import sys
from enum import IntEnum
from pathlib import Path
-from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
+from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast
import numpy as np
import torch
from convert import HfVocab
-# check for any of the given keys in the dictionary and return the value of the first key found
-def get_key_opts(d, keys):
- for k in keys:
- if k in d:
- return d[k]
- print(f"Could not find any of {keys}")
- sys.exit()
-
-
###### MODEL DEFINITIONS ######
class SentencePieceTokenTypes(IntEnum):
self.hparams = Model.load_hparams(self.dir_model)
self.model_arch = self._get_model_architecture()
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
+ self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
+
+ def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
+ key = next((k for k in keys if k in self.hparams), None)
+ if key is not None:
+ return self.hparams[key]
+ if optional:
+ return None
+ raise KeyError(f"could not find any of: {keys}")
def set_vocab(self):
self._set_vocab_gpt2()
def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name)
- self.gguf_writer.add_block_count(self.hparams.get(
- "n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
- ))
- if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
+ self.gguf_writer.add_block_count(self.block_count)
+
+ if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
self.gguf_writer.add_context_length(n_ctx)
- if (n_embd := self.hparams.get("hidden_size")) is not None:
- self.gguf_writer.add_embedding_length(n_embd)
- if (n_ff := self.hparams.get("intermediate_size")) is not None:
+
+ n_embd = self.find_hparam(["hidden_size", "n_embd"])
+ self.gguf_writer.add_embedding_length(n_embd)
+
+ if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
self.gguf_writer.add_feed_forward_length(n_ff)
- if (n_head := self.hparams.get("num_attention_heads")) is not None:
- self.gguf_writer.add_head_count(n_head)
+
+ n_head = self.find_hparam(["num_attention_heads", "n_head"])
+ self.gguf_writer.add_head_count(n_head)
+
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
- if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
- self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps)
+ if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
+ self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
+ if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None:
+ self.gguf_writer.add_layer_norm_eps(f_norm_eps)
if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
- self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
+ self.gguf_writer.add_file_type(self.ftype)
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
return MiniCPMModel
if model_architecture == "BertModel":
return BertModel
+ if model_architecture == "NomicBertModel":
+ return NomicBertModel
return Model
def _is_model_safetensors(self) -> bool:
return gguf.MODEL_ARCH.MINICPM
if arch == "BertModel":
return gguf.MODEL_ARCH.BERT
+ if arch == "NomicBertModel":
+ return gguf.MODEL_ARCH.NOMIC_BERT
raise NotImplementedError(f'Architecture "{arch}" not supported!')
class Phi2Model(Model):
def set_gguf_parameters(self):
- block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"])
+ block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
- rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"])
- n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"])
- n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"])
+ rot_pct = self.find_hparam(["partial_rotary_factor"])
+ n_embd = self.find_hparam(["hidden_size", "n_embd"])
+ n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_name("Phi2")
- self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"]))
+ self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(4 * n_embd)
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
- self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"]))
+ self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_add_bos_token(False)
class BertModel(Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
- self.block_count = self.hparams["num_hidden_layers"]
+ self.vocab_size = None
def set_gguf_parameters(self):
- # TODO(cebtenzzre): merge with parent class
- self.gguf_writer.add_name(self.dir_model.name)
- self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
- self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
- self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
- self.gguf_writer.add_block_count(self.block_count)
- self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
- self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
+ super().set_gguf_parameters()
self.gguf_writer.add_causal_attention(False)
self.gguf_writer.add_pooling_layer(True)
- self.gguf_writer.add_file_type(self.ftype)
def set_vocab(self):
path = self.dir_model
vocab = HfVocab(path, added_tokens_path)
tokens, scores, toktypes = zip(*vocab.all_tokens())
assert len(tokens) == vocab.vocab_size
+ self.vocab_size = vocab.vocab_size
# we need this to validate the size of the token_type embeddings
# though currently we are passing all zeros to the token_type embeddings
if tok.startswith(b"##"):
return tok[2:]
return b"\xe2\x96\x81" + tok
- tokens = [phantom(t, y) for t, y in zip(tokens, toktypes)]
+ tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes))
# set up bos and eos tokens (cls and sep)
self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id)
self.gguf_writer.add_tensor(new_name, data)
+class NomicBertModel(BertModel):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # the HF config claims n_ctx=8192, but it uses RoPE scaling
+ self.hparams["n_ctx"] = 2048
+
+ # SwigLU activation
+ assert self.hparams["activation_function"] == "swiglu"
+ # this doesn't do anything in the HF version
+ assert self.hparams["causal"] is False
+ # no bias tensors
+ assert self.hparams["qkv_proj_bias"] is False
+ assert self.hparams["mlp_fc1_bias"] is False
+ assert self.hparams["mlp_fc2_bias"] is False
+ # norm at end of layer
+ assert self.hparams["prenorm"] is False
+ # standard RoPE
+ assert self.hparams["rotary_emb_fraction"] == 1.0
+ assert self.hparams["rotary_emb_interleaved"] is False
+ assert self.hparams["rotary_emb_scale_base"] is None
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
+
+ def get_tensors(self):
+ assert self.vocab_size is not None
+ for name, data in super().get_tensors():
+ # Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly.
+ if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size:
+ rounded_vocab_size = (self.vocab_size + 63) // 64 * 64
+ assert data.shape == (rounded_vocab_size, self.hparams["n_embd"])
+ data = data[:self.vocab_size, :]
+ yield name, data
+
+
###### CONVERSION LOGIC ######
LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT,
LLM_ARCH_BERT,
+ LLM_ARCH_NOMIC_BERT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
};
static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
- { LLM_ARCH_LLAMA, "llama" },
- { LLM_ARCH_FALCON, "falcon" },
- { LLM_ARCH_GPT2, "gpt2" },
- { LLM_ARCH_GPTJ, "gptj" },
- { LLM_ARCH_GPTNEOX, "gptneox" },
- { LLM_ARCH_MPT, "mpt" },
- { LLM_ARCH_BAICHUAN, "baichuan" },
- { LLM_ARCH_STARCODER, "starcoder" },
- { LLM_ARCH_PERSIMMON, "persimmon" },
- { LLM_ARCH_REFACT, "refact" },
- { LLM_ARCH_BERT, "bert" },
- { LLM_ARCH_BLOOM, "bloom" },
- { LLM_ARCH_STABLELM, "stablelm" },
- { LLM_ARCH_QWEN, "qwen" },
- { LLM_ARCH_QWEN2, "qwen2" },
- { LLM_ARCH_PHI2, "phi2" },
- { LLM_ARCH_PLAMO, "plamo" },
- { LLM_ARCH_CODESHELL, "codeshell" },
- { LLM_ARCH_ORION, "orion" },
- { LLM_ARCH_INTERNLM2, "internlm2" },
- { LLM_ARCH_MINICPM, "minicpm" },
+ { LLM_ARCH_LLAMA, "llama" },
+ { LLM_ARCH_FALCON, "falcon" },
+ { LLM_ARCH_GPT2, "gpt2" },
+ { LLM_ARCH_GPTJ, "gptj" },
+ { LLM_ARCH_GPTNEOX, "gptneox" },
+ { LLM_ARCH_MPT, "mpt" },
+ { LLM_ARCH_BAICHUAN, "baichuan" },
+ { LLM_ARCH_STARCODER, "starcoder" },
+ { LLM_ARCH_PERSIMMON, "persimmon" },
+ { LLM_ARCH_REFACT, "refact" },
+ { LLM_ARCH_BERT, "bert" },
+ { LLM_ARCH_NOMIC_BERT, "nomic-bert" },
+ { LLM_ARCH_BLOOM, "bloom" },
+ { LLM_ARCH_STABLELM, "stablelm" },
+ { LLM_ARCH_QWEN, "qwen" },
+ { LLM_ARCH_QWEN2, "qwen2" },
+ { LLM_ARCH_PHI2, "phi2" },
+ { LLM_ARCH_PLAMO, "plamo" },
+ { LLM_ARCH_CODESHELL, "codeshell" },
+ { LLM_ARCH_ORION, "orion" },
+ { LLM_ARCH_INTERNLM2, "internlm2" },
+ { LLM_ARCH_MINICPM, "minicpm" },
};
enum llm_kv {
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2,
+ LLM_TENSOR_ATTN_OUT_NORM,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_UP_EXP,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
+ LLM_TENSOR_LAYER_OUT_NORM,
};
static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_POS_EMBD, "position_embd" },
- { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_output_norm" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_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_FFN_NORM, "blk.%d.layer_output_norm" },
+ { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_NOMIC_BERT,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+ { LLM_TENSOR_TOKEN_TYPES, "token_types" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
MODEL_22M,
MODEL_33M,
MODEL_109M,
+ MODEL_137M,
MODEL_335M,
MODEL_0_5B,
MODEL_1B,
struct ggml_tensor * attn_q_norm_b;
struct ggml_tensor * attn_k_norm;
struct ggml_tensor * attn_k_norm_b;
+ struct ggml_tensor * attn_out_norm;
+ struct ggml_tensor * attn_out_norm_b;
// attention
struct ggml_tensor * wq;
// normalization
struct ggml_tensor * ffn_norm;
struct ggml_tensor * ffn_norm_b;
+ struct ggml_tensor * layer_out_norm;
+ struct ggml_tensor * layer_out_norm_b;
// ff
struct ggml_tensor * ffn_gate; // w1
static const char * llama_model_type_name(e_model type) {
switch (type) {
+ case MODEL_22M: return "22M";
+ case MODEL_33M: return "33M";
+ case MODEL_109M: return "109M";
+ case MODEL_137M: return "137M";
+ case MODEL_0_5B: return "0.5B";
case MODEL_1B: return "1B";
case MODEL_2B: return "2B";
case MODEL_3B: return "3B";
model.type = e_model::MODEL_335M; break; // bge-large
}
} break;
+ case LLM_ARCH_NOMIC_BERT:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
+ ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+ ml.get_key(LLM_KV_POOLING_LAYER, hparams.pooling_layer);
+
+ if (hparams.n_layer == 12 && hparams.n_embd == 768) {
+ model.type = e_model::MODEL_137M;
+ }
+ } break;
case LLM_ARCH_BLOOM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
}
} break;
case LLM_ARCH_BERT:
+ case LLM_ARCH_NOMIC_BERT:
{
- model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
- model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
- model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
+ if (model.arch == LLM_ARCH_BERT) {
+ model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
+ }
+
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
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});
+ if (model.arch == LLM_ARCH_BERT) {
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "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.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
- layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
- layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+ } else {
+ layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ }
- layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
- layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
- layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
- layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+ layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+ layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
- 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_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ 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});
- layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ if (model.arch == LLM_ARCH_BERT) {
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
- 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_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+ } else {
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ }
+
+ layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+ layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
}
} break;
case LLM_ARCH_BLOOM:
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;
// token types are hardcoded to zero ("Sentence A")
struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
inpL = ggml_add(ctx0, inpL, type_row0);
- inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
+ if (model.arch == LLM_ARCH_BERT) {
+ inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
+ }
cb(inpL, "inp_embd", -1);
// embed layer norm
struct ggml_tensor * cur = inpL;
// self-attention
- {
+ if (model.arch == LLM_ARCH_BERT) {
struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
cb(Qcur, "Qcur", il);
// seems like we just need to do this for Q?
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ cb(cur, "kqv_out", il);
+ } else {
+ // compute Q and K and RoPE them
+ cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", 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_custom(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+ hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+ hparams.n_rot, 2, 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, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cur = ggml_add(ctx0, cur, inpL);
// attention layer norm
- cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, cb, il);
+ cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
struct ggml_tensor * ffn_inp = cur;
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
- 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);
+ if (model.arch == LLM_ARCH_BERT) {
+ 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);
+ } else {
+ cur = llm_build_ffn(ctx0, cur,
+ model.layers[il].ffn_up, NULL,
+ model.layers[il].ffn_gate, NULL,
+ model.layers[il].ffn_down, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ }
cb(cur, "ffn_out", il);
// attentions bypass the intermediate layer
cur = ggml_add(ctx0, cur, ffn_inp);
// output layer norm
- cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM, cb, il);
+ cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);
// input for next layer
inpL = cur;
result = llm.build_refact();
} break;
case LLM_ARCH_BERT:
+ case LLM_ARCH_NOMIC_BERT:
{
result = llm.build_bert();
} break;
overview / pkg / ggml / sources / llama.cpp / commitdiff