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Unverified Commit 099ee24d authored by qirun-uiuc's avatar qirun-uiuc Committed by GitHub
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[Refactor] Replace bbox_overlaps op of MLU backend with mlu-ops (#2879)

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mmcv/ops/csrc/common/mlu/bbox_overlaps_mlu_kernel.mlu deleted 100644 → 0
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/*************************************************************************
* Copyright (C) 2021 Cambricon.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
* OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*************************************************************************/
#include <float.h>
#include "common_mlu_helper.hpp"
#define COORD_NUM 4
__nram__ char nmem_buf[MAX_NRAM_SIZE];
template <typename T>
__mlu_func__ void computeDiv(void *nram_dst, void *nram_src0, void *nram_src1,
void *nram_addition, const int32_t deal_num) {
__bang_active_reciphp((T *)nram_dst, (T *)nram_src1, deal_num);
__bang_mul((T *)nram_dst, (T *)nram_src0, (T *)nram_dst, deal_num);
}
template <>
__mlu_func__ void computeDiv<half>(void *nram_dst, void *nram_src0,
void *nram_src1, void *nram_addition,
const int32_t deal_num) {
__bang_half2float((float *)nram_addition, (half *)nram_src1, deal_num);
__bang_active_reciphp((float *)nram_addition, (float *)nram_addition,
deal_num);
__bang_float2half_rd((half *)nram_src1, (float *)nram_addition, deal_num);
__bang_mul((half *)nram_dst, (half *)nram_src0, (half *)nram_src1, deal_num);
}
template <typename T>
__mlu_func__ void bboxOverlapsWorkflow(
T *vec_b1_x1, T *vec_b1_y1, T *vec_b1_x2, T *vec_b1_y2, T *vec_b2_x1,
T *vec_b2_y1, T *vec_b2_x2, T *vec_b2_y2, T *vec_left, T *vec_right,
T *vec_top, T *vec_bottom, const T *bbox1, const T *bbox2, void *ious,
const int32_t offset, const int32_t mode, const int32_t batches_stride,
const int32_t num_bbox1, const int32_t num_bbox2, const bool aligned) {
int32_t task_batch_stride = (num_bbox1 + taskDim - 1) / taskDim;
int32_t batch_start = taskId * task_batch_stride;
int32_t batch_per_task = batch_start + task_batch_stride < num_bbox1
? task_batch_stride
: num_bbox1 - batch_start;
batch_per_task = batch_per_task > 0 ? batch_per_task : (0);
if (aligned) {
int32_t num_loop_cpy = batch_per_task / batches_stride;
int32_t num_rem_cpy_batches = batch_per_task % batches_stride;
num_loop_cpy = num_rem_cpy_batches > 0 ? num_loop_cpy + 1 : num_loop_cpy;
for (int32_t i = 0; i < num_loop_cpy; i++) {
int32_t index = batch_start + i * batches_stride;
int32_t handle_batches = index + batches_stride > num_bbox1
? num_rem_cpy_batches
: batches_stride;
int32_t b1 = index;
int32_t b2 = index;
int32_t base1 = b1 * COORD_NUM;
__memcpy(vec_b1_x1, &bbox1[base1], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b1_y1, &bbox1[base1 + 1], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b1_x2, &bbox1[base1 + 2], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b1_y2, &bbox1[base1 + 3], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
int32_t base2 = b2 * COORD_NUM;
__memcpy(vec_b2_x1, &bbox2[base2], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b2_y1, &bbox2[base2 + 1], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b2_x2, &bbox2[base2 + 2], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b2_y2, &bbox2[base2 + 3], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
// get the width and height
__bang_maxequal(vec_left, vec_b1_x1, vec_b2_x1, batches_stride);
__bang_minequal(vec_right, vec_b1_x2, vec_b2_x2, batches_stride);
__bang_maxequal(vec_top, vec_b1_y1, vec_b2_y1, batches_stride);
__bang_minequal(vec_bottom, vec_b1_y2, vec_b2_y2, batches_stride);
// right - left + offset ---> left
__bang_sub(vec_left, vec_right, vec_left, batches_stride);
__bang_add_scalar(vec_left, vec_left, (T)offset, batches_stride);
// bottom - top + offset ---> right
__bang_sub(vec_right, vec_bottom, vec_top, batches_stride);
__bang_add_scalar(vec_right, vec_right, (T)offset, batches_stride);
// zero vector ---> bottom
__bang_write_value(vec_bottom, batches_stride, 0.f);
// width --> vec_left
__bang_maxequal(vec_left, vec_bottom, vec_left, batches_stride);
T *width = vec_left;
// height --> vec_right
__bang_maxequal(vec_right, vec_bottom, vec_right, batches_stride);
T *height = vec_right;
// get the b1_area
// (b1_x2 - b1_x1 + offset) ---> vec_top
__bang_sub(vec_top, vec_b1_x2, vec_b1_x1, batches_stride);
__bang_add_scalar(vec_top, vec_top, (T)offset, batches_stride);
// (b1_y2 - b1_y1 + offset) ---> vec_bottom
__bang_sub(vec_bottom, vec_b1_y2, vec_b1_y1, batches_stride);
__bang_add_scalar(vec_bottom, vec_bottom, (T)offset, batches_stride);
// b1_area = (b1_x2 - b1_x1 + offset) * (b1_y2 - b1_y1 + offset)
// ---> vec_top;
__bang_mul(vec_top, vec_top, vec_bottom, batches_stride);
T *b1_area = vec_top;
// get the b2_area
// (b2_x2 - b2_x1 + offset) ---> b2_x1
__bang_sub(vec_b2_x1, vec_b2_x2, vec_b2_x1, batches_stride);
__bang_add_scalar(vec_b2_x1, vec_b2_x1, (T)offset, batches_stride);
// (b2_y2 - b2_y1 + offset) ---> b2_y1
__bang_sub(vec_b2_y1, vec_b2_y2, vec_b2_y1, batches_stride);
__bang_add_scalar(vec_b2_y1, vec_b2_y1, (T)offset, batches_stride);
// b2_area = (b2_x2 - b2_x1 + offset) * (b2_y2 - b2_y1 + offset)
// ---> b2_x1;
__bang_mul(vec_b2_x1, vec_b2_x1, vec_b2_y1, batches_stride);
T *b2_area = vec_b2_x1;
// inter_s = width * height
__bang_mul(height, width, height, batches_stride);
T *inter_s = height;
// offset vector ---> vec_b2_y1
__bang_write_value(vec_b2_y1, batches_stride, T(offset));
T *vec_offset = vec_b2_y1;
if (mode == 0) {
__bang_add(b1_area, b1_area, b2_area, batches_stride);
__bang_sub(b1_area, b1_area, inter_s, batches_stride);
__bang_maxequal(b1_area, vec_offset, b1_area, batches_stride);
} else {
__bang_maxequal(b1_area, vec_offset, b1_area, batches_stride);
}
T *base_s = b1_area;
// ious = inter_s / base_s
computeDiv<T>(width, inter_s, base_s, vec_b2_x2, batches_stride);
__memcpy((T *)ious + index, width, handle_batches * sizeof(T),
NRAM2GDRAM);
}
} else {
int32_t num_loop_cpy = num_bbox2 / batches_stride;
int32_t num_rem_cpy_batches = num_bbox2 % batches_stride;
num_loop_cpy = num_rem_cpy_batches > 0 ? num_loop_cpy + 1 : num_loop_cpy;
for (int32_t i = 0; i < batch_per_task; i++) {
int32_t index1 = batch_start + i;
int32_t b1 = index1;
int32_t base1 = b1 * COORD_NUM;
// set bbox1 and bbox2 to nram
__bang_write_value(vec_b1_x1, batches_stride, bbox1[base1]);
__bang_write_value(vec_b1_y1, batches_stride, bbox1[base1 + 1]);
__bang_write_value(vec_b1_x2, batches_stride, bbox1[base1 + 2]);
__bang_write_value(vec_b1_y2, batches_stride, bbox1[base1 + 3]);
for (int32_t j = 0; j < num_loop_cpy; j++) {
int32_t index2 = j * batches_stride;
int32_t handle_batches = index2 + batches_stride > num_bbox2
? num_rem_cpy_batches
: batches_stride;
int32_t b2 = index2;
int32_t base2 = b2 * COORD_NUM;
// copy bbox2 to nram
__memcpy(vec_b2_x1, &bbox2[base2], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b2_y1, &bbox2[base2 + 1], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b2_x2, &bbox2[base2 + 2], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
__memcpy(vec_b2_y2, &bbox2[base2 + 3], sizeof(T), GDRAM2NRAM, sizeof(T),
COORD_NUM * sizeof(T), handle_batches - 1);
// get the width and height
__bang_maxequal(vec_left, vec_b1_x1, vec_b2_x1, batches_stride);
__bang_minequal(vec_right, vec_b1_x2, vec_b2_x2, batches_stride);
__bang_maxequal(vec_top, vec_b1_y1, vec_b2_y1, batches_stride);
__bang_minequal(vec_bottom, vec_b1_y2, vec_b2_y2, batches_stride);
// right - left + offset ---> left
__bang_sub(vec_left, vec_right, vec_left, batches_stride);
__bang_add_scalar(vec_left, vec_left, (T)offset, batches_stride);
// bottom - top + offset ---> right
__bang_sub(vec_right, vec_bottom, vec_top, batches_stride);
__bang_add_scalar(vec_right, vec_right, (T)offset, batches_stride);
// zero vector ---> bottom
__bang_write_value(vec_bottom, batches_stride, (T)0);
// width --> vec_left
__bang_maxequal(vec_left, vec_bottom, vec_left, batches_stride);
T *width = vec_left;
// height --> vec_right
__bang_maxequal(vec_right, vec_bottom, vec_right, batches_stride);
T *height = vec_right;
// get the b1_area
// (b1_x2 - b1_x1 + offset) ---> vec_top
__bang_sub(vec_top, vec_b1_x2, vec_b1_x1, batches_stride);
__bang_add_scalar(vec_top, vec_top, (T)offset, batches_stride);
// (b1_y2 - b1_y1 + offset) ---> vec_bottom
__bang_sub(vec_bottom, vec_b1_y2, vec_b1_y1, batches_stride);
__bang_add_scalar(vec_bottom, vec_bottom, (T)offset, batches_stride);
// b1_area = (b1_x2 - b1_x1 + offset) * (b1_y2 - b1_y1 + offset)
// ---> vec_top;
__bang_mul(vec_top, vec_top, vec_bottom, batches_stride);
T *b1_area = vec_top;
// get the b2_area
// (b2_x2 - b2_x1 + offset) ---> b2_x1
__bang_sub(vec_b2_x1, vec_b2_x2, vec_b2_x1, batches_stride);
__bang_add_scalar(vec_b2_x1, vec_b2_x1, (T)offset, batches_stride);
// (b2_y2 - b2_y1 + offset) ---> b2_y1
__bang_sub(vec_b2_y1, vec_b2_y2, vec_b2_y1, batches_stride);
__bang_add_scalar(vec_b2_y1, vec_b2_y1, (T)offset, batches_stride);
// b2_area = (b2_x2 - b2_x1 + offset) * (b2_y2 - b2_y1 + offset)
// ---> b2_x1;
__bang_mul(vec_b2_x1, vec_b2_x1, vec_b2_y1, batches_stride);
T *b2_area = vec_b2_x1;
// inter_s = width * height
__bang_mul(height, width, height, batches_stride);
T *inter_s = height;
// offset vector ---> vec_b2_y1
__bang_write_value(vec_b2_y1, batches_stride, T(offset));
T *vec_offset = vec_b2_y1;
if (mode == 0) {
__bang_add(b1_area, b1_area, b2_area, batches_stride);
__bang_sub(b1_area, b1_area, inter_s, batches_stride);
__bang_maxequal(b1_area, vec_offset, b1_area, batches_stride);
} else {
__bang_maxequal(b1_area, vec_offset, b1_area, batches_stride);
}
T *base_s = b1_area;
// ious = inter_s / base_s
computeDiv<T>(width, inter_s, base_s, vec_b2_x2, batches_stride);
int32_t gdram_offset = index1 * num_bbox2 + index2;
__memcpy((T *)ious + gdram_offset, width, handle_batches * sizeof(T),
NRAM2GDRAM);
}
}
}
}
template <typename T>
__mlu_global__ void MLUUnion1KernelBBoxOverlaps(
const void *bbox1, const void *bbox2, void *ious, const int32_t num_bbox1,
const int32_t num_bbox2, const int32_t mode, const bool aligned,
const int32_t offset) {
/*
* NRAM partition
* |-------------------------------------------------------------|
* | vec_b1_x1 | vec_b1_y1 | vec_b1_x2 | vec_b1_y2 |
* |-------------------------------------------------------------|
* | vec_b2_x1 | vec_b2_y1 | vec_b2_x2 | vec_b2_y2 |
* |-------------------------------------------------------------|
* | vec_left | vec_right | vec_top | vec_bottom |
* |-------------------------------------------------------------|
*
*/
const int32_t align_bytes = PAD_DOWN(MAX_NRAM_SIZE, NFU_ALIGN_SIZE);
const int32_t split_nram_num = 12;
const int32_t nram_stride =
align_bytes / NFU_ALIGN_SIZE / split_nram_num * NFU_ALIGN_SIZE;
void *vec_b1_x1 = nmem_buf;
void *vec_b1_y1 = nmem_buf + nram_stride;
void *vec_b1_x2 = nmem_buf + 2 * nram_stride;
void *vec_b1_y2 = nmem_buf + 3 * nram_stride;
void *vec_b2_x1 = nmem_buf + 4 * nram_stride;
void *vec_b2_y1 = nmem_buf + 5 * nram_stride;
void *vec_b2_x2 = nmem_buf + 6 * nram_stride;
void *vec_b2_y2 = nmem_buf + 7 * nram_stride;
void *vec_left = nmem_buf + 8 * nram_stride;
void *vec_right = nmem_buf + 9 * nram_stride;
void *vec_top = nmem_buf + 10 * nram_stride;
void *vec_bottom = nmem_buf + 11 * nram_stride;
const int32_t vec_length = nram_stride / sizeof(T);
bboxOverlapsWorkflow((T *)vec_b1_x1, (T *)vec_b1_y1, (T *)vec_b1_x2,
(T *)vec_b1_y2, (T *)vec_b2_x1, (T *)vec_b2_y1,
(T *)vec_b2_x2, (T *)vec_b2_y2, (T *)vec_left,
(T *)vec_right, (T *)vec_top, (T *)vec_bottom,
(T *)bbox1, (T *)bbox2, (T *)ious, offset, mode,
vec_length, num_bbox1, num_bbox2, aligned);
}
void KernelBBoxOverlaps(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
cnrtQueue_t queue, const cnrtDataType_t d_type,
const void *bbox1, const void *bbox2, void *ious,
const int32_t num_bbox1, const int32_t num_bbox2,
const int32_t mode, const bool aligned,
const int32_t offset) {
if (d_type == CNRT_FLOAT16) {
MLUUnion1KernelBBoxOverlaps<half><<<k_dim, k_type, queue>>>(
bbox1, bbox2, ious, num_bbox1, num_bbox2, mode, aligned, offset);
} else {
MLUUnion1KernelBBoxOverlaps<float><<<k_dim, k_type, queue>>>(
bbox1, bbox2, ious, num_bbox1, num_bbox2, mode, aligned, offset);
}
}
mmcv/ops/csrc/pytorch/mlu/bbox_overlaps_mlu.cpp
View file @ 099ee24d
...@@ -10,36 +10,11 @@ ...@@ -10,36 +10,11 @@
* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*************************************************************************/ *************************************************************************/
#include "pytorch_device_registry.hpp" #include "mlu_common_helper.h"
#include "pytorch_mlu_helper.hpp"
void KernelBBoxOverlaps(cnrtDim3_t k_dim, cnrtFunctionType_t k_type, void bbox_overlaps_mlu(const Tensor bboxes1, const Tensor bboxes2, Tensor ious,
cnrtQueue_t queue, const cnrtDataType_t d_type, const int32_t mode, const bool aligned,
const void *bbox1, const void *bbox2, void *ious, const int32_t offset) {
const int32_t num_bbox1, const int32_t num_bbox2,
const int32_t mode, const bool aligned,
const int32_t offset);
static void policyFunc(cnrtDim3_t *k_dim, cnrtFunctionType_t *k_type,
const int32_t batch_num_all) {
auto union_num = torch_mlu::getDeviceAttr(cnrtAttrClusterCount);
auto core_dim = torch_mlu::getDeviceAttr(cnrtAttrMcorePerCluster);
auto core_num = union_num * core_dim;
// Union1 policyFunc
*k_type = CNRT_FUNC_TYPE_UNION1;
k_dim->x = core_dim;
auto need_core_num = PAD_UP(batch_num_all, core_dim);
k_dim->y =
(need_core_num < core_num) ? (need_core_num / core_dim) : union_num;
k_dim->z = 1;
return;
}
void BBoxOverlapsMLUKernelLauncher(const Tensor bboxes1, const Tensor bboxes2,
Tensor ious, const int32_t mode,
const bool aligned, const int32_t offset) {
// check dtype // check dtype
TORCH_CHECK( TORCH_CHECK(
bboxes1.scalar_type() == at::kFloat || bboxes1.scalar_type() == at::kHalf, bboxes1.scalar_type() == at::kFloat || bboxes1.scalar_type() == at::kHalf,
...@@ -63,38 +38,19 @@ void BBoxOverlapsMLUKernelLauncher(const Tensor bboxes1, const Tensor bboxes2, ...@@ -63,38 +38,19 @@ void BBoxOverlapsMLUKernelLauncher(const Tensor bboxes1, const Tensor bboxes2,
return; return;
} }
// calculate task dimension INITIAL_MLU_PARAM_WITH_TENSOR(bboxes1);
cnrtDim3_t k_dim; INITIAL_MLU_PARAM_WITH_TENSOR(bboxes2);
cnrtFunctionType_t k_type; INITIAL_MLU_PARAM_WITH_TENSOR(ious);
policyFunc(&k_dim, &k_type, batch_num_all);
// get compute queue // get compute handle
cnrtQueue_t queue = torch_mlu::getCurQueue(); auto handle = mluOpGetCurrentHandle();
// get dtype of input TORCH_MLUOP_CHECK(mluOpBboxOverlaps(
cnrtDataType_t d_type = torch_mlu::toCnrtDtype(bboxes1.dtype()); handle, mode, aligned, offset, bboxes1_desc.desc(), bboxes1_ptr,
bboxes2_desc.desc(), bboxes2_ptr, ious_desc.desc(), ious_ptr));
// get ptr of tensors
auto bboxes1_impl = torch_mlu::getMluTensorImpl(bboxes1);
auto bboxes1_ptr = bboxes1_impl->cnnlMalloc();
auto bboxes2_impl = torch_mlu::getMluTensorImpl(bboxes2);
auto bboxes2_ptr = bboxes2_impl->cnnlMalloc();
auto ious_impl = torch_mlu::getMluTensorImpl(ious);
auto ious_ptr = ious_impl->cnnlMalloc();
// launch kernel
CNLOG(INFO) << "Launch Kernel MLUUnion1BboxOverlapsKernel";
CNLOG(INFO) << "kDim :[ " << k_dim.x << ", " << k_dim.y << ", " << k_dim.z
<< " ]";
KernelBBoxOverlaps(k_dim, k_type, queue, d_type, bboxes1_ptr, bboxes2_ptr,
ious_ptr, rows, cols, mode, aligned, offset);
}
void bbox_overlaps_mlu(const Tensor bboxes1, const Tensor bboxes2, Tensor ious,
const int mode, const bool aligned, const int offset) {
BBoxOverlapsMLUKernelLauncher(bboxes1, bboxes2, ious, mode, aligned, offset);
} }
void bbox_overlaps_impl(const Tensor bboxes1, const Tensor bboxes2, Tensor ious, void bbox_overlaps_impl(const Tensor bboxes1, const Tensor bboxes2, Tensor ious,
const int mode, const bool aligned, const int offset); const int mode, const bool aligned, const int offset);
REGISTER_DEVICE_IMPL(bbox_overlaps_impl, MLU, bbox_overlaps_mlu); REGISTER_DEVICE_IMPL(bbox_overlaps_impl, MLU, bbox_overlaps_mlu);
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