File size: 8,205 Bytes
2eca14b 47bd780 2eca14b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 |
"""
HuggingFace wrapper for FrawdLLM.
This allows the model to be loaded with:
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tsingla1998/frawdllm-100m", trust_remote_code=True)
"""
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig, PreTrainedModel, GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from .config import ModelConfig
from .gpt import FrawdLLM
class FrawdLLMConfig(PretrainedConfig):
"""HuggingFace-compatible configuration for FrawdLLM."""
model_type = "frawdllm"
def __init__(
self,
vocab_size: int = 32000,
n_embd: int = 768,
n_layer: int = 12,
n_head: int = 12,
context_length: int = 1024,
dropout: float = 0.1,
use_rope: bool = True,
use_rmsnorm: bool = False,
use_swiglu: bool = False,
pad_token_id: int = 0,
bos_token_id: int = 2,
eos_token_id: int = 3,
**kwargs,
):
self.vocab_size = vocab_size
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.context_length = context_length
self.dropout = dropout
self.use_rope = use_rope
self.use_rmsnorm = use_rmsnorm
self.use_swiglu = use_swiglu
# Aliases for HuggingFace compatibility
self.num_hidden_layers = n_layer
self.hidden_size = n_embd
self.num_attention_heads = n_head
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
def to_model_config(self) -> ModelConfig:
"""Convert to internal ModelConfig for the model."""
return ModelConfig(
vocab_size=self.vocab_size,
n_embd=self.n_embd,
n_layer=self.n_layer,
n_head=self.n_head,
context_length=self.context_length,
dropout=self.dropout,
use_rope=self.use_rope,
use_rmsnorm=self.use_rmsnorm,
use_swiglu=self.use_swiglu,
pad_token_id=self.pad_token_id,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
)
@classmethod
def from_model_config(cls, config: ModelConfig) -> "FrawdLLMConfig":
"""Create from internal ModelConfig."""
return cls(
vocab_size=config.vocab_size,
n_embd=config.n_embd,
n_layer=config.n_layer,
n_head=config.n_head,
context_length=config.context_length,
dropout=config.dropout,
use_rope=config.use_rope,
use_rmsnorm=config.use_rmsnorm,
use_swiglu=config.use_swiglu,
pad_token_id=config.pad_token_id,
bos_token_id=config.bos_token_id,
eos_token_id=config.eos_token_id,
)
class FrawdLLMForCausalLM(PreTrainedModel, GenerationMixin):
"""HuggingFace-compatible wrapper for FrawdLLM."""
config_class = FrawdLLMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = ["TransformerBlock"]
_tied_weights_keys = ["model.lm_head.weight"]
def __init__(self, config: FrawdLLMConfig):
super().__init__(config)
# Convert HF config to internal config
model_config = config.to_model_config()
# Create the actual model
self.model = FrawdLLM(model_config)
# For generation
self.main_input_name = "input_ids"
def get_input_embeddings(self):
return self.model.embeddings.token_emb
def set_input_embeddings(self, value):
self.model.embeddings.token_emb = value
def get_output_embeddings(self):
return self.model.lm_head
def set_output_embeddings(self, new_embeddings):
self.model.lm_head = new_embeddings
def tie_weights(self):
"""Tie input and output embeddings."""
self.model.lm_head.weight = self.model.embeddings.token_emb.weight
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
"""
Forward pass compatible with HuggingFace API.
Note: attention_mask, past_key_values, use_cache are accepted but
not fully implemented (our model doesn't use KV caching yet).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Get logits from our model
logits, _ = self.model(input_ids, None)
# Compute loss if labels provided
loss = None
if labels is not None:
# Shift for causal LM loss
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
ignore_index=-100,
)
if not return_dict:
output = (logits,)
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=None,
hidden_states=None,
attentions=None,
)
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[Tuple] = None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
):
"""Prepare inputs for generation (called by HF generate())."""
# Our model doesn't use KV cache yet, so just return input_ids
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
@classmethod
def from_frawdllm_checkpoint(
cls,
checkpoint_path: str,
device: str = "cpu",
) -> "FrawdLLMForCausalLM":
"""
Load from a FrawdLLM .pt checkpoint.
Args:
checkpoint_path: Path to the .pt checkpoint file
device: Device to load the model on
Returns:
FrawdLLMForCausalLM instance
"""
# Load the checkpoint
checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False)
# Get the internal config
model_config = checkpoint["config"]
# Create HF config
hf_config = FrawdLLMConfig.from_model_config(model_config)
# Create the wrapper model
model = cls(hf_config)
# Load the weights
model.model.load_state_dict(checkpoint["model_state_dict"])
return model
def save_pretrained_simple(self, save_directory: str):
"""
Save in HuggingFace format.
This saves:
- config.json
- model.safetensors (or pytorch_model.bin)
"""
import os
from safetensors.torch import save_file
os.makedirs(save_directory, exist_ok=True)
# Save config
self.config.save_pretrained(save_directory)
# Save model weights
# Note: We have weight tying (token_emb.weight == lm_head.weight)
# Remove the duplicate to avoid safetensors error
state_dict = self.state_dict()
if "model.lm_head.weight" in state_dict:
del state_dict["model.lm_head.weight"]
save_file(state_dict, os.path.join(save_directory, "model.safetensors"))
print(f"Saved model to {save_directory}")
# Register for AutoClass - this adds auto_map to config when saving
FrawdLLMConfig.register_for_auto_class()
FrawdLLMForCausalLM.register_for_auto_class("AutoModelForCausalLM")
|