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gptneox-main.cpp : map tensor names

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klosax 2023-08-09 00:54:21 +02:00 committed by GitHub
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1 changed files with 34 additions and 32 deletions
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gptneox-main.cpp
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@ -549,56 +549,58 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
model.layers.resize(n_layer);
model.wte = ggml_get_tensor(ctx, "gpt_neox.embed_in.weight");
model.ln_f_g = ggml_get_tensor(ctx, "gpt_neox.final_layer_norm.weight");
model.ln_f_b = ggml_get_tensor(ctx, "gpt_neox.final_layer_norm.bias");
model.lmh_g = ggml_get_tensor(ctx, "embed_out.weight");
model.wte = ggml_get_tensor(ctx, "transformer.token_embd.weight");
model.ln_f_g = ggml_get_tensor(ctx, "transformer.output_norm.weight");
model.ln_f_b = ggml_get_tensor(ctx, "transformer.output_norm.bias");
model.lmh_g = ggml_get_tensor(ctx, "transformer.output.weight");
// map by name
model.tensors["gpt_neox.embed_in.weight"] = model.wte;
model.tensors["gpt_neox.final_layer_norm.weight"] = model.ln_f_g;
model.tensors["gpt_neox.final_layer_norm.bias"] = model.ln_f_b;
model.tensors["embed_out.weight"] = model.lmh_g;
model.tensors["transformer.token_embd.weight"] = model.wte;
model.tensors["transformer.output_norm.weight"] = model.ln_f_g;
model.tensors["transformer.output_norm.bias"] = model.ln_f_b;
model.tensors["transformer.output.weight"] = model.lmh_g;
for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
layer.ln_1_g = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight" );
layer.ln_1_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias" );
std::string blocknamestart = "transformer.blocks." + std::to_string(i) + ".";
layer.c_attn_attn_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight" );
layer.c_attn_attn_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias" );
layer.ln_1_g = get_tensor_ex(ctx, blocknamestart + "attn_norm_1.weight" );
layer.ln_1_b = get_tensor_ex(ctx, blocknamestart + "attn_norm_1.bias" );
layer.c_attn_proj_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight" );
layer.c_attn_proj_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias" );
layer.c_attn_attn_w = get_tensor_ex(ctx, blocknamestart + "attn_qkv.weight" );
layer.c_attn_attn_b = get_tensor_ex(ctx ,blocknamestart + "attn_qkv.bias" );
layer.ln_2_g = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight" );
layer.ln_2_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias");
layer.c_attn_proj_w = get_tensor_ex(ctx, blocknamestart + "attn_output.weight" );
layer.c_attn_proj_b = get_tensor_ex(ctx, blocknamestart + "attn_output.bias" );
layer.c_mlp_fc_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight" );
layer.c_mlp_fc_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias" );
layer.ln_2_g = get_tensor_ex(ctx, blocknamestart + "ffn_norm.weight" );
layer.ln_2_b = get_tensor_ex(ctx, blocknamestart + "ffn_norm.bias");
layer.c_mlp_proj_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight" );
layer.c_mlp_proj_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias" );
layer.c_mlp_fc_w = get_tensor_ex(ctx, blocknamestart + "ffn_up.weight" );
layer.c_mlp_fc_b = get_tensor_ex(ctx, blocknamestart + "ffn_up.bias" );
layer.c_mlp_proj_w = get_tensor_ex(ctx, blocknamestart + "ffn_down.weight" );
layer.c_mlp_proj_b = get_tensor_ex(ctx, blocknamestart + "ffn_down.bias" );
// map by name
model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight"] = layer.ln_1_g;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias"] = layer.ln_1_b;
model.tensors[blocknamestart + "attn_norm_1.weight"] = layer.ln_1_g;
model.tensors[blocknamestart + "attn_norm_1.bias"] = layer.ln_1_b;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight"] = layer.c_attn_attn_w;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias"] = layer.c_attn_attn_b;
model.tensors[blocknamestart + "attn_qkv.weight"] = layer.c_attn_attn_w;
model.tensors[blocknamestart + "attn_qkv.bias"] = layer.c_attn_attn_b;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight"] = layer.c_attn_proj_w;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias"] = layer.c_attn_proj_b;
model.tensors[blocknamestart + "attn_output.weight"] = layer.c_attn_proj_w;
model.tensors[blocknamestart + "attn_output.bias"] = layer.c_attn_proj_b;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight"] = layer.ln_2_g;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias"] = layer.ln_2_b;
model.tensors[blocknamestart + "ffn_norm.weight"] = layer.ln_2_g;
model.tensors[blocknamestart + "ffn_norm.bias"] = layer.ln_2_b;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight"] = layer.c_mlp_fc_w;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias"] = layer.c_mlp_fc_b;
model.tensors[blocknamestart + "ffn_up.weight"] = layer.c_mlp_fc_w;
model.tensors[blocknamestart + "ffn_up.bias"] = layer.c_mlp_fc_b;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight"] = layer.c_mlp_proj_w;
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias"] = layer.c_mlp_proj_b;
model.tensors[blocknamestart + "ffn_down.weight"] = layer.c_mlp_proj_w;
model.tensors[blocknamestart + "ffn_down.bias"] = layer.c_mlp_proj_b;
}
}