Merge branch 'master' into concedo_experimental
# Conflicts: # README.md # tests/test-grad0.cpp # tests/test-opt.cpp # tests/test-quantize-perf.cpp
This commit is contained in:
Concedo
commit
5beb773320
8 changed files with 1444 additions and 328 deletions
|
|
@ -11,11 +11,14 @@ import sys
|
|||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any
|
||||
import itertools
|
||||
import gguf
|
||||
import numpy as np
|
||||
import torch
|
||||
from sentencepiece import SentencePieceProcessor # type: ignore[import]
|
||||
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
|
||||
import gguf
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from typing import TypeAlias
|
||||
|
|
@ -174,8 +177,11 @@ if not tokenizer_model_file.is_file():
|
|||
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
|
||||
|
||||
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
|
||||
vocab_size = hparams.get('vocab_size')
|
||||
if vocab_size is None:
|
||||
vocab_size = tokenizer.vocab_size()
|
||||
|
||||
for i in range(tokenizer.vocab_size()):
|
||||
for i in range(vocab_size):
|
||||
text: bytes
|
||||
score: float
|
||||
|
||||
|
|
|
|||
318
convert-refact-hf-to-gguf.py
Executable file
318
convert-refact-hf-to-gguf.py
Executable file
|
|
@ -0,0 +1,318 @@
|
|||
#!/usr/bin/env python3
|
||||
# HF refact--> gguf conversion
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import AutoTokenizer # type: ignore[import]
|
||||
|
||||
if "NO_LOCAL_GGUF" not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / "gguf-py" / "gguf"))
|
||||
import gguf
|
||||
|
||||
|
||||
def bytes_to_unicode():
|
||||
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
|
||||
"""
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||
The reversible bpe codes work on unicode strings.
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||
This is a significant percentage of your normal, say, 32K bpe vocab.
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||
"""
|
||||
bs = (
|
||||
list(range(ord("!"), ord("~") + 1))
|
||||
+ list(range(ord("¡"), ord("¬") + 1))
|
||||
+ list(range(ord("®"), ord("ÿ") + 1))
|
||||
)
|
||||
cs = bs[:]
|
||||
n = 0
|
||||
for b in range(2**8):
|
||||
if b not in bs:
|
||||
bs.append(b)
|
||||
cs.append(2**8 + n)
|
||||
n += 1
|
||||
return dict(zip(bs, (chr(n) for n in cs)))
|
||||
|
||||
|
||||
def count_model_parts(dir_model: Path) -> int:
|
||||
num_parts = 0
|
||||
for filename in os.listdir(dir_model):
|
||||
if filename.startswith("pytorch_model-"):
|
||||
num_parts += 1
|
||||
|
||||
if num_parts > 0:
|
||||
print("gguf: found " + str(num_parts) + " model parts")
|
||||
return num_parts
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Convert a Refact model to a GGML compatible file"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--vocab-only",
|
||||
action="store_true",
|
||||
help="extract only the vocab",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--outfile",
|
||||
type=Path,
|
||||
help="path to write to; default: based on input",
|
||||
)
|
||||
parser.add_argument(
|
||||
"model",
|
||||
type=Path,
|
||||
help="directory containing model file, or model file itself (*.bin)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"ftype",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
default=1,
|
||||
nargs="?",
|
||||
help="output format - use 0 for float32, 1 for float16",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
args = parse_args()
|
||||
|
||||
dir_model = args.model
|
||||
ftype = args.ftype
|
||||
if not dir_model.is_dir():
|
||||
print(f"Error: {args.model} is not a directory", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
# possible tensor data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
if args.outfile is not None:
|
||||
fname_out = args.outfile
|
||||
else:
|
||||
# output in the same directory as the model by default
|
||||
fname_out = dir_model / f"ggml-model-{ftype_str[ftype]}.gguf"
|
||||
|
||||
print("gguf: loading model " + dir_model.name)
|
||||
|
||||
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
|
||||
hparams = json.load(f)
|
||||
|
||||
if hparams["architectures"][0] != "GPTRefactForCausalLM":
|
||||
print("Model architecture not supported: " + hparams["architectures"][0])
|
||||
|
||||
sys.exit(1)
|
||||
|
||||
# get number of model parts
|
||||
num_parts = count_model_parts(dir_model)
|
||||
|
||||
ARCH = gguf.MODEL_ARCH.REFACT
|
||||
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
|
||||
|
||||
print("gguf: get model metadata")
|
||||
|
||||
# Get refact feed forward dimension
|
||||
hidden_dim = hparams["n_embd"]
|
||||
inner_dim = 4 * hidden_dim
|
||||
hidden_dim = int(2 * inner_dim / 3)
|
||||
multiple_of = 256
|
||||
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
||||
|
||||
block_count = hparams["n_layer"]
|
||||
|
||||
gguf_writer.add_name("Refact")
|
||||
# refact uses Alibi. So this is from config.json which might be used by training.
|
||||
gguf_writer.add_context_length(hparams["n_positions"])
|
||||
gguf_writer.add_embedding_length(hparams["n_embd"])
|
||||
|
||||
gguf_writer.add_feed_forward_length(ff_dim)
|
||||
gguf_writer.add_block_count(block_count)
|
||||
gguf_writer.add_head_count(hparams["n_head"])
|
||||
gguf_writer.add_head_count_kv(1)
|
||||
gguf_writer.add_layer_norm_rms_eps(hparams["layer_norm_epsilon"])
|
||||
gguf_writer.add_file_type(ftype)
|
||||
|
||||
# TOKENIZATION
|
||||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: list[bytearray] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
tokenizer_json_file = dir_model / "tokenizer.json"
|
||||
if not tokenizer_json_file.is_file():
|
||||
print(f"Error: Missing {tokenizer_json_file}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
# gpt2 tokenizer
|
||||
gguf_writer.add_tokenizer_model("gpt2")
|
||||
|
||||
with open(tokenizer_json_file, "r", encoding="utf-8") as f:
|
||||
tokenizer_json = json.load(f)
|
||||
|
||||
print("gguf: get gpt2 tokenizer vocab")
|
||||
|
||||
# The number of tokens in tokenizer.json can differ from the expected vocab size.
|
||||
# This causes downstream issues with mismatched tensor sizes when running the inference
|
||||
vocab_size = (
|
||||
hparams["vocab_size"]
|
||||
if "vocab_size" in hparams
|
||||
else len(tokenizer_json["model"]["vocab"])
|
||||
)
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
|
||||
|
||||
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
|
||||
byte_encoder = bytes_to_unicode()
|
||||
byte_decoder = {v: k for k, v in byte_encoder.items()}
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i in reverse_vocab:
|
||||
text = reverse_vocab[i]
|
||||
try:
|
||||
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
|
||||
except KeyError:
|
||||
text = bytearray()
|
||||
for c in reverse_vocab[i]:
|
||||
if ord(c) < 256: # single byte character
|
||||
text.append(byte_decoder[ord(c)])
|
||||
else: # multibyte special token character
|
||||
text.extend(c.encode("utf-8"))
|
||||
else:
|
||||
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
|
||||
pad_token = f"[PAD{i}]".encode("utf8")
|
||||
text = bytearray(pad_token)
|
||||
|
||||
tokens.append(text)
|
||||
scores.append(0.0) # dymmy
|
||||
toktypes.append(gguf.TokenType.NORMAL) # dummy
|
||||
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_scores(scores)
|
||||
gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
|
||||
special_vocab.add_to_gguf(gguf_writer)
|
||||
|
||||
# TENSORS
|
||||
|
||||
tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
|
||||
|
||||
# params for qkv transform
|
||||
n_head = hparams["n_head"]
|
||||
n_head_kv = 1
|
||||
|
||||
head_dim = hparams["n_embd"] // n_head
|
||||
|
||||
# tensor info
|
||||
print("gguf: get tensor metadata")
|
||||
|
||||
if num_parts == 0:
|
||||
part_names = iter(("pytorch_model.bin",))
|
||||
else:
|
||||
part_names = (
|
||||
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
|
||||
)
|
||||
for part_name in part_names:
|
||||
if args.vocab_only:
|
||||
break
|
||||
print("gguf: loading model part '" + part_name + "'")
|
||||
model_part = torch.load(dir_model / part_name, map_location="cpu")
|
||||
|
||||
for i in range(block_count):
|
||||
if f"transformer.h.{i}.attn.kv.weight" in model_part:
|
||||
data = model_part[f"transformer.h.{i}.attn.kv.weight"]
|
||||
model_part[f"model.layers.{i}.self_attn.k_proj.weight"] = data[
|
||||
: n_head_kv * head_dim
|
||||
]
|
||||
model_part[f"model.layers.{i}.self_attn.v_proj.weight"] = data[
|
||||
n_head_kv * head_dim :
|
||||
]
|
||||
del model_part[f"transformer.h.{i}.attn.kv.weight"]
|
||||
if f"transformer.h.{i}.attn.q.weight" in model_part:
|
||||
model_part[f"model.layers.{i}.self_attn.q_proj.weight"] = model_part[
|
||||
f"transformer.h.{i}.attn.q.weight"
|
||||
]
|
||||
del model_part[f"transformer.h.{i}.attn.q.weight"]
|
||||
if f"transformer.h.{i}.mlp.gate_up_proj.weight" in model_part:
|
||||
data = model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
|
||||
model_part[f"model.layers.{i}.mlp.gate_proj.weight"] = data[:ff_dim]
|
||||
model_part[f"model.layers.{i}.mlp.up_proj.weight"] = data[ff_dim:]
|
||||
del model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
|
||||
|
||||
for name in model_part.keys():
|
||||
data = model_part[name]
|
||||
|
||||
old_dtype = data.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data.dtype != torch.float16 and data.dtype != torch.float32:
|
||||
data = data.to(torch.float32)
|
||||
|
||||
data = data.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight",))
|
||||
if new_name is None:
|
||||
print("Can not map tensor '" + name + "'")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if ftype == 0 and data_dtype == np.float16:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
||||
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
||||
if (
|
||||
ftype == 1
|
||||
and data_dtype == np.float32
|
||||
and name.endswith(".weight")
|
||||
and n_dims == 2
|
||||
):
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(
|
||||
new_name
|
||||
+ ", n_dims = "
|
||||
+ str(n_dims)
|
||||
+ ", "
|
||||
+ str(old_dtype)
|
||||
+ " --> "
|
||||
+ str(data.dtype)
|
||||
)
|
||||
|
||||
gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
print("gguf: write header")
|
||||
gguf_writer.write_header_to_file()
|
||||
print("gguf: write metadata")
|
||||
gguf_writer.write_kv_data_to_file()
|
||||
if not args.vocab_only:
|
||||
print("gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
print(f"gguf: model successfully exported to '{fname_out}'")
|
||||
print("")
|
||||
|
|
@ -61,7 +61,7 @@ For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' L
|
|||
--lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin
|
||||
```
|
||||
|
||||
The scale numbers don't need to add up to one, and you can also use numbers creater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values.
|
||||
The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values.
|
||||
|
||||
Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
|
||||
If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.
|
||||
|
|
|
|||
13
ggml.h
13
ggml.h
|
|
@ -401,10 +401,14 @@ extern "C" {
|
|||
GGML_OP_CLAMP,
|
||||
GGML_OP_CONV_1D,
|
||||
GGML_OP_CONV_2D,
|
||||
GGML_OP_CONV_TRANSPOSE_1D,
|
||||
GGML_OP_CONV_TRANSPOSE_2D,
|
||||
GGML_OP_POOL_1D,
|
||||
GGML_OP_POOL_2D,
|
||||
|
||||
GGML_OP_CONV_1D_STAGE_0, // internal
|
||||
GGML_OP_CONV_1D_STAGE_1, // internal
|
||||
|
||||
GGML_OP_UPSCALE, // nearest interpolate
|
||||
|
||||
GGML_OP_FLASH_ATTN,
|
||||
|
|
@ -1386,6 +1390,14 @@ extern "C" {
|
|||
int s,
|
||||
int d);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
int s0,
|
||||
int p0,
|
||||
int d0);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_conv_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
@ -1759,6 +1771,7 @@ extern "C" {
|
|||
GGML_OPT_NO_CONTEXT,
|
||||
GGML_OPT_INVALID_WOLFE,
|
||||
GGML_OPT_FAIL,
|
||||
GGML_OPT_CANCEL,
|
||||
|
||||
GGML_LINESEARCH_FAIL = -128,
|
||||
GGML_LINESEARCH_MINIMUM_STEP,
|
||||
|
|
|
|||
|
|
@ -85,6 +85,7 @@ class MODEL_ARCH(IntEnum):
|
|||
GPTNEOX : int = auto()
|
||||
MPT : int = auto()
|
||||
STARCODER : int = auto()
|
||||
REFACT : int = auto()
|
||||
BERT : int = auto()
|
||||
|
||||
|
||||
|
|
@ -118,6 +119,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.GPTNEOX: "gptneox",
|
||||
MODEL_ARCH.MPT: "mpt",
|
||||
MODEL_ARCH.STARCODER: "starcoder",
|
||||
MODEL_ARCH.REFACT: "refact",
|
||||
MODEL_ARCH.BERT: "bert",
|
||||
}
|
||||
|
||||
|
|
@ -247,6 +249,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.REFACT: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.GPT2: [
|
||||
# TODO
|
||||
],
|
||||
|
|
@ -271,7 +287,7 @@ class TensorNameMap:
|
|||
# Token embeddings
|
||||
MODEL_TENSOR.TOKEN_EMBD: (
|
||||
"gpt_neox.embed_in", # gptneox
|
||||
"transformer.wte", # gpt2 gpt-j mpt
|
||||
"transformer.wte", # gpt2 gpt-j mpt refact
|
||||
"transformer.word_embeddings", # falcon
|
||||
"model.embed_tokens", # llama-hf
|
||||
"tok_embeddings", # llama-pth
|
||||
|
|
@ -304,6 +320,7 @@ class TensorNameMap:
|
|||
"norm", # llama-pth
|
||||
"embeddings.LayerNorm", # bert
|
||||
"transformer.norm_f", # mpt
|
||||
"ln_f", # refact
|
||||
),
|
||||
|
||||
# Rope frequencies
|
||||
|
|
@ -316,7 +333,7 @@ class TensorNameMap:
|
|||
# Attention norm
|
||||
MODEL_TENSOR.ATTN_NORM: (
|
||||
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
|
||||
"transformer.h.{bid}.ln_1", # gpt2 gpt-j
|
||||
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
|
||||
"transformer.blocks.{bid}.norm_1", # mpt
|
||||
"transformer.h.{bid}.input_layernorm", # falcon7b
|
||||
"transformer.h.{bid}.ln_mlp", # falcon40b
|
||||
|
|
@ -365,7 +382,7 @@ class TensorNameMap:
|
|||
# Attention output
|
||||
MODEL_TENSOR.ATTN_OUT: (
|
||||
"gpt_neox.layers.{bid}.attention.dense", # gptneox
|
||||
"transformer.h.{bid}.attn.c_proj", # gpt2
|
||||
"transformer.h.{bid}.attn.c_proj", # gpt2 refact
|
||||
"transformer.blocks.{bid}.attn.out_proj", # mpt
|
||||
"transformer.h.{bid}.self_attention.dense", # falcon
|
||||
"model.layers.{bid}.self_attn.o_proj", # llama-hf
|
||||
|
|
@ -383,7 +400,7 @@ class TensorNameMap:
|
|||
# Feed-forward norm
|
||||
MODEL_TENSOR.FFN_NORM: (
|
||||
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
|
||||
"transformer.h.{bid}.ln_2", # gpt2
|
||||
"transformer.h.{bid}.ln_2", # gpt2 refact
|
||||
"transformer.blocks.{bid}.norm_2", # mpt
|
||||
"model.layers.{bid}.post_attention_layernorm", # llama-hf
|
||||
"layers.{bid}.ffn_norm", # llama-pth
|
||||
|
|
@ -396,7 +413,7 @@ class TensorNameMap:
|
|||
"transformer.h.{bid}.mlp.c_fc", # gpt2
|
||||
"transformer.blocks.{bid}.ffn.up_proj", # mpt
|
||||
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
|
||||
"model.layers.{bid}.mlp.up_proj", # llama-hf
|
||||
"model.layers.{bid}.mlp.up_proj", # llama-hf refact
|
||||
"layers.{bid}.feed_forward.w3", # llama-pth
|
||||
"encoder.layer.{bid}.intermediate.dense", # bert
|
||||
"transformer.h.{bid}.mlp.fc_in", # gpt-j
|
||||
|
|
@ -404,14 +421,14 @@ class TensorNameMap:
|
|||
|
||||
# Feed-forward gate
|
||||
MODEL_TENSOR.FFN_GATE: (
|
||||
"model.layers.{bid}.mlp.gate_proj", # llama-hf
|
||||
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
|
||||
"layers.{bid}.feed_forward.w1", # llama-pth
|
||||
),
|
||||
|
||||
# Feed-forward down
|
||||
MODEL_TENSOR.FFN_DOWN: (
|
||||
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
|
||||
"transformer.h.{bid}.mlp.c_proj", # gpt2
|
||||
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact
|
||||
"transformer.blocks.{bid}.ffn.down_proj", # mpt
|
||||
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
|
||||
"model.layers.{bid}.mlp.down_proj", # llama-hf
|
||||
|
|
|
|||
|
|
@ -69,7 +69,6 @@ inline static int32_t vaddvq_s32(int32x4_t v) {
|
|||
// 2-6 bit quantization in super-blocks
|
||||
//
|
||||
|
||||
|
||||
//
|
||||
// ===================== Helper functions
|
||||
//
|
||||
|
|
@ -348,7 +347,6 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
|
|||
const float q4scale = 15.f;
|
||||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
|
||||
float max_scale = 0; // as we are deducting the min, scales are always positive
|
||||
float max_min = 0;
|
||||
for (int j = 0; j < QK_K/16; ++j) {
|
||||
|
|
|
|||
382
llama.cpp
382
llama.cpp
|
|
@ -166,6 +166,7 @@ enum llm_arch {
|
|||
LLM_ARCH_GPTNEOX,
|
||||
LLM_ARCH_MPT,
|
||||
LLM_ARCH_STARCODER,
|
||||
LLM_ARCH_REFACT,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -178,6 +179,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_MPT, "mpt" },
|
||||
{ LLM_ARCH_BAICHUAN, "baichuan" },
|
||||
{ LLM_ARCH_STARCODER, "starcoder" },
|
||||
{ LLM_ARCH_REFACT, "refact" },
|
||||
};
|
||||
|
||||
enum llm_kv {
|
||||
|
|
@ -398,6 +400,23 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
|
|||
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_REFACT,
|
||||
{
|
||||
{ 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_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.ffn_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" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_UNKNOWN,
|
||||
{
|
||||
|
|
@ -1933,6 +1952,14 @@ static void llm_load_hparams(
|
|||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_REFACT:
|
||||
{
|
||||
GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
|
||||
switch (hparams.n_layer) {
|
||||
case 32: model.type = e_model::MODEL_1B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
default: (void)0;
|
||||
}
|
||||
|
||||
|
|
@ -2170,6 +2197,7 @@ static void llm_load_tensors(
|
|||
const auto tn = LLM_TN(model.arch);
|
||||
switch (model.arch) {
|
||||
case LLM_ARCH_LLAMA:
|
||||
case LLM_ARCH_REFACT:
|
||||
{
|
||||
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
||||
|
||||
|
|
@ -3363,6 +3391,353 @@ static struct ggml_cgraph * llm_build_baichaun(
|
|||
return gf;
|
||||
}
|
||||
|
||||
static struct ggml_cgraph * llm_build_refact(
|
||||
llama_context & lctx,
|
||||
const llama_batch & batch) {
|
||||
const auto & model = lctx.model;
|
||||
const auto & hparams = model.hparams;
|
||||
const auto & cparams = lctx.cparams;
|
||||
|
||||
const auto & kv_self = lctx.kv_self;
|
||||
|
||||
GGML_ASSERT(!!kv_self.ctx);
|
||||
|
||||
const int64_t n_embd = hparams.n_embd;
|
||||
const int64_t n_layer = hparams.n_layer;
|
||||
const int64_t n_ctx = cparams.n_ctx;
|
||||
const int64_t n_head = hparams.n_head;
|
||||
const int64_t n_head_kv = hparams.n_head_kv;
|
||||
const int64_t n_embd_head = hparams.n_embd_head();
|
||||
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
||||
|
||||
const float norm_rms_eps = hparams.f_norm_rms_eps;
|
||||
|
||||
const int n_gpu_layers = model.n_gpu_layers;
|
||||
|
||||
const int32_t n_tokens = batch.n_tokens;
|
||||
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
|
||||
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
|
||||
|
||||
// printf("n_kv = %d\n", n_kv);
|
||||
|
||||
auto & buf_compute = lctx.buf_compute;
|
||||
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ buf_compute.size,
|
||||
/*.mem_buffer =*/ buf_compute.data,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
params.no_alloc = true;
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
|
||||
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
if (batch.token) {
|
||||
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
|
||||
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
|
||||
}
|
||||
ggml_set_name(inp_tokens, "inp_tokens");
|
||||
|
||||
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
||||
} else {
|
||||
#ifdef GGML_USE_MPI
|
||||
GGML_ASSERT(false && "not implemented");
|
||||
#endif
|
||||
|
||||
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
||||
|
||||
ggml_allocr_alloc(lctx.alloc, inpL);
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
|
||||
}
|
||||
}
|
||||
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
(void) i_gpu_start;
|
||||
|
||||
// offload functions set the tensor output backend to GPU
|
||||
// tensors are GPU-accelerated if any input or the output has been offloaded
|
||||
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
|
||||
offload_func_t offload_func_kq = llama_nop;
|
||||
offload_func_t offload_func_v = llama_nop;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (n_gpu_layers > n_layer) {
|
||||
offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
|
||||
}
|
||||
if (n_gpu_layers > n_layer + 1) {
|
||||
offload_func_v = ggml_cuda_assign_buffers_no_alloc;
|
||||
}
|
||||
if (n_gpu_layers > n_layer + 2) {
|
||||
offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
// KQ_scale
|
||||
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
||||
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
||||
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
|
||||
}
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
||||
offload_func_kq(KQ_mask);
|
||||
ggml_set_name(KQ_mask, "KQ_mask");
|
||||
ggml_allocr_alloc(lctx.alloc, KQ_mask);
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
float * data = (float *) KQ_mask->data;
|
||||
memset(data, 0, ggml_nbytes(KQ_mask));
|
||||
|
||||
for (int h = 0; h < 1; ++h) {
|
||||
for (int j = 0; j < n_tokens; ++j) {
|
||||
const llama_pos pos = batch.pos[j];
|
||||
const llama_seq_id seq_id = batch.seq_id[j];
|
||||
|
||||
for (int i = 0; i < n_kv; ++i) {
|
||||
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
|
||||
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_format_name(inpL, "layer_inp_%d", il);
|
||||
|
||||
offload_func_t offload_func = llama_nop;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (il >= i_gpu_start) {
|
||||
offload_func = ggml_cuda_assign_buffers_no_alloc;
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_0");
|
||||
|
||||
// cur = cur*attn_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "attention_norm_0");
|
||||
}
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// compute Q and K
|
||||
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
||||
offload_func_kq(tmpk);
|
||||
ggml_set_name(tmpk, "tmpk");
|
||||
|
||||
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
||||
offload_func_kq(tmpq);
|
||||
ggml_set_name(tmpq, "tmpq");
|
||||
|
||||
struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
|
||||
offload_func_kq(Kcur);
|
||||
ggml_set_name(Kcur, "Kcur");
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
|
||||
offload_func_kq(Qcur);
|
||||
ggml_set_name(Qcur, "Qcur");
|
||||
|
||||
// store key and value to memory
|
||||
{
|
||||
// compute the transposed [n_tokens, n_embd] V matrix
|
||||
|
||||
struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
||||
offload_func_v(tmpv);
|
||||
ggml_set_name(tmpv, "tmpv");
|
||||
|
||||
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
|
||||
offload_func_v(Vcur);
|
||||
ggml_set_name(Vcur, "Vcur");
|
||||
|
||||
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
|
||||
offload_func_kq(k);
|
||||
ggml_set_name(k, "k");
|
||||
|
||||
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
||||
( n_ctx)*ggml_element_size(kv_self.v),
|
||||
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
||||
offload_func_v(v);
|
||||
ggml_set_name(v, "v");
|
||||
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
||||
}
|
||||
|
||||
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
||||
offload_func_kq(Q);
|
||||
ggml_set_name(Q, "Q");
|
||||
|
||||
struct ggml_tensor * K =
|
||||
ggml_view_3d(ctx0, kv_self.k,
|
||||
n_embd_head, n_kv, n_head_kv,
|
||||
ggml_element_size(kv_self.k)*n_embd_gqa,
|
||||
ggml_element_size(kv_self.k)*n_embd_head,
|
||||
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
||||
offload_func_kq(K);
|
||||
ggml_set_name(K, "K");
|
||||
|
||||
// K * Q
|
||||
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
||||
offload_func_kq(KQ);
|
||||
ggml_set_name(KQ, "KQ");
|
||||
|
||||
// KQ_scaled = KQ / sqrt(n_embd_head)
|
||||
// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
|
||||
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
||||
offload_func_kq(KQ_scaled);
|
||||
ggml_set_name(KQ_scaled, "KQ_scaled");
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
|
||||
ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
|
||||
|
||||
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
|
||||
offload_func_kq(KQ_masked);
|
||||
ggml_set_name(KQ_masked, "KQ_masked");
|
||||
|
||||
// KQ = soft_max(KQ_masked)
|
||||
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
||||
offload_func_v(KQ_soft_max);
|
||||
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
||||
|
||||
// split cached V into n_head heads
|
||||
struct ggml_tensor * V =
|
||||
ggml_view_3d(ctx0, kv_self.v,
|
||||
n_kv, n_embd_head, n_head_kv,
|
||||
ggml_element_size(kv_self.v)*n_ctx,
|
||||
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
||||
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
||||
offload_func_v(V);
|
||||
ggml_set_name(V, "V");
|
||||
|
||||
#if 1
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
||||
offload_func_v(KQV);
|
||||
ggml_set_name(KQV, "KQV");
|
||||
#else
|
||||
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
|
||||
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
|
||||
// is there a better way?
|
||||
struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
|
||||
#endif
|
||||
|
||||
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
||||
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||
offload_func_v(KQV_merged);
|
||||
ggml_set_name(KQV_merged, "KQV_merged");
|
||||
|
||||
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
|
||||
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
||||
offload_func_v(cur);
|
||||
ggml_set_name(cur, "KQV_merged_contiguous");
|
||||
|
||||
// projection (no bias)
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].wo,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
}
|
||||
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
||||
offload_func(inpFF);
|
||||
ggml_set_name(inpFF, "inpFF");
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_1");
|
||||
|
||||
// cur = cur*ffn_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "ffn_norm");
|
||||
}
|
||||
|
||||
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w3,
|
||||
cur);
|
||||
offload_func(tmp);
|
||||
ggml_set_name(tmp, "result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w1,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w1");
|
||||
|
||||
// SILU activation
|
||||
cur = ggml_silu(ctx0, cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu");
|
||||
|
||||
cur = ggml_mul(ctx0, cur, tmp);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu_x_result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w2,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "inpFF_+_result_w2");
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
|
||||
offload_func_nr(cur);
|
||||
ggml_set_name(cur, "rms_norm_2");
|
||||
|
||||
// cur = cur*norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.output_norm);
|
||||
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
||||
ggml_set_name(cur, "result_norm");
|
||||
}
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
ggml_set_name(cur, "result_output");
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
static struct ggml_cgraph * llm_build_falcon(
|
||||
llama_context & lctx,
|
||||
const llama_batch & batch) {
|
||||
|
|
@ -4003,6 +4378,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm_build_starcoder(lctx, batch);
|
||||
} break;
|
||||
case LLM_ARCH_REFACT:
|
||||
{
|
||||
result = llm_build_refact(lctx, batch);
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -4136,7 +4515,8 @@ static int llama_decode_internal(
|
|||
// If all tensors can be run on the GPU then using more than 1 thread is detrimental.
|
||||
const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
|
||||
model.arch == LLM_ARCH_BAICHUAN ||
|
||||
model.arch == LLM_ARCH_FALCON;
|
||||
model.arch == LLM_ARCH_FALCON ||
|
||||
model.arch == LLM_ARCH_REFACT;
|
||||
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
|
||||
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
|
||||
n_threads = 1;
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue