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feat: add ln 2, rename vars

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pull/520/head
Zach Nussbaum 1 year ago committed by Adam Treat
parent aef524b460
commit 525b703984
1 changed files with 50 additions and 38 deletions
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88
llmodel/mpt.cpp
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@ -32,8 +32,10 @@ struct mpt_hparams {
struct mpt_layer { struct mpt_layer {
// normalization // normalization
struct ggml_tensor * ln_1_g; struct ggml_tensor * norm_1_g;
struct ggml_tensor * ln_1_b; struct ggml_tensor * norm_1_b;
struct ggml_tensor * norm_2_g;
struct ggml_tensor * norm_2_b;
// attention // attention
struct ggml_tensor * c_attn_q_proj_w; struct ggml_tensor * c_attn_q_proj_w;
@ -43,11 +45,11 @@ struct mpt_layer {
struct ggml_tensor * c_attn_proj_w; struct ggml_tensor * c_attn_proj_w;
// ff // ff
struct ggml_tensor * c_mlp_fc_w; struct ggml_tensor * up_proj_w;
struct ggml_tensor * c_mlp_fc_b; struct ggml_tensor * up_proj_b;
struct ggml_tensor * c_mlp_proj_w; struct ggml_tensor * down_proj_w;
struct ggml_tensor * c_mlp_proj_b; struct ggml_tensor * down_proj_b;
}; };
struct mpt_buffer { 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) { 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()); 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));
// uint32_t magic; if (magic != 0x67676d6c) {
// fin.read((char *) &magic, sizeof(magic)); fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
// if (magic != 0x67676d6c) { return false;
// fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str()); }
// return false; }
// }
// }
// load hparams // 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) { for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i]; auto & layer = model.layers[i];
layer.ln_1_g = 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.ln_1_b = 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_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); 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_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.up_proj_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_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.down_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_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
// map by name // map by name
model.tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_g; model.tensors["transformer.block." + std::to_string(i) + ".norm_1.weight"] = layer.norm_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.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.block." + 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.block." + 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.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.block." + 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.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.block." + 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.bias"] = layer.c_mlp_proj_b;
} }
// key + value memory // key + value memory
@ -531,9 +531,9 @@ bool mpt_eval(
// cur = ln_1_g*cur + ln_1_b // cur = ln_1_g*cur + ln_1_b
cur = ggml_add(ctx0, cur = ggml_add(ctx0,
ggml_mul(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), 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; struct ggml_tensor * inpSA = cur;
@ -615,6 +615,18 @@ bool mpt_eval(
cur); 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; struct ggml_tensor * inpFF = cur;
// feed-forward network // feed-forward network
@ -622,11 +634,11 @@ bool mpt_eval(
{ {
// note here we pass inpSA instead of cur // note here we pass inpSA instead of cur
cur = ggml_mul_mat(ctx0, cur = ggml_mul_mat(ctx0,
model.layers[il].c_mlp_fc_w, model.layers[il].up_proj_w,
inpSA); inpSA);
cur = ggml_add(ctx0, 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); cur);
// RELU activation // RELU activation
@ -635,11 +647,11 @@ bool mpt_eval(
// projection // projection
// cur = proj_w*cur + proj_b // cur = proj_w*cur + proj_b
cur = ggml_mul_mat(ctx0, cur = ggml_mul_mat(ctx0,
model.layers[il].c_mlp_proj_w, model.layers[il].down_proj_w,
cur); cur);
cur = ggml_add(ctx0, 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); cur);
} }

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