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fix mul_mat_id() for new input, make the ut pass (#6682)

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Neo Zhang Jianyu 2024-04-15 17:12:26 +08:00 committed by GitHub
parent 1958f7e06c
commit 17e98d4c96
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1 changed files with 53 additions and 49 deletions
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102
ggml-sycl.cpp
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@ -15996,73 +15996,76 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
const ggml_tensor *src1,
ggml_tensor *dst) try {
#if 0
ggml_sycl_mul_mat_id_sycl(dst);
// TODO: mmq/mmv support
#endif
const int64_t nb11 = src1->nb[1];
const int64_t nb1 = dst->nb[1];
const struct ggml_tensor * ids = src0;
const int32_t id = ((int32_t *) dst->op_params)[0];
const int32_t n_as = ((int32_t *) dst->op_params)[1];
std::vector<char> ids_host(ggml_nbytes(ids));
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT &&
"mul_mat_id does not support split buffers");
const ggml_tensor *ids = dst->src[2];
const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
if (ids->backend == GGML_BACKEND_TYPE_GPU) {
const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
SYCL_CHECK(CHECK_TRY_ERROR(
stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)).wait()));
// SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
} else {
memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
}
const size_t nb11 = src1->nb[1];
const size_t nb1 = dst->nb[1];
const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra;
const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra;
const int32_t id = ((int32_t *)dst->op_params)[0];
const int32_t n_as = src0->ne[2];
std::vector<char> ids_host(ggml_nbytes(ids));
const char *ids_dev = (const char *)ids->data;
SYCL_CHECK(CHECK_TRY_ERROR(
stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
const ggml_tensor_extra_gpu *src0_extra =
(const ggml_tensor_extra_gpu *)src0->extra;
const ggml_tensor_extra_gpu *src1_extra =
(const ggml_tensor_extra_gpu *)src1->extra;
const ggml_tensor_extra_gpu *dst_extra =
(const ggml_tensor_extra_gpu *)dst->extra;
ggml_tensor_extra_gpu src0_row_extra;
ggml_tensor_extra_gpu src1_row_extra;
ggml_tensor_extra_gpu dst_row_extra;
ggml_tensor src0_row = *src0;
ggml_tensor src1_row = *src1;
ggml_tensor dst_row = *dst;
src1_row.backend = GGML_BACKEND_TYPE_GPU;
dst_row.backend = GGML_BACKEND_TYPE_GPU;
src0_row.extra = &src0_row_extra;
src1_row.extra = &src1_row_extra;
dst_row.extra = &dst_row_extra;
char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ?
(char *) src1->data : (char *) src1_extra->data_device[g_main_device];
char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ?
(char *) dst->data : (char *) dst_extra->data_device[g_main_device];
char *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU
? (char *)src0->data
: (char *)src0_extra->data_device[g_main_device];
char *src1_original = src1->backend == GGML_BACKEND_TYPE_CPU
? (char *)src1->data
: (char *)src1_extra->data_device[g_main_device];
char *dst_original = dst->backend == GGML_BACKEND_TYPE_CPU
? (char *)dst->data
: (char *)dst_extra->data_device[g_main_device];
src0_row.ne[2] = 1;
src0_row.ne[3] = 1;
src0_row.nb[3] = src0->nb[2];
if (src1->ne[1] == 1) {
GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
//int32_t row_id;
//SYCL_CHECK(syclMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), syclMemcpyDeviceToHost, g_syclStreams[g_main_device][0]));
//SYCL_CHECK(syclStreamSynchronize(g_syclStreams[g_main_device][0]));
const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
const int32_t row_id =
*(const int32_t *)(ids_host.data() + i01 * ids->nb[1] +
id * ids->nb[0]);
GGML_ASSERT(row_id >= 0 && row_id < n_as);
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
src0_row_extra.data_device[g_main_device] =
src0_original + row_id * src0->nb[2];
src1_row_extra.data_device[g_main_device] =
src1_original + i01 * src1->nb[1];
dst_row_extra.data_device[g_main_device] =
dst_original + i01 * dst->nb[1];
src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1];
src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set?
dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1];
dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set?
ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row);
ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
}
} else {
sycl_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1));
@ -16072,8 +16075,6 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
dst_row_extra.data_device[g_main_device] = dst_contiguous.get();
for (int32_t row_id = 0; row_id < n_as; ++row_id) {
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
int64_t num_src1_rows = 0;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
@ -16086,7 +16087,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
SYCL_CHECK(CHECK_TRY_ERROR(
stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11,
src1_original + i01 * nb11, nb11).wait()));
src1_original + i01 * nb11, nb11)));
num_src1_rows++;
}
@ -16094,6 +16095,9 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
continue;
}
src0_row_extra.data_device[g_main_device] =
src0_original + row_id * src0->nb[2];
src1_row.ne[1] = num_src1_rows;
dst_row.ne[1] = num_src1_rows;
@ -16105,7 +16109,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
dst_row.nb[2] = num_src1_rows*nb1;
dst_row.nb[3] = num_src1_rows*nb1;
ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row);
ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
num_src1_rows = 0;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
@ -16119,7 +16123,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
dst_original + i01 * nb1,
dst_contiguous.get() + num_src1_rows * nb1, nb1).wait()));
dst_contiguous.get() + num_src1_rows * nb1, nb1)));
num_src1_rows++;
}
}