root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-28 12:24:35 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

attn

Browse Source
This commit is contained in:
Georgi Gerganov 2024-12-10 21:59:45 +02:00
parent 33dffbc57a
commit 00afcb4820
No known key found for this signature in database
GPG Key ID: 449E073F9DC10735
2 changed files with 152 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
convert_hf_to_gguf.py
View File

@ -296,7 +296,9 @@ class Model:
break
for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
data = data_torch.squeeze().numpy()
# TODO: why do we squeeze here?
#data = data_torch.squeeze().numpy()
data = data_torch.numpy()
# if data ends up empty, it means data_torch was a scalar tensor -> restore
if len(data.shape) == 0:
@ -2044,6 +2046,8 @@ class OuteTTSVocoderModel(Model):
logger.debug(f"Skipping {name!r}")
return []
print(f"{self.map_tensor_name(name)} -> {data_torch.shape}")
return [(self.map_tensor_name(name), data_torch)]
def set_vocab(self):

169
src/llama.cpp
View File

@ -3055,9 +3055,11 @@ struct llama_model {
struct ggml_tensor * cls_out_b = nullptr;
// outetts vocoder
// TODO: dedup
struct ggml_tensor * conv_1d = nullptr;
struct ggml_tensor * conv_1d_b = nullptr;
// resnet 0
struct ggml_tensor * posnet_0_norm1 = nullptr;
struct ggml_tensor * posnet_0_norm1_b = nullptr;
@ -3070,6 +3072,7 @@ struct llama_model {
struct ggml_tensor * posnet_0_conv2 = nullptr;
struct ggml_tensor * posnet_0_conv2_b = nullptr;
// resnet 1
struct ggml_tensor * posnet_1_norm1 = nullptr;
struct ggml_tensor * posnet_1_norm1_b = nullptr;
@ -3082,6 +3085,48 @@ struct llama_model {
struct ggml_tensor * posnet_1_conv2 = nullptr;
struct ggml_tensor * posnet_1_conv2_b = nullptr;
// attn 2
struct ggml_tensor * posnet_2_attn_norm = nullptr;
struct ggml_tensor * posnet_2_attn_norm_b = nullptr;
struct ggml_tensor * posnet_2_attn_q = nullptr;
struct ggml_tensor * posnet_2_attn_q_b = nullptr;
struct ggml_tensor * posnet_2_attn_k = nullptr;
struct ggml_tensor * posnet_2_attn_k_b = nullptr;
struct ggml_tensor * posnet_2_attn_v = nullptr;
struct ggml_tensor * posnet_2_attn_v_b = nullptr;
struct ggml_tensor * posnet_2_attn_o = nullptr;
struct ggml_tensor * posnet_2_attn_o_b = nullptr;
// resnet 3
struct ggml_tensor * posnet_3_norm1 = nullptr;
struct ggml_tensor * posnet_3_norm1_b = nullptr;
struct ggml_tensor * posnet_3_conv1 = nullptr;
struct ggml_tensor * posnet_3_conv1_b = nullptr;
struct ggml_tensor * posnet_3_norm2 = nullptr;
struct ggml_tensor * posnet_3_norm2_b = nullptr;
struct ggml_tensor * posnet_3_conv2 = nullptr;
struct ggml_tensor * posnet_3_conv2_b = nullptr;
// resnet 4
struct ggml_tensor * posnet_4_norm1 = nullptr;
struct ggml_tensor * posnet_4_norm1_b = nullptr;
struct ggml_tensor * posnet_4_conv1 = nullptr;
struct ggml_tensor * posnet_4_conv1_b = nullptr;
struct ggml_tensor * posnet_4_norm2 = nullptr;
struct ggml_tensor * posnet_4_norm2_b = nullptr;
struct ggml_tensor * posnet_4_conv2 = nullptr;
struct ggml_tensor * posnet_4_conv2_b = nullptr;
std::vector<llama_layer> layers;
// gguf metadata
@ -7386,6 +7431,11 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
{LLM_TENSOR_POS_NET_NORM2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_POS_NET_CONV1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
{LLM_TENSOR_POS_NET_CONV2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
{LLM_TENSOR_POS_NET_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_POS_NET_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_POS_NET_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_POS_NET_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_POS_NET_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
};
// checks if the weight tensor can be used with the specified buffer type and device
@ -9491,6 +9541,45 @@ static bool llm_load_tensors(
model.posnet_1_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 1), {3, 768, 768}, 0);
model.posnet_1_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 1), {768}, 0);
model.posnet_2_attn_norm = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", 2), {768}, 0);
model.posnet_2_attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias", 2), {768}, 0);
model.posnet_2_attn_q = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "weight", 2), {1, 768, 768}, 0);
model.posnet_2_attn_q_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "bias", 2), {768}, 0);
model.posnet_2_attn_k = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "weight", 2), {1, 768, 768}, 0);
model.posnet_2_attn_k_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "bias", 2), {768}, 0);
model.posnet_2_attn_v = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "weight", 2), {1, 768, 768}, 0);
model.posnet_2_attn_v_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "bias", 2), {768}, 0);
model.posnet_2_attn_o = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "weight", 2), {1, 768, 768}, 0);
model.posnet_2_attn_o_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "bias", 2), {768}, 0);
model.posnet_3_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 3), {768}, 0);
model.posnet_3_norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", 3), {768}, 0);
model.posnet_3_conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", 3), {3, 768, 768}, 0);
model.posnet_3_conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", 3), {768}, 0);
model.posnet_3_norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", 3), {768}, 0);
model.posnet_3_norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", 3), {768}, 0);
model.posnet_3_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 3), {3, 768, 768}, 0);
model.posnet_3_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 3), {768}, 0);
model.posnet_4_norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", 4), {768}, 0);
model.posnet_4_norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", 4), {768}, 0);
model.posnet_4_conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", 4), {3, 768, 768}, 0);
model.posnet_4_conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", 4), {768}, 0);
model.posnet_4_norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", 4), {768}, 0);
model.posnet_4_norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", 4), {768}, 0);
model.posnet_4_conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", 4), {3, 768, 768}, 0);
model.posnet_4_conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", 4), {768}, 0);
// output
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {768}, 0);
model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {768, 1282}, llama_model_loader::TENSOR_NOT_REQUIRED);
@ -17088,58 +17177,94 @@ struct llm_build_context {
cur = ggml_conv_1d_ph(ctx0, model.conv_1d, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.conv_1d_b, 1, model.conv_1d_b->ne[0]));
inpL = cur;
// resnet block 0
{
struct ggml_tensor * cur_rnet = cur;
cur_rnet = llm_build_norm(ctx0, cur, hparams,
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_0_norm1, 1, model.posnet_0_norm1->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_0_norm1_b, 1, model.posnet_0_norm1_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_0_conv1, cur_rnet, 1, 1);
cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_0_conv1_b, 1, model.posnet_0_conv1_b->ne[0]));
cur = ggml_conv_1d_ph(ctx0, model.posnet_0_conv1, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_0_conv1_b, 1, model.posnet_0_conv1_b->ne[0]));
cur_rnet = llm_build_norm(ctx0, cur_rnet, hparams,
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_0_norm2, 1, model.posnet_0_norm2->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_0_norm2_b, 1, model.posnet_0_norm2_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_0_conv2, cur_rnet, 1, 1);
cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_0_conv2_b, 1, model.posnet_0_conv2_b->ne[0]));
cur = ggml_conv_1d_ph(ctx0, model.posnet_0_conv2, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_0_conv2_b, 1, model.posnet_0_conv2_b->ne[0]));
cur = ggml_add(ctx0, cur_rnet, cur);
cur = ggml_add(ctx0, cur, inpL);
}
inpL = cur;
// resnet block 1
{
struct ggml_tensor * cur_rnet = cur;
cur_rnet = llm_build_norm(ctx0, cur, hparams,
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_1_norm1, 1, model.posnet_1_norm1->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_1_norm1_b, 1, model.posnet_1_norm1_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_1_conv1, cur_rnet, 1, 1);
cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_1_conv1_b, 1, model.posnet_1_conv1_b->ne[0]));
cur = ggml_conv_1d_ph(ctx0, model.posnet_1_conv1, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_1_conv1_b, 1, model.posnet_1_conv1_b->ne[0]));
cur_rnet = llm_build_norm(ctx0, cur_rnet, hparams,
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_1_norm2, 1, model.posnet_1_norm2->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_1_norm2_b, 1, model.posnet_1_norm2_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
cur_rnet = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur_rnet), cur_rnet);
cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
cur_rnet = ggml_conv_1d_ph(ctx0, model.posnet_1_conv2, cur_rnet, 1, 1);
cur_rnet = ggml_add(ctx0, cur_rnet, ggml_reshape_2d(ctx0, model.posnet_1_conv2_b, 1, model.posnet_1_conv2_b->ne[0]));
cur = ggml_conv_1d_ph(ctx0, model.posnet_1_conv2, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_1_conv2_b, 1, model.posnet_1_conv2_b->ne[0]));
cur = ggml_add(ctx0, cur_rnet, cur);
cur = ggml_add(ctx0, cur, inpL);
}
inpL = cur;
// attention block
{
cur = llm_build_norm(ctx0, cur, hparams,
ggml_reshape_2d(ctx0, model.posnet_2_attn_norm, 1, model.posnet_2_attn_norm->ne[0]),
ggml_reshape_2d(ctx0, model.posnet_2_attn_norm_b, 1, model.posnet_2_attn_norm_b->ne[0]),
LLM_NORM_GROUP, cb, 0);
struct ggml_tensor * q;
struct ggml_tensor * k;
struct ggml_tensor * v;
q = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_q, cur, 1, 1);
k = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_k, cur, 1, 1);
v = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_v, cur, 1, 1);
q = ggml_add(ctx0, q, ggml_reshape_2d(ctx0, model.posnet_2_attn_q_b, 1, model.posnet_2_attn_q_b->ne[0]));
k = ggml_add(ctx0, k, ggml_reshape_2d(ctx0, model.posnet_2_attn_k_b, 1, model.posnet_2_attn_k_b->ne[0]));
v = ggml_add(ctx0, v, ggml_reshape_2d(ctx0, model.posnet_2_attn_v_b, 1, model.posnet_2_attn_v_b->ne[0]));
q = ggml_cont(ctx0, ggml_transpose(ctx0, q));
k = ggml_cont(ctx0, ggml_transpose(ctx0, k));
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
kq = ggml_soft_max_ext(ctx0, kq, nullptr, 1.0f/sqrtf(float(768)), 0.0f);
cur = ggml_mul_mat(ctx0, kq, v);
cur = ggml_conv_1d_ph(ctx0, model.posnet_2_attn_o, cur, 1, 1);
cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, model.posnet_2_attn_o_b, 1, model.posnet_2_attn_o_b->ne[0]));
cur = ggml_add(ctx0, cur, inpL);
}
printf("cur: %d %d %d\n", cur->ne[0], cur->ne[1], cur->ne[2]);