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The model intentionally uses the same readable reference tensor names as the
published safetensors files. It implements the full-prefix path and does not
claim a KV cache; ``use_cache`` is therefore false by default.
"""
from __future__ import annotations
import math
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_vortex import VortexConfig
class VortexRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return F.rms_norm(hidden_states, (hidden_states.shape[-1],), self.weight, self.eps)
class VortexRotaryEmbedding(nn.Module):
def __init__(self, dim: int, max_seq_len: int, theta: float) -> None:
super().__init__()
inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
positions = torch.arange(max_seq_len, dtype=torch.float32)
frequencies = torch.outer(positions, inv_freq)
angles = torch.cat((frequencies, frequencies), dim=-1)
self.register_buffer("cos_cached", angles.cos()[None, None], persistent=False)
self.register_buffer("sin_cached", angles.sin()[None, None], persistent=False)
@staticmethod
def rotate_half(x: torch.Tensor) -> torch.Tensor:
half = x.shape[-1] // 2
return torch.cat((-x[..., half:], x[..., :half]), dim=-1)
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
position_ids: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
if position_ids is None:
cos = self.cos_cached[:, :, : query.shape[-2]]
sin = self.sin_cached[:, :, : query.shape[-2]]
else:
cos = self.cos_cached[0, 0, position_ids].unsqueeze(1)
sin = self.sin_cached[0, 0, position_ids].unsqueeze(1)
cos = cos.to(dtype=query.dtype, device=query.device)
sin = sin.to(dtype=query.dtype, device=query.device)
return (
query * cos + self.rotate_half(query) * sin,
key * cos + self.rotate_half(key) * sin,
)
class VortexAttention(nn.Module):
def __init__(self, config: VortexConfig) -> None:
super().__init__()
if config.num_attention_heads % config.num_key_value_heads:
raise ValueError("num_attention_heads must divide evenly by num_key_value_heads")
if config.num_attention_heads * config.head_dim != config.hidden_size:
raise ValueError("num_attention_heads * head_dim must equal hidden_size")
self.num_heads = config.num_attention_heads
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.head_dim
kv_dim = config.num_key_value_heads * config.head_dim
self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
self.k_proj = nn.Linear(config.hidden_size, kv_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(config.hidden_size, kv_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
self.q_norm = VortexRMSNorm(config.head_dim, config.rms_norm_eps)
self.k_norm = VortexRMSNorm(config.head_dim, config.rms_norm_eps)
self.rope = VortexRotaryEmbedding(config.head_dim, config.max_position_embeddings, config.rope_theta)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
) -> torch.Tensor:
batch, seq_len, _ = hidden_states.shape
query = self.q_proj(hidden_states).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
key = self.k_proj(hidden_states).view(batch, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value = self.v_proj(hidden_states).view(batch, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
query, key = self.rope(self.q_norm(query), self.k_norm(key), position_ids)
if self.num_key_value_heads != self.num_heads:
repeats = self.num_heads // self.num_key_value_heads
key = key.repeat_interleave(repeats, dim=1)
value = value.repeat_interleave(repeats, dim=1)
sdpa_mask = None
query_valid = None
is_causal = attention_mask is None and seq_len > 1
if attention_mask is not None:
if attention_mask.ndim == 2:
valid = attention_mask.to(dtype=torch.bool, device=hidden_states.device)
valid_keys = valid[:, None, None, :]
query_valid = valid[:, None, :, None]
causal = torch.ones((seq_len, seq_len), dtype=torch.bool, device=hidden_states.device).tril()
sdpa_mask = valid_keys & causal[None, None, :, :]
# SDPA returns NaN for an all-masked query row. Left padding
# creates exactly those rows, so give pad queries one harmless
# fallback key and zero their outputs after attention.
fallback = torch.zeros_like(sdpa_mask)
fallback[..., 0] = True
sdpa_mask = torch.where(query_valid, sdpa_mask, fallback)
elif attention_mask.ndim == 4:
sdpa_mask = attention_mask
else:
raise ValueError("Vortex expects a 2-D or 4-D attention mask")
output = F.scaled_dot_product_attention(
query,
key,
value,
attn_mask=sdpa_mask,
dropout_p=0.0,
is_causal=is_causal,
)
if query_valid is not None:
output = output * query_valid.to(dtype=output.dtype)
output = output.transpose(1, 2).contiguous().view(batch, seq_len, -1)
return self.o_proj(output)
class VortexMLP(nn.Module):
def __init__(self, config: VortexConfig) -> None:
super().__init__()
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias)
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.attention_bias)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return self.down_proj(F.silu(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
class VortexDecoderBlock(nn.Module):
def __init__(self, config: VortexConfig) -> None:
super().__init__()
self.norm1 = VortexRMSNorm(config.hidden_size, config.rms_norm_eps)
self.attn = VortexAttention(config)
self.norm2 = VortexRMSNorm(config.hidden_size, config.rms_norm_eps)
self.ffn = VortexMLP(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = hidden_states + self.attn(self.norm1(hidden_states), attention_mask, position_ids)
hidden_states = hidden_states + self.ffn(self.norm2(hidden_states))
return hidden_states
class VortexPreTrainedModel(PreTrainedModel):
config_class = VortexConfig
base_model_prefix = "vortex"
supports_gradient_checkpointing = False
class VortexForCausalLM(VortexPreTrainedModel):
# The output projection is implemented directly with embed_tokens.weight,
# so there is no second lm_head parameter to tie or load.
_tied_weights_keys = None
main_input_name = "input_ids"
def __init__(self, config: VortexConfig) -> None:
super().__init__(config)
# Transformers 5.x uses this expanded mapping while loading from a
# checkpoint. It is empty because the single embedding parameter is
# both the input table and the output projection.
self.all_tied_weights_keys = {}
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList(VortexDecoderBlock(config) for _ in range(config.num_hidden_layers))
self.norm = VortexRMSNorm(config.hidden_size, config.rms_norm_eps)
self.post_init()
def _init_weights(self, module: nn.Module) -> None:
"""Initialization hook used by both Transformers 4.x and 5.x."""
if isinstance(module, VortexRMSNorm):
nn.init.ones_(module.weight)
elif isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
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=0.02)
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.embed_tokens
def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
self.embed_tokens = new_embeddings
def prepare_inputs_for_generation(self, input_ids: torch.Tensor, **kwargs) -> dict:
# No KV cache is exported. Returning the full prefix keeps generation
# correct, though slower than a cache-enabled implementation.
return {
"input_ids": input_ids,
"attention_mask": kwargs.get("attention_mask"),
"use_cache": False,
}
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_values=None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> CausalLMOutputWithPast:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("pass either input_ids or inputs_embeds, not both")
if inputs_embeds is None:
if input_ids is None:
raise ValueError("input_ids or inputs_embeds is required")
hidden_states = self.embed_tokens(input_ids)
else:
hidden_states = inputs_embeds
if position_ids is None:
if attention_mask is not None and attention_mask.ndim == 2:
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids = position_ids.masked_fill(attention_mask == 0, 0)
else:
position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
all_hidden_states = () if output_hidden_states else None
for block in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
hidden_states = block(hidden_states, attention_mask, position_ids)
if output_hidden_states:
all_hidden_states += (hidden_states,)
hidden_states = self.norm(hidden_states)
logits = F.linear(hidden_states, self.embed_tokens.weight)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous().float()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if return_dict is False:
output = (logits, None, all_hidden_states, None)
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,
)
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