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c305fa5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | from __future__ import annotations
import json
from pathlib import Path
from typing import Callable, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf, logger
@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
class MambaModel(TextModel):
model_arch = gguf.MODEL_ARCH.MAMBA
def __init__(self, dir_model: Path, *args, **kwargs):
# Avoid using AutoConfig for hparams
hparams = kwargs.pop("hparams", None)
if hparams is None:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
super().__init__(dir_model, *args, hparams=hparams, **kwargs)
def set_vocab(self):
vocab_size = self.hparams["vocab_size"]
# Round vocab size to next multiple of 8
pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
# pad using ceiling division
# ref: https://stackoverflow.com/a/17511341/22827863
vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
self.hparams["vocab_size"] = vocab_size
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
elif (self.dir_model / "tokenizer.model").is_file():
self._set_vocab_sentencepiece()
else:
# Use the GPT-NeoX tokenizer when no tokenizer files are present
self._set_vocab_builtin("gpt-neox", vocab_size)
def set_gguf_parameters(self):
d_model = self.find_hparam(["hidden_size", "d_model"])
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
# ceiling division
# ref: https://stackoverflow.com/a/17511341/22827863
# ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
use_dt_b_c_norm = False
# For falconmamba we do apply RMS norm on B / DT and C layers
if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",):
use_dt_b_c_norm = True
# Fail early for models which don't have a block expansion factor of 2
assert d_inner == 2 * d_model
self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
self.gguf_writer.add_embedding_length(d_model)
self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_ssm_conv_kernel(d_conv)
self.gguf_writer.add_ssm_inner_size(d_inner)
self.gguf_writer.add_ssm_state_size(d_state)
self.gguf_writer.add_ssm_time_step_rank(dt_rank)
self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
self.gguf_writer.add_ssm_dt_b_c_rms(use_dt_b_c_norm) # For classic Mamba we don't apply rms norm on B / DT layers
self.gguf_writer.add_file_type(self.ftype)
_tok_embd = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)
new_name = self.map_tensor_name(name)
if name.endswith(".A_log"):
logger.debug("A_log --> A ==> " + new_name)
data_torch = -torch.exp(data_torch)
# [4 1 8192 1] -> [4 8192 1 1]
if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
data_torch = data_torch.squeeze()
# assuming token_embd.weight is seen before output.weight
if self._tok_embd is not None and new_name == output_name:
if torch.equal(self._tok_embd, data_torch):
logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting")
return
elif new_name == tok_embd_name:
self._tok_embd = data_torch
yield from super().modify_tensors(data_torch, new_name, bid)
@ModelBase.register("Mamba2ForCausalLM")
class Mamba2Model(TextModel):
model_arch = gguf.MODEL_ARCH.MAMBA2
def __init__(self, dir_model: Path, *args, **kwargs):
# Avoid using AutoConfig for hparams
# It wrongly assumes all Mamba2 models are Mamba-Codestral-7B-v0.1
hparams = kwargs.pop("hparams", None)
if hparams is None:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if "llm_config" in hparams:
hparams["text_config"] = hparams["llm_config"]
super().__init__(dir_model, *args, hparams=hparams, **kwargs)
self.d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
self.expand = self.find_hparam(["mamba_expand", "expand"], optional=True) or 2
self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or self.expand * self.d_model
self.n_group = self.find_hparam(["n_groups"], optional=True) or 1
def set_vocab(self):
vocab_size = self.hparams["vocab_size"]
# Round vocab size to next multiple of 16
pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
# pad using ceiling division
# ref: https://stackoverflow.com/a/17511341/22827863
vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
self.hparams["vocab_size"] = vocab_size
if (self.dir_model / "tokenizer.model").is_file():
self._set_vocab_sentencepiece()
elif (self.dir_model / "tokenizer.model.v3").is_file():
# mamba-codestral
raise NotImplementedError(f"Please rename {self.dir_model / 'tokenizer.model.v3'} to {self.dir_model / 'tokenizer.model'}")
elif (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
else:
# Use the GPT-NeoX tokenizer when no tokenizer files are present
self._set_vocab_builtin("gpt-neox", vocab_size)
def set_gguf_parameters(self):
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 128
head_dim = self.find_hparam(["mamba_d_head", "head_dim"], optional=True) or 64
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
# skip the assertion for FalconH1 Model
if self.model_arch != gguf.MODEL_ARCH.FALCON_H1:
assert self.d_inner == self.expand * self.d_model
assert self.d_inner % head_dim == 0
self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
self.gguf_writer.add_embedding_length(self.d_model)
self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_ssm_conv_kernel(d_conv)
self.gguf_writer.add_ssm_inner_size(self.d_inner)
self.gguf_writer.add_ssm_state_size(d_state)
self.gguf_writer.add_ssm_time_step_rank(self.d_inner // head_dim)
self.gguf_writer.add_ssm_group_count(self.n_group)
self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
self.gguf_writer.add_file_type(self.ftype)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
if name.startswith(("model.backbone", "model.lm_head")):
# map Mamba-Codestral-7B-v0.1 tensor names to the names used by Mamba-2
name = name.removeprefix("model.")
if name.endswith(".dt_bias"):
name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
return super().filter_tensors((name, gen))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
new_name = self.map_tensor_name(name)
if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
data_torch = data_torch.squeeze()
elif any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [
gguf.MODEL_TENSOR.SSM_A,
gguf.MODEL_TENSOR.SSM_D,
]):
# unsqueeze A to use similar shape semantics as Mamba-1
# (D is also unsqueezed, but for more straightforward broadcast internally)
data_torch = data_torch.reshape((*data_torch.shape, 1))
elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid):
data_torch = data_torch.reshape((self.n_group, self.d_inner // self.n_group))
if name.endswith(".A_log"):
logger.debug("A_log --> A ==> " + new_name)
data_torch = -torch.exp(data_torch)
yield (new_name, data_torch)
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