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https://github.com/ggerganov/llama.cpp.git
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cleanup convert-hf-to-gguf.py
This commit is contained in:
Jared Van Bortel
parent
b8ff85efe0
commit
c2f407e398
@ -10,7 +10,7 @@ import re
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import sys
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from enum import IntEnum
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
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from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast
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import numpy as np
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import torch
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@ -25,15 +25,6 @@ import gguf
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from convert import HfVocab
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# check for any of the given keys in the dictionary and return the value of the first key found
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def get_key_opts(d, keys):
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for k in keys:
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if k in d:
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return d[k]
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print(f"Could not find any of {keys}")
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sys.exit()
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###### MODEL DEFINITIONS ######
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class SentencePieceTokenTypes(IntEnum):
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@ -58,6 +49,15 @@ class Model:
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self.hparams = Model.load_hparams(self.dir_model)
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self.model_arch = self._get_model_architecture()
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self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
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self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
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def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
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key = next((k for k in keys if k in self.hparams), None)
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if key is not None:
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return self.hparams[key]
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if optional:
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return None
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raise KeyError(f"could not find any of: {keys}")
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def set_vocab(self):
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self._set_vocab_gpt2()
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@ -79,28 +79,33 @@ class Model:
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def set_gguf_parameters(self):
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_block_count(self.hparams.get(
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"n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
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))
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self.gguf_writer.add_block_count(self.block_count)
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if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
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self.gguf_writer.add_context_length(n_ctx)
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if (n_embd := self.hparams.get("hidden_size")) is not None:
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self.gguf_writer.add_embedding_length(n_embd)
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if (n_ff := self.hparams.get("intermediate_size")) is not None:
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n_embd = self.find_hparam(["hidden_size", "n_embd"])
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self.gguf_writer.add_embedding_length(n_embd)
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if (n_ff := self.find_hparam(["intermediate_size", "n_inner"])) is not None:
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self.gguf_writer.add_feed_forward_length(n_ff)
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if (n_head := self.hparams.get("num_attention_heads")) is not None:
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self.gguf_writer.add_head_count(n_head)
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n_head = self.find_hparam(["num_attention_heads", "n_head"])
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self.gguf_writer.add_head_count(n_head)
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if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
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self.gguf_writer.add_head_count_kv(n_head_kv)
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if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
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self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps)
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if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
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self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
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if (f_norm_eps := self.hparams.get("layer_norm_eps")) is not None:
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self.gguf_writer.add_layer_norm_eps(f_norm_eps)
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if (n_experts := self.hparams.get("num_local_experts")) is not None:
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self.gguf_writer.add_expert_count(n_experts)
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if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
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self.gguf_writer.add_expert_used_count(n_experts_used)
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self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
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self.gguf_writer.add_file_type(self.ftype)
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def write_tensors(self):
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block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
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@ -1301,21 +1306,21 @@ class GPT2Model(Model):
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class Phi2Model(Model):
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def set_gguf_parameters(self):
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block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"])
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block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
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rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"])
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n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"])
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n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"])
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rot_pct = self.find_hparam(["partial_rotary_factor"])
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n_embd = self.find_hparam(["hidden_size", "n_embd"])
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n_head = self.find_hparam(["num_attention_heads", "n_head"])
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self.gguf_writer.add_name("Phi2")
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self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"]))
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self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
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self.gguf_writer.add_embedding_length(n_embd)
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self.gguf_writer.add_feed_forward_length(4 * n_embd)
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_head_count(n_head)
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self.gguf_writer.add_head_count_kv(n_head)
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self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"]))
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self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
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self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
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self.gguf_writer.add_file_type(self.ftype)
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self.gguf_writer.add_add_bos_token(False)
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@ -1640,21 +1645,12 @@ in chat mode so that the conversation can end normally.")
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class BertModel(Model):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.block_count = self.hparams["num_hidden_layers"]
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self.vocab_size = None
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def set_gguf_parameters(self):
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# TODO(cebtenzzre): merge with parent class
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
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self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
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self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
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self.gguf_writer.add_block_count(self.block_count)
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self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
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self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
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super().set_gguf_parameters()
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self.gguf_writer.add_causal_attention(False)
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self.gguf_writer.add_pooling_layer(True)
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self.gguf_writer.add_file_type(self.ftype)
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def set_vocab(self):
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path = self.dir_model
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@ -1678,7 +1674,7 @@ class BertModel(Model):
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if tok.startswith(b"##"):
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return tok[2:]
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return b"\xe2\x96\x81" + tok
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tokens = [phantom(t, y) for t, y in zip(tokens, toktypes)]
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tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes))
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# set up bos and eos tokens (cls and sep)
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self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id)
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@ -1732,34 +1728,34 @@ class BertModel(Model):
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class NomicBertModel(BertModel):
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def __init__(self, *args, **kwargs):
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Model.__init__(self, *args, **kwargs)
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self.block_count = self.hparams["n_layer"]
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super().__init__(*args, **kwargs)
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# the HF config claims n_ctx=8192, but it uses RoPE scaling
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self.hparams["n_ctx"] = 2048
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# SwigLU activation
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assert self.hparams["activation_function"] == "swiglu"
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assert self.hparams["causal"] is False # True is untested
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# this doesn't do anything in the HF version
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assert self.hparams["causal"] is False
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# no bias tensors
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assert self.hparams["qkv_proj_bias"] is False
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assert self.hparams["mlp_fc1_bias"] is False
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assert self.hparams["mlp_fc2_bias"] is False
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# norm at end of layer
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assert self.hparams["prenorm"] is False
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# standard RoPE
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assert self.hparams["rotary_emb_fraction"] == 1.0
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assert self.hparams["rotary_emb_interleaved"] is False
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assert self.hparams["rotary_emb_scale_base"] is None
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def set_gguf_parameters(self):
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# TODO(cebtenzzre): merge with parent class
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self.gguf_writer.add_name(self.dir_model.name)
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# the HF config claims n_ctx=8192, but it uses RoPE scaling
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self.gguf_writer.add_context_length(2048)
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self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
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self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
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self.gguf_writer.add_block_count(self.block_count)
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self.gguf_writer.add_head_count(self.hparams["n_head"])
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self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
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super().set_gguf_parameters()
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self.gguf_writer.add_causal_attention(self.hparams["causal"])
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self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
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self.gguf_writer.add_pooling_layer(True)
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self.gguf_writer.add_file_type(self.ftype)
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def get_tensors(self):
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assert self.vocab_size is not None
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for name, data in super().get_tensors():
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# Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly.
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if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size:
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