mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
Add support for sqrt on CUDA (#7953)
* cuda sqrt support * enable cuda in pca * fix comments in pca * add test * add sqrt to ggml_backend_cuda_supports_op * fix test * new line * Use F32 sqrtf instead of F64 sqrt Co-authored-by: Johannes Gäßler <johannesg@5d6.de> --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
This commit is contained in:
parent
19b7a836f6
commit
43b35e38ba
@ -64,15 +64,15 @@ struct pca_model {
|
|||||||
struct ggml_tensor * dev_eigenvector;
|
struct ggml_tensor * dev_eigenvector;
|
||||||
|
|
||||||
pca_model(struct ggml_tensor * t_input) {
|
pca_model(struct ggml_tensor * t_input) {
|
||||||
// TODO: enable GPU support when support for GGML_OP_SQRT is added
|
#ifdef GGML_USE_CUDA
|
||||||
// #ifdef GGML_USE_CUDA
|
fprintf(stderr, "%s: using CUDA backend\n", __func__);
|
||||||
// fprintf(stderr, "%s: using CUDA backend\n", __func__);
|
backend = ggml_backend_cuda_init(0); // init device 0
|
||||||
// backend = ggml_backend_cuda_init(0); // init device 0
|
if (!backend) {
|
||||||
// if (!backend) {
|
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
|
||||||
// fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
|
}
|
||||||
// }
|
#endif
|
||||||
// #endif
|
|
||||||
|
|
||||||
|
// TODO: enable Metal support when support for GGML_OP_SQRT is added
|
||||||
// #ifdef GGML_USE_METAL
|
// #ifdef GGML_USE_METAL
|
||||||
// fprintf(stderr, "%s: using Metal backend\n", __func__);
|
// fprintf(stderr, "%s: using Metal backend\n", __func__);
|
||||||
// backend = ggml_backend_metal_init();
|
// backend = ggml_backend_metal_init();
|
||||||
|
@ -2267,6 +2267,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
|
|||||||
case GGML_OP_SQR:
|
case GGML_OP_SQR:
|
||||||
ggml_cuda_op_sqr(ctx, dst);
|
ggml_cuda_op_sqr(ctx, dst);
|
||||||
break;
|
break;
|
||||||
|
case GGML_OP_SQRT:
|
||||||
|
ggml_cuda_op_sqrt(ctx, dst);
|
||||||
|
break;
|
||||||
case GGML_OP_CLAMP:
|
case GGML_OP_CLAMP:
|
||||||
ggml_cuda_op_clamp(ctx, dst);
|
ggml_cuda_op_clamp(ctx, dst);
|
||||||
break;
|
break;
|
||||||
@ -2830,6 +2833,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
|
|||||||
case GGML_OP_RMS_NORM:
|
case GGML_OP_RMS_NORM:
|
||||||
case GGML_OP_SCALE:
|
case GGML_OP_SCALE:
|
||||||
case GGML_OP_SQR:
|
case GGML_OP_SQR:
|
||||||
|
case GGML_OP_SQRT:
|
||||||
case GGML_OP_CLAMP:
|
case GGML_OP_CLAMP:
|
||||||
case GGML_OP_CONT:
|
case GGML_OP_CONT:
|
||||||
case GGML_OP_DIAG_MASK_INF:
|
case GGML_OP_DIAG_MASK_INF:
|
||||||
|
@ -92,6 +92,15 @@ static __global__ void sqr_f32(const float * x, float * dst, const int k) {
|
|||||||
dst[i] = x[i] * x[i];
|
dst[i] = x[i] * x[i];
|
||||||
}
|
}
|
||||||
|
|
||||||
|
static __global__ void sqrt_f32(const float * x, float * dst, const int k) {
|
||||||
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
||||||
|
|
||||||
|
if (i >= k) {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
dst[i] = sqrtf(x[i]);
|
||||||
|
}
|
||||||
|
|
||||||
static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
||||||
const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
|
const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
|
||||||
gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
||||||
@ -142,6 +151,11 @@ static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t
|
|||||||
sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
static void sqrt_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
||||||
|
const int num_blocks = (k + CUDA_SQRT_BLOCK_SIZE - 1) / CUDA_SQRT_BLOCK_SIZE;
|
||||||
|
sqrt_f32<<<num_blocks, CUDA_SQRT_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
||||||
|
}
|
||||||
|
|
||||||
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||||
const ggml_tensor * src0 = dst->src[0];
|
const ggml_tensor * src0 = dst->src[0];
|
||||||
const float * src0_d = (const float *)src0->data;
|
const float * src0_d = (const float *)src0->data;
|
||||||
@ -284,3 +298,17 @@ void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
|||||||
|
|
||||||
sqr_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
|
sqr_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||||
|
const ggml_tensor * src0 = dst->src[0];
|
||||||
|
const float * src0_d = (const float *)src0->data;
|
||||||
|
float * dst_d = (float *)dst->data;
|
||||||
|
cudaStream_t stream = ctx.stream();
|
||||||
|
|
||||||
|
GGML_ASSERT(ggml_is_contiguous(src0));
|
||||||
|
|
||||||
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||||
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
||||||
|
|
||||||
|
sqrt_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
|
||||||
|
}
|
||||||
|
@ -8,6 +8,7 @@
|
|||||||
#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
|
#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
|
||||||
#define CUDA_HARDSWISH_BLOCK_SIZE 256
|
#define CUDA_HARDSWISH_BLOCK_SIZE 256
|
||||||
#define CUDA_SQR_BLOCK_SIZE 256
|
#define CUDA_SQR_BLOCK_SIZE 256
|
||||||
|
#define CUDA_SQRT_BLOCK_SIZE 256
|
||||||
|
|
||||||
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||||
|
|
||||||
@ -28,3 +29,5 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
|||||||
void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||||
|
|
||||||
void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||||
|
|
||||||
|
void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||||
|
@ -1063,6 +1063,33 @@ struct test_sqr : public test_case {
|
|||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|
||||||
|
// GGML_OP_SQRT
|
||||||
|
struct test_sqrt : public test_case {
|
||||||
|
const ggml_type type;
|
||||||
|
const std::array<int64_t, 4> ne;
|
||||||
|
|
||||||
|
std::string vars() override {
|
||||||
|
return VARS_TO_STR2(type, ne);
|
||||||
|
}
|
||||||
|
|
||||||
|
test_sqrt(ggml_type type = GGML_TYPE_F32,
|
||||||
|
std::array<int64_t, 4> ne = {10, 10, 10, 10})
|
||||||
|
: type(type), ne(ne) {}
|
||||||
|
|
||||||
|
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||||
|
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||||||
|
ggml_tensor * out = ggml_sqrt(ctx, a);
|
||||||
|
return out;
|
||||||
|
}
|
||||||
|
|
||||||
|
void initialize_tensors(ggml_context * ctx) override {
|
||||||
|
// fill with positive values
|
||||||
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
||||||
|
init_tensor_uniform(t, 0.0f, 100.0f);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
// GGML_OP_CLAMP
|
// GGML_OP_CLAMP
|
||||||
struct test_clamp : public test_case {
|
struct test_clamp : public test_case {
|
||||||
const ggml_type type;
|
const ggml_type type;
|
||||||
@ -2200,6 +2227,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
|||||||
}
|
}
|
||||||
|
|
||||||
test_cases.emplace_back(new test_sqr());
|
test_cases.emplace_back(new test_sqr());
|
||||||
|
test_cases.emplace_back(new test_sqrt());
|
||||||
test_cases.emplace_back(new test_clamp());
|
test_cases.emplace_back(new test_clamp());
|
||||||
|
|
||||||
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));
|
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));
|
||||||
|
Loading…
Reference in New Issue
Block a user