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cann: Fix ggml_cann_im2col for 1D im2col (#8819)

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* fix ggml_cann_im2col for 1D im2col

* fix build warning
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Mengqing Cao 2024-08-02 16:50:53 +08:00 committed by GitHub
parent 0fbbd88458
commit e09a800f9a
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2 changed files with 142 additions and 28 deletions
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167
ggml/src/ggml-cann/aclnn_ops.cpp
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@ -1312,6 +1312,111 @@ aclnnStatus aclnnIm2col(void* workspace, uint64_t workspaceSize,
#ifdef __cplusplus
}
#endif
static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx,
ggml_tensor* dst,
ggml_tensor* src1,
aclTensor* tmp_cast_tensor,
aclTensor* tmp_im2col_tensor) {
// Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW]
int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]};
size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]};
aclTensor* acl_dst =
ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1);
int64_t permute_dim[] = {0, 2, 1};
if (src1->type != dst->type) {
aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3);
} else {
aclnn_permute(ctx, tmp_im2col_tensor, acl_dst, permute_dim, 3);
}
// release
ACL_CHECK(aclDestroyTensor(acl_dst));
}
static void ggml_cann_im2col_1d_post_process(
ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_tensor* src1,
aclTensor* tmp_cast_tensor, aclTensor* tmp_im2col_tensor,
const std::vector<int64_t>& im2col_op_params) {
// get params
const int64_t KH = im2col_op_params[0];
const int64_t KW = im2col_op_params[1];
const int64_t IW = im2col_op_params[2];
const int64_t IC = im2col_op_params[3];
const int64_t N = im2col_op_params[4];
const int64_t OH = im2col_op_params[5];
const int64_t OW = im2col_op_params[6];
const int64_t s0 = im2col_op_params[7];
const int64_t p0 = im2col_op_params[8];
const int64_t d0 = im2col_op_params[9];
const int64_t n_bytes_factor = im2col_op_params[10];
// Permute: [N, IC * KH * KW, OW * OH] ->
// [N, OW * OH * n_bytes_factor, IC * KH * KW]
aclTensor* tmp_permute_tensor = nullptr;
ggml_cann_pool_alloc tmp_permute_allocator(ctx.pool());
tmp_permute_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
void* tmp_permute_buffer = tmp_permute_allocator.get();
int64_t tmp_permute_ne[] = {IC * KH * KW, OW * OH * n_bytes_factor, N};
size_t tmp_permute_nb[GGML_MAX_DIMS - 1];
tmp_permute_nb[0] = ggml_type_size(dst->type);
for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1];
}
tmp_permute_tensor = ggml_cann_create_tensor(
tmp_permute_buffer, ggml_cann_type_mapping(dst->type),
ggml_type_size(dst->type), tmp_permute_ne, tmp_permute_nb,
GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
int64_t permute_dim[] = {0, 2, 1};
if (src1->type != dst->type) {
aclnn_permute(ctx, tmp_cast_tensor, tmp_permute_tensor, permute_dim, 3);
} else {
aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor, permute_dim,
3);
}
// number of times the kernel moves in W dimension
const int n_step_w = (IW + 2 * p0 - d0 * (KW - 1) - 1) / s0 + 1;
size_t offset;
void *cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer;
// memory copy with offset to restore 1D im2col from 2d
if (IC > 1) {
offset = IC * KH * KW * n_step_w * ggml_type_size(dst->type);
size_t size_cpy = KH * KW * ggml_type_size(dst->type);
for (int c = 0; c < IC; c++) {
cur_permute_buffer = (char*)tmp_permute_buffer + offset +
KH * KW * c * ggml_type_size(dst->type);
cur_dst_buffer = (char*)dst->data +
c * KH * KW * n_step_w * ggml_type_size(dst->type);
for (int i = 0; i < n_step_w; i++) {
ACL_CHECK(aclrtMemcpyAsync(
cur_dst_buffer, size_cpy, cur_permute_buffer, size_cpy,
ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
cur_dst_buffer =
(char*)cur_dst_buffer + KH * KW * ggml_type_size(dst->type);
cur_permute_buffer = (char*)cur_permute_buffer +
KH * KW * IC * ggml_type_size(dst->type);
}
}
} else {
offset = KH * KW * n_step_w *
ggml_type_size(dst->type); // equal to ggml_nbytes(dst)
ACL_CHECK(aclrtMemcpyAsync(dst->data, offset,
(char*)tmp_permute_buffer + offset, offset,
ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
}
// release
ACL_CHECK(aclDestroyTensor(tmp_permute_tensor));
}
void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_tensor* src0 = dst->src[0]; // kernel
ggml_tensor* src1 = dst->src[1]; // input
@ -1320,21 +1425,23 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
GGML_TENSOR_BINARY_OP_LOCALS;
const int64_t N = is_2D ? ne13 : ne12;
const int64_t IC = is_2D ? ne12 : ne11;
// aclnnIm2col only works on 2D. set s1, p1, d1 to 1 to perform 2D
// im2col and do post-processing to restore it to 1D.
const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
const int32_t s1 = is_2D ? ((const int32_t*)(dst->op_params))[1] : 1;
const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
const int32_t p1 = is_2D ? ((const int32_t*)(dst->op_params))[3] : 1;
const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
const int32_t d1 = is_2D ? ((const int32_t*)(dst->op_params))[5] : 1;
const int64_t KH = is_2D ? ne01 : 1;
const int64_t N = ne13;
const int64_t IC = ne12;
const int64_t KH = ne01;
const int64_t KW = ne00;
const int64_t IW = ne10;
const int64_t OH = is_2D ? ne2 : 1;
const int64_t OW = ne1;
@ -1342,9 +1449,12 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
GGML_ASSERT(nb10 == sizeof(float));
// im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH]
// memory allocated increased to 3x when is_2D == false
const int64_t n_bytes_factor = is_2D ? 1 : 3;
// im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH * n_bytes_factor]
aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
int64_t tmp_im2col_ne[] = {OW * OH, IC * KH * KW, N};
int64_t tmp_im2col_ne[] = {OW * OH * n_bytes_factor, IC * KH * KW, N};
size_t tmp_im2col_nb[GGML_MAX_DIMS - 1];
tmp_im2col_nb[0] = ggml_type_size(src1->type);
@ -1356,8 +1466,10 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
// If dst is f16, tmp_buffer is f32, we need alloc src.typesize *
// dst.elemcount.
ggml_cann_pool_alloc im2col_allocator(
ctx.pool(), ggml_nelements(dst) * ggml_element_size(src1));
ctx.pool(),
ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor);
void* tmp_im2col_buffer = im2col_allocator.get();
aclTensor* tmp_im2col_tensor = ggml_cann_create_tensor(
tmp_im2col_buffer, ggml_cann_type_mapping(src1->type),
ggml_type_size(src1->type), tmp_im2col_ne, tmp_im2col_nb,
@ -1380,8 +1492,9 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
paddings, strides, tmp_im2col_tensor,
&workspaceSize, &executor));
ggml_cann_pool_alloc workspace_allocator(ctx.pool());
if (workspaceSize > 0) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
workspace_allocator.alloc(workspaceSize);
workspaceAddr = workspace_allocator.get();
}
@ -1391,9 +1504,10 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
// Cast if dst is f16.
aclTensor* tmp_cast_tensor = nullptr;
ggml_cann_pool_alloc tmp_cast_allocator(ctx.pool());
void* tmp_cast_buffer = nullptr;
if (src1->type != dst->type) {
tmp_cast_allocator.alloc(ggml_nbytes(dst));
void* tmp_cast_buffer = tmp_cast_allocator.get();
tmp_cast_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
tmp_cast_buffer = tmp_cast_allocator.get();
size_t temp_cast_nb[GGML_MAX_DIMS - 1];
temp_cast_nb[0] = ggml_type_size(dst->type);
for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
@ -1408,24 +1522,21 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_cann_type_mapping(dst->type));
}
// Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW]
int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]};
size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]};
aclTensor* acl_dst =
ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1);
int64_t permute_dim[] = {0, 2, 1};
if (src1->type != dst->type) {
aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3);
// post-processing
if (is_2D) {
ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor,
tmp_im2col_tensor);
} else {
aclnn_permute(ctx, tmp_im2col_tensor, acl_dst, permute_dim, 3);
std::vector<int64_t> im2col_op_params = {
KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor};
ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor,
tmp_im2col_tensor, im2col_op_params);
}
// release
ACL_CHECK(aclDestroyTensor(acl_src1));
ACL_CHECK(aclDestroyTensor(tmp_im2col_tensor));
ACL_CHECK(aclDestroyTensor(tmp_cast_tensor));
ACL_CHECK(aclDestroyTensor(acl_dst));
ACL_CHECK(aclDestroyIntArray(kernel_size));
ACL_CHECK(aclDestroyIntArray(dilations));
ACL_CHECK(aclDestroyIntArray(paddings));

3
tests/test-backend-ops.cpp
View File

@ -2139,6 +2139,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
// test cases for 1D im2col
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
test_cases.emplace_back(new test_conv_transpose_1d());
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 3, 0, 1));