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llama : add support for Nomic Embed (#5468)

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Jared Van Bortel 2024-02-13 12:03:53 -05:00 committed by GitHub
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4 changed files with 273 additions and 113 deletions
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113
convert-hf-to-gguf.py
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@ -10,7 +10,7 @@ import re
import sys import sys
from enum import IntEnum from enum import IntEnum
from pathlib import Path 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 numpy as np
import torch import torch
@ -25,15 +25,6 @@ import gguf
from convert import HfVocab 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 ###### ###### MODEL DEFINITIONS ######
class SentencePieceTokenTypes(IntEnum): class SentencePieceTokenTypes(IntEnum):
@ -58,6 +49,15 @@ class Model:
self.hparams = Model.load_hparams(self.dir_model) self.hparams = Model.load_hparams(self.dir_model)
self.model_arch = self._get_model_architecture() 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.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): def set_vocab(self):
self._set_vocab_gpt2() self._set_vocab_gpt2()
@ -79,28 +79,33 @@ class Model:
def set_gguf_parameters(self): def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_block_count(self.hparams.get( self.gguf_writer.add_block_count(self.block_count)
"n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
)) if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
self.gguf_writer.add_context_length(n_ctx) self.gguf_writer.add_context_length(n_ctx)
if (n_embd := self.hparams.get("hidden_size")) is not None:
n_embd = self.find_hparam(["hidden_size", "n_embd"])
self.gguf_writer.add_embedding_length(n_embd) self.gguf_writer.add_embedding_length(n_embd)
if (n_ff := self.hparams.get("intermediate_size")) is not None:
if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
self.gguf_writer.add_feed_forward_length(n_ff) self.gguf_writer.add_feed_forward_length(n_ff)
if (n_head := self.hparams.get("num_attention_heads")) is not None:
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count(n_head)
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_head_count_kv(n_head_kv)
if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None: if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps) 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: if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts) self.gguf_writer.add_expert_count(n_experts)
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: 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_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): def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
@ -211,6 +216,8 @@ class Model:
return MiniCPMModel return MiniCPMModel
if model_architecture == "BertModel": if model_architecture == "BertModel":
return BertModel return BertModel
if model_architecture == "NomicBertModel":
return NomicBertModel
return Model return Model
def _is_model_safetensors(self) -> bool: def _is_model_safetensors(self) -> bool:
@ -268,6 +275,8 @@ class Model:
return gguf.MODEL_ARCH.MINICPM return gguf.MODEL_ARCH.MINICPM
if arch == "BertModel": if arch == "BertModel":
return gguf.MODEL_ARCH.BERT return gguf.MODEL_ARCH.BERT
if arch == "NomicBertModel":
return gguf.MODEL_ARCH.NOMIC_BERT
raise NotImplementedError(f'Architecture "{arch}" not supported!') raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -1297,21 +1306,21 @@ class GPT2Model(Model):
class Phi2Model(Model): class Phi2Model(Model):
def set_gguf_parameters(self): 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"]) rot_pct = self.find_hparam(["partial_rotary_factor"])
n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"]) n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"]) n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_name("Phi2") 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_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(4 * n_embd) self.gguf_writer.add_feed_forward_length(4 * n_embd)
self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(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_rope_dimension_count(int(rot_pct * n_embd) // n_head)
self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_add_bos_token(False) self.gguf_writer.add_add_bos_token(False)
@ -1636,20 +1645,12 @@ in chat mode so that the conversation can end normally.")
class BertModel(Model): class BertModel(Model):
def __init__(self, *args, **kwargs): def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs) super().__init__(*args, **kwargs)
self.block_count = self.hparams["num_hidden_layers"] self.vocab_size = None
def set_gguf_parameters(self): def set_gguf_parameters(self):
# TODO(cebtenzzre): merge with parent class super().set_gguf_parameters()
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"])
self.gguf_writer.add_causal_attention(False) self.gguf_writer.add_causal_attention(False)
self.gguf_writer.add_pooling_layer(True) self.gguf_writer.add_pooling_layer(True)
self.gguf_writer.add_file_type(self.ftype)
def set_vocab(self): def set_vocab(self):
path = self.dir_model path = self.dir_model
@ -1659,6 +1660,7 @@ class BertModel(Model):
vocab = HfVocab(path, added_tokens_path) vocab = HfVocab(path, added_tokens_path)
tokens, scores, toktypes = zip(*vocab.all_tokens()) tokens, scores, toktypes = zip(*vocab.all_tokens())
assert len(tokens) == vocab.vocab_size assert len(tokens) == vocab.vocab_size
self.vocab_size = vocab.vocab_size
# we need this to validate the size of the token_type embeddings # we need this to validate the size of the token_type embeddings
# though currently we are passing all zeros to the token_type embeddings # though currently we are passing all zeros to the token_type embeddings
@ -1672,7 +1674,7 @@ class BertModel(Model):
if tok.startswith(b"##"): if tok.startswith(b"##"):
return tok[2:] return tok[2:]
return b"\xe2\x96\x81" + tok 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) # 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_bos_token_id(vocab.tokenizer.cls_token_id)
@ -1724,6 +1726,43 @@ class BertModel(Model):
self.gguf_writer.add_tensor(new_name, data) 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 ###### ###### CONVERSION LOGIC ######

16
gguf-py/gguf/constants.py
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@ -98,6 +98,7 @@ class MODEL_ARCH(IntEnum):
PERSIMMON = auto() PERSIMMON = auto()
REFACT = auto() REFACT = auto()
BERT = auto() BERT = auto()
NOMIC_BERT = auto()
BLOOM = auto() BLOOM = auto()
STABLELM = auto() STABLELM = auto()
QWEN = auto() QWEN = auto()
@ -153,6 +154,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.PERSIMMON: "persimmon", MODEL_ARCH.PERSIMMON: "persimmon",
MODEL_ARCH.REFACT: "refact", MODEL_ARCH.REFACT: "refact",
MODEL_ARCH.BERT: "bert", MODEL_ARCH.BERT: "bert",
MODEL_ARCH.NOMIC_BERT: "nomic-bert",
MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.STABLELM: "stablelm",
MODEL_ARCH.QWEN: "qwen", MODEL_ARCH.QWEN: "qwen",
@ -282,6 +284,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.LAYER_OUT_NORM, MODEL_TENSOR.LAYER_OUT_NORM,
], ],
MODEL_ARCH.NOMIC_BERT: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.TOKEN_EMBD_NORM,
MODEL_TENSOR.TOKEN_TYPES,
MODEL_TENSOR.POS_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_OUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.LAYER_OUT_NORM,
],
MODEL_ARCH.MPT: [ MODEL_ARCH.MPT: [
MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT_NORM,

12
gguf-py/gguf/tensor_mapping.py
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@ -15,7 +15,7 @@ class TensorNameMap:
"word_embeddings", # bloom "word_embeddings", # bloom
"model.embed_tokens", # llama-hf "model.embed_tokens", # llama-hf
"tok_embeddings", # llama-pth "tok_embeddings", # llama-pth
"embeddings.word_embeddings", # bert "embeddings.word_embeddings", # bert nomic-bert
"language_model.embedding.word_embeddings", # persimmon "language_model.embedding.word_embeddings", # persimmon
"wte", # gpt2 "wte", # gpt2
"transformer.embd.wte", # phi2 "transformer.embd.wte", # phi2
@ -24,13 +24,14 @@ class TensorNameMap:
# Token type embeddings # Token type embeddings
MODEL_TENSOR.TOKEN_TYPES: ( MODEL_TENSOR.TOKEN_TYPES: (
"embeddings.token_type_embeddings", # bert "embeddings.token_type_embeddings", # bert nomic-bert
), ),
# Normalization of token embeddings # Normalization of token embeddings
MODEL_TENSOR.TOKEN_EMBD_NORM: ( MODEL_TENSOR.TOKEN_EMBD_NORM: (
"word_embeddings_layernorm", # bloom "word_embeddings_layernorm", # bloom
"embeddings.LayerNorm", # bert "embeddings.LayerNorm", # bert
"emb_ln", # nomic-bert
), ),
# Position embeddings # Position embeddings
@ -103,6 +104,7 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.query_key_value", # persimmon "model.layers.{bid}.self_attn.query_key_value", # persimmon
"h.{bid}.attn.c_attn", # gpt2 "h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.mixer.Wqkv", # phi2 "transformer.h.{bid}.mixer.Wqkv", # phi2
"encoder.layers.{bid}.attn.Wqkv", # nomic-bert
), ),
# Attention query # Attention query
@ -152,11 +154,13 @@ class TensorNameMap:
"transformer.h.{bid}.mixer.out_proj", # phi2 "transformer.h.{bid}.mixer.out_proj", # phi2
"model.layers.layers.{bid}.self_attn.o_proj", # plamo "model.layers.layers.{bid}.self_attn.o_proj", # plamo
"model.layers.{bid}.attention.wo", # internlm2 "model.layers.{bid}.attention.wo", # internlm2
"encoder.layers.{bid}.attn.out_proj", # nomic-bert
), ),
# Attention output norm # Attention output norm
MODEL_TENSOR.ATTN_OUT_NORM: ( MODEL_TENSOR.ATTN_OUT_NORM: (
"encoder.layer.{bid}.attention.output.LayerNorm", # bert "encoder.layer.{bid}.attention.output.LayerNorm", # bert
"encoder.layers.{bid}.norm1", # nomic-bert
), ),
# Rotary embeddings # Rotary embeddings
@ -205,6 +209,7 @@ class TensorNameMap:
"model.layers.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.fc1", # phi2
"model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.layers.{bid}.mlp.up_proj", # plamo
"model.layers.{bid}.feed_forward.w3", # internlm2 "model.layers.{bid}.feed_forward.w3", # internlm2
"encoder.layers.{bid}.mlp.fc11", # nomic-bert
), ),
MODEL_TENSOR.FFN_UP_EXP: ( MODEL_TENSOR.FFN_UP_EXP: (
@ -224,6 +229,7 @@ class TensorNameMap:
"transformer.h.{bid}.mlp.w2", # qwen "transformer.h.{bid}.mlp.w2", # qwen
"model.layers.layers.{bid}.mlp.gate_proj", # plamo "model.layers.layers.{bid}.mlp.gate_proj", # plamo
"model.layers.{bid}.feed_forward.w1", # internlm2 "model.layers.{bid}.feed_forward.w1", # internlm2
"encoder.layers.{bid}.mlp.fc12", # nomic-bert
), ),
MODEL_TENSOR.FFN_GATE_EXP: ( MODEL_TENSOR.FFN_GATE_EXP: (
@ -249,6 +255,7 @@ class TensorNameMap:
"model.layers.{bid}.mlp.fc2", # phi2 "model.layers.{bid}.mlp.fc2", # phi2
"model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.layers.{bid}.mlp.down_proj", # plamo
"model.layers.{bid}.feed_forward.w2", # internlm2 "model.layers.{bid}.feed_forward.w2", # internlm2
"encoder.layers.{bid}.mlp.fc2", # nomic-bert
), ),
MODEL_TENSOR.FFN_DOWN_EXP: ( MODEL_TENSOR.FFN_DOWN_EXP: (
@ -272,6 +279,7 @@ class TensorNameMap:
MODEL_TENSOR.LAYER_OUT_NORM: ( MODEL_TENSOR.LAYER_OUT_NORM: (
"encoder.layer.{bid}.output.LayerNorm", # bert "encoder.layer.{bid}.output.LayerNorm", # bert
"encoder.layers.{bid}.norm2", # nomic-bert
) )
} }

123
llama.cpp
View File

@ -197,6 +197,7 @@ enum llm_arch {
LLM_ARCH_PERSIMMON, LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT, LLM_ARCH_REFACT,
LLM_ARCH_BERT, LLM_ARCH_BERT,
LLM_ARCH_NOMIC_BERT,
LLM_ARCH_BLOOM, LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM, LLM_ARCH_STABLELM,
LLM_ARCH_QWEN, LLM_ARCH_QWEN,
@ -222,6 +223,7 @@ static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_PERSIMMON, "persimmon" }, { LLM_ARCH_PERSIMMON, "persimmon" },
{ LLM_ARCH_REFACT, "refact" }, { LLM_ARCH_REFACT, "refact" },
{ LLM_ARCH_BERT, "bert" }, { LLM_ARCH_BERT, "bert" },
{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
{ LLM_ARCH_BLOOM, "bloom" }, { LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_STABLELM, "stablelm" },
{ LLM_ARCH_QWEN, "qwen" }, { LLM_ARCH_QWEN, "qwen" },
@ -375,6 +377,7 @@ enum llm_tensor {
LLM_TENSOR_ATTN_OUT, LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_NORM, LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2, LLM_TENSOR_ATTN_NORM_2,
LLM_TENSOR_ATTN_OUT_NORM,
LLM_TENSOR_ATTN_ROT_EMBD, LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE_INP, LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_NORM, LLM_TENSOR_FFN_NORM,
@ -387,6 +390,7 @@ enum llm_tensor {
LLM_TENSOR_FFN_UP_EXP, LLM_TENSOR_FFN_UP_EXP,
LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_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 = { static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@ -552,12 +556,27 @@ 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_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" }, { LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_POS_EMBD, "position_embd" }, { 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_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, { 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_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
}, },
@ -1485,6 +1504,7 @@ enum e_model {
MODEL_22M, MODEL_22M,
MODEL_33M, MODEL_33M,
MODEL_109M, MODEL_109M,
MODEL_137M,
MODEL_335M, MODEL_335M,
MODEL_0_5B, MODEL_0_5B,
MODEL_1B, MODEL_1B,
@ -1620,6 +1640,8 @@ struct llama_layer {
struct ggml_tensor * attn_q_norm_b; struct ggml_tensor * attn_q_norm_b;
struct ggml_tensor * attn_k_norm; struct ggml_tensor * attn_k_norm;
struct ggml_tensor * attn_k_norm_b; struct ggml_tensor * attn_k_norm_b;
struct ggml_tensor * attn_out_norm;
struct ggml_tensor * attn_out_norm_b;
// attention // attention
struct ggml_tensor * wq; struct ggml_tensor * wq;
@ -1638,6 +1660,8 @@ struct llama_layer {
// normalization // normalization
struct ggml_tensor * ffn_norm; struct ggml_tensor * ffn_norm;
struct ggml_tensor * ffn_norm_b; struct ggml_tensor * ffn_norm_b;
struct ggml_tensor * layer_out_norm;
struct ggml_tensor * layer_out_norm_b;
// ff // ff
struct ggml_tensor * ffn_gate; // w1 struct ggml_tensor * ffn_gate; // w1
@ -2855,6 +2879,11 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
static const char * llama_model_type_name(e_model type) { static const char * llama_model_type_name(e_model type) {
switch (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_1B: return "1B";
case MODEL_2B: return "2B"; case MODEL_2B: return "2B";
case MODEL_3B: return "3B"; case MODEL_3B: return "3B";
@ -3073,6 +3102,17 @@ static void llm_load_hparams(
model.type = e_model::MODEL_335M; break; // bge-large model.type = e_model::MODEL_335M; break; // bge-large
} }
} break; } 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: case LLM_ARCH_BLOOM:
{ {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -3875,10 +3915,14 @@ static bool llm_load_tensors(
} }
} break; } break;
case LLM_ARCH_BERT: 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.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.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.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 = 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}); model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
@ -3888,12 +3932,7 @@ static bool llm_load_tensors(
auto & layer = model.layers[i]; auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); if (model.arch == LLM_ARCH_BERT) {
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "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.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); 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.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
@ -3902,15 +3941,29 @@ static bool llm_load_tensors(
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "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.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {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.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, 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.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.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); 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});
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_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; } break;
case LLM_ARCH_BLOOM: case LLM_ARCH_BLOOM:
@ -5773,6 +5826,7 @@ struct llm_build_context {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); 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_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); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur; struct ggml_tensor * cur;
@ -5789,7 +5843,9 @@ struct llm_build_context {
// token types are hardcoded to zero ("Sentence A") // token types are hardcoded to zero ("Sentence A")
struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0); 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, inpL, type_row0);
if (model.arch == LLM_ARCH_BERT) {
inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL); inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
}
cb(inpL, "inp_embd", -1); cb(inpL, "inp_embd", -1);
// embed layer norm // embed layer norm
@ -5805,7 +5861,7 @@ struct llm_build_context {
struct ggml_tensor * cur = inpL; struct ggml_tensor * cur = inpL;
// self-attention // 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); struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
cb(Qcur, "Qcur", il); cb(Qcur, "Qcur", il);
@ -5818,6 +5874,37 @@ struct llm_build_context {
// seems like we just need to do this for Q? // seems like we just need to do this for Q?
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); 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, cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo, 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); Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
@ -5828,25 +5915,34 @@ struct llm_build_context {
cur = ggml_add(ctx0, cur, inpL); cur = ggml_add(ctx0, cur, inpL);
// attention layer norm // 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; struct ggml_tensor * ffn_inp = cur;
cb(ffn_inp, "ffn_inp", il); cb(ffn_inp, "ffn_inp", il);
// feed-forward network // feed-forward network
if (model.arch == LLM_ARCH_BERT) {
cur = llm_build_ffn(ctx0, cur, cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, 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, model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL, NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); 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); cb(cur, "ffn_out", il);
// attentions bypass the intermediate layer // attentions bypass the intermediate layer
cur = ggml_add(ctx0, cur, ffn_inp); cur = ggml_add(ctx0, cur, ffn_inp);
// output layer norm // 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 // input for next layer
inpL = cur; inpL = cur;
@ -7289,6 +7385,7 @@ static struct ggml_cgraph * llama_build_graph(
result = llm.build_refact(); result = llm.build_refact();
} break; } break;
case LLM_ARCH_BERT: case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
{ {
result = llm.build_bert(); result = llm.build_bert();
} break; } break;