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CPUBlas.cpp
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/native/CPUBlas.h>
#include <ATen/native/mkl/LinearAlgebra.h>
#include <ATen/native/mkldnn/Matmul.h>
#include <ATen/Config.h>
#include <c10/util/SmallBuffer.h>
#include <c10/util/irange.h>
#include <climits>
#if !defined(__s390x__ ) && !defined(__powerpc__)
#include <cpuinfo.h>
#endif
#if AT_BUILD_WITH_BLAS()
#if C10_IOS
#include <Accelerate/Accelerate.h>
#else
extern "C" void dgemm_(char *transa, char *transb, int *m, int *n, int *k, double *alpha, const double *a, int *lda, const double *b, int *ldb, double *beta, double *c, int *ldc);
extern "C" void sgemm_(char *transa, char *transb, int *m, int *n, int *k, float *alpha, const float *a, int *lda, const float *b, int *ldb, float *beta, float *c, int *ldc);
extern "C" void cgemm_(char *transa, char *transb, int *m, int *n, int *k, void *alpha, const void *a, int *lda, const void *b, int *ldb, void *beta, void *c, int *ldc);
extern "C" void zgemm_(char *transa, char *transb, int *m, int *n, int *k, void *alpha, const void *a, int *lda, const void *b, int *ldb, void *beta, void *c, int *ldc);
#ifdef BLAS_HAS_SBGEMM
extern "C" void sbgemm_(char *transa, char *transb, int *m, int *n, int *k,
float *alpha,
const at::BFloat16 *a, int *lda,
const at::BFloat16 *b, int *ldb,
float *beta,
float *c, int *ldc);
#endif // BLAS_HAS_SBGEMM
extern "C" void cswap_(int *n, const void *x, int *incx, void *y, int *incy);
extern "C" void dcopy_(int *n, const double *x, int *incx, double *y, int *incy);
extern "C" void scopy_(int *n, const float *x, int *incx, float *y, int *incy);
extern "C" void zcopy_(int *n, const void *x, int *incx, void *y, int *incy);
extern "C" void ccopy_(int *n, const void *x, int *incx, void *y, int *incy);
extern "C" void daxpy_(int *n, double *a, const double *x, int *incx, double *y, int *incy);
extern "C" void saxpy_(int *n, float *a, const float *x, int *incx, float *y, int *incy);
extern "C" void caxpy_(int *n, void *a, const void *x, int *incx, void *y, int *incy);
extern "C" void zaxpy_(int *n, void *a, const void *x, int *incx, void *y, int *incy);
#endif // C10_IOS
#endif // AT_BUILD_WITH_BLAS
#ifdef USE_FBGEMM
#include <fbgemm/FbgemmI64.h>
#endif // USE_FBGEMM
#if AT_MKLDNN_ENABLED()
#include <ideep.hpp>
// Add uKernel API versioning to be compatible with different oneDNN versions
// oneDNN 3.6.x updates the ukernel APIs of brgemm and brgemm_pack_B
// brgemm_pack_B is changed to transform and the setting of brgemm beta is changed to set_add_C
#if (IDEEP_VERSION_MAJOR == 3 && IDEEP_VERSION_MINOR == 5)
#define ONEDNN_UKERNEL_1
#elif (IDEEP_VERSION_MAJOR >= 3 && IDEEP_VERSION_MINOR >= 6)
#define ONEDNN_UKERNEL_2
#endif
#if ((defined(ONEDNN_UKERNEL_1) || defined(ONEDNN_UKERNEL_2)) && (defined(__x86_64__) || (defined(_M_X64) && !defined(_M_ARM64EC))))
#define ONEDNN_UKERNEL_ENABLED
#endif
#endif // AT_MKLDNN_ENABLED()
#if defined(ONEDNN_UKERNEL_ENABLED)
#include <oneapi/dnnl/dnnl_ukernel.hpp>
#include <oneapi/dnnl/dnnl.hpp>
#endif // oneDNN BRGEMM
namespace at::native::cpublas {
namespace internal {
void normalize_last_dims(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
int64_t *lda, int64_t *ldb, int64_t *ldc) {
if (n == 1) {
*ldc = m;
}
if(transa != TransposeType::NoTranspose) {
if (m == 1) {
*lda = k;
}
} else if(k == 1) {
*lda = m;
}
if(transb != TransposeType::NoTranspose) {
if (k == 1) {
*ldb = n;
}
} else if (n == 1) {
*ldb = k;
}
}
} // namespace internal
namespace {
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunneeded-internal-declaration")
bool use_blas_gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
int64_t lda, int64_t ldb, int64_t ldc) {
const bool transa_ = transa != TransposeType::NoTranspose;
const bool transb_ = transb != TransposeType::NoTranspose;
return (
(m <= INT_MAX) && (n <= INT_MAX) && (k <= INT_MAX) &&
(lda <= INT_MAX) && (ldb <= INT_MAX) && (ldc <= INT_MAX) &&
(lda >= std::max(int64_t{1}, (transa_ ? k : m))) &&
(ldb >= std::max(int64_t{1}, (transb_ ? n : k))) &&
(ldc >= std::max(int64_t{1}, m)));
}
C10_DIAGNOSTIC_POP()
#ifdef USE_FBGEMM
fbgemm::matrix_op_t to_fbgemm(TransposeType trans) {
switch (trans) {
case TransposeType::Transpose: return fbgemm::matrix_op_t::Transpose;
case TransposeType::NoTranspose: return fbgemm::matrix_op_t::NoTranspose;
case TransposeType::ConjTranspose: TORCH_INTERNAL_ASSERT(false, "ConjTranspose type is not supported in fbgemm");
}
TORCH_INTERNAL_ASSERT(false, "Invalid transpose type");
}
#endif // USE_FBGEMM
#if (AT_BUILD_WITH_BLAS() && C10_IOS)
CBLAS_TRANSPOSE to_apple_accelerate_transpose(TransposeType trans) {
switch (trans) {
case TransposeType::Transpose: return CblasTrans;
case TransposeType::NoTranspose: return CblasNoTrans;
case TransposeType::ConjTranspose: return CblasConjTrans;
}
TORCH_INTERNAL_ASSERT(false, "Invalid transpose type");
}
#endif
} // namespace (anonymous)
DEFINE_DISPATCH(gemm_stub);
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const double alpha,
const double *a, int64_t lda,
const double *b, int64_t ldb,
const double beta,
double *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#if AT_BUILD_WITH_BLAS()
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
double alpha_ = alpha, beta_ = beta;
#if C10_IOS
CBLAS_TRANSPOSE transa_ = to_apple_accelerate_transpose(transa);
CBLAS_TRANSPOSE transb_ = to_apple_accelerate_transpose(transb);
cblas_dgemm(CblasColMajor,
transa_, transb_,
m_, n_, k_,
alpha_,
a, lda_,
b, ldb_,
beta_,
c, ldc_);
#else
char transa_ = to_blas(transa), transb_ = to_blas(transb);
dgemm_(
&transa_, &transb_,
&m_, &n_, &k_,
&alpha_,
a, &lda_,
b, &ldb_,
&beta_,
c, &ldc_);
#endif
return;
}
#endif
gemm_stub(
at::kCPU, at::kDouble,
transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const float alpha,
const float *a, int64_t lda,
const float *b, int64_t ldb,
const float beta,
float *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#if AT_MKLDNN_ENABLED()
if (mkldnn_bf32_gemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc)) {
return;
}
#endif
#if AT_BUILD_WITH_BLAS()
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
float alpha_ = alpha, beta_ = beta;
#if C10_IOS
CBLAS_TRANSPOSE transa_ = to_apple_accelerate_transpose(transa);
CBLAS_TRANSPOSE transb_ = to_apple_accelerate_transpose(transb);
cblas_sgemm(CblasColMajor,
transa_, transb_,
m_, n_, k_,
alpha_,
a, lda_,
b, ldb_,
beta_,
c, ldc_);
#else
char transa_ = to_blas(transa), transb_ = to_blas(transb);
sgemm_(
&transa_, &transb_,
&m_, &n_, &k_,
&alpha_,
a, &lda_,
b, &ldb_,
&beta_,
c, &ldc_);
#endif
return;
}
#endif
gemm_stub(
at::kCPU, at::kFloat,
transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const c10::complex<double> alpha,
const c10::complex<double> *a, int64_t lda,
const c10::complex<double> *b, int64_t ldb,
const c10::complex<double> beta,
c10::complex<double> *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#if AT_BUILD_WITH_BLAS()
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
c10::complex<double> alpha_ = alpha, beta_ = beta;
#if C10_IOS
CBLAS_TRANSPOSE transa_ = to_apple_accelerate_transpose(transa);
CBLAS_TRANSPOSE transb_ = to_apple_accelerate_transpose(transb);
cblas_zgemm(CblasColMajor,
transa_, transb_,
m_, n_, k_,
&alpha_,
a, lda_,
b, ldb_,
&beta_,
c, ldc_);
#else
char transa_ = to_blas(transa), transb_ = to_blas(transb);
zgemm_(
&transa_, &transb_,
&m_, &n_, &k_,
&alpha_,
a, &lda_,
b, &ldb_,
&beta_,
c, &ldc_);
#endif
return;
}
#endif
gemm_stub(
at::kCPU, at::kComplexDouble,
transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const c10::complex<float> alpha,
const c10::complex<float> *a, int64_t lda,
const c10::complex<float> *b, int64_t ldb,
const c10::complex<float> beta,
c10::complex<float> *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#if AT_BUILD_WITH_BLAS()
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
c10::complex<float> alpha_ = alpha, beta_ = beta;
#if C10_IOS
CBLAS_TRANSPOSE transa_ = to_apple_accelerate_transpose(transa);
CBLAS_TRANSPOSE transb_ = to_apple_accelerate_transpose(transb);
cblas_cgemm(CblasColMajor,
transa_, transb_,
m_, n_, k_,
&alpha_,
a, lda_,
b, ldb_,
&beta_,
c, ldc_);
#else
char transa_ = to_blas(transa), transb_ = to_blas(transb);
cgemm_(
&transa_, &transb_,
&m_, &n_, &k_,
&alpha_,
a, &lda_,
b, &ldb_,
&beta_,
c, &ldc_);
#endif
return;
}
#endif
gemm_stub(
at::kCPU, at::kComplexFloat,
transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const float alpha,
const at::BFloat16 *a, int64_t lda,
const at::BFloat16 *b, int64_t ldb,
const float beta,
at::BFloat16 *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#if AT_BUILD_WITH_BLAS() && defined(BLAS_HAS_SBGEMM)
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
char transa_ = to_blas(transa), transb_ = to_blas(transb);
float alpha_ = alpha, beta_ = beta;
int c_size = n_ * ldc_;
// C matrix in OpenBLAS sbgemm are of type "float" so we have to convert, copy and copy back.
std::vector<float> float_v(c, c + c_size);
sbgemm_(&transa_, &transb_,
&m_, &n_, &k_,
&alpha_,
a, &lda_,
b, &ldb_,
&beta_,
float_v.data(), &ldc_);
for (auto cv: float_v) {
*(c++) = c10::convert<at::BFloat16>(cv);
}
return;
}
#endif
#if AT_MKLDNN_ENABLED()
#ifdef __aarch64__
// MKLDNN also supports ARM for bf16, and the bypass is only
// currently intended for x86/x86_64.
const bool use_bf16_gemv_trans = false;
#else
const bool bf16_gemv_trans_would_be_faster = cpuinfo_initialize() &&
!cpuinfo_has_x86_avx512bf16();
const bool use_bf16_gemv_trans = bf16_gemv_trans_would_be_faster &&
transa == TransposeType::Transpose &&
transb == TransposeType::NoTranspose && n == 1 && alpha == 1.0;
#endif
if (!use_bf16_gemv_trans && mkldnn_bf16_gemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc)) {
return;
}
#endif
gemm_stub(
at::kCPU, at::kBFloat16,
transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const float alpha,
const at::Half *a, int64_t lda,
const at::Half *b, int64_t ldb,
const float beta,
at::Half *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#if AT_MKLDNN_ENABLED()
// Per https://github.com/pytorch/pytorch/pull/137918#discussion_r1825460179 ,
// we should not bother checking for !cpuinfo_has_x86_avx512fp16() here,
// because "onednn (mkldnn) won't use avx512fp16 to compute gemms by default
// because the avx512fp16 fma would incur accuracy loss".
const bool fp16_gemv_trans_would_be_faster = cpuinfo_initialize() &&
cpuinfo_has_x86_f16c();
const bool use_fp16_gemv_trans = fp16_gemv_trans_would_be_faster &&
transa == TransposeType::Transpose &&
transb == TransposeType::NoTranspose && n == 1 && alpha == 1.0;
if (!use_fp16_gemv_trans &&
mkldnn_fp16_gemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc)) {
return;
}
#endif
gemm_stub(
at::kCPU, at::kHalf,
transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const float alpha,
const at::BFloat16 *a, int64_t lda,
const at::BFloat16 *b, int64_t ldb,
const float beta,
float *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#if AT_BUILD_WITH_BLAS() && defined(BLAS_HAS_SBGEMM)
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
char transa_ = to_blas(transa), transb_ = to_blas(transb);
float alpha_ = alpha, beta_ = beta;
sbgemm_(&transa_, &transb_,
&m_, &n_, &k_,
&alpha_,
a, &lda_,
b, &ldb_,
&beta_,
c, &ldc_);
return;
}
#endif
#ifdef MKL_HAS_SBGEMM
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
mkl_gemm_bf16bf16f32(transa, transb, m_, n_, k_, alpha, a, lda_, b, ldb_, beta, c, ldc_);
return;
}
#endif
// for the fallback path, first compute gemm with beta = 0,
// and then add c in full precision.
int64_t c_size = n * m;
std::vector<at::BFloat16> bfloat_c(c_size, 0.f);
gemm_stub(
at::kCPU, at::kBFloat16,
transa, transb, m, n, k, alpha, a, lda, b, ldb, 0.f, bfloat_c.data(), m);
for (const auto j : c10::irange(n)) {
for (const auto i : c10::irange(m)) {
auto offset = j * ldc + i;
// beta == 0 won't propagate NaN from C
if (beta == 0.f) {
c[offset] = c10::convert<float>(bfloat_c[j * m + i]);
} else {
c[offset] = beta * c[offset] + c10::convert<float>(bfloat_c[j * m + i]);
}
}
}
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const float alpha,
const at::Half *a, int64_t lda,
const at::Half *b, int64_t ldb,
const float beta,
float *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#ifdef MKL_HAS_SHGEMM
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
int m_ = m, n_ = n, k_ = k, lda_ = lda, ldb_ = ldb, ldc_ = ldc;
mkl_gemm_f16f16f32(transa, transb, m_, n_, k_, alpha, a, lda_, b, ldb_, beta, c, ldc_);
return;
}
#endif
// for the fallback path, first compute gemm with beta = 0,
// and then add c in full precision.
int64_t c_size = n * m;
std::vector<at::Half> float16_c(c_size, 0.f);
gemm_stub(
at::kCPU, at::kHalf,
transa, transb, m, n, k, alpha, a, lda, b, ldb, 0.f, float16_c.data(), m);
for (const auto j : c10::irange(n)) {
for (const auto i : c10::irange(m)) {
auto offset = j * ldc + i;
// beta == 0 won't propagate NaN from C
if (beta == 0.f) {
c[offset] = c10::convert<float>(float16_c[j * m + i]);
} else {
c[offset] = beta * c[offset] + c10::convert<float>(float16_c[j * m + i]);
}
}
}
}
void gemm(
TransposeType transa, TransposeType transb,
int64_t m, int64_t n, int64_t k,
const int64_t alpha,
const int64_t *a, int64_t lda,
const int64_t *b, int64_t ldb,
const int64_t beta,
int64_t *c, int64_t ldc) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
#ifdef USE_FBGEMM
if (alpha == 1 && (beta == 0 || beta == 1)) {
// In FBGEMM, we assume row-major ordering; However, here we assume the
// column-major ordering following the FORTRAN tradition in BLAS interface
// in this function: we can configure the layout (row/column-major ordering)
// of A and B by changing transa_ and transb_, but we cannot change the
// layout of C with this FORTRAN-style BLAS interface.
//
// The workaround is that we compute
// C^T (n x m) = B^T (n x k) * A^T (k x m) instead.
//
// In this way we view C^T as the row-major ordering when passing to FBGEMM.
fbgemm::cblas_gemm_i64_i64acc(
to_fbgemm(transb),
to_fbgemm(transa),
n,
m,
k,
b,
ldb,
a,
lda,
beta == 1,
c,
ldc);
return;
}
#endif
gemm_stub(
kCPU, kLong,
transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
template <typename scalar_t>
static void gemm_batched_mkl_impl(
TransposeType transa, TransposeType transb,
int64_t batch_size, int64_t m, int64_t n, int64_t k,
scalar_t alpha,
const scalar_t **a, int64_t lda,
const scalar_t **b, int64_t ldb,
scalar_t beta,
scalar_t **c, int64_t ldc) {
for (int64_t i = 0; i < batch_size;) {
int sub_batch = std::min(batch_size - i, int64_t{INT_MAX});
mkl_gemm_batched(transa, transb, sub_batch, m, n, k, alpha,
&a[i], lda, &b[i], ldb, beta, &c[i], ldc);
i += sub_batch;
}
}
template <typename scalar_t>
using is_blas_library_type = std::integral_constant<bool,
std::is_same_v<scalar_t, double> ||
std::is_same_v<scalar_t, float> ||
std::is_same_v<scalar_t, c10::complex<double>> ||
std::is_same_v<scalar_t, c10::complex<float>>>;
template <typename scalar_t>
void gemm_batched_generic(
TransposeType transa, TransposeType transb,
int64_t batch_size, int64_t m, int64_t n, int64_t k,
scalar_t alpha,
const scalar_t **a, int64_t lda,
const scalar_t **b, int64_t ldb,
scalar_t beta,
scalar_t **c, int64_t ldc) {
for (const auto batch : c10::irange(batch_size)) {
gemm(transa, transb, m, n, k, alpha, a[batch], lda, b[batch], ldb, beta, c[batch], ldc);
}
}
template <typename scalar_t>
void gemm_batched(
TransposeType transa, TransposeType transb,
int64_t batch_size, int64_t m, int64_t n, int64_t k,
scalar_t alpha,
const scalar_t **a, int64_t lda,
const scalar_t **b, int64_t ldb,
scalar_t beta,
scalar_t **c, int64_t ldc) {
if (batch_size == 1) {
return gemm(transa, transb, m, n, k, alpha, a[0], lda, b[0], ldb, beta, c[0], ldc);
}
if constexpr (AT_MKL_ENABLED() && is_blas_library_type<scalar_t>::value) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
gemm_batched_mkl_impl(
transa, transb, batch_size, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
} else {
gemm_batched_generic(
transa, transb, batch_size, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
} else {
gemm_batched_generic(
transa, transb, batch_size, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
}
template <typename scalar_t>
void gemm_batched_with_stride_generic(
TransposeType transa, TransposeType transb,
int64_t batch_size, int64_t m, int64_t n, int64_t k,
scalar_t alpha,
const scalar_t *a, int64_t lda, int64_t batch_stride_a,
const scalar_t *b, int64_t ldb, int64_t batch_stride_b,
scalar_t beta,
scalar_t *c, int64_t ldc, int64_t batch_stride_c) {
for (const auto batch : c10::irange(batch_size)) {
const auto a_batch = a + batch_stride_a * batch;
const auto b_batch = b + batch_stride_b * batch;
const auto c_batch = c + batch_stride_c * batch;
gemm(transa, transb, m, n, k, alpha, a_batch, lda, b_batch, ldb, beta, c_batch, ldc);
}
}
template <typename scalar_t>
void gemm_batched_with_stride(
TransposeType transa, TransposeType transb,
int64_t batch_size, int64_t m, int64_t n, int64_t k,
scalar_t alpha,
const scalar_t *a, int64_t lda, int64_t batch_stride_a,
const scalar_t *b, int64_t ldb, int64_t batch_stride_b,
scalar_t beta,
scalar_t *c, int64_t ldc, int64_t batch_stride_c) {
if (batch_size == 1) {
return gemm(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
}
if constexpr (AT_MKL_ENABLED() && is_blas_library_type<scalar_t>::value) {
internal::normalize_last_dims(transa, transb, m, n, k, &lda, &ldb, &ldc);
if (use_blas_gemm(transa, transb, m, n, k, lda, ldb, ldc)) {
c10::SmallBuffer<const scalar_t*, 16> a_ptrs(batch_size);
c10::SmallBuffer<const scalar_t*, 16> b_ptrs(batch_size);
c10::SmallBuffer<scalar_t*, 16> c_ptrs(batch_size);
for (const auto batch : c10::irange(batch_size)) {
a_ptrs[batch] = a + batch_stride_a * batch;
b_ptrs[batch] = b + batch_stride_b * batch;
c_ptrs[batch] = c + batch_stride_c * batch;
}
gemm_batched_mkl_impl(
transa, transb, batch_size, m, n, k, alpha, a_ptrs.data(), lda,
b_ptrs.data(), ldb, beta, c_ptrs.data(), ldc);
} else {
gemm_batched_with_stride_generic(
transa, transb, batch_size, m, n, k, alpha, a, lda, batch_stride_a,
b, ldb, batch_stride_b, beta, c, ldc, batch_stride_c);
}
} else {
gemm_batched_with_stride_generic(transa, transb, batch_size, m, n, k, alpha,
a, lda, batch_stride_a, b, ldb, batch_stride_b,
beta, c, ldc, batch_stride_c);
}
}
#define INSTANTIATE_BATCHED_GEMM(scalar_t, DType) \
template void gemm_batched( \
TransposeType transa, TransposeType transb, \
int64_t batch_size, int64_t m, int64_t n, int64_t k, \
scalar_t alpha, \
const scalar_t **a, int64_t lda, \
const scalar_t **b, int64_t ldb, \
scalar_t beta, \
scalar_t **c, int64_t ldc); \
template void gemm_batched_with_stride( \
TransposeType transa, TransposeType transb, \
int64_t batch_size, int64_t m, int64_t n, int64_t k, \
scalar_t alpha, \
const scalar_t *a, int64_t lda, int64_t batch_stride_a, \
const scalar_t *b, int64_t ldb, int64_t batch_stride_b, \
scalar_t beta, \
scalar_t *c, int64_t ldc, int64_t batch_stride_c);
AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF_F8NZ(INSTANTIATE_BATCHED_GEMM)
DEFINE_DISPATCH(axpy_stub);
void axpy(int64_t n, double a, const double *x, int64_t incx, double *y, int64_t incy) {
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
{
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_daxpy(i_n, a, x, i_incx, y, i_incy);
#else
daxpy_(&i_n, &a, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
axpy_stub(
kCPU, at::kDouble,
n, a, x, incx, y, incy);
}
void axpy(int64_t n, float a, const float *x, int64_t incx, float *y, int64_t incy) {
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
{
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_saxpy(i_n, a, x, i_incx, y, i_incy);
#else
saxpy_(&i_n, &a, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
axpy_stub(
kCPU, at::kFloat,
n, a, x, incx, y, incy);
}
void axpy(int64_t n, c10::complex<double> a, const c10::complex<double> *x, int64_t incx, c10::complex<double> *y, int64_t incy) {
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
{
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_zaxpy(i_n, &a, x, i_incx, y, i_incy);
#else
zaxpy_(&i_n, &a, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
axpy_stub(
kCPU, at::kComplexDouble,
n, a, x, incx, y, incy);
}
void axpy(int64_t n, c10::complex<float> a, const c10::complex<float> *x, int64_t incx, c10::complex<float> *y, int64_t incy) {
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
{
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_caxpy(i_n, &a, x, i_incx, y, i_incy);
#else
caxpy_(&i_n, &a, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
axpy_stub(
kCPU, at::kComplexFloat,
n, a, x, incx, y, incy);
}
DEFINE_DISPATCH(copy_stub);
void copy(int64_t n, const double *x, int64_t incx, double *y, int64_t incy) {
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_dcopy(i_n, x, i_incx, y, i_incy);
#else
dcopy_(&i_n, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
copy_stub(
kCPU, at::kDouble,
n, x, incx, y, incy);
}
void copy(int64_t n, const float *x, int64_t incx, float *y, int64_t incy) {
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_scopy(i_n, x, i_incx, y, i_incy);
#else
scopy_(&i_n, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
copy_stub(
kCPU, at::kFloat,
n, x, incx, y, incy);
}
void copy(int64_t n, const c10::complex<double> *x, int64_t incx, c10::complex<double> *y, int64_t incy) {
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_zcopy(i_n, x, i_incx, y, i_incy);
#else
zcopy_(&i_n, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
copy_stub(
kCPU, at::kComplexDouble,
n, x, incx, y, incy);
}
void copy(int64_t n, const c10::complex<float> *x, int64_t incx, c10::complex<float> *y, int64_t incy){
if(n == 1)
{
incx = 1;
incy = 1;
}
#if AT_BUILD_WITH_BLAS()
if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
int i_n = (int)n;
int i_incx = (int)incx;
int i_incy = (int)incy;
#if C10_IOS
cblas_ccopy(i_n, &x, i_incx, y, i_incy);
#else
ccopy_(&i_n, x, &i_incx, y, &i_incy);
#endif
return;
}
#endif
copy_stub(
kCPU, at::kComplexFloat,
n, x, incx, y, incy);
}
// oneDNN BRGEMM
#if defined(ONEDNN_UKERNEL_ENABLED)
struct BrgemmKey {
int64_t M;
int64_t N;
int64_t K;
int64_t batch_size;
int64_t lda;
int64_t ldb;
int64_t ldc;
ScalarType dt_a;
ScalarType dt_b;
ScalarType dt_c;
bool add_C;
BrgemmKey(
int64_t M,
int64_t N,
int64_t K,
int64_t batch_size,
int64_t lda,
int64_t ldb,
int64_t ldc,
ScalarType dt_a,
ScalarType dt_b,
ScalarType dt_c,
bool add_C)
: M(M),
N(N),
K(K),
batch_size(batch_size),
lda(lda),
ldb(ldb),
ldc(ldc),
dt_a(dt_a),
dt_b(dt_b),
dt_c(dt_c),
add_C(add_C) {}
bool operator==(const BrgemmKey& other) const {
return M == other.M && N == other.N && K == other.K &&
batch_size == other.batch_size && lda == other.lda &&
ldb == other.ldb && ldc == other.ldc && dt_a == other.dt_a &&
dt_b == other.dt_b && dt_c == other.dt_c && add_C == other.add_C;
}
};
struct PackKey {
int64_t K;
int64_t N;
int64_t ld_in;
int64_t ld_out;
ScalarType dt_in;
ScalarType dt_out;
PackKey(
int64_t K,
int64_t N,
int64_t ld_in,
int64_t ld_out,
ScalarType dt_in,
ScalarType dt_out)
: K(K),
N(N),
ld_in(ld_in),
ld_out(ld_out),
dt_in(dt_in),
dt_out(dt_out) {}
bool operator==(const PackKey& other) const {
return N == other.N && K == other.K && ld_in == other.ld_in &&
ld_out == other.ld_out && dt_in == other.dt_in &&
dt_out == other.dt_out;
}
};
inline dnnl::memory::data_type get_dnnl_dtype(ScalarType dtype) {
if (dtype == ScalarType::Float) {
return dnnl::memory::data_type::f32;
} else if (dtype == ScalarType::BFloat16) {
return dnnl::memory::data_type::bf16;
} else if (dtype == ScalarType::Half) {
return dnnl::memory::data_type::f16;
} else if (dtype == ScalarType::Byte) {
return dnnl::memory::data_type::u8;
} else if (dtype == ScalarType::Char) {
return dnnl::memory::data_type::s8;
} else {
TORCH_CHECK(false, "get_dnnl_dtype expects float/bfloat16/half/int8 tensor input");
}
}
template<typename key_t>
struct UnsafeUkernelKeyHasher {
std::size_t operator()(const key_t& key) const;
};
template<>
std::size_t UnsafeUkernelKeyHasher<BrgemmKey>::operator()(const BrgemmKey& key) const {
// Use M, N, K add_C, and ldc to compute hash to reduce the overhead as
// batch size and data types are unlikely to change within the same kernel and
// lda/ldb are likely to be related to M, K, N or use fixed values.
std::size_t h = std::hash<int64_t>()(key.M);
h = std::hash<int64_t>()(key.N) ^ (h << 1);
h = std::hash<int64_t>()(key.K) ^ (h << 1);
h = std::hash<bool>()(key.add_C) ^ (h << 1);
h = std::hash<int64_t>()(key.ldc) ^ (h << 1);
return h;
}
template<>
std::size_t UnsafeUkernelKeyHasher<PackKey>::operator()(const PackKey& key) const {
// Use K and N to compute hash to reduce the overhead as
// data types are unlikely to change and
// ld_in/ld_out is likely to be related to K, N or use fixed values
std::size_t h = std::hash<int64_t>()(key.K);
h = std::hash<int64_t>()(key.N) ^ (h << 1);
return h;
}
template <typename key_t, typename value_t>
struct KernelCache {
using kstore_t = std::unordered_map<key_t, std::shared_ptr<value_t>, UnsafeUkernelKeyHasher<key_t>>;
static inline std::shared_ptr<value_t>&& fetch_or_create(
const key_t& key,
const std::function<std::shared_ptr<value_t>()>& callback) {
auto&& search = get_store().find(key);
if (search != get_store().end()) {
return std::move(search->second);
} else {
get_store().insert({key, callback()});
return std::move(get_store()[key]);
}
}