mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-02 09:40:42 +00:00
feat: add ln 2, rename vars
This commit is contained in:
Zach Nussbaum
Adam Treat
parent
21f2aa4911
commit
199a585ad1
@ -32,8 +32,10 @@ struct mpt_hparams {
|
||||
|
||||
struct mpt_layer {
|
||||
// normalization
|
||||
struct ggml_tensor * ln_1_g;
|
||||
struct ggml_tensor * ln_1_b;
|
||||
struct ggml_tensor * norm_1_g;
|
||||
struct ggml_tensor * norm_1_b;
|
||||
struct ggml_tensor * norm_2_g;
|
||||
struct ggml_tensor * norm_2_b;
|
||||
|
||||
// attention
|
||||
struct ggml_tensor * c_attn_q_proj_w;
|
||||
@ -43,11 +45,11 @@ struct mpt_layer {
|
||||
struct ggml_tensor * c_attn_proj_w;
|
||||
|
||||
// ff
|
||||
struct ggml_tensor * c_mlp_fc_w;
|
||||
struct ggml_tensor * c_mlp_fc_b;
|
||||
struct ggml_tensor * up_proj_w;
|
||||
struct ggml_tensor * up_proj_b;
|
||||
|
||||
struct ggml_tensor * c_mlp_proj_w;
|
||||
struct ggml_tensor * c_mlp_proj_b;
|
||||
struct ggml_tensor * down_proj_w;
|
||||
struct ggml_tensor * down_proj_b;
|
||||
};
|
||||
|
||||
struct mpt_buffer {
|
||||
@ -154,16 +156,14 @@ struct mpt_vocab {
|
||||
bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & model, mpt_vocab & vocab) {
|
||||
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
|
||||
|
||||
// verify magic
|
||||
// TODO: Do we really need this?
|
||||
// {
|
||||
// uint32_t magic;
|
||||
// fin.read((char *) &magic, sizeof(magic));
|
||||
// if (magic != 0x67676d6c) {
|
||||
// fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
|
||||
// return false;
|
||||
// }
|
||||
// }
|
||||
{
|
||||
uint32_t magic;
|
||||
fin.read((char *) &magic, sizeof(magic));
|
||||
if (magic != 0x67676d6c) {
|
||||
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
// load hparams
|
||||
{
|
||||
@ -313,8 +313,8 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.norm_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.norm_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
layer.c_attn_q_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
@ -322,27 +322,27 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
|
||||
|
||||
layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
|
||||
layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
|
||||
layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
|
||||
layer.up_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
|
||||
layer.up_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
|
||||
|
||||
layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
|
||||
layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.down_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
|
||||
layer.down_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
// map by name
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_g;
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"] = layer.ln_1_b;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".norm_1.weight"] = layer.norm_1_g;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".norm_1.bias"] = layer.norm_1_b;
|
||||
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;
|
||||
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
|
||||
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"] = layer.c_mlp_fc_w;
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"] = layer.c_mlp_fc_b;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".mlp.fc_in.weight"] = layer.c_mlp_fc_w;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".mlp.fc_in.bias"] = layer.c_mlp_fc_b;
|
||||
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"] = layer.c_mlp_proj_w;
|
||||
model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"] = layer.c_mlp_proj_b;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".mlp.fc_out.weight"] = layer.c_mlp_proj_w;
|
||||
model.tensors["transformer.block." + std::to_string(i) + ".mlp.fc_out.bias"] = layer.c_mlp_proj_b;
|
||||
}
|
||||
|
||||
// key + value memory
|
||||
@ -531,9 +531,9 @@ bool mpt_eval(
|
||||
// cur = ln_1_g*cur + ln_1_b
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
|
||||
ggml_repeat(ctx0, model.layers[il].norm_1_g, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
|
||||
ggml_repeat(ctx0, model.layers[il].norm_1_b, cur));
|
||||
}
|
||||
|
||||
struct ggml_tensor * inpSA = cur;
|
||||
@ -615,6 +615,18 @@ bool mpt_eval(
|
||||
cur);
|
||||
}
|
||||
|
||||
// norm 2
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
|
||||
// cur = ln_1_g*cur + ln_1_b
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].norm_2_g, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, model.layers[il].norm_2_b, cur));
|
||||
}
|
||||
|
||||
struct ggml_tensor * inpFF = cur;
|
||||
|
||||
// feed-forward network
|
||||
@ -622,11 +634,11 @@ bool mpt_eval(
|
||||
{
|
||||
// note here we pass inpSA instead of cur
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].c_mlp_fc_w,
|
||||
model.layers[il].up_proj_w,
|
||||
inpSA);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
|
||||
ggml_repeat(ctx0, model.layers[il].up_proj_b, cur),
|
||||
cur);
|
||||
|
||||
// RELU activation
|
||||
@ -635,11 +647,11 @@ bool mpt_eval(
|
||||
// projection
|
||||
// cur = proj_w*cur + proj_b
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].c_mlp_proj_w,
|
||||
model.layers[il].down_proj_w,
|
||||
cur);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
|
||||
ggml_repeat(ctx0, model.layers[il].down_proj_b, cur),
|
||||
cur);
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user