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cuda : fix dmmv cols requirement to 2*GGML_CUDA_DMMV_X (#8800)

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* cuda : fix dmmv cols requirement to 2*GGML_CUDA_DMMV_X

* update asserts

* only use dmmv for supported types

* add test
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slaren 2024-08-01 15:26:22 +02:00 committed by GitHub
parent c8a0090922
commit 7a11eb3a26
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4 changed files with 22 additions and 11 deletions
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5
ggml/src/ggml-cuda.cu
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@ -1885,10 +1885,9 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16)
bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src0->ne[0] >= GGML_CUDA_DMMV_X*2
&& src1->ne[1] == 1;
&& src0->ne[0] % (GGML_CUDA_DMMV_X*2) == 0 && src1->ne[1] == 1;
bool use_mul_mat_vec_q = ggml_is_quantized(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;

21
ggml/src/ggml-cuda/dmmv.cu
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@ -500,7 +500,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
}
static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
// the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
const dim3 block_nums(block_num_y, 1, 1);
@ -510,7 +510,7 @@ static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y,
}
static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@ -519,7 +519,7 @@ static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y,
}
static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@ -528,7 +528,7 @@ static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y,
}
static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@ -537,7 +537,7 @@ static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y,
}
static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@ -588,7 +588,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
}
static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@ -672,3 +672,12 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
GGML_UNUSED(src1_ncols);
GGML_UNUSED(src1_padded_row_size);
}
bool ggml_cuda_dmmv_type_supported(ggml_type src0_type) {
return src0_type == GGML_TYPE_Q4_0 || src0_type == GGML_TYPE_Q4_1 ||
src0_type == GGML_TYPE_Q5_0 || src0_type == GGML_TYPE_Q5_1 ||
src0_type == GGML_TYPE_Q8_0 || src0_type == GGML_TYPE_Q2_K ||
src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q4_K ||
src0_type == GGML_TYPE_Q5_K || src0_type == GGML_TYPE_Q6_K ||
src0_type == GGML_TYPE_F16;
}

2
ggml/src/ggml-cuda/dmmv.cuh
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@ -16,3 +16,5 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream);
bool ggml_cuda_dmmv_type_supported(ggml_type src0_type);

5
tests/test-backend-ops.cpp
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@ -804,8 +804,7 @@ struct test_cpy : public test_case {
test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 1},
std::array<int64_t, 4> permute = {0, 0, 0, 0},
bool _dst_use_permute = false)
std::array<int64_t, 4> permute = {0, 0, 0, 0})
: type_src(type_src), type_dst(type_dst), ne(ne), permute(permute),
_src_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
@ -2269,6 +2268,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
for (ggml_type type_a : other_types) {
for (ggml_type type_b : {GGML_TYPE_F32}) {
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, ggml_blck_size(type_a), { 1, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1}));
}
}