haraldwolff/llama.cpp
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examples : fix gpt-neox (#2943)

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Co-authored-by: mmnga <mmnga1mmnga@gmail.com>
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momonga 2023-09-03 14:36:28 +09:00 committed by GitHub
parent 2753415afd
commit c42f0ec6b3
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2 changed files with 51 additions and 8 deletions
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13
examples/gptneox-wip/gptneox-main.cpp
View file

@ -660,9 +660,10 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
ggml_tensor * gpt_neox_ff(
const gpt_neox_block &block,
ggml_context * ctx0,
ggml_tensor * inp) {
ggml_tensor * inp,
const gpt_neox_hparams &hparams) {
ggml_tensor * cur = ggml_norm(ctx0, inp);
ggml_tensor * cur = ggml_norm(ctx0, inp, hparams.norm_eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur));
cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur);
@ -753,7 +754,7 @@ bool gpt_neox_eval(
// self-attention
{
{
cur = ggml_norm(ctx0, inpL);
cur = ggml_norm(ctx0, inpL, hparams.norm_eps);
cur = ggml_add(ctx0,
ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur),
@ -844,7 +845,7 @@ bool gpt_neox_eval(
if (hparams.par_res == 0) {
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL);
cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF);
cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF, hparams);
// input for next layer
inpL = ggml_add(ctx0, cur, inpFF);
@ -853,7 +854,7 @@ bool gpt_neox_eval(
// this is independent of the self-attention result, so it could be done in parallel to the self-attention
// note here we pass inpL instead of cur
cur = gpt_neox_ff(model.blocks[il], ctx0, inpL);
cur = gpt_neox_ff(model.blocks[il], ctx0, inpL, hparams);
// layer input + FF
cur = ggml_add(ctx0, cur, inpFF);
@ -867,7 +868,7 @@ bool gpt_neox_eval(
// norm
{
inpL = ggml_norm(ctx0, inpL);
inpL = ggml_norm(ctx0, inpL, hparams.norm_eps);
// inpL = ln_f_g*inpL + ln_f_b
inpL = ggml_add(ctx0,

46
llama.cpp
View file

@ -325,6 +325,44 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_GPT2,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_GPTJ,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_GPTNEOX,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_MPT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_UNKNOWN,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
};
static llm_arch llm_arch_from_string(const std::string & name) {
@ -1605,9 +1643,13 @@ static void llm_load_hparams(
GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
}
}
// gpt-neox n_rot = rotary_pct * (n_embd / n_head)
// gpt-j n_rot = rotary_dim
}
// arch-specific KVs