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[Feature](mluOpTest): access variable in tensor struct through function in mlu_op_gtest #1152

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merged 4 commits into from
Dec 31, 2024
Merged

[Feature](mluOpTest): access variable in tensor struct through function in mlu_op_gtest #1152

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a438fc5
[Feature](mluOpTest): access variable in tensor struct through functi…
Nov 19, 2024
af38b4c
Merge branch 'master' into nl22222
nth-BYTE Dec 18, 2024
92959ab
Merge branch 'master' into nl22222
nth-BYTE Dec 23, 2024
6242c68
Merge branch 'master' into nl22222
nth-BYTE Dec 31, 2024
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10 changes: 5 additions & 5 deletions test/mlu_op_gtest/pb_gtest/src/internal_kernel/transpose_cpu/transpose_cpu.cpp
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Expand Up @@ -89,15 +89,15 @@ mluOpStatus_t mluOpTransposeCpu(const int64_t dim_desc,
PARAM_CHECK("[cnnlTransposeCpu]", y_desc != NULL);
uint64_t sum = mluOpGetTensorElementNum(x_desc);
// zero elements, return success
if (sum == 0 || x_desc->dim == 0 || y_desc->dim == 0) {
if (sum == 0 || x_desc->getDim() == 0 || y_desc->getDim() == 0) {
VLOG(5) << "cnnlTransposeCpu:: zero elements, return success.";
return MLUOP_STATUS_SUCCESS;
}
PARAM_CHECK("[cnnlTransposeCpu]", x != NULL);
PARAM_CHECK("[cnnlTransposeCpu]", y != NULL);

const uint64_t dim_all = dim_desc;
auto data_type = x_desc->dtype;
auto data_type = x_desc->getDtype();
int loop_d = 1;
if (data_type == MLUOP_DTYPE_INT31) {
loop_d = 2;
Expand All @@ -112,17 +112,17 @@ mluOpStatus_t mluOpTransposeCpu(const int64_t dim_desc,
uint64_t DIM[TRANSPOSE_MAX_DIM + 1] = {1, 1, 1, 1, 1, 1, 1, 1, 1};
uint64_t dim[TRANSPOSE_MAX_DIM + 1] = {0};

if (x_desc->dim != dim_all || y_desc->dim != dim_all) {
if (x_desc->getDim() != dim_all || y_desc->getDim() != dim_all) {
LOG(ERROR)
<< "cnnlTransposeCpu: dimension information mismatch, dim of x: "
<< x_desc->dim << ", dim of y: " << y_desc->dim
<< x_desc->getDim() << ", dim of y: " << y_desc->getDim()
<< ", dim of descriptor: " << dim_all;
return MLUOP_STATUS_BAD_PARAM;
}

for (int i = 0; i < dim_all; i++) {
permute[i] = permute_desc[i];
DIM[i] = x_desc->dims[i];
DIM[i] = x_desc->getDimIndex(i);
}
if (MLUOP_DTYPE_INT31 == data_type) {
transposeCpuNd(loop_d, (int16_t *)x, (int16_t *)y, sum, dim, DIM, permute);
Expand Down
4 changes: 2 additions & 2 deletions test/mlu_op_gtest/pb_gtest/src/zoo/bbox_overlaps/bbox_overlaps.cpp
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Expand Up @@ -152,8 +152,8 @@ void BboxOverlapsExecutor::cpuCompute() {
auto bbox1_desc = tensor_desc_[0].tensor;
auto bbox2_desc = tensor_desc_[1].tensor;

int rows = bbox1_desc->dims[0];
int cols = bbox2_desc->dims[0];
int rows = bbox1_desc->getDimIndex(0);
int cols = bbox2_desc->getDimIndex(0);

// get struct param
int mode = parser_->getProtoNode()->bbox_overlaps_param().mode();
Expand Down
30 changes: 15 additions & 15 deletions test/mlu_op_gtest/pb_gtest/src/zoo/border_align_backward/border_align_backward.cpp
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Expand Up @@ -113,19 +113,19 @@ void BorderAlignBackwardExecutor::cpuCompute() {
float *boxes = cpu_fp32_input_[1];
float *argmax_idx = cpu_fp32_input_[2];
float *grad_input = cpu_fp32_output_[0];
const int32_t box_size = boxes_desc->dims[1];
const int32_t channels = grad_output_desc->dims[3];
const int32_t height = grad_input_desc->dims[1];
const int32_t width = grad_input_desc->dims[2];
const int32_t N = grad_output_desc->dims[0];
const int32_t H = grad_output_desc->dims[1];
const int32_t W = grad_output_desc->dims[2];
const int32_t C = grad_output_desc->dims[3];
const int32_t box_size = boxes_desc->getDimIndex(1);
const int32_t channels = grad_output_desc->getDimIndex(3);
const int32_t height = grad_input_desc->getDimIndex(1);
const int32_t width = grad_input_desc->getDimIndex(2);
const int32_t N = grad_output_desc->getDimIndex(0);
const int32_t H = grad_output_desc->getDimIndex(1);
const int32_t W = grad_output_desc->getDimIndex(2);
const int32_t C = grad_output_desc->getDimIndex(3);

const int32_t N1 = grad_input_desc->dims[0];
const int32_t H1 = grad_input_desc->dims[1];
const int32_t W1 = grad_input_desc->dims[2];
const int32_t C1 = grad_input_desc->dims[3];
const int32_t N1 = grad_input_desc->getDimIndex(0);
const int32_t H1 = grad_input_desc->getDimIndex(1);
const int32_t W1 = grad_input_desc->getDimIndex(2);
const int32_t C1 = grad_input_desc->getDimIndex(3);
float x_stride = 0;
float y_stride = 0;
float stride = 0;
Expand Down Expand Up @@ -260,9 +260,9 @@ void BorderAlignBackwardExecutor::cpuCompute() {
int64_t BorderAlignBackwardExecutor::getTheoryOps() {
auto input_desc = parser_->getMetaTensor(0).tensor;
auto boxes_desc = parser_->getMetaTensor(1).tensor;
const int32_t N = input_desc->dims[0];
const int32_t C = input_desc->dims[3] / 4;
const int32_t K = boxes_desc->dims[1];
const int32_t N = input_desc->getDimIndex(0);
const int32_t C = input_desc->getDimIndex(3) / 4;
const int32_t K = boxes_desc->getDimIndex(1);

const int64_t theory_ops = N * K * 4 * C * 3;
return theory_ops;
Expand Down
16 changes: 8 additions & 8 deletions test/mlu_op_gtest/pb_gtest/src/zoo/border_align_forward/border_align_forward.cpp
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Expand Up @@ -113,11 +113,11 @@ float bilinear_interpolate(const float *input, const int32_t H, const int32_t W,
void BorderAlignForwardExecutor::cpuCompute() {
auto input_desc = parser_->getMetaTensor(0).tensor;
auto boxes_desc = parser_->getMetaTensor(1).tensor;
const int32_t N = input_desc->dims[0];
const int32_t H = input_desc->dims[1];
const int32_t W = input_desc->dims[2];
const int32_t C = input_desc->dims[3] / 4;
const int32_t K = boxes_desc->dims[1];
const int32_t N = input_desc->getDimIndex(0);
const int32_t H = input_desc->getDimIndex(1);
const int32_t W = input_desc->getDimIndex(2);
const int32_t C = input_desc->getDimIndex(3) / 4;
const int32_t K = boxes_desc->getDimIndex(1);
float x1, x2, y1, y2;
float x_stride = 0;
float y_stride = 0;
Expand Down Expand Up @@ -195,9 +195,9 @@ void BorderAlignForwardExecutor::cpuCompute() {
int64_t BorderAlignForwardExecutor::getTheoryOps() {
auto input_desc = parser_->getMetaTensor(0).tensor;
auto boxes_desc = parser_->getMetaTensor(1).tensor;
const int32_t N = input_desc->dims[0];
const int32_t C = input_desc->dims[3] / 4;
const int32_t K = boxes_desc->dims[1];
const int32_t N = input_desc->getDimIndex(0);
const int32_t C = input_desc->getDimIndex(3) / 4;
const int32_t K = boxes_desc->getDimIndex(1);

const int64_t theory_ops = N * K * 4 * C * 14;
return theory_ops;
Expand Down
14 changes: 7 additions & 7 deletions test/mlu_op_gtest/pb_gtest/src/zoo/box_iou_rotated/box_iou_rotated.cpp
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Expand Up @@ -62,8 +62,8 @@ void BoxIouRotatedExecutor::cpuCompute() {

auto box1_desc = tensor_desc_[0].tensor;
auto box2_desc = tensor_desc_[1].tensor;
auto num_box1 = box1_desc->dims[0];
auto num_box2 = box2_desc->dims[0];
auto num_box1 = box1_desc->getDimIndex(0);
auto num_box2 = box2_desc->getDimIndex(0);

int mode = parser_->getProtoNode()->box_iou_rotated_param().mode();
bool aligned = parser_->getProtoNode()->box_iou_rotated_param().aligned();
Expand All @@ -85,17 +85,17 @@ void BoxIouRotatedExecutor::cpuBoxIouRotated(const T *box1_raw,
VLOG(4) << "num box1: " << num_box1;
VLOG(4) << "num box2: " << num_box2;
if (aligned) {
int num_ious = tensor_desc_[2].tensor->dims[0];
int num_ious = tensor_desc_[2].tensor->getDimIndex(0);
VLOG(4) << "num_ious: " << num_ious;
GTEST_CHECK(num_box1 == num_ious,
"when aligned, num_box1 should equal to num_ious.");
} else {
int num_ious = tensor_desc_[2].tensor->dims[0];
int num_ious = tensor_desc_[2].tensor->getDimIndex(0);
VLOG(4) << "num_ious[0]: " << num_ious;
num_ious = tensor_desc_[2].tensor->dims[1];
num_ious = tensor_desc_[2].tensor->getDimIndex(1);
VLOG(4) << "num_ious[1]: " << num_ious;
GTEST_CHECK(((num_box1 == tensor_desc_[2].tensor->dims[0]) ||
(num_box2 == tensor_desc_[2].tensor->dims[1])),
GTEST_CHECK(((num_box1 == tensor_desc_[2].tensor->getDimIndex(0)) ||
(num_box2 == tensor_desc_[2].tensor->getDimIndex(1))),
"when not aligned, num_ious should equal to num_box1*num_box2");
}

Expand Down
8 changes: 4 additions & 4 deletions test/mlu_op_gtest/pb_gtest/src/zoo/dcn_backward_data/dcn_backward_data.cpp
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Expand Up @@ -101,8 +101,8 @@ void DcnBackwardDataExecutor::workspaceMalloc() {
grad_offset_desc_ = parser_->outputs()[1].tensor;
grad_mask_desc_ = use_grad_mask_ ? parser_->outputs()[2].tensor : nullptr;

grad_output_desc_->onchip_dtype = grad_output_oc_dt_;
weight_desc_->onchip_dtype = weight_oc_dt_;
grad_output_desc_->setOnchipDtype(grad_output_oc_dt_);
weight_desc_->setOnchipDtype(weight_oc_dt_);

dcn_desc_ = cpu_runtime_.allocate(mluOpCreateDCNDescriptor,
mluOpDestroyDCNDescriptor);
Expand Down Expand Up @@ -153,8 +153,8 @@ void DcnBackwardDataExecutor::compute() {
void *dev_grad_mask =
use_mask_ ? data_vector_[6 + use_mask_].device_ptr : nullptr;

grad_output_desc_->onchip_dtype = grad_output_oc_dt_;
weight_desc_->onchip_dtype = weight_oc_dt_;
grad_output_desc_->setOnchipDtype(grad_output_oc_dt_);
weight_desc_->setOnchipDtype(weight_oc_dt_);

VLOG(4) << "call mluOpDCNBackwardData()";
interface_timer_.start();
Expand Down
58 changes: 29 additions & 29 deletions test/mlu_op_gtest/pb_gtest/src/zoo/dcn_backward_weight/dcn_backward_weight.cpp
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Expand Up @@ -163,8 +163,8 @@ void DcnBackwardWeightExecutor::workspaceMalloc() {
parser_->getOutputNum() == 1 ? nullptr : tensor_desc_[5].tensor;
}

input_desc->onchip_dtype = input_onchip_dtype;
grad_output_desc->onchip_dtype = grad_output_onchip_dtype;
input_desc->setOnchipDtype(input_onchip_dtype);
grad_output_desc->setOnchipDtype(grad_output_onchip_dtype);
MLUOP_CHECK(mluOpGetDCNBackwardWeightWorkspaceSize(
handle_, dcn_desc, input_desc, offset_desc, mask_desc, grad_output_desc,
grad_weight_desc, grad_bias_desc, &workspace_size));
Expand All @@ -191,7 +191,7 @@ void DcnBackwardWeightExecutor::compute() {
if (dcn_param.has_compute_type()) {
compute_type = cvtProtoDtypeToMluOp(dcn_param.compute_type());
} else {
compute_type = input_desc->dtype;
compute_type = input_desc->getDtype();
}

mluOpDCNDescriptor_t dcn_desc = cpu_runtime_.allocate(
Expand Down Expand Up @@ -226,8 +226,8 @@ void DcnBackwardWeightExecutor::compute() {
parser_->getOutputNum() == 1 ? nullptr : data_vector_[5].device_ptr;
}

input_desc->onchip_dtype = input_onchip_dtype;
grad_output_desc->onchip_dtype = grad_output_onchip_dtype;
input_desc->setOnchipDtype(input_onchip_dtype);
grad_output_desc->setOnchipDtype(grad_output_onchip_dtype);

VLOG(4) << "call mluOpDCNBackwardWeight()";
interface_timer_.start();
Expand Down Expand Up @@ -444,15 +444,15 @@ void DcnBackwardWeightExecutor::computeDCNBackwardWeightCPU(
const mluOpTensorDescriptor_t grad_bias_desc, void *cpu_grad_bias,
float *buffer, int pad[], int stride[], int dilation[],
int64_t &theory_ops) {
const int N = input_desc->dims[0];
const int hi = input_desc->dims[1];
const int wi = input_desc->dims[2];
const int ci = input_desc->dims[3];
const int ho = offset_desc->dims[1];
const int wo = offset_desc->dims[2];
const int co = grad_output_desc->dims[3];
const int kh = grad_weight_desc->dims[1];
const int kw = grad_weight_desc->dims[2];
const int N = input_desc->getDimIndex(0);
const int hi = input_desc->getDimIndex(1);
const int wi = input_desc->getDimIndex(2);
const int ci = input_desc->getDimIndex(3);
const int ho = offset_desc->getDimIndex(1);
const int wo = offset_desc->getDimIndex(2);
const int co = grad_output_desc->getDimIndex(3);
const int kh = grad_weight_desc->getDimIndex(1);
const int kw = grad_weight_desc->getDimIndex(2);
const int pt = pad[0];
const int pb = pad[1];
const int pl = pad[2];
Expand Down Expand Up @@ -579,12 +579,12 @@ void DcnBackwardWeightExecutor::cpuCompute() {
parser_->getOutputNum() == 1 ? nullptr : cpu_fp32_output_[1];
}

const int ho = offset_desc->dims[1];
const int wo = offset_desc->dims[2];
const int kh = grad_weight_desc->dims[1];
const int kw = grad_weight_desc->dims[2];
const int ci = input_desc->dims[3];
const int co = grad_output_desc->dims[3];
const int ho = offset_desc->getDimIndex(1);
const int wo = offset_desc->getDimIndex(2);
const int kh = grad_weight_desc->getDimIndex(1);
const int kw = grad_weight_desc->getDimIndex(2);
const int ci = input_desc->getDimIndex(3);
const int co = grad_output_desc->getDimIndex(3);

size_t cpu_buffer_size = 0;
if (g == 1) {
Expand Down Expand Up @@ -634,15 +634,15 @@ int64_t DcnBackwardWeightExecutor::getTheoryOps() {
grad_bias_desc =
parser_->getOutputNum() == 1 ? nullptr : tensor_desc_[5].tensor;
}
const int N = input_desc->dims[0];
const int hi = input_desc->dims[1];
const int wi = input_desc->dims[2];
const int ci = input_desc->dims[3];
const int ho = offset_desc->dims[1];
const int wo = offset_desc->dims[2];
const int co = grad_output_desc->dims[3];
const int kh = grad_weight_desc->dims[1];
const int kw = grad_weight_desc->dims[2];
const int N = input_desc->getDimIndex(0);
const int hi = input_desc->getDimIndex(1);
const int wi = input_desc->getDimIndex(2);
const int ci = input_desc->getDimIndex(3);
const int ho = offset_desc->getDimIndex(1);
const int wo = offset_desc->getDimIndex(2);
const int co = grad_output_desc->getDimIndex(3);
const int kh = grad_weight_desc->getDimIndex(1);
const int kw = grad_weight_desc->getDimIndex(2);
int coeff = getCoefficientOfLT2CT();
const int k = im2col_step * ho * wo;
const int m = co / g;
Expand Down
56 changes: 28 additions & 28 deletions test/mlu_op_gtest/pb_gtest/src/zoo/dcn_forward/dcn_forward.cpp
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Expand Up @@ -158,8 +158,8 @@ void DcnForwardExecutor::workspaceMalloc() {
output_desc = tensor_desc_[5].tensor;
}

input_desc->onchip_dtype = input_onchip_dtype;
weight_desc->onchip_dtype = weight_onchip_dtype;
input_desc->setOnchipDtype(input_onchip_dtype);
weight_desc->setOnchipDtype(weight_onchip_dtype);

MLUOP_CHECK(mluOpGetDCNForwardWorkspaceSize(
handle_, dcn_desc, input_desc, offset_desc, mask_desc, weight_desc,
Expand Down Expand Up @@ -236,8 +236,8 @@ void DcnForwardExecutor::compute() {
output = data_vector_[5].device_ptr;
}

input_desc->onchip_dtype = input_onchip_dtype;
weight_desc->onchip_dtype = weight_onchip_dtype;
input_desc->setOnchipDtype(input_onchip_dtype);
weight_desc->setOnchipDtype(weight_onchip_dtype);
VLOG(4) << "call mluOpDCNForward()";
interface_timer_.start();

Expand Down Expand Up @@ -447,15 +447,15 @@ void DcnForwardExecutor::computeDCNForwardCPU(
const mluOpTensorDescriptor_t output_desc, const void *cpu_output,
float *buffer, int pad[], int stride[], int dilation[],
int64_t &theory_ops) {
const int N = input_desc->dims[0];
const int hi = input_desc->dims[1];
const int wi = input_desc->dims[2];
const int ci = input_desc->dims[3];
const int ho = offset_desc->dims[1];
const int wo = offset_desc->dims[2];
const int co = output_desc->dims[3];
const int kh = weight_desc->dims[1];
const int kw = weight_desc->dims[2];
const int N = input_desc->getDimIndex(0);
const int hi = input_desc->getDimIndex(1);
const int wi = input_desc->getDimIndex(2);
const int ci = input_desc->getDimIndex(3);
const int ho = offset_desc->getDimIndex(1);
const int wo = offset_desc->getDimIndex(2);
const int co = output_desc->getDimIndex(3);
const int kh = weight_desc->getDimIndex(1);
const int kw = weight_desc->getDimIndex(2);
const int pt = pad[0];
const int pb = pad[1];
const int pl = pad[2];
Expand Down Expand Up @@ -594,12 +594,12 @@ void DcnForwardExecutor::cpuCompute() {
cpu_output = cpu_fp32_output_[0];
}

const int ho = offset_desc->dims[1];
const int wo = offset_desc->dims[2];
const int kh = weight_desc->dims[1];
const int kw = weight_desc->dims[2];
const int ci = input_desc->dims[3];
const int co = output_desc->dims[3];
const int ho = offset_desc->getDimIndex(1);
const int wo = offset_desc->getDimIndex(2);
const int kh = weight_desc->getDimIndex(1);
const int kw = weight_desc->getDimIndex(2);
const int ci = input_desc->getDimIndex(3);
const int co = output_desc->getDimIndex(3);

size_t cpu_buffer_size = 0;
if (g == 1) {
Expand Down Expand Up @@ -652,15 +652,15 @@ int64_t DcnForwardExecutor::getTheoryOps() {
output_desc = tensor_desc_[5].tensor;
}

const int N = input_desc->dims[0];
const int hi = input_desc->dims[1];
const int wi = input_desc->dims[2];
const int ci = input_desc->dims[3];
const int ho = offset_desc->dims[1];
const int wo = offset_desc->dims[2];
const int co = output_desc->dims[3];
const int kh = weight_desc->dims[1];
const int kw = weight_desc->dims[2];
const int N = input_desc->getDimIndex(0);
const int hi = input_desc->getDimIndex(1);
const int wi = input_desc->getDimIndex(2);
const int ci = input_desc->getDimIndex(3);
const int ho = offset_desc->getDimIndex(1);
const int wo = offset_desc->getDimIndex(2);
const int co = output_desc->getDimIndex(3);
const int kh = weight_desc->getDimIndex(1);
const int kw = weight_desc->getDimIndex(2);
int coeff = getCoefficientOfLT2CT();
const int k = kh * kw * ci / g;
const int m = im2col_step * ho * wo;
Expand Down
10 changes: 5 additions & 5 deletions test/mlu_op_gtest/pb_gtest/src/zoo/deform_roi_pool_backward/deform_roi_pool_backward.cpp
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Expand Up @@ -58,11 +58,11 @@ void DeformRoiPoolBackwardExecutor::initData() {
grad_input_desc = tensor_desc_[3].tensor;
}

batchs = input_desc->dims[0];
height = input_desc->dims[1];
width = input_desc->dims[2];
channels = input_desc->dims[3];
rois_num = rois_desc->dims[0];
batchs = input_desc->getDimIndex(0);
height = input_desc->getDimIndex(1);
width = input_desc->getDimIndex(2);
channels = input_desc->getDimIndex(3);
rois_num = rois_desc->getDimIndex(0);

// get params
auto deform_roi_pool_backward_proto_desc =
Expand Down
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