from __future__ import annotations
import argparse
+import contextlib
import json
import os
import struct
import gguf
-def count_model_parts(dir_model: Path) -> int:
+def count_model_parts(dir_model: Path, prefix: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
- if filename.startswith("pytorch_model-"):
+ if filename.startswith(prefix):
num_parts += 1
if num_parts > 0:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
-if hparams["architectures"][0] != "RWForCausalLM":
+if hparams["architectures"][0] != "FalconForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit(1)
# 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
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
print("gguf: get model metadata")
-block_count = hparams["n_layer"]
+block_count = hparams["num_hidden_layers"]
gguf_writer.add_name("Falcon")
gguf_writer.add_context_length(2048) # not in config.json
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
-gguf_writer.add_head_count(hparams["n_head"])
-if "n_head_kv" in hparams:
- gguf_writer.add_head_count_kv(hparams["n_head_kv"])
+gguf_writer.add_head_count(hparams["num_attention_heads"])
+if "num_kv_heads" in hparams:
+ gguf_writer.add_head_count_kv(hparams["num_kv_heads"])
else:
gguf_writer.add_head_count_kv(1)
gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
# params for qkv transform
-n_head = hparams["n_head"]
-n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
+n_head = hparams["num_attention_heads"]
+n_head_kv = hparams["num_kv_heads"] if "num_kv_heads" in hparams else 1
head_dim = hparams["hidden_size"] // n_head
if num_parts == 0:
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:
part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
if args.vocab_only:
break
print("gguf: loading model part '" + part_name + "'")
- model_part = torch.load(dir_model / part_name, map_location="cpu")
-
- 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)
-
- # QKV tensor transform
- # 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
- # 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.
- # 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,
- # in contiguous fashion.
- # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
-
- if "query_key_value" in name:
- 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)
- 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)
- data = torch.cat((q,k,v)).reshape_as(data)
-
- data = data.squeeze().numpy()
-
- # map tensor names
- new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
- 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)
+ 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"))
+
+ with ctx as model_part:
+ for name in model_part.keys():
+ data = model_part.get_tensor(name) if is_safetensors else 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)
+
+ # QKV tensor transform
+ # 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
+ # 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.
+ # 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,
+ # in contiguous fashion.
+ # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
+
+ if "query_key_value" in name:
+ 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)
+ 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)
+ data = torch.cat((q,k,v)).reshape_as(data)
+
+ data = data.squeeze().numpy()
+
+ # map tensor names
+ new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+ 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")
overview / pkg / ggml / sources / llama.cpp / commitdiff