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InfiniTensor
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Fix mlu (#87) 

* fix some operator code

* fix some code of mlu operator

* fix some code of cast and elementwise

* clang format

* remove copy kernel

* fix cast

* fix clang-format

---------

Co-authored-by: wanghailu <wanghailu@qiyuanlab.com>
Co-authored-by: Haojie Wang <haojie0429@gmail.com>
This commit is contained in:
Hardy 2023-09-04 08:33:28 +08:00 committed by GitHub
parent 2412c25e67
commit b4eda85e67
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
24 changed files with 375 additions and 550 deletions
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69
src/kernels/bang/activation.cc
View File

@ -16,25 +16,17 @@ class UnaryCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
int len = dim.size();
int size = 1;
for (int i = 0; i < len; ++i) {
size *= dim[i];
}
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[1] = {size};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, 1, dim_array));
// get outputs
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, 1, dim_array));
// get op descriptor
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlActivationDescriptor_t opDesc;
checkCnnlError(cnnlCreateActivationDescriptor(&opDesc));
checkCnnlError(cnnlSetActivationDescriptor(
@ -46,9 +38,6 @@ class UnaryCnnl : public BangKernelWithoutConfig {
aData, &beta, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyActivationDescriptor(opDesc));
@ -65,28 +54,21 @@ class RoundCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlRound(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
@ -103,31 +85,28 @@ class PReluCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto bDim = op->getInputs(1)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
int alpha_array[4] = {1, 1, 1, 1};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(
bDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, alpha_array));
// get outputs
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, bDim.size(),
bDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat = cnnlPrelu(context->cnnlHandle(), aDesc, aData,
bDesc, bData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));

31
src/kernels/bang/activation_backward.cc
View File

@ -18,28 +18,27 @@ class ActivationBackwardCnnl : public BangKernelWithoutConfig {
void *const diffXData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t yDesc, diffYDesc, xDesc, diffXDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto yDim = op->getInputs(0)->getDims();
auto diffyDim = op->getInputs(1)->getDims();
auto xDim = op->getInputs(2)->getDims();
auto diffxDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&yDesc));
checkCnnlError(cnnlSetTensorDescriptor(yDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get inputs
CNNL_DTYPE_FLOAT, yDim.size(),
yDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&diffYDesc));
checkCnnlError(cnnlSetTensorDescriptor(diffYDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get inputs
checkCnnlError(cnnlSetTensorDescriptor(
diffYDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, diffyDim.size(),
diffyDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&xDesc));
checkCnnlError(cnnlSetTensorDescriptor(xDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, xDim.size(),
xDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&diffXDesc));
checkCnnlError(cnnlSetTensorDescriptor(diffXDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
diffXDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, diffxDim.size(),
diffxDim.data()));
// get op descriptor
cnnlActivationDescriptor_t opDesc;
checkCnnlError(cnnlCreateActivationDescriptor(&opDesc));
@ -53,8 +52,6 @@ class ActivationBackwardCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(yDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(diffYDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(xDesc));

207
src/kernels/bang/cast.cc
View File

@ -14,154 +14,189 @@ class CastCnnl : public BangKernelWithoutConfig {
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
cnnlCastDataType_t NlCastType;
CastType type = op->getType();
switch (type) {
case CastType::Float2Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT64, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_FLOAT_TO_INT64;
break;
case CastType::Float2Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_FLOAT_TO_INT32;
break;
case CastType::Float2Int16:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT16, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_FLOAT_TO_INT16;
break;
case CastType::Float2Int8:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT8, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_FLOAT_TO_INT8;
break;
case CastType::Int322Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT32_TO_FLOAT;
break;
case CastType::Int322Int8:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT8, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT32_TO_INT8;
break;
case CastType::Int322Int16:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT16, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT32_TO_INT16;
break;
case CastType::Int162Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT16, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT16_TO_FLOAT;
break;
case CastType::Int162Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT16, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT16_TO_INT32;
break;
case CastType::Int82Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT8, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT8_TO_FLOAT;
break;
case CastType::Int82Int16:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT8, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT16, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT8_TO_INT16;
break;
case CastType::Int82Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT8, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT8_TO_INT32;
break;
case CastType::Uint82Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_UINT8, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_UINT8_TO_FLOAT;
break;
case CastType::Uint82Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_UINT8, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_UINT8_TO_INT32;
break;
case CastType::Uint82Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_UINT8, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT64, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_UINT8_TO_INT64;
break;
case CastType::Int322Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT64, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT32_TO_INT64;
break;
case CastType::Int642Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT64, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT64_TO_INT32;
break;
case CastType::Int642Uint32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT64, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_UINT32,
dim.size(), dim.data()));
NlCastType = CNNL_CAST_INT64_TO_UINT32;
break;
case CastType::Int642Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT64, dim.size(),
dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_INT64_TO_FLOAT;
break;
case CastType::Uint322Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_UINT32,
dim.size(), dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT64, dim.size(),
dim.data()));
NlCastType = CNNL_CAST_UINT32_TO_INT64;
break;
default:
@ -172,8 +207,6 @@ class CastCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

17
src/kernels/bang/ceil.cc
View File

@ -13,28 +13,23 @@ class CeilCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlCeil(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

12
src/kernels/bang/clip.cc
View File

@ -15,23 +15,17 @@ class ClipCnnl : public BangKernelWithoutConfig {
float max = op->getMax().value();
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
cnnlStatus_t stat =
cnnlClip(context->cnnlHandle(), aDesc, aData, &min, &max, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
}
};

47
src/kernels/bang/concat.cc
View File

@ -10,40 +10,29 @@ class ConcatCnnl : public BangKernelWithoutConfig {
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
int num = op->numInputs();
int axis = op->getDim();
auto cDim = op->getOutput()->getDims();
cnnlTensorDescriptor_t desc;
checkCnnlError(cnnlCreateTensorDescriptor(&desc));
checkCnnlError(cnnlSetTensorDescriptor(desc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlTensorDescriptor_t descArray[num];
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlCreateTensorDescriptor(&descArray[i]));
checkCnnlError(cnnlSetTensorDescriptor(
descArray[i], CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT,
op->getInputs(i)->getDims().size(),
op->getInputs(i)->getDims().data()));
}
void *argv[num];
for (int i = 0; i < num; ++i) {
argv[i] = op->getInputs(i)->getRawDataPtr<void *>();
}
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t desc;
int dim_array[num][4];
for (int i = 0; i < num; ++i) {
auto dim = op->getInputs(i)->getDims();
if (dim.size() != 4) {
IT_TODO_HALT();
}
dim_array[i][0] = dim[0];
dim_array[i][1] = dim[1];
dim_array[i][2] = dim[2];
dim_array[i][3] = dim[3];
}
auto dim = op->getOutput()->getDims();
int dimout_array[4] = {dim[0], dim[1], dim[2], dim[3]};
checkCnnlError(cnnlCreateTensorDescriptor(&desc));
checkCnnlError(cnnlSetTensorDescriptor(
desc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dimout_array));
cnnlTensorDescriptor_t descArray[num];
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlCreateTensorDescriptor(&descArray[i]));
checkCnnlError(
cnnlSetTensorDescriptor(descArray[i], CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array[i]));
}
size_t wsSize;
cnnlGetConcatWorkspaceSize(context->cnnlHandle(), num, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
@ -54,8 +43,6 @@ class ConcatCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlDestroyTensorDescriptor(descArray[i]));
}

18
src/kernels/bang/det.cc
View File

@ -21,28 +21,22 @@ class DetCnnl : public BangKernelWithoutConfig {
cnnlTensorDescriptor_t aDesc, cDesc;
auto dimin = op->getInputs(0)->getDims();
auto dimout = op->getOutput()->getDims();
if (dimin.size() != 4 || dimout.size() != 2)
IT_TODO_HALT();
int dimin_array[4] = {dimin[0], dimin[1], dimin[2], dimin[3]};
int dimout_array[2] = {dimout[0], dimout[1]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 4, dimin_array));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, dimin.size(),
dimin.data()));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 2, dimout_array));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, dimout.size(),
dimout.data()));
cnnlStatus_t stat =
cnnlDet(context->cnnlHandle(), nlMode, aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

259
src/kernels/bang/element_wise.cc
View File

@ -22,24 +22,21 @@ class ElementWiseCnnl : public BangKernelWithoutConfig {
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
if (a_dim.size() > 4 || b_dim.size() > 4 || c_dim.size() > 4)
IT_TODO_HALT();
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, a_dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(
bDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, b_dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, c_dim.data()));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
// get op descriptor
cnnlOpTensorDescriptor_t opDesc;
checkCnnlError(cnnlCreateOpTensorDescriptor(&opDesc));
checkCnnlError(cnnlSetOpTensorDescriptor(
@ -58,8 +55,6 @@ class ElementWiseCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -79,29 +74,26 @@ class LogicOpCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
size_t wsSize;
cnnlGetLogicOpWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
@ -110,8 +102,6 @@ class LogicOpCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -130,29 +120,26 @@ class BitComputeCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, 4, dim_array));
CNNL_DTYPE_INT32, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, 4, dim_array));
// get outputs
CNNL_DTYPE_INT32, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, 4, dim_array));
CNNL_DTYPE_INT32, c_dim.size(),
c_dim.data()));
size_t wsSize;
cnnlGetBitComputeWorkspaceSize(context->cnnlHandle(), aDesc, bDesc,
cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
@ -161,8 +148,6 @@ class BitComputeCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -180,29 +165,26 @@ class DivCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
size_t wsSize;
cnnlGetDivWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat = cnnlDiv_v2(
@ -211,8 +193,6 @@ class DivCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -230,26 +210,23 @@ class MaximumCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
// get op descriptor
size_t wsSize;
cnnlGetMaximumWorkspaceSize(context->cnnlHandle(), cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
@ -260,8 +237,6 @@ class MaximumCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -279,26 +254,23 @@ class MinimumCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
// get op descriptor
size_t wsSize;
cnnlGetMinimumWorkspaceSize(context->cnnlHandle(), cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
@ -309,8 +281,6 @@ class MinimumCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -328,30 +298,23 @@ class MSELossCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
MSELossObj::Reduction reduction = op->getReduction();
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
int dim_out[4] = {1, 1, 1, 1};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
if (reduction == MSELossObj::None) {
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
} else {
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_out));
}
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
cnnlStatus_t stat;
if (reduction == MSELossObj::None) {
stat = cnnlMSELoss(context->cnnlHandle(), CNNL_MSE_LOSS_NONE, aDesc,
@ -367,8 +330,6 @@ class MSELossCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -386,26 +347,23 @@ class PowerCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
// get op descriptor
size_t wsSize;
cnnlGetPowWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
@ -417,8 +375,6 @@ class PowerCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -436,29 +392,26 @@ class FloorDivCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
size_t wsSize;
cnnlGetFloorDivWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat = cnnlFloorDiv_v2(
@ -467,8 +420,6 @@ class FloorDivCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -486,29 +437,26 @@ class FloorModCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
size_t wsSize;
cnnlGetFloorModWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
@ -517,8 +465,6 @@ class FloorModCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
@ -536,29 +482,26 @@ class SquaredDifferenceCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, a_dim.size(),
a_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, b_dim.size(),
b_dim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, c_dim.size(),
c_dim.data()));
size_t wsSize;
cnnlGetSquaredDifferenceWorkspaceSize(context->cnnlHandle(), aDesc,
bDesc, cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
@ -567,8 +510,6 @@ class SquaredDifferenceCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));

17
src/kernels/bang/erf.cc
View File

@ -13,20 +13,17 @@ class ErfCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlErf_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
@ -34,8 +31,6 @@ class ErfCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

17
src/kernels/bang/exp.cc
View File

@ -13,20 +13,17 @@ class ExpCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlExp_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
@ -34,8 +31,6 @@ class ExpCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

11
src/kernels/bang/fill.cc
View File

@ -13,23 +13,18 @@ class FillCnnl : public BangKernelWithoutConfig {
float value = op->getValue();
cnnlTensorDescriptor_t cDesc;
auto dim = op->getOutput()->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlFill(context->cnnlHandle(), value, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};

17
src/kernels/bang/floor.cc
View File

@ -13,28 +13,23 @@ class FloorCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlFloor(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

10
src/kernels/bang/hardtanh.cc
View File

@ -16,22 +16,16 @@ class HardtanhCnnl : public BangKernelWithoutConfig {
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, dim.size(), dim.data()));
cnnlStatus_t stat = cnnlHardtanh(context->cnnlHandle(), aDesc, aData,
max, min, aDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
}
};

10
src/kernels/bang/l2loss.cc
View File

@ -14,22 +14,16 @@ class L2LossCnnl : public BangKernelWithoutConfig {
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, dim.size(), dim.data()));
cnnlStatus_t stat =
cnnlL2Loss(context->cnnlHandle(), aDesc, aData, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
}
};

17
src/kernels/bang/log.cc
View File

@ -28,20 +28,17 @@ class LogCnnl : public BangKernelWithoutConfig {
}
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlLog_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
@ -49,8 +46,6 @@ class LogCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

4
src/kernels/bang/matmul.cc
View File

@ -22,7 +22,6 @@ class MatmulCnnl : public BangKernelWithoutConfig {
int32_t transA = op->getTransA();
int32_t transB = op->getTransB();
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(
cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT,
@ -33,7 +32,6 @@ class MatmulCnnl : public BangKernelWithoutConfig {
cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT,
dimInputs1.size(), dimInputs1.data()));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(
cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT,
@ -68,8 +66,6 @@ class MatmulCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));

17
src/kernels/bang/negtensor.cc
View File

@ -13,28 +13,23 @@ class NegTensorCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlNegTensor(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

17
src/kernels/bang/reciprocal.cc
View File

@ -13,28 +13,23 @@ class ReciprocalCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlReciprocal(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

10
src/kernels/bang/reshape.cc
View File

@ -11,17 +11,11 @@ class CopyBang : public BangKernelWithoutConfig {
auto outData = op->getOutputs()[0]->getRawDataPtr<void *>();
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
int len = dim.size();
int size = 1;
for (int i = 0; i < len; ++i) {
size *= dim[i];
}
int dim_array[1] = {size};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, 1, dim_array));
CNNL_DTYPE_FLOAT, dim.size(),
dim.data()));
cnnlStatus_t stat =
cnnlCopy(context->cnnlHandle(), aDesc, inData, aDesc, outData);
if (stat != CNNL_STATUS_SUCCESS)

17
src/kernels/bang/rsqrt.cc
View File

@ -13,20 +13,17 @@ class RsqrtCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlRsqrt_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
@ -34,8 +31,6 @@ class RsqrtCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

43
src/kernels/bang/split.cc
View File

@ -10,39 +10,26 @@ class SplitCnnl : public BangKernelWithoutConfig {
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
int num = op->numOutputs();
int axis = op->getDim();
void *argv[num];
for (int i = 0; i < num; ++i) {
argv[i] = op->getOutput(i)->getRawDataPtr<void *>();
}
void *const inputData = (op->getInputs(0)->getRawDataPtr<void *>());
cnnlTensorDescriptor_t desc;
int dimout_array[num][4];
for (int i = 0; i < num; ++i) {
auto dim = op->getOutput(i)->getDims();
if (dim.size() != 4) {
IT_TODO_HALT();
}
dimout_array[i][0] = dim[0];
dimout_array[i][1] = dim[1];
dimout_array[i][2] = dim[2];
dimout_array[i][3] = dim[3];
}
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4) {
IT_TODO_HALT();
}
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
cnnlTensorDescriptor_t desc;
checkCnnlError(cnnlCreateTensorDescriptor(&desc));
checkCnnlError(cnnlSetTensorDescriptor(desc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
desc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, dim.size(), dim.data()));
cnnlTensorDescriptor_t descArray[num];
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlCreateTensorDescriptor(&descArray[i]));
checkCnnlError(
cnnlSetTensorDescriptor(descArray[i], CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dimout_array[i]));
checkCnnlError(cnnlSetTensorDescriptor(
descArray[i], CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT,
op->getOutput(i)->getDims().size(),
op->getOutput(i)->getDims().data()));
}
void *const inputData = (op->getInputs(0)->getRawDataPtr<void *>());
void *argv[num];
for (int i = 0; i < num; ++i) {
argv[i] = op->getOutput(i)->getRawDataPtr<void *>();
}
size_t wsSize;
@ -55,8 +42,6 @@ class SplitCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlDestroyTensorDescriptor(descArray[i]));
}

17
src/kernels/bang/sqrt.cc
View File

@ -13,20 +13,17 @@ class SqrtCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
cnnlStatus_t stat =
cnnlSqrt_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
@ -34,8 +31,6 @@ class SqrtCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}

23
src/kernels/bang/transpose.cc
View File

@ -15,26 +15,21 @@ class TransposeCnnl : public BangKernelWithoutConfig {
cnnlTensorDescriptor_t aDesc, cDesc;
auto dimin = op->getInputs(0)->getDims();
auto dimout = op->getOutput()->getDims();
if (dimin.size() != 4 || dimout.size() != 4)
IT_TODO_HALT();
int dimin_array[4] = {dimin[0], dimin[1], dimin[2], dimin[3]};
int dimout_array[4] = {dimout[0], dimout[1], dimout[2], dimout[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 4, dimin_array));
// get outputs
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, dimin.size(),
dimin.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 4, dimout_array));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, dimout.size(),
dimout.data()));
// get op descriptor
auto permute = op->getPermute();
cnnlTransposeDescriptor_t opDesc;
checkCnnlError(cnnlCreateTransposeDescriptor(&opDesc));
checkCnnlError(cnnlSetTransposeDescriptor(opDesc, 4, permute.data()));
checkCnnlError(
cnnlSetTransposeDescriptor(opDesc, permute.size(), permute.data()));
size_t wsSize;
cnnlGetTransposeWorkspaceSize(context->cnnlHandle(), aDesc, opDesc,
@ -47,8 +42,6 @@ class TransposeCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyTransposeDescriptor(opDesc));

18
src/kernels/bang/trigon.cc
View File

@ -15,22 +15,18 @@ class TrigonCnnl : public BangKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
auto aDim = op->getInputs(0)->getDims();
auto cDim = op->getOutput()->getDims();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
CNNL_DTYPE_FLOAT, aDim.size(),
aDim.data()));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
CNNL_DTYPE_FLOAT, cDim.size(),
cDim.data()));
// get op descriptor
cnnlTrigonDescriptor_t opDesc;
checkCnnlError(cnnlCreateTrigonDescriptor(&opDesc));
checkCnnlError(cnnlSetTrigonDescriptor(opDesc, getOpType()));
@ -40,8 +36,6 @@ class TrigonCnnl : public BangKernelWithoutConfig {
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyTrigonDescriptor(opDesc));