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Hugging Face model class for MINDI 1.0 420M.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_mindi import MindiConfig
@dataclass
class _Cfg:
vocab_size: int
max_seq_len: int
d_model: int
n_layers: int
n_heads: int
d_ff: int
dropout: float
tie_embeddings: bool
init_std: float
rms_norm_eps: float
@property
def head_dim(self) -> int:
if self.d_model % self.n_heads != 0:
raise ValueError("d_model must be divisible by n_heads")
return self.d_model // self.n_heads
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5) -> None:
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
norm = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(norm + self.eps)
return self.weight * x
class RotaryEmbedding(nn.Module):
def __init__(self, head_dim: int, max_seq_len: int) -> None:
super().__init__()
if head_dim % 2 != 0:
raise ValueError("head_dim must be even for rotary embeddings")
inv_freq = 1.0 / (10000 ** (torch.arange(0, head_dim, 2).float() / head_dim))
t = torch.arange(max_seq_len, dtype=torch.float32)
freqs = torch.outer(t, inv_freq)
self.register_buffer("cos_cached", torch.cos(freqs), persistent=False)
self.register_buffer("sin_cached", torch.sin(freqs), persistent=False)
def forward(self, q: torch.Tensor, k: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
cos = self.cos_cached[:seq_len].unsqueeze(0).unsqueeze(0)
sin = self.sin_cached[:seq_len].unsqueeze(0).unsqueeze(0)
return self._apply_rotary(q, cos, sin), self._apply_rotary(k, cos, sin)
@staticmethod
def _apply_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
x1 = x[..., ::2]
x2 = x[..., 1::2]
xe = x1 * cos - x2 * sin
xo = x1 * sin + x2 * cos
return torch.stack((xe, xo), dim=-1).flatten(-2)
class CausalSelfAttention(nn.Module):
def __init__(self, cfg: _Cfg) -> None:
super().__init__()
self.n_heads = cfg.n_heads
self.head_dim = cfg.head_dim
self.scale = self.head_dim ** -0.5
self.q_proj = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
self.k_proj = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
self.v_proj = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
self.o_proj = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
self.dropout = nn.Dropout(cfg.dropout)
self.rotary = RotaryEmbedding(self.head_dim, cfg.max_seq_len)
def forward(self, x: torch.Tensor) -> torch.Tensor:
bsz, seq_len, _ = x.shape
q = self.q_proj(x).view(bsz, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(bsz, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(bsz, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
q, k = self.rotary(q, k, seq_len=seq_len)
out = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=None,
dropout_p=self.dropout.p if self.training else 0.0,
is_causal=True,
scale=self.scale,
)
out = out.transpose(1, 2).contiguous().view(bsz, seq_len, -1)
return self.o_proj(out)
class FeedForward(nn.Module):
def __init__(self, cfg: _Cfg) -> None:
super().__init__()
self.fc1 = nn.Linear(cfg.d_model, cfg.d_ff, bias=False)
self.fc2 = nn.Linear(cfg.d_ff, cfg.d_model, bias=False)
self.dropout = nn.Dropout(cfg.dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.fc1(x)
x = F.gelu(x, approximate="tanh")
x = self.fc2(x)
x = self.dropout(x)
return x
class TransformerBlock(nn.Module):
def __init__(self, cfg: _Cfg) -> None:
super().__init__()
self.norm1 = RMSNorm(cfg.d_model, cfg.rms_norm_eps)
self.attn = CausalSelfAttention(cfg)
self.norm2 = RMSNorm(cfg.d_model, cfg.rms_norm_eps)
self.ffn = FeedForward(cfg)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.norm1(x))
x = x + self.ffn(self.norm2(x))
return x
class MindiForCausalLM(PreTrainedModel):
config_class = MindiConfig
base_model_prefix = "mindi"
supports_gradient_checkpointing = False
def __init__(self, config: MindiConfig):
super().__init__(config)
cfg = _Cfg(
vocab_size=config.vocab_size,
max_seq_len=config.max_seq_len,
d_model=config.d_model,
n_layers=config.n_layers,
n_heads=config.n_heads,
d_ff=config.d_ff,
dropout=config.dropout,
tie_embeddings=config.tie_embeddings,
init_std=config.init_std,
rms_norm_eps=config.rms_norm_eps,
)
self.embed_tokens = nn.Embedding(cfg.vocab_size, cfg.d_model)
self.dropout = nn.Dropout(cfg.dropout)
self.blocks = nn.ModuleList([TransformerBlock(cfg) for _ in range(cfg.n_layers)])
self.norm_final = RMSNorm(cfg.d_model, cfg.rms_norm_eps)
self.lm_head = nn.Linear(cfg.d_model, cfg.vocab_size, bias=False)
if cfg.tie_embeddings:
self.lm_head.weight = self.embed_tokens.weight
self.post_init()
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=self.config.init_std)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=self.config.init_std)
def get_input_embeddings(self) -> nn.Module:
return self.embed_tokens
def set_input_embeddings(self, value: nn.Module) -> None:
self.embed_tokens = value
def get_output_embeddings(self) -> nn.Module:
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
self.lm_head = new_embeddings
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
**kwargs,
) -> CausalLMOutputWithPast:
del attention_mask, kwargs
x = self.embed_tokens(input_ids)
x = self.dropout(x)
for block in self.blocks:
x = block(x)
x = self.norm_final(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
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,
)
return CausalLMOutputWithPast(loss=loss, logits=logits)
@torch.no_grad()
def prepare_inputs_for_generation(self, input_ids: torch.Tensor, **kwargs):
del kwargs
return {"input_ids": input_ids}
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