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[Contrib] cblas batch_matmul
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@hlu1
hlu1 committed May 20, 2019
1 parent 78a0f47 commit 6cf8c21
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Showing 5 changed files with 351 additions and 92 deletions.
6 changes: 5 additions & 1 deletion cmake/modules/contrib/BLAS.cmake
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,11 @@ elseif(USE_BLAS STREQUAL "mkl")
if(NOT IS_DIRECTORY ${USE_MKL_PATH})
set(USE_MKL_PATH /opt/intel/mkl)
endif()
find_library(BLAS_LIBRARY NAMES mkl_rt mklml_gnu HINTS ${USE_MKL_PATH}/lib/ ${USE_MKL_PATH}/lib/intel64)
if(APPLE)
find_library(BLAS_LIBRARY NAMES mklml HINTS ${USE_MKL_PATH}/lib/ ${USE_MKL_PATH}/lib/intel64)
elseif(UNIX)
find_library(BLAS_LIBRARY NAMES mkl_rt mklml_gnu HINTS ${USE_MKL_PATH}/lib/ ${USE_MKL_PATH}/lib/intel64)
endif()
include_directories(${USE_MKL_PATH}/include)
list(APPEND TVM_RUNTIME_LINKER_LIBS ${BLAS_LIBRARY})
list(APPEND RUNTIME_SRCS ${CBLAS_CONTRIB_SRC})
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55 changes: 49 additions & 6 deletions python/tvm/contrib/cblas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,10 +17,10 @@
"""External function interface to BLAS libraries."""
from __future__ import absolute_import as _abs

from .. import api as _api
from .. import intrin as _intrin
from .. import api as _api, intrin as _intrin

def matmul(lhs, rhs, transa=False, transb=False):

def matmul(lhs, rhs, transa=False, transb=False, **kwargs):
"""Create an extern op that compute matrix mult of A and rhs with CrhsLAS
This function serves as an example on how to call external libraries.
Expand All @@ -44,7 +44,50 @@ def matmul(lhs, rhs, transa=False, transb=False):
n = lhs.shape[1] if transa else lhs.shape[0]
m = rhs.shape[0] if transb else rhs.shape[1]
return _api.extern(
(n, m), [lhs, rhs],
(n, m),
[lhs, rhs],
lambda ins, outs: _intrin.call_packed(
"tvm.contrib.cblas.matmul", ins[0], ins[1], outs[0], transa, transb
),
name="C",
**kwargs
)


def batch_matmul(lhs, rhs, transa=False, transb=False, iterative=False, **kwargs):
"""Create an extern op that compute batched matrix mult of A and rhs with CBLAS
This function serves as an example on how to call external libraries.
Parameters
----------
lhs : Tensor
The left matrix operand
rhs : Tensor
The right matrix operand
transa : bool
Whether transpose lhs
transb : bool
Whether transpose rhs
Returns
-------
C : Tensor
The result tensor.
"""
b = lhs.shape[0]
n = lhs.shape[2] if transa else lhs.shape[1]
m = rhs.shape[1] if transb else rhs.shape[2]
return _api.extern(
(b, n, m),
[lhs, rhs],
lambda ins, outs: _intrin.call_packed(
"tvm.contrib.cblas.matmul",
ins[0], ins[1], outs[0], transa, transb), name="C")
"tvm.contrib.cblas.batch_matmul"
if not iterative
else "tvm.contrib.cblas.batch_matmul_iterative",
ins[0],
ins[1],
outs[0],
transa,
transb,
),
name="C",
**kwargs
)
171 changes: 131 additions & 40 deletions src/contrib/cblas/cblas.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -6,9 +6,9 @@
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
*
* http://www.apache.org/licenses/LICENSE-2.0
*
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
Expand All @@ -21,12 +21,11 @@
* Copyright (c) 2017 by Contributors
* \file Use external cblas library call.
*/
#include <dmlc/logging.h>
#include <tvm/runtime/registry.h>
#include <tvm/runtime/util.h>
#include <dmlc/logging.h>
#include "gemm_common.h"


extern "C" {
#if USE_MKL_BLAS == 1
#include <mkl_cblas.h>
Expand All @@ -40,56 +39,148 @@ namespace contrib {

using namespace runtime;

inline CBLAS_TRANSPOSE BooleanToTranspose(bool trans) {
return trans ? CblasTrans : CblasNoTrans;
}
inline CBLAS_TRANSPOSE BooleanToTranspose(bool trans) { return trans ? CblasTrans : CblasNoTrans; }

struct CblasSgemmOp {
typedef float TDatatype;
void operator()(bool ta, bool tb,
int M, int N, int K,
float alpha, float* A, int lda,
float* B, int ldb,
float beta, float* C, int ldc) {
cblas_sgemm(CblasColMajor,
BooleanToTranspose(ta),
BooleanToTranspose(tb),
M, N, K,
alpha, A, lda,
B, ldb,
beta, C, ldc);
void operator()(bool ta, bool tb, int M, int N, int K, float alpha, float* A, int lda, float* B,
int ldb, float beta, float* C, int ldc) {
cblas_sgemm(CblasColMajor, BooleanToTranspose(ta), BooleanToTranspose(tb), M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
};

struct CblasDgemmOp {
typedef double TDatatype;
void operator()(bool ta, bool tb,
int M, int N, int K,
double alpha, double* A, int lda,
double* B, int ldb,
double beta, double* C, int ldc) {
cblas_dgemm(CblasColMajor,
BooleanToTranspose(ta),
BooleanToTranspose(tb),
M, N, K,
alpha, A, lda,
B, ldb,
beta, C, ldc);
void operator()(bool ta, bool tb, int M, int N, int K, double alpha, double* A, int lda,
double* B, int ldb, double beta, double* C, int ldc) {
cblas_dgemm(CblasColMajor, BooleanToTranspose(ta), BooleanToTranspose(tb), M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
};

struct CblasSgemmBatchOp {
typedef float TDatatype;
void operator()(int batch_size, bool ta, bool tb, int M, int N, int K, float alpha, float* A,
int a_stride, int lda, float* B, int b_stride, int ldb, float beta, float* C,
int c_stride, int ldc) {
CBLAS_TRANSPOSE trans_a = BooleanToTranspose(ta);
CBLAS_TRANSPOSE trans_b = BooleanToTranspose(tb);
#if USE_MKL_BLAS == 1
std::vector<const float*> A_array(batch_size);
std::vector<const float*> B_array(batch_size);
std::vector<float*> C_array(batch_size);
for (int i = 0; i < batch_size; ++i) {
A_array[i] = A + i * a_stride;
B_array[i] = B + i * b_stride;
C_array[i] = C + i * c_stride;
}
cblas_sgemm_batch(CblasColMajor, &trans_a, &trans_b, &M, &N, &K, &alpha, A_array.data(), &lda,
B_array.data(), &ldb, &beta, C_array.data(), &ldc, 1, &batch_size);
#else
for (int i = 0; i < batch_size; ++i) {
cblas_sgemm(CblasColMajor, trans_a, trans_b, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);
A += a_stride;
B += b_stride;
C += c_stride;
}
#endif
}
};

struct CblasSgemmBatchIterativeOp {
typedef float TDatatype;
void operator()(int batch_size, bool ta, bool tb, int M, int N, int K, float alpha, float* A,
int a_stride, int lda, float* B, int b_stride, int ldb, float beta, float* C,
int c_stride, int ldc) {
CBLAS_TRANSPOSE trans_a = BooleanToTranspose(ta);
CBLAS_TRANSPOSE trans_b = BooleanToTranspose(tb);
for (int i = 0; i < batch_size; ++i) {
cblas_sgemm(CblasColMajor, trans_a, trans_b, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);
A += a_stride;
B += b_stride;
C += c_stride;
}
}
};

struct CblasDgemmBatchOp {
typedef double TDatatype;
void operator()(int batch_size, bool ta, bool tb, int M, int N, int K, double alpha, double* A,
int a_stride, int lda, double* B, int b_stride, int ldb, double beta, double* C,
int c_stride, int ldc) {
CBLAS_TRANSPOSE trans_a = BooleanToTranspose(ta);
CBLAS_TRANSPOSE trans_b = BooleanToTranspose(tb);
#if USE_MKL_BLAS == 1
std::vector<const double*> A_array(batch_size);
std::vector<const double*> B_array(batch_size);
std::vector<double*> C_array(batch_size);
for (int i = 0; i < batch_size; ++i) {
A_array[i] = A + i * a_stride;
B_array[i] = B + i * b_stride;
C_array[i] = C + i * c_stride;
}
cblas_dgemm_batch(CblasColMajor, &trans_a, &trans_b, &M, &N, &K, &alpha, A_array.data(), &lda,
B_array.data(), &ldb, &beta, C_array.data(), &ldc, 1, &batch_size);
#else
for (int i = 0; i < batch_size; ++i) {
cblas_dgemm(CblasColMajor, trans_a, trans_b, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);
A += a_stride;
B += b_stride;
C += c_stride;
}
#endif
}
};

struct CblasDgemmBatchIterativeOp {
typedef double TDatatype;
void operator()(int batch_size, bool ta, bool tb, int M, int N, int K, double alpha, double* A,
int a_stride, int lda, double* B, int b_stride, int ldb, double beta, double* C,
int c_stride, int ldc) {
CBLAS_TRANSPOSE trans_a = BooleanToTranspose(ta);
CBLAS_TRANSPOSE trans_b = BooleanToTranspose(tb);
for (int i = 0; i < batch_size; ++i) {
cblas_dgemm(CblasColMajor, trans_a, trans_b, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);
A += a_stride;
B += b_stride;
C += c_stride;
}
}
};

// matrix multiplication for row major
TVM_REGISTER_GLOBAL("tvm.contrib.cblas.matmul")
.set_body([](TVMArgs args, TVMRetValue *ret) {
DLTensor* A = args[0];
CHECK(TypeMatch(A->dtype, kDLFloat, 32) ||
TypeMatch(A->dtype, kDLFloat, 64));
.set_body([](TVMArgs args, TVMRetValue* ret) {
DLTensor* A = args[0];
CHECK(TypeMatch(A->dtype, kDLFloat, 32) || TypeMatch(A->dtype, kDLFloat, 64));

if (TypeMatch(A->dtype, kDLFloat, 32))
CallGemm(args, ret, CblasSgemmOp());
else
CallGemm(args, ret, CblasDgemmOp());
});

if (TypeMatch(A->dtype, kDLFloat, 32))
CallGemm(args, ret, CblasSgemmOp());
else
CallGemm(args, ret, CblasDgemmOp());
});
TVM_REGISTER_GLOBAL("tvm.contrib.cblas.batch_matmul")
.set_body([](TVMArgs args, TVMRetValue* ret) {
DLTensor* A = args[0];
CHECK(TypeMatch(A->dtype, kDLFloat, 32) || TypeMatch(A->dtype, kDLFloat, 64));
if (TypeMatch(A->dtype, kDLFloat, 32)) {
CallBatchGemm(args, ret, CblasSgemmBatchOp());
} else {
CallBatchGemm(args, ret, CblasDgemmBatchOp());
}
});

TVM_REGISTER_GLOBAL("tvm.contrib.cblas.batch_matmul_iterative")
.set_body([](TVMArgs args, TVMRetValue* ret) {
DLTensor* A = args[0];
CHECK(TypeMatch(A->dtype, kDLFloat, 32) || TypeMatch(A->dtype, kDLFloat, 64));
if (TypeMatch(A->dtype, kDLFloat, 32)) {
CallBatchGemm(args, ret, CblasSgemmBatchIterativeOp());
} else {
CallBatchGemm(args, ret, CblasDgemmBatchIterativeOp());
}
});
} // namespace contrib
} // namespace tvm
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