ikawrakow/ik_llama.cpp
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Check if MMQ should be used before using it (#603)

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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-07-13 07:43:15 +02:00
committed by GitHub
parent c53cb65251
commit b5ddec9516
1 changed files with 12 additions and 6 deletions
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18
ggml/src/ggml-cuda.cu
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@@ -2679,13 +2679,18 @@ static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor
bool use_quantized_src1 = false;
int64_t src1_padded_num_cols = 0, src1_padded_row_size = 0, src1_quantized_size = 0;
if (ggml_is_quantized(src0_1->type) && src0_1->type == src0_2->type && src1->ne[1] == 1 && src1->ne[3] == 1) {
src1_padded_num_cols = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
src1_padded_row_size = src1_padded_num_cols/ggml_blck_size(GGML_TYPE_Q8_1)*ggml_type_size(GGML_TYPE_Q8_1);
src1_quantized_size = src1_padded_row_size*src1->ne[2] + get_mmq_x_max_host(ggml_cuda_info().devices[ctx.device].cc)*sizeof(block_q8_1_mmq);
src1_quantized.alloc(src1_quantized_size);
use_quantized_src1 = true;
if (ggml_cuda_should_use_mmq(src0_1->type, ggml_cuda_info().devices[ctx.device].cc, src1->ne[2])) {
src1_padded_num_cols = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
src1_padded_row_size = src1_padded_num_cols/ggml_blck_size(GGML_TYPE_Q8_1)*ggml_type_size(GGML_TYPE_Q8_1);
src1_quantized_size = src1_padded_row_size*src1->ne[2] + get_mmq_x_max_host(ggml_cuda_info().devices[ctx.device].cc)*sizeof(block_q8_1_mmq);
src1_quantized.alloc(src1_quantized_size);
use_quantized_src1 = true;
}
}
ggml_cuda_pool_alloc<char> src1_contiguous(ctx.pool());
if (!use_quantized_src1) {
src1_contiguous.alloc(sizeof(float)*ggml_nelements(src1));
}
ggml_cuda_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
ggml_cuda_pool_alloc<char> dst_up_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
ggml_cuda_pool_alloc<char> dst_gate_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
ggml_cuda_pool_alloc<char> final_dst_contiguous(ctx.pool());
@@ -2728,6 +2733,7 @@ static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor
k_copy_src_to_contiguous<<<grid_dims, block_dims, 0, stream>>>(
src1_original, src1_contiguous.get(), dev_row_mapping.get() + mapping_offset, ne10, ne11, nb11, nb12);
CUDA_CHECK(cudaGetLastError());
src1_row.data = src1_contiguous.get();
}
src0_1_row.data = src0_1_original + i02*nb02;