,
const Tensor& bag_size_,
int64_t num_weights,
bool scale_grad_by_freq,
int64_t mode,
const Tensor& per_sample_weights_,
Tensor& index_grad_weight,
int64_t padding_idx) {
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
Tensor &offset2bag_ = const_cast<Tensor &>(offset2bag__);
auto ind_sort_ = indices_.sort();
auto indices = std::get<0>(ind_sort_);
auto ind_sort = std::get<1>(ind_sort_);
auto offset2bag = offset2bag_.index_select(0, ind_sort);
optional<Tensor> per_sample_weights;
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
scalar_t* per_sample_weights_data;
optional<int64_t> per_sample_weights_stride;
if (per_sample_weights_.defined()) {
per_sample_weights = per_sample_weights_.index_select(0, ind_sort);
per_sample_weights_data = per_sample_weights->data_ptr<scalar_t>();
per_sample_weights_stride = per_sample_weights->strides()[0];
}
int64_t numel = indices.numel();
// explicitly capture all required variables to work around windows build
// TODO: fix this when windows can correctly capture variables in nested lambda
AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "_embedding_bag_dense_backward_cpu_sum_mean",
[&indices, &offset2bag, &bag_size_, &num_weights, &numel, &per_sample_weights,
&per_sample_weights_data, &per_sample_weights_stride, &mode, &scale_grad_by_freq,
&grad, &index_grad_weight, &padding_idx] {
auto* indices_data = indices.data_ptr<index_t>();
auto* offset2bag_data = offset2bag.data_ptr<index_t>();
auto* bag_size_data = bag_size_.data_ptr<index_t>();
auto counts = compute_counts(num_weights, indices_data, numel);
auto next_unique_index_idx =
compute_counts_uniq(num_weights, indices_data, numel, counts);
auto loop =
[&next_unique_index_idx, &indices_data, &offset2bag_data, &bag_size_data, &per_sample_weights,
&mode, &per_sample_weights_data, &per_sample_weights_stride, &scale_grad_by_freq,
&counts, &grad, &index_grad_weight, &padding_idx
](index_t start, index_t end) {
for (index_t i = start; i < end; i++) {
index_t start = i == 0 ? 0 : next_unique_index_idx[i - 1];