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Unify CPU matmuls, remove unused accelerate conv (#1814)

Browse Source 
* unify matmuls

* Update mlx/backend/common/matmul.cpp

Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>

---------

Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>
This commit is contained in:
Awni Hannun 2025-01-31 14:43:37 -08:00 committed by GitHub
parent ded914f442
commit c6fc07f1f4
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GPG Key ID: B5690EEEBB952194
14 changed files with 354 additions and 397 deletions
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8
mlx/backend/accelerate/CMakeLists.txt
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@ -1,6 +1,2 @@
target_sources( target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
mlx ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp)
PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp)

20
mlx/backend/accelerate/conv.cpp
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@ -1,20 +0,0 @@
// Copyright © 2023-2024 Apple Inc.
#include <cassert>
#include <Accelerate/Accelerate.h>
#include <simd/vector.h>
#include "mlx/backend/common/copy.h"
#include "mlx/primitives.h"
#include "mlx/utils.h"
namespace mlx::core {
void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
eval(inputs, out);
// TODO: Add accelerate based optimizations for CPU conv
}
} // namespace mlx::core

253
mlx/backend/accelerate/matmul.cpp
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@ -1,253 +0,0 @@
// Copyright © 2023-2024 Apple Inc.
#include <cassert>
#include <Accelerate/Accelerate.h>
#include "mlx/backend/accelerate/utils.h"
#include "mlx/backend/common/copy.h"
#include "mlx/primitives.h"
#include "mlx/utils.h"
namespace mlx::core {
namespace {
std::tuple<bool, size_t, array> check_transpose(const array& arr) {
auto stx = arr.strides()[arr.ndim() - 2];
auto sty = arr.strides()[arr.ndim() - 1];
if (stx == arr.shape(-1) && sty == 1) {
return std::make_tuple(false, stx, arr);
} else if (stx == 1 && sty == arr.shape(-2)) {
return std::make_tuple(true, sty, arr);
} else {
array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
copy(arr, arr_copy, CopyType::General);
size_t stx = arr.shape(-1);
return std::make_tuple(false, stx, arr_copy);
}
}
inline void matmul_cblas_general(
const array& a_pre,
const array& b_pre,
array& out,
float alpha = 1.0f,
float beta = 0.0f) {
if (out.dtype() != float32) {
throw std::runtime_error(
"[matmul_cblas] on CPU currently only supports float32");
}
auto [a_transposed, lda, a] = check_transpose(a_pre);
auto [b_transposed, ldb, b] = check_transpose(b_pre);
size_t M = a.shape(-2);
size_t N = b.shape(-1);
size_t K = a.shape(-1);
if (M == 0 || N == 0) {
return;
}
if (K == 0) {
std::memset(static_cast<void*>(out.data<float>()), 0, out.nbytes());
return;
}
for (int i = 0; i < (a.size() / (M * K)); ++i) {
cblas_sgemm(
CblasRowMajor,
a_transposed ? CblasTrans : CblasNoTrans, // transA
b_transposed ? CblasTrans : CblasNoTrans, // transB
M,
N,
K,
alpha, // alpha
a.data<float>() + elem_to_loc(M * K * i, a.shape(), a.strides()),
lda,
b.data<float>() + elem_to_loc(K * N * i, b.shape(), b.strides()),
ldb,
beta, // beta
out.data<float>() + M * N * i,
out.shape(-1) // ldc
);
}
}
inline void matmul_cblas(const array& a_pre, const array& b_pre, array& out) {
if (out.dtype() != float32) {
throw std::runtime_error(
"[matmul_cblas] on CPU currently only supports float32");
}
out.set_data(allocator::malloc_or_wait(out.nbytes()));
return matmul_cblas_general(a_pre, b_pre, out);
}
inline void matmul_bnns_general(
const array& a_pre,
const array& b_pre,
array& out,
float alpha = 1.0f,
float beta = 0.0f) {
// TODO: Update to utilize BNNS broadcasting
auto [a_transposed, lda, a] = check_transpose(a_pre);
auto [b_transposed, ldb, b] = check_transpose(b_pre);
size_t M = a.shape(-2);
size_t N = b.shape(-1);
size_t K = a.shape(-1);
if (M == 0 || N == 0) {
return;
}
if (K == 0) {
std::memset(static_cast<void*>(out.data<float>()), 0, out.nbytes());
return;
}
BNNSDataType bnns_dtype = to_bnns_dtype(out.dtype());
const BNNSLayerParametersBroadcastMatMul gemm_params{
/* float alpha = */ alpha,
/* float beta = */ beta,
/* bool transA = */ a_transposed,
/* bool transB = */ b_transposed,
/* bool quadratic = */ false,
/* bool a_is_weights = */ false,
/* bool b_is_weights = */ false,
/* BNNSNDArrayDescriptor iA_desc = */
BNNSNDArrayDescriptor{
/* BNNSNDArrayFlags flags = */ BNNSNDArrayFlagBackpropSet,
/* BNNSDataLayout layout = */ BNNSDataLayoutRowMajorMatrix,
/* size_t size[BNNS_MAX_TENSOR_DIMENSION] = */
{lda, (M * K) / lda, 0, 0, 0, 0, 0, 0},
/* size_t stride[BNNS_MAX_TENSOR_DIMENSION] = */
{1, lda, 0, 0, 0, 0, 0, 0},
/* void * _Nullable data = */ nullptr,
/* BNNSDataType data_type = */ bnns_dtype,
/* void * _Nullable table_data = */ nullptr,
/* BNNSDataType table_data_type = */ bnns_dtype,
/* float data_scale = */ 1.0,
/* float data_bias = */ 0.0,
},
/* BNNSNDArrayDescriptor iB_desc = */
BNNSNDArrayDescriptor{
/* BNNSNDArrayFlags flags = */ BNNSNDArrayFlagBackpropSet,
/* BNNSDataLayout layout = */ BNNSDataLayoutRowMajorMatrix,
/* size_t size[BNNS_MAX_TENSOR_DIMENSION] = */
{ldb, (K * N) / ldb, 0, 0, 0, 0, 0, 0},
/* size_t stride[BNNS_MAX_TENSOR_DIMENSION] = */
{1, ldb, 0, 0, 0, 0, 0, 0},
/* void * _Nullable data = */ nullptr,
/* BNNSDataType data_type = */ bnns_dtype,
/* void * _Nullable table_data = */ nullptr,
/* BNNSDataType table_data_type = */ bnns_dtype,
/* float data_scale = */ 1.0,
/* float data_bias = */ 0.0,
},
/* BNNSNDArrayDescriptor o_desc = */
BNNSNDArrayDescriptor{
/* BNNSNDArrayFlags flags = */ BNNSNDArrayFlagBackpropSet,
/* BNNSDataLayout layout = */ BNNSDataLayoutRowMajorMatrix,
/* size_t size[BNNS_MAX_TENSOR_DIMENSION] = */
{N, M, 0, 0, 0, 0, 0, 0},
/* size_t stride[BNNS_MAX_TENSOR_DIMENSION] = */
{1, N, 0, 0, 0, 0, 0, 0},
/* void * _Nullable data = */ nullptr,
/* BNNSDataType data_type = */ bnns_dtype,
/* void * _Nullable table_data = */ nullptr,
/* BNNSDataType table_data_type = */ bnns_dtype,
/* float data_scale = */ 1.0,
/* float data_bias = */ 0.0,
},
};
auto bnns_filter =
BNNSFilterCreateLayerBroadcastMatMul(&gemm_params, nullptr);
for (int i = 0; i < (a.size() / (M * K)); ++i) {
BNNSFilterApplyTwoInput(
bnns_filter,
a.data<uint8_t>() +
elem_to_loc(M * K * i, a.shape(), a.strides()) * a.itemsize(),
b.data<uint8_t>() +
elem_to_loc(K * N * i, b.shape(), b.strides()) * b.itemsize(),
out.data<uint8_t>() + M * N * i * out.itemsize());
}
BNNSFilterDestroy(bnns_filter);
}
inline void matmul_bnns(const array& a_pre, const array& b_pre, array& out) {
// TODO: Update to utilize BNNS broadcasting
out.set_data(allocator::malloc_or_wait(out.nbytes()));
return matmul_bnns_general(a_pre, b_pre, out);
}
template <typename T>
inline void mask_matrix(
T* data,
const bool* mask,
int tile_size,
const int X,
const int Y,
const size_t X_data_str,
const size_t Y_data_str,
const size_t X_mask_str,
const size_t Y_mask_str) {
int tX = (X + tile_size - 1) / tile_size;
int tY = (Y + tile_size - 1) / tile_size;
for (int i = 0; i < tX; i++) {
for (int j = 0; j < tY; j++) {
bool do_mask = mask[i * X_mask_str + j * Y_mask_str];
if (!do_mask) {
int loc_x = i * tile_size;
int loc_y = j * tile_size;
T* data_block = data + loc_x * X_data_str + loc_y * Y_data_str;
int size_x = std::min(tile_size, X - loc_x);
int size_y = std::min(tile_size, Y - loc_y);
for (int ii = 0; ii < size_x; ii++) {
for (int jj = 0; jj < size_y; jj++) {
data_block[ii * X_data_str + jj * Y_data_str] = T(0.);
}
}
}
}
}
}
} // namespace
void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
if (out.dtype() == float32) {
return matmul_cblas(inputs[0], inputs[1], out);
}
return matmul_bnns(inputs[0], inputs[1], out);
}
void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
// Fill output with C
auto& c = inputs[2];
CopyType ctype = c.data_size() == 1 ? CopyType::Scalar : CopyType::General;
copy(c, out, ctype);
if (out.dtype() == float32) {
return matmul_cblas_general(inputs[0], inputs[1], out, alpha_, beta_);
}
return matmul_bnns_general(inputs[0], inputs[1], out, alpha_, beta_);
}
} // namespace mlx::core

28
mlx/backend/accelerate/utils.h
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@ -1,28 +0,0 @@
// Copyright © 2023-2024 Apple Inc.
#pragma once
#include <Accelerate/Accelerate.h>
#include "mlx/dtype.h"
namespace mlx::core {
BNNSDataType to_bnns_dtype(Dtype mlx_dtype) {
uint32_t size_bits = size_of(mlx_dtype) * 8;
switch (kindof(mlx_dtype)) {
case Dtype::Kind::b:
return BNNSDataTypeBoolean;
case Dtype::Kind::u:
return BNNSDataType(BNNSDataTypeUIntBit | size_bits);
case Dtype::Kind::i:
return BNNSDataType(BNNSDataTypeIntBit | size_bits);
case Dtype::Kind::f:
return BNNSDataType(BNNSDataTypeFloatBit | size_bits);
case Dtype::Kind::V:
return BNNSDataTypeBFloat16;
case Dtype::Kind::c:
throw std::invalid_argument("BNNS does not support complex types");
}
}
} // namespace mlx::core

9
mlx/backend/common/CMakeLists.txt
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@ -50,6 +50,8 @@ target_sources(
${CMAKE_CURRENT_SOURCE_DIR}/erf.cpp ${CMAKE_CURRENT_SOURCE_DIR}/erf.cpp
${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
${CMAKE_CURRENT_SOURCE_DIR}/hadamard.cpp ${CMAKE_CURRENT_SOURCE_DIR}/hadamard.cpp
${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
${CMAKE_CURRENT_SOURCE_DIR}/gemms/cblas.cpp
${CMAKE_CURRENT_SOURCE_DIR}/masked_mm.cpp ${CMAKE_CURRENT_SOURCE_DIR}/masked_mm.cpp
${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp
@ -71,6 +73,13 @@ target_sources(
${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
${CMAKE_CURRENT_BINARY_DIR}/compiled_preamble.cpp) ${CMAKE_CURRENT_BINARY_DIR}/compiled_preamble.cpp)
if(MLX_BUILD_ACCELERATE)
target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/bnns.cpp)
else()
target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_fp16.cpp
${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_bf16.cpp)
endif()
if(IOS) if(IOS)
target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/compiled_nocpu.cpp) target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/compiled_nocpu.cpp)
else() else()

2
mlx/backend/common/conv.cpp
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@ -1128,7 +1128,7 @@ void conv_3D_cpu(
} // namespace } // namespace
void Convolution::eval(const std::vector<array>& inputs, array& out) { void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
out.set_data(allocator::malloc_or_wait(out.nbytes())); out.set_data(allocator::malloc_or_wait(out.nbytes()));
auto& in = inputs[0]; auto& in = inputs[0];

87
mlx/backend/common/default_primitives.cpp
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@ -1,11 +1,6 @@
// Copyright © 2023-2024 Apple Inc. // Copyright © 2023-2024 Apple Inc.
#include <cstring>
#include "mlx/array.h" #include "mlx/array.h"
#include "mlx/backend/common/copy.h"
#include "mlx/backend/common/lapack.h"
#include "mlx/backend/common/utils.h"
#include "mlx/primitives.h" #include "mlx/primitives.h"
#define DEFAULT(primitive) \ #define DEFAULT(primitive) \
@ -21,89 +16,7 @@
namespace mlx::core { namespace mlx::core {
DEFAULT(Convolution)
DEFAULT(Reduce) DEFAULT(Reduce)
DEFAULT(Scan) DEFAULT(Scan)
namespace {
inline void matmul_common_general(
const array& a_pre,
const array& b_pre,
array& out,
float alpha = 1.0f,
float beta = 0.0f) {
auto check_transpose = [](const array& arr) {
auto stx = arr.strides()[arr.ndim() - 2];
auto sty = arr.strides()[arr.ndim() - 1];
if (stx == arr.shape(-1) && sty == 1) {
return std::make_tuple(false, stx, arr);
} else if (stx == 1 && sty == arr.shape(-2)) {
return std::make_tuple(true, sty, arr);
} else {
array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
copy(arr, arr_copy, CopyType::General);
stx = arr.shape(-1);
return std::make_tuple(false, stx, arr_copy);
}
};
auto [a_transposed, lda, a] = check_transpose(a_pre);
auto [b_transposed, ldb, b] = check_transpose(b_pre);
size_t M = a.shape(-2);
size_t N = b.shape(-1);
size_t K = a.shape(-1);
if (M == 0 || N == 0) {
return;
}
if (K == 0) {
std::memset(static_cast<void*>(out.data<float>()), 0, out.nbytes());
return;
}
for (int i = 0; i < (a.size() / (M * K)); ++i) {
cblas_sgemm(
CblasRowMajor,
a_transposed ? CblasTrans : CblasNoTrans, // transA
b_transposed ? CblasTrans : CblasNoTrans, // transB
M,
N,
K,
alpha, // alpha
a.data<float>() + elem_to_loc(M * K * i, a.shape(), a.strides()),
lda,
b.data<float>() + elem_to_loc(K * N * i, b.shape(), b.strides()),
ldb,
beta, // beta
out.data<float>() + M * N * i,
out.shape(-1) // ldc
);
}
}
} // namespace
void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
if (out.dtype() != float32) {
throw std::runtime_error(
"[Matmul::eval_cpu] Currently only supports float32.");
}
out.set_data(allocator::malloc_or_wait(out.nbytes()));
return matmul_common_general(inputs[0], inputs[1], out);
}
void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
if (out.dtype() != float32) {
throw std::runtime_error(
"[AddMM::eval_cpu] Currently only supports float32.");
}
// Fill output with C
auto& c = inputs[2];
CopyType ctype = c.data_size() == 1 ? CopyType::Scalar : CopyType::General;
copy(c, out, ctype);
return matmul_common_general(inputs[0], inputs[1], out, alpha_, beta_);
}
} // namespace mlx::core } // namespace mlx::core

20
mlx/backend/common/gemm.h Normal file
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@ -0,0 +1,20 @@
// Copyright © 2025 Apple Inc.
#pragma once
#include "mlx/array.h"
namespace mlx::core {
template <typename T>
void matmul(
const array& a,
const array& b,
array& out,
bool a_transposed,
bool b_transposed,
size_t lda,
size_t ldb,
float alpha,
float beta);
} // namespace mlx::core

157
mlx/backend/common/gemms/bnns.cpp Normal file
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@ -0,0 +1,157 @@
// Copyright © 2023-2024 Apple Inc.
#include <Accelerate/Accelerate.h>
#include "mlx/array.h"
#include "mlx/backend/common/gemm.h"
#include "mlx/backend/common/utils.h"
#include "mlx/dtype.h"
namespace mlx::core {
BNNSDataType to_bnns_dtype(Dtype mlx_dtype) {
uint32_t size_bits = size_of(mlx_dtype) * 8;
switch (kindof(mlx_dtype)) {
case Dtype::Kind::b:
return BNNSDataTypeBoolean;
case Dtype::Kind::u:
return BNNSDataType(BNNSDataTypeUIntBit | size_bits);
case Dtype::Kind::i:
return BNNSDataType(BNNSDataTypeIntBit | size_bits);
case Dtype::Kind::f:
return BNNSDataType(BNNSDataTypeFloatBit | size_bits);
case Dtype::Kind::V:
return BNNSDataTypeBFloat16;
case Dtype::Kind::c:
throw std::invalid_argument("BNNS does not support complex types");
}
}
void matmul_bnns(
const array& a,
const array& b,
array& out,
bool a_transposed,
bool b_transposed,
size_t lda,
size_t ldb,
float alpha,
float beta) {
size_t M = a.shape(-2);
size_t N = b.shape(-1);
size_t K = a.shape(-1);
BNNSDataType bnns_dtype = to_bnns_dtype(out.dtype());
const BNNSLayerParametersBroadcastMatMul gemm_params{
/* float alpha = */ alpha,
/* float beta = */ beta,
/* bool transA = */ a_transposed,
/* bool transB = */ b_transposed,
/* bool quadratic = */ false,
/* bool a_is_weights = */ false,
/* bool b_is_weights = */ false,
/* BNNSNDArrayDescriptor iA_desc = */
BNNSNDArrayDescriptor{
/* BNNSNDArrayFlags flags = */ BNNSNDArrayFlagBackpropSet,
/* BNNSDataLayout layout = */ BNNSDataLayoutRowMajorMatrix,
/* size_t size[BNNS_MAX_TENSOR_DIMENSION] = */
{lda, (M * K) / lda, 0, 0, 0, 0, 0, 0},
/* size_t stride[BNNS_MAX_TENSOR_DIMENSION] = */
{1, lda, 0, 0, 0, 0, 0, 0},
/* void * _Nullable data = */ nullptr,
/* BNNSDataType data_type = */ bnns_dtype,
/* void * _Nullable table_data = */ nullptr,
/* BNNSDataType table_data_type = */ bnns_dtype,
/* float data_scale = */ 1.0,
/* float data_bias = */ 0.0,
},
/* BNNSNDArrayDescriptor iB_desc = */
BNNSNDArrayDescriptor{
/* BNNSNDArrayFlags flags = */ BNNSNDArrayFlagBackpropSet,
/* BNNSDataLayout layout = */ BNNSDataLayoutRowMajorMatrix,
/* size_t size[BNNS_MAX_TENSOR_DIMENSION] = */
{ldb, (K * N) / ldb, 0, 0, 0, 0, 0, 0},
/* size_t stride[BNNS_MAX_TENSOR_DIMENSION] = */
{1, ldb, 0, 0, 0, 0, 0, 0},
/* void * _Nullable data = */ nullptr,
/* BNNSDataType data_type = */ bnns_dtype,
/* void * _Nullable table_data = */ nullptr,
/* BNNSDataType table_data_type = */ bnns_dtype,
/* float data_scale = */ 1.0,
/* float data_bias = */ 0.0,
},
/* BNNSNDArrayDescriptor o_desc = */
BNNSNDArrayDescriptor{
/* BNNSNDArrayFlags flags = */ BNNSNDArrayFlagBackpropSet,
/* BNNSDataLayout layout = */ BNNSDataLayoutRowMajorMatrix,
/* size_t size[BNNS_MAX_TENSOR_DIMENSION] = */
{N, M, 0, 0, 0, 0, 0, 0},
/* size_t stride[BNNS_MAX_TENSOR_DIMENSION] = */
{1, N, 0, 0, 0, 0, 0, 0},
/* void * _Nullable data = */ nullptr,
/* BNNSDataType data_type = */ bnns_dtype,
/* void * _Nullable table_data = */ nullptr,
/* BNNSDataType table_data_type = */ bnns_dtype,
/* float data_scale = */ 1.0,
/* float data_bias = */ 0.0,
},
};
auto bnns_filter =
BNNSFilterCreateLayerBroadcastMatMul(&gemm_params, nullptr);
for (int i = 0; i < (a.size() / (M * K)); ++i) {
BNNSFilterApplyTwoInput(
bnns_filter,
a.data<uint8_t>() +
elem_to_loc(M * K * i, a.shape(), a.strides()) * a.itemsize(),
b.data<uint8_t>() +
elem_to_loc(K * N * i, b.shape(), b.strides()) * b.itemsize(),
out.data<uint8_t>() + M * N * i * out.itemsize());
}
BNNSFilterDestroy(bnns_filter);
}
template <>
void matmul<float16_t>(
const array& a,
const array& b,
array& out,
bool a_transposed,
bool b_transposed,
size_t lda,
size_t ldb,
float alpha,
float beta) {
matmul_bnns(a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
}
template <>
void matmul<bfloat16_t>(
const array& a,
const array& b,
array& out,
bool a_transposed,
bool b_transposed,
size_t lda,
size_t ldb,
float alpha,
float beta) {
matmul_bnns(a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
}
} // namespace mlx::core

44
mlx/backend/common/gemms/cblas.cpp Normal file
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@ -0,0 +1,44 @@
// Copyright © 2025 Apple Inc.
#include "mlx/backend/common/gemm.h"
#include "mlx/backend/common/lapack.h"
#include "mlx/backend/common/utils.h"
namespace mlx::core {
template <>
void matmul<float>(
const array& a,
const array& b,
array& out,
bool a_transposed,
bool b_transposed,
size_t lda,
size_t ldb,
float alpha,
float beta) {
size_t M = a.shape(-2);
size_t N = b.shape(-1);
size_t K = a.shape(-1);
for (int i = 0; i < (a.size() / (M * K)); ++i) {
cblas_sgemm(
CblasRowMajor,
a_transposed ? CblasTrans : CblasNoTrans, // transA
b_transposed ? CblasTrans : CblasNoTrans, // transB
M,
N,
K,
alpha, // alpha
a.data<float>() + elem_to_loc(M * K * i, a.shape(), a.strides()),
lda,
b.data<float>() + elem_to_loc(K * N * i, b.shape(), b.strides()),
ldb,
beta, // beta
out.data<float>() + M * N * i,
out.shape(-1) // ldc
);
}
}
} // namespace mlx::core

21
mlx/backend/common/gemms/no_bf16.cpp Normal file
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@ -0,0 +1,21 @@
// Copyright © 2025 Apple Inc.
#include "mlx/backend/common/gemm.h"
namespace mlx::core {
template <>
void matmul<bfloat16_t>(
const array&,
const array&,
array&,
bool,
bool,
size_t,
size_t,
float,
float) {
throw std::runtime_error("[Matmul::eval_cpu] bfloat16 not supported.");
}
} // namespace mlx::core

21
mlx/backend/common/gemms/no_fp16.cpp Normal file
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@ -0,0 +1,21 @@
// Copyright © 2025 Apple Inc.
#include "mlx/backend/common/gemm.h"
namespace mlx::core {
template <>
void matmul<float16_t>(
const array&,
const array&,
array&,
bool,
bool,
size_t,
size_t,
float,
float) {
throw std::runtime_error("[Matmul::eval_cpu] float16 not supported.");
}
} // namespace mlx::core

79
mlx/backend/common/matmul.cpp Normal file
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@ -0,0 +1,79 @@
// Copyright © 2023-2024 Apple Inc.
#include <cstring>
#include "mlx/array.h"
#include "mlx/backend/common/copy.h"
#include "mlx/backend/common/gemm.h"
#include "mlx/primitives.h"
namespace mlx::core {
void matmul_general(
const array& a_pre,
const array& b_pre,
array& out,
float alpha = 1.0f,
float beta = 0.0f) {
auto check_transpose = [](const array& arr) {
auto stx = arr.strides()[arr.ndim() - 2];
auto sty = arr.strides()[arr.ndim() - 1];
if (stx == arr.shape(-1) && sty == 1) {
return std::make_tuple(false, stx, arr);
} else if (stx == 1 && sty == arr.shape(-2)) {
return std::make_tuple(true, sty, arr);
} else {
array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
copy(arr, arr_copy, CopyType::General);
stx = arr.shape(-1);
return std::make_tuple(false, stx, arr_copy);
}
};
auto [a_transposed, lda, a] = check_transpose(a_pre);
auto [b_transposed, ldb, b] = check_transpose(b_pre);
size_t M = a.shape(-2);
size_t N = b.shape(-1);
size_t K = a.shape(-1);
if (M == 0 || N == 0) {
return;
}
if (out.dtype() == float32) {
matmul<float>(a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
} else if (out.dtype() == float16) {
matmul<float16_t>(
a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
} else if (out.dtype() == bfloat16) {
matmul<bfloat16_t>(
a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta);
} else {
throw std::runtime_error("[Matmul::eval_cpu] Invalid type.");
}
}
void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
out.set_data(allocator::malloc_or_wait(out.nbytes()));
if (inputs[0].shape(-1) == 0) {
std::memset(out.data<void>(), 0, out.nbytes());
return;
}
return matmul_general(inputs[0], inputs[1], out);
}
void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
if (out.dtype() != float32) {
throw std::runtime_error(
"[AddMM::eval_cpu] Currently only supports float32.");
}
// Fill output with C
auto& c = inputs[2];
CopyType ctype = c.data_size() == 1
? CopyType::Scalar
: (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
copy(c, out, ctype);
return matmul_general(inputs[0], inputs[1], out, alpha_, beta_);
}
} // namespace mlx::core

2
mlx/primitives.h
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@ -704,8 +704,6 @@ class Convolution : public UnaryPrimitive {
std::vector<int> input_dilation_; std::vector<int> input_dilation_;
int groups_; int groups_;
bool flip_; bool flip_;
void eval(const std::vector<array>& inputs, array& out);
}; };
class Copy : public UnaryPrimitive { class Copy : public UnaryPrimitive {