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SoftMaxKernel.cpp
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SoftMaxKernel.cpp
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#include <ATen/native/cpu/SoftmaxKernel.h>
#include <algorithm>
#include <iterator>
#include <numeric>
#include <ATen/Dispatch.h>
#include <ATen/Parallel.h>
#include <ATen/cpu/vec256/functional.h>
#include <ATen/cpu/vec256/vec256.h>
#include <c10/util/Optional.h>
// [Note AVX-SSE transitions] In general we avoid calls into cmath for code
// compiled with AVX/AVX2 This is because of SSE-AVX transitions and a bug in
// Glibc2.23 See https://bugs.launchpad.net/ubuntu/+source/glibc/+bug/1663280
//
// On grainsize: The grainsize is chosen to roughly get GRAIN_SIZE number of
// computations per task. Each task works across dim_size elements. 16 should be
// a very rough approximation of the number of computations per dim_size element
// by counting simple computations (*, +, -) as 1 and exp or log as 4.
namespace at { namespace native {
namespace {
template <typename scalar_t>
inline void _vec_log_softmax_lastdim(
scalar_t* input_data_base,
scalar_t* output_data_base,
int64_t outer_size,
int64_t dim_size) {
using Vec = vec256::Vec256<scalar_t>;
static constexpr int64_t CHUNK_SIZE = (128 / sizeof(scalar_t)) * Vec::size();
int64_t grain_size = internal::GRAIN_SIZE / (16 * dim_size * CHUNK_SIZE);
if (grain_size < CHUNK_SIZE)
grain_size = CHUNK_SIZE;
parallel_for(
0,
outer_size,
grain_size,
[&](int64_t begin, int64_t end) {
for (int64_t ii = begin; ii < end; ii += CHUNK_SIZE) {
scalar_t tmp_sum_scalar[CHUNK_SIZE];
scalar_t max_input_arr[CHUNK_SIZE];
int64_t loop_end = CHUNK_SIZE;
if (ii + CHUNK_SIZE > end)
loop_end = end - ii;
for (int64_t j = 0; j < loop_end; j++) {
int64_t i = ii + j;
scalar_t* input_data = input_data_base + i * dim_size;
max_input_arr[j] = vec256::reduce_all<scalar_t>(
[](Vec& x, Vec& y) { return vec256::maximum(x, y); },
input_data,
dim_size);
}
for (int64_t j = 0; j < loop_end; j++) {
int64_t i = ii + j;
scalar_t* input_data = input_data_base + i * dim_size;
scalar_t max_input = max_input_arr[j];
tmp_sum_scalar[j] = vec256::map_reduce_all<scalar_t>(
[max_input](Vec x) { return (x - Vec(max_input)).exp(); },
[](Vec x, Vec y) { return x + y; },
input_data,
dim_size);
}
// See [Note AVX-SSE transitions] for why this should call the
// vectorized version (aside from perf improvements).
vec256::map(
[](Vec x) { return x.log(); },
tmp_sum_scalar,
tmp_sum_scalar,
loop_end);
for (int64_t j = 0; j < loop_end; j++) {
int64_t i = ii + j;
scalar_t* input_data = input_data_base + i * dim_size;
scalar_t* output_data = output_data_base + i * dim_size;
scalar_t tmp_sum = tmp_sum_scalar[j];
scalar_t max_input = max_input_arr[j];
// It's necessary to keep the order of the operations below.
// In some cases that input is large digits and the difference
// is small, if we compute `max_input` plus `tmp_sum` before,
// there would be a numerical problem. See an example in
// https://github.com/pytorch/pytorch/issues/11752#issuecomment-422883379
vec256::map(
[tmp_sum, max_input](Vec x) { return x - Vec(max_input) - Vec(tmp_sum); },
output_data,
input_data,
dim_size);
}
}
});
}
template <typename scalar_t>
inline void _vec_softmax_lastdim(
scalar_t* input_data_base,
scalar_t* output_data_base,
int64_t outer_size,
int64_t dim_size) {
using Vec = vec256::Vec256<scalar_t>;
int64_t grain_size = internal::GRAIN_SIZE / (16 * dim_size);
if (grain_size < 1)
grain_size = 1;
parallel_for(
0,
outer_size,
grain_size,
[&](int64_t begin, int64_t end) {
for (int64_t i = begin; i < end; i++) {
scalar_t* input_data = input_data_base + i * dim_size;
scalar_t* output_data = output_data_base + i * dim_size;
scalar_t max_input = vec256::reduce_all<scalar_t>(
[](Vec& x, Vec& y) { return vec256::maximum(x, y); },
input_data,
dim_size);
vec256::map(
[max_input](Vec x) { return (x - Vec(max_input)).exp(); },
output_data,
input_data,
dim_size);
scalar_t tmp_sum = vec256::reduce_all<scalar_t>(
[](Vec x, Vec y) { return x + y; }, output_data, dim_size);
tmp_sum = 1 / tmp_sum;
vec256::map(
[tmp_sum](Vec x) { return x * Vec(tmp_sum); },
output_data,
output_data,
dim_size);
}
});
}
template <typename scalar_t, bool log_softmax>
inline void _vec_host_softmax_backward_lastdim(
scalar_t* grad_input_data_base,
scalar_t* grad_data_base,
scalar_t* output_data_base,
int64_t outer_size,
int64_t dim_size) {
using Vec = vec256::Vec256<scalar_t>;
int64_t grain_size = internal::GRAIN_SIZE / (16 * dim_size);
if (grain_size < 1)
grain_size = 1;
parallel_for(
0,
outer_size,
grain_size,
[&](int64_t begin, int64_t end) {
for (int64_t i = begin; i < end; i++) {
scalar_t* grad_input_data = grad_input_data_base + i * dim_size;
scalar_t* grad_data = grad_data_base + i * dim_size;
scalar_t* output_data = output_data_base + i * dim_size;
scalar_t sum;
if (log_softmax) {
sum = vec256::reduce_all<scalar_t>(
[](Vec& x, Vec& y) { return x + y; }, grad_data, dim_size);
} else {
sum = vec256::map2_reduce_all<scalar_t>(
[](Vec x, Vec y) { return x * y; },
[](Vec x, Vec y) { return x + y; },
grad_data,
output_data,
dim_size);
}
if (log_softmax) {
vec256::map2(
[sum](Vec x, Vec y) { return x - ((y.exp()) * Vec(sum)); },
grad_input_data,
grad_data,
output_data,
dim_size);
} else {
vec256::map2(
[sum](Vec x, Vec y) { return (x - Vec(sum)) * y; },
grad_input_data,
grad_data,
output_data,
dim_size);
}
}
});
}
template <typename scalar_t, bool LogSoftMax>
struct vec_host_softmax_lastdim {
static void apply(Tensor& output, const Tensor& input) {
int64_t outer_size = 1;
int64_t dim_size = input.size(input.ndimension() - 1);
for (int64_t i = 0; i < input.ndimension() - 1; ++i)
outer_size *= input.size(i);
scalar_t* input_data_base = input.data_ptr<scalar_t>();
scalar_t* output_data_base = output.data_ptr<scalar_t>();
if (LogSoftMax) {
_vec_log_softmax_lastdim(
input_data_base, output_data_base, outer_size, dim_size);
} else {
_vec_softmax_lastdim(
input_data_base, output_data_base, outer_size, dim_size);
}
}
};
template <typename scalar_t, bool LogSoftMax>
struct vec_host_softmax_backward_lastdim {
static void
apply(Tensor& grad_input, const Tensor& grad, const Tensor& output) {
int64_t outer_size = 1;
int64_t dim_size = grad.size(grad.ndimension() - 1);
for (int64_t i = 0; i < grad.ndimension() - 1; ++i)
outer_size *= grad.size(i);
scalar_t* grad_input_data_base = grad_input.data_ptr<scalar_t>();
scalar_t* grad_data_base = grad.data_ptr<scalar_t>();
scalar_t* output_data_base = output.data_ptr<scalar_t>();
_vec_host_softmax_backward_lastdim<scalar_t, LogSoftMax>(
grad_input_data_base,
grad_data_base,
output_data_base,
outer_size,
dim_size);
}
};
static void softmax_lastdim_kernel_impl(Tensor& result, const Tensor& self) {
AT_DISPATCH_FLOATING_TYPES(self.scalar_type(), "softmax_lastdim_kernel_impl", [&] {
vec_host_softmax_lastdim<scalar_t, false>::apply(result, self);
});
}
static void log_softmax_lastdim_kernel_impl(
Tensor& result,
const Tensor& self) {
AT_DISPATCH_FLOATING_TYPES(
self.scalar_type(), "log_softmax_lastdim_kernel_impl", [&] {
vec_host_softmax_lastdim<scalar_t, true>::apply(result, self);
});
}
static void softmax_backward_lastdim_kernel_impl(
Tensor& grad_input,
const Tensor& grad,
const Tensor& output) {
AT_DISPATCH_FLOATING_TYPES(
grad.scalar_type(), "softmax_backward_lastdim_kernel_impl", [&] {
vec_host_softmax_backward_lastdim<scalar_t, false>::apply(
grad_input, grad, output);
});
}
static void log_softmax_backward_lastdim_kernel_impl(
Tensor& grad_input,
const Tensor& grad,
const Tensor& output) {
AT_DISPATCH_FLOATING_TYPES(
grad.scalar_type(), "log_softmax_backward_lastdim_kernel_impl", [&] {
vec_host_softmax_backward_lastdim<scalar_t, true>::apply(
grad_input, grad, output);
});
}
} // anonymous namespace
REGISTER_DISPATCH(softmax_lastdim_kernel, &softmax_lastdim_kernel_impl);
REGISTER_DISPATCH(log_softmax_lastdim_kernel, &log_softmax_lastdim_kernel_impl);
REGISTER_DISPATCH(
softmax_backward_lastdim_kernel,
&softmax_backward_lastdim_kernel_impl);
REGISTER_DISPATCH(
log_softmax_backward_lastdim_kernel,
&log_softmax_backward_lastdim_kernel_impl);
}} // namespace at::native