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Add Nemotron/Minitron GGUF Conversion & Inference Support (#8922)

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* Add nemotron GGUF conversion & inference support

* Fix formatting issues

* Remove unnecessary write_tensors()

* Update convert_hf_to_gguf.py

Co-authored-by: compilade <git@compilade.net>

* Update src/llama.cpp

Co-authored-by: compilade <git@compilade.net>

* Address comments by @compilade

* Replace ggml_mul_mat()->llm_build_lora_mm()

* Remove mutable variable

* Use  for bias tensors

* Cover corner case for role_scaling not in config.json

---------

Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
Yoshi Suhara 2024-08-15 19:23:33 -07:00 committed by GitHub
parent e3f6fd56b1
commit 2a24c8caa6
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4 changed files with 271 additions and 11 deletions
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41
convert_hf_to_gguf.py
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@ -3740,6 +3740,47 @@ class ChatGLMModel(Model):
name = name.removeprefix("transformer.") name = name.removeprefix("transformer.")
return [(self.map_tensor_name(name), data_torch)] return [(self.map_tensor_name(name), data_torch)]
@Model.register("NemotronForCausalLM")
class NemotronModel(Model):
model_arch = gguf.MODEL_ARCH.NEMOTRON
def set_vocab(self):
self._set_vocab_sentencepiece()
self.gguf_writer.add_pad_token_id(0)
self.gguf_writer.add_unk_token_id(1)
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"])
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
# * Partial RoPE
rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
# * RopeScaling for Nemotron
if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
else:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
# model.layers.{l}.input_layernorm.weight
# model.layers.{l}.post_attention_layernorm.weight
# model.norm.weight
if name.endswith("norm.weight"):
data_torch = data_torch + 1
return [(self.map_tensor_name(name), data_torch)]
###### CONVERSION LOGIC ###### ###### CONVERSION LOGIC ######

21
gguf-py/gguf/constants.py
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@ -219,6 +219,7 @@ class MODEL_ARCH(IntEnum):
T5 = auto() T5 = auto()
T5ENCODER = auto() T5ENCODER = auto()
JAIS = auto() JAIS = auto()
NEMOTRON = auto()
class MODEL_TENSOR(IntEnum): class MODEL_TENSOR(IntEnum):
@ -347,6 +348,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.T5: "t5", MODEL_ARCH.T5: "t5",
MODEL_ARCH.T5ENCODER: "t5encoder", MODEL_ARCH.T5ENCODER: "t5encoder",
MODEL_ARCH.JAIS: "jais", MODEL_ARCH.JAIS: "jais",
MODEL_ARCH.NEMOTRON: "nemotron",
} }
TENSOR_NAMES: dict[MODEL_TENSOR, str] = { TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -1065,6 +1067,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_UP,
], ],
MODEL_ARCH.NEMOTRON: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
# TODO # TODO
} }
@ -1105,6 +1122,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.CHATGLM: [ MODEL_ARCH.CHATGLM: [
MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ROPE_FREQS,
], ],
MODEL_ARCH.NEMOTRON: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
} }
# #

21
gguf-py/gguf/tensor_mapping.py
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@ -13,7 +13,7 @@ class TensorNameMap:
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais
"transformer.word_embeddings", # falcon "transformer.word_embeddings", # falcon
"word_embeddings", # bloom "word_embeddings", # bloom
"model.embed_tokens", # llama-hf "model.embed_tokens", # llama-hf nemotron
"tok_embeddings", # llama-pth "tok_embeddings", # llama-pth
"embeddings.word_embeddings", # bert nomic-bert "embeddings.word_embeddings", # bert nomic-bert
"language_model.embedding.word_embeddings", # persimmon "language_model.embedding.word_embeddings", # persimmon
@ -52,7 +52,7 @@ class TensorNameMap:
# Output # Output
MODEL_TENSOR.OUTPUT: ( MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox "embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron
"output", # llama-pth bloom internlm2 "output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon "word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2 "lm_head.linear", # phi2
@ -75,6 +75,7 @@ class TensorNameMap:
"transformer.rms_norm", # Grok "transformer.rms_norm", # Grok
"encoder.final_layernorm", # chatglm "encoder.final_layernorm", # chatglm
"transformer.norm", # openelm "transformer.norm", # openelm
"model.norm", # nemotron
), ),
# Rope frequencies # Rope frequencies
@ -93,7 +94,7 @@ class TensorNameMap:
"transformer.h.{bid}.input_layernorm", # falcon7b "transformer.h.{bid}.input_layernorm", # falcon7b
"h.{bid}.input_layernorm", # bloom "h.{bid}.input_layernorm", # bloom
"transformer.h.{bid}.ln_mlp", # falcon40b "transformer.h.{bid}.ln_mlp", # falcon40b
"model.layers.{bid}.input_layernorm", # llama-hf "model.layers.{bid}.input_layernorm", # llama-hf nemotron
"layers.{bid}.attention_norm", # llama-pth "layers.{bid}.attention_norm", # llama-pth
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon "language_model.encoder.layers.{bid}.input_layernorm", # persimmon
"model.layers.{bid}.ln1", # yi "model.layers.{bid}.ln1", # yi
@ -135,7 +136,7 @@ class TensorNameMap:
# Attention query # Attention query
MODEL_TENSOR.ATTN_Q: ( MODEL_TENSOR.ATTN_Q: (
"model.layers.{bid}.self_attn.q_proj", # llama-hf "model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron
"layers.{bid}.attention.wq", # llama-pth "layers.{bid}.attention.wq", # llama-pth
"encoder.layer.{bid}.attention.self.query", # bert "encoder.layer.{bid}.attention.self.query", # bert
"transformer.h.{bid}.attn.q_proj", # gpt-j "transformer.h.{bid}.attn.q_proj", # gpt-j
@ -146,7 +147,7 @@ class TensorNameMap:
# Attention key # Attention key
MODEL_TENSOR.ATTN_K: ( MODEL_TENSOR.ATTN_K: (
"model.layers.{bid}.self_attn.k_proj", # llama-hf "model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron
"layers.{bid}.attention.wk", # llama-pth "layers.{bid}.attention.wk", # llama-pth
"encoder.layer.{bid}.attention.self.key", # bert "encoder.layer.{bid}.attention.self.key", # bert
"transformer.h.{bid}.attn.k_proj", # gpt-j "transformer.h.{bid}.attn.k_proj", # gpt-j
@ -158,7 +159,7 @@ class TensorNameMap:
# Attention value # Attention value
MODEL_TENSOR.ATTN_V: ( MODEL_TENSOR.ATTN_V: (
"model.layers.{bid}.self_attn.v_proj", # llama-hf "model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron
"layers.{bid}.attention.wv", # llama-pth "layers.{bid}.attention.wv", # llama-pth
"encoder.layer.{bid}.attention.self.value", # bert "encoder.layer.{bid}.attention.self.value", # bert
"transformer.h.{bid}.attn.v_proj", # gpt-j "transformer.h.{bid}.attn.v_proj", # gpt-j
@ -175,7 +176,7 @@ class TensorNameMap:
"transformer.blocks.{bid}.attn.out_proj", # mpt "transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon "transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom "h.{bid}.self_attention.dense", # bloom
"model.layers.{bid}.self_attn.o_proj", # llama-hf "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron
"layers.{bid}.attention.wo", # llama-pth "layers.{bid}.attention.wo", # llama-pth
"encoder.layer.{bid}.attention.output.dense", # bert "encoder.layer.{bid}.attention.output.dense", # bert
"transformer.h.{bid}.attn.out_proj", # gpt-j "transformer.h.{bid}.attn.out_proj", # gpt-j
@ -218,7 +219,7 @@ class TensorNameMap:
"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais "transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
"h.{bid}.post_attention_layernorm", # bloom "h.{bid}.post_attention_layernorm", # bloom
"transformer.blocks.{bid}.norm_2", # mpt "transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf "model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron
"layers.{bid}.ffn_norm", # llama-pth "layers.{bid}.ffn_norm", # llama-pth
"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
"model.layers.{bid}.ln2", # yi "model.layers.{bid}.ln2", # yi
@ -258,7 +259,7 @@ class TensorNameMap:
"transformer.blocks.{bid}.ffn.up_proj", # mpt "transformer.blocks.{bid}.ffn.up_proj", # mpt
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
"h.{bid}.mlp.dense_h_to_4h", # bloom "h.{bid}.mlp.dense_h_to_4h", # bloom
"model.layers.{bid}.mlp.up_proj", # llama-hf refact "model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron
"layers.{bid}.feed_forward.w3", # llama-pth "layers.{bid}.feed_forward.w3", # llama-pth
"encoder.layer.{bid}.intermediate.dense", # bert "encoder.layer.{bid}.intermediate.dense", # bert
"transformer.h.{bid}.mlp.fc_in", # gpt-j "transformer.h.{bid}.mlp.fc_in", # gpt-j
@ -329,7 +330,7 @@ class TensorNameMap:
"transformer.blocks.{bid}.ffn.down_proj", # mpt "transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"h.{bid}.mlp.dense_4h_to_h", # bloom "h.{bid}.mlp.dense_4h_to_h", # bloom
"model.layers.{bid}.mlp.down_proj", # llama-hf "model.layers.{bid}.mlp.down_proj", # llama-hf nemotron
"layers.{bid}.feed_forward.w2", # llama-pth "layers.{bid}.feed_forward.w2", # llama-pth
"encoder.layer.{bid}.output.dense", # bert "encoder.layer.{bid}.output.dense", # bert
"transformer.h.{bid}.mlp.fc_out", # gpt-j "transformer.h.{bid}.mlp.fc_out", # gpt-j

199
src/llama.cpp
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@ -210,6 +210,7 @@ enum llm_arch {
LLM_ARCH_T5, LLM_ARCH_T5,
LLM_ARCH_T5ENCODER, LLM_ARCH_T5ENCODER,
LLM_ARCH_JAIS, LLM_ARCH_JAIS,
LLM_ARCH_NEMOTRON,
LLM_ARCH_UNKNOWN, LLM_ARCH_UNKNOWN,
}; };
@ -255,6 +256,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_T5, "t5" }, { LLM_ARCH_T5, "t5" },
{ LLM_ARCH_T5ENCODER, "t5encoder" }, { LLM_ARCH_T5ENCODER, "t5encoder" },
{ LLM_ARCH_JAIS, "jais" }, { LLM_ARCH_JAIS, "jais" },
{ LLM_ARCH_NEMOTRON, "nemotron" },
{ LLM_ARCH_UNKNOWN, "(unknown)" }, { LLM_ARCH_UNKNOWN, "(unknown)" },
}; };
@ -1296,6 +1298,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
}, },
}, },
{
LLM_ARCH_NEMOTRON,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ 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_UNKNOWN, LLM_ARCH_UNKNOWN,
{ {
@ -5235,6 +5255,14 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN; default: model.type = e_model::MODEL_UNKNOWN;
} }
} break; } break;
case LLM_ARCH_NEMOTRON:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_4B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0; default: (void)0;
} }
@ -7568,6 +7596,48 @@ static bool llm_load_tensors(
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
} }
} break; } break;
case LLM_ARCH_NEMOTRON:
{
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.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
// optional bias tensors
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
// optional MLP bias
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
}
} break;
default: default:
throw std::runtime_error("unknown architecture"); throw std::runtime_error("unknown architecture");
} }
@ -8254,7 +8324,7 @@ static struct ggml_tensor * llm_build_kqv(
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
cb(kq, "kq", il); cb(kq, "kq", il);
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2) { if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2 || model.arch == LLM_ARCH_NEMOTRON) {
// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
ggml_mul_mat_set_prec(kq, GGML_PREC_F32); ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
@ -13755,6 +13825,128 @@ struct llm_build_context {
return gf; return gf;
} }
struct ggml_cgraph * build_nemotron() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
//GGML_ASSERT(n_embd_head == hparams.n_rot);
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) {
struct ggml_tensor * inpSA = inpL;
// norm
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
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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, n_ctx_orig, 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, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, lctx, 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);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
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, lctx, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL,
LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = llm_build_lora_mm(lctx, 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) { static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@ -14010,6 +14202,10 @@ static struct ggml_cgraph * llama_build_graph(
{ {
result = llm.build_jais(); result = llm.build_jais();
} break; } break;
case LLM_ARCH_NEMOTRON:
{
result = llm.build_nemotron();
} break;
default: default:
GGML_ABORT("fatal error"); GGML_ABORT("fatal error");
} }
@ -17080,6 +17276,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_OPENELM: case LLM_ARCH_OPENELM:
case LLM_ARCH_GPTNEOX: case LLM_ARCH_GPTNEOX:
case LLM_ARCH_CODESHELL: case LLM_ARCH_CODESHELL:
case LLM_ARCH_NEMOTRON:
return LLAMA_ROPE_TYPE_NEOX; return LLAMA_ROPE_TYPE_NEOX;
// all model arches should be listed explicitly here // all model arches should be listed explicitly here