ikawrakow/ik_llama.cpp
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Doesn't do much on the GPU either

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Iwan Kawrakow
2025-08-31 08:48:51 +03:00
parent cef57a6b13
commit ff4f403231
2 changed files with 77 additions and 14 deletions
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87
ggml/src/ggml-cuda.cu
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@@ -2143,7 +2143,62 @@ 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) {
static int ggml_cuda_mul_mat_q(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const ggml_cgraph * cgraph, int node_n, bool is_gemv) {
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*ggml_nrows(src1);
if (!is_gemv) {
quantized_size += get_mmq_x_max_host(ggml_cuda_info().devices[ctx.device].cc)*sizeof(block_q8_1_mmq);
}
ggml_cuda_pool_alloc<char> src1_quantized(ctx.pool(), quantized_size);
if (is_gemv) {
quantize_row_q8_1_cuda((const float *)src1->data, (void *)src1_quantized.get(), src1->ne[0], src1->ne[1], src1->ne[2], ne10_padded,
src0->type, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_vec_q(ctx, src0, src1, dst, (const char *)src0->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0->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->type, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_q(ctx, src0, src1, dst, (const char *)src0->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
}
if (!cgraph) return node_n;
while (node_n + 1 < cgraph->n_nodes) {
dst = cgraph->nodes[node_n+1];
if (ggml_is_empty(dst) || dst->op == GGML_OP_RESHAPE || dst->op == GGML_OP_TRANSPOSE || dst->op == GGML_OP_VIEW
|| dst->op == GGML_OP_PERMUTE || dst->op == GGML_OP_NONE) {
++node_n; continue;
}
if (dst->op != GGML_OP_MUL_MAT || dst->src[1] != src1 || !ggml_is_quantized(dst->src[0]->type)) break;
if (!is_gemv && mmq_get_q8_1_ds_layout(src0->type) != mmq_get_q8_1_ds_layout(dst->src[0]->type)) break;
if (is_gemv) {
ggml_cuda_op_mul_mat_vec_q(ctx, dst->src[0], src1, dst, (const char *)dst->src[0]->data, nullptr, src1_quantized.get(),
(float *)dst->data, 0, dst->src[0]->ne[1], src1->ne[1], ne10_padded, stream);
} else {
ggml_cuda_op_mul_mat_q(ctx, dst->src[0], src1, dst, (const char *)dst->src[0]->data, nullptr, src1_quantized.get(),
(float *)dst->data, 0, dst->src[0]->ne[1], src1->ne[1], ne10_padded, stream);
}
CUDA_CHECK(cudaGetLastError());
++node_n;
}
return node_n;
}
static int ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const ggml_cgraph * cgraph, int node_n) {
const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
// If src0 is a temporary compute buffer it may have some padding that needs to be cleared for mul_mat_vec_q or mul_mat_q.
@@ -2188,6 +2243,10 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc);
}
if (!split && (use_mul_mat_vec_q || use_mul_mat_q) && src1->ne[2]*src1->ne[3] == 1) {
return ggml_cuda_mul_mat_q(ctx, src0, src1, dst, cgraph, node_n, use_mul_mat_vec_q);
}
// debug helpers
//printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
//printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
@@ -2215,6 +2274,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
} else {
ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, nullptr);
}
return node_n;
}
struct mmid_row_mapping {
@@ -2454,7 +2514,7 @@ static bool ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
src1_row.data = src1_original + i11*nb11 + i12*nb12;
dst_row.data = dst_original + i1*nb1 + i2*nb2;
ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row, nullptr, 0);
}
}
} else {
@@ -2505,7 +2565,7 @@ static bool ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
dst_row.nb[2] = num_src1_rows*nb1;
dst_row.nb[3] = num_src1_rows*nb1;
ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row, nullptr, 0);
{
dim3 block_dims(std::min((unsigned int)ne0, 768u));
@@ -2889,7 +2949,7 @@ static bool ggml_cuda_moe_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_te
ggml_cuda_op_mul_mat_q(ctx, &src0_1_row, &src1_row, &dst_row, (const char *)src0_1_row.data, nullptr, src1_quantized.get(), (float *)dst_row.data,
0, src0_1_row.ne[1], num_src1_rows, src1_padded_num_cols, stream);
} else {
ggml_cuda_mul_mat(ctx, &src0_1_row, &src1_row, &dst_row);
ggml_cuda_mul_mat(ctx, &src0_1_row, &src1_row, &dst_row, nullptr, 0);
}
CUDA_CHECK(cudaGetLastError());
@@ -2906,7 +2966,7 @@ static bool ggml_cuda_moe_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_te
ggml_cuda_op_mul_mat_q(ctx, &src0_2_row, &src1_row, &dst_row, (const char *)src0_2_row.data, nullptr, src1_quantized.get(), (float *)dst_row.data,
0, src0_2_row.ne[1], num_src1_rows, src1_padded_num_cols, stream);
} else {
ggml_cuda_mul_mat(ctx, &src0_2_row, &src1_row, &dst_row);
ggml_cuda_mul_mat(ctx, &src0_2_row, &src1_row, &dst_row, nullptr, 0);
}
CUDA_CHECK(cudaGetLastError());
@@ -2947,8 +3007,7 @@ static bool ggml_cuda_moe_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_te
(int)dst_row.ne[0], (int)dst_row.ne[1], (int)dst_row.ne[2], (int)dst_row.ne[3]);
first = false;
}
ggml_cuda_mul_mat(ctx, &final_src, &dst_row, &final_dst);
//ggml_cuda_mul_mat(ctx, next->src[0], &dst_row, &final_dst);
ggml_cuda_mul_mat(ctx, &final_src, &dst_row, &final_dst, nullptr, 0);
CUDA_CHECK(cudaGetLastError());
dim3 block_dims(std::min((unsigned int)next->ne[0], 768u));
@@ -3031,8 +3090,7 @@ static void ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor
}
static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst, struct ggml_tensor * next,
const ggml_cgraph * cgraph, int & i) {
static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst, const ggml_cgraph * cgraph, int & i) {
// why is this here instead of mul_mat?
if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) {
ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device);
@@ -3042,6 +3100,8 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
int64_t tim1 = ggml_time_us();
#endif
auto next = i < cgraph->n_nodes - 1 ? cgraph->nodes[i+1] : nullptr;
switch (dst->op) {
case GGML_OP_REPEAT:
ggml_cuda_op_repeat(ctx, dst);
@@ -3112,7 +3172,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
ggml_cuda_op_hardswish(ctx, dst);
break;
default:
return false;
return -1;
}
break;
case GGML_OP_NORM:
@@ -3148,9 +3208,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_MUL_MAT:
if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
GGML_CUDA_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
return false;
return -1;
} else {
ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
i = ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst, cgraph, i);
}
break;
case GGML_OP_MUL_MAT_ID:
@@ -3569,7 +3629,6 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
ggml_tensor * next = i < cgraph->n_nodes-1 ? cgraph->nodes[i+1] : nullptr;
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
@@ -3604,7 +3663,7 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
GGML_UNUSED(integrated);
#endif // NDEBUG
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node, next, cgraph, i);
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node, cgraph, i);
if (!ok) {
GGML_CUDA_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
}

4
src/llama.cpp
View File

@@ -11108,6 +11108,10 @@ struct llm_build_context {
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
ggml_build_forward_expand(gf, Qcur);
ggml_build_forward_expand(gf, Kcur);
ggml_build_forward_expand(gf, Vcur);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
cb(Qcur, "Qcur_normed", il);