root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-25 10:54:36 +00:00
Code Issues Actions 9 Packages Projects Releases Wiki Activity

convert : update Falcon script for new HF config (#3448)

Browse Source 
Also adds Falcon-180B support.
Closes #3049

Co-authored-by: jb <jonathan.t.barnard@gmail.com>
This commit is contained in:
cebtenzzre 2023-10-05 15:00:34 -04:00 committed by GitHub
parent 45eba9369f
commit 48edda30ee
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 66 additions and 51 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

117
convert-falcon-hf-to-gguf.py
View File

@ -4,6 +4,7 @@
from __future__ import annotations from __future__ import annotations
import argparse import argparse
import contextlib
import json import json
import os import os
import struct import struct
@ -20,10 +21,10 @@ if 'NO_LOCAL_GGUF' not in os.environ:
import gguf import gguf
def count_model_parts(dir_model: Path) -> int: def count_model_parts(dir_model: Path, prefix: str) -> int:
num_parts = 0 num_parts = 0
for filename in os.listdir(dir_model): for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"): if filename.startswith(prefix):
num_parts += 1 num_parts += 1
if num_parts > 0: if num_parts > 0:
@ -77,20 +78,26 @@ print("gguf: loading model "+dir_model.name)
with open(dir_model / "config.json", "r", encoding="utf-8") as f: with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f) hparams = json.load(f)
if hparams["architectures"][0] != "RWForCausalLM": if hparams["architectures"][0] != "FalconForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0]) print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit(1) sys.exit(1)
# get number of model parts # get number of model parts
num_parts = count_model_parts(dir_model) num_parts = count_model_parts(dir_model, "model-00")
if num_parts:
is_safetensors = True
from safetensors import safe_open
else:
is_safetensors = False
num_parts = count_model_parts(dir_model, "pytorch_model-")
ARCH=gguf.MODEL_ARCH.FALCON ARCH=gguf.MODEL_ARCH.FALCON
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
print("gguf: get model metadata") print("gguf: get model metadata")
block_count = hparams["n_layer"] block_count = hparams["num_hidden_layers"]
gguf_writer.add_name("Falcon") gguf_writer.add_name("Falcon")
gguf_writer.add_context_length(2048) # not in config.json gguf_writer.add_context_length(2048) # not in config.json
@ -98,9 +105,9 @@ gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
gguf_writer.add_embedding_length(hparams["hidden_size"]) gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"]) gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
gguf_writer.add_block_count(block_count) gguf_writer.add_block_count(block_count)
gguf_writer.add_head_count(hparams["n_head"]) gguf_writer.add_head_count(hparams["num_attention_heads"])
if "n_head_kv" in hparams: if "num_kv_heads" in hparams:
gguf_writer.add_head_count_kv(hparams["n_head_kv"]) gguf_writer.add_head_count_kv(hparams["num_kv_heads"])
else: else:
gguf_writer.add_head_count_kv(1) gguf_writer.add_head_count_kv(1)
gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
@ -146,8 +153,8 @@ special_vocab.add_to_gguf(gguf_writer)
tensor_map = gguf.get_tensor_name_map(ARCH,block_count) tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
# params for qkv transform # params for qkv transform
n_head = hparams["n_head"] n_head = hparams["num_attention_heads"]
n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1 n_head_kv = hparams["num_kv_heads"] if "num_kv_heads" in hparams else 1
head_dim = hparams["hidden_size"] // n_head head_dim = hparams["hidden_size"] // n_head
@ -156,6 +163,10 @@ print("gguf: get tensor metadata")
if num_parts == 0: if num_parts == 0:
part_names = iter(("pytorch_model.bin",)) part_names = iter(("pytorch_model.bin",))
elif is_safetensors:
part_names = (
f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1)
)
else: else:
part_names = ( part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
@ -165,60 +176,64 @@ for part_name in part_names:
if args.vocab_only: if args.vocab_only:
break break
print("gguf: loading model part '" + part_name + "'") print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(dir_model / part_name, map_location="cpu") if is_safetensors:
ctx = safe_open(dir_model / part_name, framework="pt", device="cpu")
else:
ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu"))
for name in model_part.keys(): with ctx as model_part:
data = model_part[name] for name in model_part.keys():
data = model_part.get_tensor(name) if is_safetensors else model_part[name]
old_dtype = data.dtype old_dtype = data.dtype
# convert any unsupported data types to float32 # convert any unsupported data types to float32
if data.dtype != torch.float16 and data.dtype != torch.float32: if data.dtype != torch.float16 and data.dtype != torch.float32:
data = data.to(torch.float32) data = data.to(torch.float32)
# QKV tensor transform # QKV tensor transform
# The original query_key_value tensor contains n_head_kv "kv groups", # The original query_key_value tensor contains n_head_kv "kv groups",
# each consisting of n_head/n_head_kv query weights followed by one key # each consisting of n_head/n_head_kv query weights followed by one key
# and one value weight (shared by all query heads in the kv group). # and one value weight (shared by all query heads in the kv group).
# This layout makes it a big pain to work with in GGML. # This layout makes it a big pain to work with in GGML.
# So we rearrange them here,, so that we have n_head query weights # So we rearrange them here,, so that we have n_head query weights
# followed by n_head_kv key weights followed by n_head_kv value weights, # followed by n_head_kv key weights followed by n_head_kv value weights,
# in contiguous fashion. # in contiguous fashion.
# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
if "query_key_value" in name: if "query_key_value" in name:
qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head) q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
data = torch.cat((q,k,v)).reshape_as(data) data = torch.cat((q,k,v)).reshape_as(data)
data = data.squeeze().numpy() data = data.squeeze().numpy()
# map tensor names # map tensor names
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None: if new_name is None:
print("Can not map tensor '" + name + "'") print("Can not map tensor '" + name + "'")
sys.exit() sys.exit()
n_dims = len(data.shape) n_dims = len(data.shape)
data_dtype = data.dtype data_dtype = data.dtype
# if f32 desired, convert any float16 to float32 # if f32 desired, convert any float16 to float32
if ftype == 0 and data_dtype == np.float16: if ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32) data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as 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: if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32) data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16 # 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: if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16) data = data.astype(np.float16)
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(new_name, data) gguf_writer.add_tensor(new_name, data)
print("gguf: write header") print("gguf: write header")