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Added tensor layer numbers #1692

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Added tensor layer numbers #1692

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82cfd1b
Added tensor layer numbers
Jun 4, 2023
d18245e
Fixed indent
Jun 8, 2023
0ae7b6e
Merge branch 'master' into tensor-layer-num
dkun7944 Jun 8, 2023
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4 changes: 4 additions & 0 deletions ggml.c
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Original file line number Diff line number Diff line change
Expand Up @@ -4605,6 +4605,10 @@ void ggml_set_name(struct ggml_tensor * tensor, const char * name) {
tensor->name[sizeof(tensor->name) - 1] = '\0';
}

void ggml_set_layer_num(struct ggml_tensor * tensor, int layer_num) {
tensor->layer_num = layer_num;
}

struct ggml_tensor * ggml_view_tensor(
struct ggml_context * ctx,
const struct ggml_tensor * src) {
Expand Down
2 changes: 2 additions & 0 deletions ggml.h
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Expand Up @@ -358,6 +358,7 @@ extern "C" {
enum ggml_type type;
enum ggml_backend backend;

int layer_num;
int n_dims;
int64_t ne[GGML_MAX_DIMS]; // number of elements
size_t nb[GGML_MAX_DIMS]; // stride in bytes:
Expand Down Expand Up @@ -550,6 +551,7 @@ extern "C" {

GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
GGML_API void ggml_set_name(struct ggml_tensor * tensor, const char * name);
GGML_API void ggml_set_layer_num(struct ggml_tensor * tensor, int layer_num);

//
// operations on tensors with backpropagation
Expand Down
42 changes: 21 additions & 21 deletions llama.cpp
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Expand Up @@ -687,21 +687,22 @@ struct llama_model_loader {
}
}

struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
auto it = tensors_map.name_to_idx.find(name);
struct ggml_tensor * get_tensor(int layer_num, const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
std::string layer_name_wnum = (layer_num == 0) ? name : "layers." + std::to_string(layer_num) + name;
auto it = tensors_map.name_to_idx.find(layer_name_wnum);
if (it == tensors_map.name_to_idx.end()) {
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", layer_name_wnum.c_str())));
}
llama_load_tensor & lt = tensors_map.tensors.at(it->second);
if (lt.ne != ne) {
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
layer_name_wnum.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
}

return get_tensor_for(lt, backend);
return get_tensor_for(lt, layer_num, backend);
}

struct ggml_tensor * get_tensor_for(llama_load_tensor & lt, ggml_backend backend) {
struct ggml_tensor * get_tensor_for(llama_load_tensor & lt, int layer_num, ggml_backend backend) {
struct ggml_tensor * tensor;
if (lt.ne.size() == 2) {
tensor = ggml_new_tensor_2d(ggml_ctx, lt.type, lt.ne.at(0), lt.ne.at(1));
Expand All @@ -710,6 +711,7 @@ struct llama_model_loader {
tensor = ggml_new_tensor_1d(ggml_ctx, lt.type, lt.ne.at(0));
}
ggml_set_name(tensor, lt.name.c_str());
ggml_set_layer_num(tensor, layer_num);
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor

tensor->backend = backend;
Expand Down Expand Up @@ -1084,8 +1086,8 @@ static void llama_model_load_internal(

ml->ggml_ctx = ctx;

model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.norm = ml->get_tensor("norm.weight", {n_embd}, GGML_BACKEND_CPU);
model.tok_embeddings = ml->get_tensor(0, "tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.norm = ml->get_tensor(0, "norm.weight", {n_embd}, GGML_BACKEND_CPU);

// "output" tensor
{
Expand All @@ -1096,7 +1098,7 @@ static void llama_model_load_internal(
backend_output = GGML_BACKEND_CPU;
}

model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output);
model.output = ml->get_tensor(0, "output.weight", {n_embd, n_vocab}, backend_output);
}

const int i_gpu_start = n_layer - n_gpu_layers;
Expand All @@ -1108,20 +1110,18 @@ static void llama_model_load_internal(

auto & layer = model.layers[i];

std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ml->get_tensor(i, ".attention_norm.weight", {n_embd}, backend);

layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
layer.wq = ml->get_tensor(i, ".attention.wq.weight", {n_embd, n_embd}, backend_split);
layer.wk = ml->get_tensor(i, ".attention.wk.weight", {n_embd, n_embd}, backend_split);
layer.wv = ml->get_tensor(i, ".attention.wv.weight", {n_embd, n_embd}, backend_split);
layer.wo = ml->get_tensor(i, ".attention.wo.weight", {n_embd, n_embd}, backend_split);

layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend_split);
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend_split);
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend_split);
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml->get_tensor(i, ".ffn_norm.weight", {n_embd}, backend);

layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend);

layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend_split);
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend_split);
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend_split);
layer.w1 = ml->get_tensor(i, ".feed_forward.w1.weight", {n_embd, n_ff}, backend_split);
layer.w2 = ml->get_tensor(i, ".feed_forward.w2.weight", { n_ff, n_embd}, backend_split);
layer.w3 = ml->get_tensor(i, ".feed_forward.w3.weight", {n_embd, n_ff}, backend_split);

if (backend == GGML_BACKEND_GPU) {
vram_weights +=
Expand Down Expand Up @@ -2747,7 +2747,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
}
size_t idx = model_loader->tensors_map.name_to_idx[base_name];
llama_load_tensor & lt = model_loader->tensors_map.tensors[idx];
base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
base_t = model_loader->get_tensor(0, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
lt.data = (uint8_t *) lt.ggml_tensor->data;
model_loader->load_data_for(lt);
lt.ggml_tensor->data = lt.data;
Expand Down