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Add support for sqrt on CUDA (#7953)

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* 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:
Calvin Laurenson
2024-06-16 15:23:04 -07:00
committed by GitHub
parent 19b7a836f6
commit 43b35e38ba
5 changed files with 71 additions and 8 deletions
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16
examples/cvector-generator/pca.hpp
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@@ -64,15 +64,15 @@ struct pca_model {
struct ggml_tensor * dev_eigenvector;
pca_model(struct ggml_tensor * t_input) {
// TODO: enable GPU support when support for GGML_OP_SQRT is added
// #ifdef GGML_USE_CUDA
// fprintf(stderr, "%s: using CUDA backend\n", __func__);
// backend = ggml_backend_cuda_init(0); // init device 0
// if (!backend) {
// fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
// }
// #endif
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
#endif
// TODO: enable Metal support when support for GGML_OP_SQRT is added
// #ifdef GGML_USE_METAL
// fprintf(stderr, "%s: using Metal backend\n", __func__);
// backend = ggml_backend_metal_init();

4
ggml-cuda.cu
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@@ -2267,6 +2267,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_SQR:
ggml_cuda_op_sqr(ctx, dst);
break;
case GGML_OP_SQRT:
ggml_cuda_op_sqrt(ctx, dst);
break;
case GGML_OP_CLAMP:
ggml_cuda_op_clamp(ctx, dst);
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_SCALE:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_CLAMP:
case GGML_OP_CONT:
case GGML_OP_DIAG_MASK_INF:

28
ggml-cuda/unary.cu
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@@ -92,6 +92,15 @@ static __global__ void sqr_f32(const float * x, float * dst, const int k) {
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) {
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);
@@ -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);
}
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) {
const ggml_tensor * src0 = dst->src[0];
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);
}
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);
}

3
ggml-cuda/unary.cuh
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@@ -8,6 +8,7 @@
#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
#define CUDA_HARDSWISH_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);
@@ -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_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

28
tests/test-backend-ops.cpp
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@@ -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
struct test_clamp : public test_case {
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_sqrt());
test_cases.emplace_back(new test_clamp());
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));