vbatts/llama.cpp
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cann: support q4_0 model (#8822)

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wangshuai09 2024-08-05 12:22:30 +08:00 • committed by GitHub
parent 0d6fb52be0
commit c02b0a8a4d
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7 changed files with 357 additions and 45 deletions
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45
ggml/src/ggml-cann/aclnn_ops.cpp
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@ -910,6 +910,13 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
((ggml_tensor*)dst->extra)->ne);
return;
}
if (dst->type == GGML_TYPE_Q4_0) {
aclrtlaunch_ascendc_quantize_f16_to_q4_0(
24, ctx.stream(), src->data, dst->data,
((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
((ggml_tensor*)dst->extra)->ne);
return;
}
if (dst->type == GGML_TYPE_F16) {
if (ggml_are_same_shape(src, dst)) {
cann_copy(ctx, acl_src, acl_dst);
@ -971,6 +978,13 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
((ggml_tensor*)dst->extra)->ne);
return;
}
if (dst->type == GGML_TYPE_Q4_0) {
aclrtlaunch_ascendc_quantize_f32_to_q4_0(
24, ctx.stream(), src->data, dst->data,
((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
((ggml_tensor*)dst->extra)->ne);
return;
}
if (dst->type == GGML_TYPE_F32) {
if (ggml_are_same_shape(src, dst)) {
cann_copy(ctx, acl_src, acl_dst);
@ -2463,21 +2477,33 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
* @param dst The destination tensor where the result of the matrix
* multiplication will be stored.
*/
static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx,
ggml_tensor* dst) {
static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
ggml_tensor* dst,
const enum ggml_type type) {
ggml_tensor* src0 = dst->src[0]; // weight
ggml_tensor* src1 = dst->src[1]; // input
// The shape of the weight is NCHW. Matrix multiplication uses HW dims. HC
// is regarded as batch. weight need transpose.
int64_t weight_ne[] = {src0->ne[1], src0->ne[0]};
size_t weight_elem_size = sizeof(uint8_t);
size_t weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
float weight_elem_size;
if (type == GGML_TYPE_Q4_0) {
weight_elem_size = float(sizeof(uint8_t)) / 2;
}
else if (type == GGML_TYPE_Q8_0) {
weight_elem_size = float(sizeof(uint8_t));
}
else {
GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT");
}
float weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
// size of one matrix is element_size * height * width.
size_t weight_stride = weight_elem_size * src0->ne[0] * src0->ne[1];
size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3];
// scale stored at the end of weight. Also need transpose.
GGML_ASSERT(QK4_0 == QK8_0);
int64_t scale_ne[] = {src0->ne[1], src0->ne[0] / QK8_0};
size_t scale_elem_size = sizeof(uint16_t);
size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size,
@ -2541,8 +2567,9 @@ static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx,
(char*)input_buffer + batch1 * input_stride, ACL_FLOAT16,
input_elem_size, input_ne, input_nb, 2);
aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
(char*)src0->data + batch0 * weight_stride, ACL_INT8,
weight_elem_size, weight_ne, weight_nb, 2);
(char*)src0->data + batch0 * weight_stride,
ggml_cann_type_mapping(type), weight_elem_size, weight_ne,
weight_nb, 2);
aclTensor* acl_scale_tensor = ggml_cann_create_tensor(
scale_offset + batch0 * scale_stride, ACL_FLOAT16,
scale_elem_size, scale_ne, scale_nb, 2);
@ -2596,11 +2623,9 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
case GGML_TYPE_F16:
ggml_cann_mat_mul_fp(ctx, dst);
break;
// case GGML_TYPE_Q4_0:
// ggml_cann_mul_mat_q4_0(ctx, dst);
// break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q8_0:
ggml_cann_mul_mat_q8_0(ctx, dst);
ggml_cann_mul_mat_quant(ctx, dst, type);
break;
default:
GGML_ABORT("fatal error");