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import sys
from typing import Optional, Tuple, Union
# Monkeypatch safetensors to handle None/missing metadata which crashes transformers
try:
import safetensors
original_safe_open = safetensors.safe_open
class SafeOpenWrapper:
def __init__(self, original_obj):
self.original_obj = original_obj
def __enter__(self):
self.original_obj.__enter__()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
return self.original_obj.__exit__(exc_type, exc_val, exc_tb)
def metadata(self):
meta = self.original_obj.metadata()
if meta is None:
return {"format": "pt"}
return meta
def __getattr__(self, name):
return getattr(self.original_obj, name)
def patched_safe_open(*args, **kwargs):
f = original_safe_open(*args, **kwargs)
return SafeOpenWrapper(f)
safetensors.safe_open = patched_safe_open
if "transformers.modeling_utils" in sys.modules:
import transformers.modeling_utils
transformers.modeling_utils.safe_open = patched_safe_open
except Exception:
pass
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from .configuration_tiny import TinyConfig
def rotate_half(x: torch.Tensor) -> torch.Tensor:
x1 = x[..., :x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
assert dim % 2 == 0
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
t = torch.arange(end)
freqs = torch.outer(t, freqs).float()
cos = torch.cos(freqs)
sin = torch.sin(freqs)
cos = torch.cat([cos, cos], dim=-1)
sin = torch.cat([sin, sin], dim=-1)
return cos, sin
def apply_rotary_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
T = x.shape[2]
cos_t = cos[:T, :].unsqueeze(0).unsqueeze(1)
sin_t = sin[:T, :].unsqueeze(0).unsqueeze(1)
return (x * cos_t) + (rotate_half(x) * sin_t)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
variance = x.pow(2).mean(-1, keepdim=True)
return x * torch.rsqrt(variance + self.eps) * self.weight
class FeedForward(nn.Module):
def __init__(self, config: TinyConfig):
super().__init__()
self.w1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.w2 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.w3 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.dropout = nn.Dropout(config.hidden_dropout) if config.hidden_dropout > 0.0 else None
def forward(self, x: torch.Tensor) -> torch.Tensor:
out = F.silu(self.w1(x)) * self.w2(x)
out = self.w3(out)
if self.dropout is not None:
out = self.dropout(out)
return out
class Attention(nn.Module):
def __init__(self, config: TinyConfig):
super().__init__()
self.n_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_dim = config.hidden_size // config.num_attention_heads
assert self.n_heads * self.head_dim == self.hidden_size
self.wq = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.wk = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.wv = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.wo = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.dropout_p = config.attention_dropout
def forward(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
B, T, C = x.shape
q = self.wq(x)
k = self.wk(x)
v = self.wv(x)
q = q.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
q = apply_rotary_emb(q, cos, sin)
k = apply_rotary_emb(k, cos, sin)
dropout_p = self.dropout_p if self.training else 0.0
if attention_mask is not None:
if torch.all(attention_mask == 1):
attn_mask = None
is_causal = True
else:
causal_mask = torch.tril(torch.ones((T, T), dtype=torch.bool, device=x.device))
padding_mask = attention_mask.to(torch.bool).unsqueeze(1).unsqueeze(2) # shape: (B, 1, 1, T)
attn_mask = causal_mask.unsqueeze(0).unsqueeze(1) & padding_mask # shape: (B, 1, T, T)
is_causal = False
else:
attn_mask = None
is_causal = True
out = F.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_mask,
dropout_p=dropout_p,
is_causal=is_causal
)
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.wo(out)
class TransformerBlock(nn.Module):
def __init__(self, config: TinyConfig):
super().__init__()
self.attention = Attention(config)
self.feed_forward = FeedForward(config)
self.attention_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
x = x + self.attention(self.attention_norm(x), cos, sin, attention_mask)
x = x + self.feed_forward(self.ffn_norm(x))
return x
class TinyPreTrainedModel(PreTrainedModel):
config_class = TinyConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["TransformerBlock"]
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (TinyModel, TinyForCausalLM)):
module.gradient_checkpointing = value
class TinyModel(TinyPreTrainedModel):
def __init__(self, config: TinyConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.tok_embeddings = nn.Embedding(
config.vocab_size, config.hidden_size, self.padding_idx
)
self.layers = nn.ModuleList(
[TransformerBlock(config) for _ in range(config.num_hidden_layers)]
)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
cos, sin = precompute_freqs_cis(
dim=config.hidden_size // config.num_attention_heads,
end=config.max_position_embeddings * 2,
theta=config.rope_theta,
)
self.register_buffer("cos", cos, persistent=False)
self.register_buffer("sin", sin, persistent=False)
self.gradient_checkpointing = False
self.post_init()
def get_input_embeddings(self):
return self.tok_embeddings
def set_input_embeddings(self, value):
self.tok_embeddings = value
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
hidden_states = self.tok_embeddings(input_ids)
elif inputs_embeds is not None:
hidden_states = inputs_embeds
else:
raise ValueError("You must specify either input_ids or inputs_embeds")
T = hidden_states.shape[1]
cos = self.cos[:T]
sin = self.sin[:T]
all_hidden_states = () if output_hidden_states else None
for layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
hidden_states = self._gradient_checkpointing_func(
layer.__call__,
hidden_states,
cos,
sin,
attention_mask,
)
else:
hidden_states = layer(
hidden_states,
cos,
sin,
attention_mask,
)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
past_key_values=None,
)
class TinyForCausalLM(TinyPreTrainedModel):
_tied_weights_keys = ["tok_embeddings.weight"]
def __init__(self, config: TinyConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.tok_embeddings = nn.Embedding(
config.vocab_size, config.hidden_size, self.padding_idx
)
self.layers = nn.ModuleList(
[TransformerBlock(config) for _ in range(config.num_hidden_layers)]
)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
cos, sin = precompute_freqs_cis(
dim=config.hidden_size // config.num_attention_heads,
end=config.max_position_embeddings * 2,
theta=config.rope_theta,
)
self.register_buffer("cos", cos, persistent=False)
self.register_buffer("sin", sin, persistent=False)
if config.tie_word_embeddings:
self.output.weight = self.tok_embeddings.weight
self.gradient_checkpointing = False
self.post_init()
def get_input_embeddings(self):
return self.tok_embeddings
def set_input_embeddings(self, value):
self.tok_embeddings = value
if self.config.tie_word_embeddings:
self.output.weight = value.weight
def get_output_embeddings(self):
return self.output
def set_output_embeddings(self, new_embeddings):
self.output = new_embeddings
def tie_weights(self):
if self.config.tie_word_embeddings:
self.tok_embeddings.weight = self.output.weight
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = 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]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
hidden_states = self.tok_embeddings(input_ids)
elif inputs_embeds is not None:
hidden_states = inputs_embeds
else:
raise ValueError("You must specify either input_ids or inputs_embeds")
T = hidden_states.shape[1]
cos = self.cos[:T]
sin = self.sin[:T]
all_hidden_states = () if output_hidden_states else None
for layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
hidden_states = self._gradient_checkpointing_func(
layer.__call__,
hidden_states,
cos,
sin,
attention_mask,
)
else:
hidden_states = layer(
hidden_states,
cos,
sin,
attention_mask,
)
hidden_states = self.norm(hidden_states)
logits = self.output(hidden_states)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
if not return_dict:
output = (logits,)
if output_hidden_states:
output = output + (all_hidden_states,)
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=None,
hidden_states=all_hidden_states,
attentions=None,
)
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
**kwargs,
) -> dict:
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs["attention_mask"] = attention_mask
model_inputs["past_key_values"] = past_key_values
return model_inputs |