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Add missing inference support for GPTNeoXForCausalLM (Pythia and GPT-…
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…NeoX base models) (ggerganov#7461)

* convert-hf : add conversion of bloom-style qkv tensor to gpt-style qkv (code borrowed from BloomModel)

* llama : add inference support for LLM_ARCH_GPTNEOX

* llama : add model types for every Pythia variant and GPT-NeoX

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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@fairydreaming @sszymczy
fairydreaming and sszymczy authored May 23, 2024
1 parent a61a94e commit 9b82476
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38 changes: 38 additions & 0 deletions convert-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -673,6 +673,44 @@ def set_gguf_parameters(self):
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused

n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))

tensors: list[tuple[str, Tensor]] = []

if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
# Map bloom-style qkv_linear to gpt-style qkv_linear
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
data_torch = torch.cat(
(
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.weight")
elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name):
qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
data_torch = torch.cat(
(
qkv_bias[:, 0, :].reshape((n_embed,)),
qkv_bias[:, 1, :].reshape((n_embed,)),
qkv_bias[:, 2, :].reshape((n_embed,)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.bias")

tensors.append((self.map_tensor_name(name), data_torch))

return tensors


@Model.register("BloomForCausalLM")
class BloomModel(Model):
Expand Down
236 changes: 235 additions & 1 deletion llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1692,17 +1692,24 @@ static llama_state g_state;
// available llama models
enum e_model {
MODEL_UNKNOWN,
MODEL_14M,
MODEL_17M,
MODEL_22M,
MODEL_33M,
MODEL_70M,
MODEL_109M,
MODEL_137M,
MODEL_160M,
MODEL_335M,
MODEL_410M,
MODEL_0_5B,
MODEL_1B,
MODEL_1_4B,
MODEL_2B,
MODEL_2_8B,
MODEL_3B,
MODEL_4B,
MODEL_6_9B,
MODEL_7B,
MODEL_8B,
MODEL_12B,
Expand Down Expand Up @@ -1734,6 +1741,7 @@ static const size_t GiB = 1024*MiB;
struct llama_hparams {
bool vocab_only;
bool rope_finetuned;
bool use_par_res;

uint32_t n_vocab;
uint32_t n_ctx_train; // context size the model was trained on
Expand Down Expand Up @@ -3773,17 +3781,24 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {

static const char * llama_model_type_name(e_model type) {
switch (type) {
case MODEL_14M: return "14M";
case MODEL_17M: return "17M";
case MODEL_22M: return "22M";
case MODEL_33M: return "33M";
case MODEL_70M: return "70M";
case MODEL_109M: return "109M";
case MODEL_137M: return "137M";
case MODEL_160M: return "160M";
case MODEL_335M: return "335M";
case MODEL_410M: return "410M";
case MODEL_0_5B: return "0.5B";
case MODEL_1B: return "1B";
case MODEL_1_4B: return "1.4B";
case MODEL_2B: return "2B";
case MODEL_2_8B: return "2.8B";
case MODEL_3B: return "3B";
case MODEL_4B: return "4B";
case MODEL_6_9B: return "6.9B";
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
case MODEL_12B: return "12B";
Expand Down Expand Up @@ -4282,6 +4297,52 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_GPTNEOX:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
switch (hparams.n_layer) {
case 6:
switch (hparams.n_ff) {
case 512: model.type = e_model::MODEL_14M; break;
case 2048: model.type = e_model::MODEL_70M; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 12:
switch (hparams.n_ff) {
case 3072: model.type = e_model::MODEL_160M; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 16:
switch (hparams.n_ff) {
case 8192: model.type = e_model::MODEL_1B; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 24:
switch (hparams.n_ff) {
case 4096: model.type = e_model::MODEL_410M; break;
case 8192: model.type = e_model::MODEL_1_4B; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 32:
switch (hparams.n_ff) {
case 10240: model.type = e_model::MODEL_2_8B; break;
case 16384: model.type = e_model::MODEL_6_9B; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 36:
switch (hparams.n_ff) {
case 20480: model.type = e_model::MODEL_12B; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 44:
switch (hparams.n_ff) {
case 24576: model.type = e_model::MODEL_20B; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}

Expand Down Expand Up @@ -6033,6 +6094,41 @@ static bool llm_load_tensors(
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_GPTNEOX:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}

for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);

auto & layer = model.layers[i];

layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});

layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});

layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});

layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});

layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});

layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
Expand Down Expand Up @@ -10560,6 +10656,140 @@ struct llm_build_context {

return gf;
}

struct ggml_cgraph * build_gptneox() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

struct ggml_tensor * cur;
struct ggml_tensor * inpL;

inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();

// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

for (int il = 0; il < n_layer; ++il) {
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);

// self-attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);

cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);

struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);

Qcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);

Kcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);

cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}

if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}

// ffn
if (hparams.use_par_res) {
// attention and ffn are computed in parallel
// x = x + attn(ln1(x)) + ffn(ln2(x))

struct ggml_tensor * attn_out = cur;

cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);

cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);

cur = ggml_add(ctx0, cur, inpL);
cb(cur, "ffn_out", il);

inpL = ggml_add(ctx0, cur, attn_out);
cb(inpL, "l_out", il);
} else {
// attention and ffn are computed sequentially
// x = x + attn(ln1(x))
// x = x + ffn(ln2(x))

struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);

cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);

cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(cur, "ffn_out", il);

inpL = ggml_add(ctx0, cur, ffn_inp);
cb(inpL, "l_out", il);
}
}

cur = llm_build_norm(ctx0, inpL, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);

cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);

ggml_build_forward_expand(gf, cur);

return gf;
}
};

static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
Expand Down Expand Up @@ -10770,6 +11000,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_olmo();
} break;
case LLM_ARCH_GPTNEOX:
{
result = llm.build_gptneox();
} break;
default:
GGML_ASSERT(false);
}
Expand Down Expand Up @@ -15762,7 +15996,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
// these models do not use RoPE
case LLM_ARCH_GPT2:
case LLM_ARCH_GPTJ:
case LLM_ARCH_GPTNEOX:
case LLM_ARCH_MPT:
case LLM_ARCH_REFACT:
case LLM_ARCH_BLOOM:
Expand Down Expand Up @@ -15798,6 +16031,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_PHI3:
case LLM_ARCH_GEMMA:
case LLM_ARCH_STARCODER2:
case LLM_ARCH_GPTNEOX:
return LLAMA_ROPE_TYPE_NEOX;

// all model arches should be listed explicitly here
Expand Down

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