ikawrakow/ik_llama.cpp
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WIP CUDA

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Iwan Kawrakow
2025-08-30 11:16:48 +03:00
parent 06aacc3167
commit 4360ff545e
2 changed files with 64 additions and 4 deletions
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67
ggml/src/ggml-cuda.cu
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@@ -2521,7 +2521,7 @@ static bool ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
return false;
}
static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * next) {
static bool ggml_cuda_moe_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * next) {
const ggml_tensor * src0_1 = dst->src[0];
const ggml_tensor * src0_2 = dst->src[1];
const ggml_tensor * src0 = src0_1;
@@ -2972,6 +2972,60 @@ static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor
return fuse_down;
}
static void ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0_1 = dst->src[0];
const ggml_tensor * src0_2 = dst->src[1];
const ggml_tensor * src1 = dst->src[2];
GGML_ASSERT(ggml_is_quantized(src0_1->type));
GGML_ASSERT(src0_1->type == src0_2->type);
GGML_ASSERT(src1->ne[2] == 1);
GGML_ASSERT(src1->ne[3] == 1);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0_1->buffer));
GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0_2->buffer));
auto stream = ctx.stream();
auto ne10_padded = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
auto nb10_padded = ne10_padded*sizeof(block_q8_1)/QK8_1;
auto quantized_size = nb10_padded*src1->ne[1];
if (src1->ne[1] > 8) {
quantized_size += get_mmq_x_max_host(ggml_cuda_info().devices[ctx.device].cc)*sizeof(block_q8_1_mmq);
}
ggml_cuda_pool_alloc<float> dst_up(ctx.pool(), ggml_nelements(dst));
ggml_cuda_pool_alloc<char> src1_quantized(ctx.pool(), quantized_size);
if (src1->ne[1] <= 8) {
quantize_row_q8_1_cuda((const float *)src1->data, (void *)src1_quantized.get(), src1->ne[0], src1->ne[1], 1, nb10_padded,
src0_1->type, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_vec_q(ctx, src0_1, src1, dst, (const char *)src0_1->data, nullptr, src1_quantized.get(), dst_up.get(),
0, src0_1->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_vec_q(ctx, src0_2, src1, dst, (const char *)src0_2->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0_2->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
} else {
quantize_mmq_q8_1_cuda((const float *)src1->data, src1_quantized.get(), src1->ne[0], src1->ne[1], 1, ne10_padded, src0_1->type, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_q(ctx, src0_1, src1, dst, (const char *)src0_1->data, nullptr, src1_quantized.get(), dst_up.get(),
0, src0_1->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_q(ctx, src0_2, src1, dst, (const char *)src0_2->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0_1->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
}
ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], ggml_nelements(dst),
(const float *)dst->data, dst_up.get(), (float *)dst->data);
CUDA_CHECK(cudaGetLastError());
}
static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst, struct ggml_tensor * next, bool& skip_next) {
// why is this here instead of mul_mat?
if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) {
@@ -3097,7 +3151,10 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
skip_next = ggml_cuda_mul_mat_id(ctx, dst, next);
break;
case GGML_OP_MOE_FUSED_UP_GATE:
skip_next = ggml_cuda_up_gate_unary(ctx, dst, next);
skip_next = ggml_cuda_moe_up_gate_unary(ctx, dst, next);
break;
case GGML_OP_FUSED_UP_GATE:
ggml_cuda_up_gate_unary(ctx, dst);
break;
case GGML_OP_SCALE:
ggml_cuda_op_scale(ctx, dst);
@@ -3950,10 +4007,12 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
case GGML_OP_MOE_FUSED_UP_GATE:
case GGML_OP_FUSED_UP_GATE:
{
bool is_fused_up_gate = op->op == GGML_OP_MOE_FUSED_UP_GATE || op->op == GGML_OP_FUSED_UP_GATE;
struct ggml_tensor * a = op->src[0];
struct ggml_tensor * b = op->op == GGML_OP_MOE_FUSED_UP_GATE ? op->src[2] : op->src[1];
if (op->op == GGML_OP_MOE_FUSED_UP_GATE && a->type != op->src[1]->type) {
struct ggml_tensor * b = is_fused_up_gate ? op->src[2] : op->src[1];
if (is_fused_up_gate && a->type != op->src[1]->type) {
printf("%s: returning false for GGML_OP_MOE_FUSED_UP_GATE because src0->type != src1->type\n", __func__);
return false;
}

1
ggml/src/ggml-cuda/mmq.cuh
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@@ -108,6 +108,7 @@ static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
case GGML_TYPE_IQ4_KT:
return MMQ_Q8_1_DS_LAYOUT_D4;
default:
fprintf(stderr, "Unhandled type %s (%d)\n", ggml_type_name(type_x), type_x);
GGML_ABORT("fatal error");
break;
}