convert : update Falcon script for new HF config (#3448)
Also adds Falcon-180B support. Closes #3049 Co-authored-by: jb <jonathan.t.barnard@gmail.com>
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cebtenzzre
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48edda30ee
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@ -4,6 +4,7 @@
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from __future__ import annotations
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import argparse
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import contextlib
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import json
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import os
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import struct
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@ -20,10 +21,10 @@ if 'NO_LOCAL_GGUF' not in os.environ:
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import gguf
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def count_model_parts(dir_model: Path) -> int:
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def count_model_parts(dir_model: Path, prefix: str) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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if filename.startswith(prefix):
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num_parts += 1
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if num_parts > 0:
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@ -77,20 +78,26 @@ print("gguf: loading model "+dir_model.name)
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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if hparams["architectures"][0] != "RWForCausalLM":
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if hparams["architectures"][0] != "FalconForCausalLM":
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print("Model architecture not supported: " + hparams["architectures"][0])
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sys.exit(1)
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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num_parts = count_model_parts(dir_model, "model-00")
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if num_parts:
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is_safetensors = True
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from safetensors import safe_open
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else:
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is_safetensors = False
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num_parts = count_model_parts(dir_model, "pytorch_model-")
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ARCH=gguf.MODEL_ARCH.FALCON
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gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
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print("gguf: get model metadata")
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block_count = hparams["n_layer"]
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block_count = hparams["num_hidden_layers"]
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gguf_writer.add_name("Falcon")
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gguf_writer.add_context_length(2048) # not in config.json
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@ -98,9 +105,9 @@ gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
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gguf_writer.add_embedding_length(hparams["hidden_size"])
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gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(hparams["n_head"])
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if "n_head_kv" in hparams:
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gguf_writer.add_head_count_kv(hparams["n_head_kv"])
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gguf_writer.add_head_count(hparams["num_attention_heads"])
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if "num_kv_heads" in hparams:
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gguf_writer.add_head_count_kv(hparams["num_kv_heads"])
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else:
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gguf_writer.add_head_count_kv(1)
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gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
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@ -146,8 +153,8 @@ special_vocab.add_to_gguf(gguf_writer)
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tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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# params for qkv transform
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n_head = hparams["n_head"]
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n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
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n_head = hparams["num_attention_heads"]
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n_head_kv = hparams["num_kv_heads"] if "num_kv_heads" in hparams else 1
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head_dim = hparams["hidden_size"] // n_head
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@ -156,6 +163,10 @@ print("gguf: get tensor metadata")
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if num_parts == 0:
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part_names = iter(("pytorch_model.bin",))
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elif is_safetensors:
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part_names = (
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f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1)
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)
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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@ -165,60 +176,64 @@ for part_name in part_names:
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if args.vocab_only:
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break
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(dir_model / part_name, map_location="cpu")
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if is_safetensors:
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ctx = safe_open(dir_model / part_name, framework="pt", device="cpu")
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else:
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ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu"))
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for name in model_part.keys():
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data = model_part[name]
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with ctx as model_part:
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for name in model_part.keys():
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data = model_part.get_tensor(name) if is_safetensors else model_part[name]
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old_dtype = data.dtype
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old_dtype = data.dtype
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# convert any unsupported data types to float32
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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# convert any unsupported data types to float32
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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# QKV tensor transform
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# The original query_key_value tensor contains n_head_kv "kv groups",
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# each consisting of n_head/n_head_kv query weights followed by one key
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# and one value weight (shared by all query heads in the kv group).
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# This layout makes it a big pain to work with in GGML.
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# So we rearrange them here,, so that we have n_head query weights
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# followed by n_head_kv key weights followed by n_head_kv value weights,
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# in contiguous fashion.
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# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
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# QKV tensor transform
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# The original query_key_value tensor contains n_head_kv "kv groups",
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# each consisting of n_head/n_head_kv query weights followed by one key
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# and one value weight (shared by all query heads in the kv group).
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# This layout makes it a big pain to work with in GGML.
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# So we rearrange them here,, so that we have n_head query weights
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# followed by n_head_kv key weights followed by n_head_kv value weights,
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# in contiguous fashion.
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# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
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if "query_key_value" in name:
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qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
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q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
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k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
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v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
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data = torch.cat((q,k,v)).reshape_as(data)
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if "query_key_value" in name:
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qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
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q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
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k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
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v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
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data = torch.cat((q,k,v)).reshape_as(data)
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data = data.squeeze().numpy()
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data = data.squeeze().numpy()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
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if new_name is None:
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print("Can not map tensor '" + name + "'")
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sys.exit()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
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if new_name is None:
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print("Can not map tensor '" + name + "'")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.add_tensor(new_name, data)
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gguf_writer.add_tensor(new_name, data)
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print("gguf: write header")
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