ViperEkura
fc47319240
- Extract _resolve_base_type and _validate_component as module-level helpers - Replace ForwardRef._evaluate private API with eval in module namespace - Remove broad except Exception in __init_subclass__, _component_base always set - Replace direct _entries mutation in strategy.py with register() call form - Remove dead TOKENIZER_CLASSES registry from AutoTokenizer - Extract ModelFactory(BaseFactory[nn.Module]) as pure factory - AutoModel now inherits only nn.Module, no factory state - Move @AutoModel.register to @ModelFactory.register in transformer.py and encoder.py
123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
from typing import Any, Dict, Mapping, Optional
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import torch
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import torch.nn as nn
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from torch import Tensor
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from astrai.config.model_config import AutoRegressiveLMConfig
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from astrai.inference.core.cache import CacheView
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from astrai.model.automodel import AutoModel, ModelFactory
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from astrai.model.components.decoder_block import DecoderBlock
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from astrai.model.components.embedding import Embedding
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from astrai.model.components.linear import Linear
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from astrai.model.components.norm import RMSNorm
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from astrai.model.components.rope import RotaryEmbedding
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def process_attention_mask(
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input_mask: Optional[Tensor],
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) -> Optional[Tensor]:
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if input_mask is None:
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return None
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if input_mask.dim() == 2:
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return input_mask[:, None, None, :]
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if input_mask.dim() == 3:
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return input_mask[:, None, :, :]
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return input_mask
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@ModelFactory.register("autoregressive_lm")
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class AutoRegressiveLM(AutoModel):
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"""Autoregressive language model with paged KV cache."""
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def __init__(self, config: AutoRegressiveLMConfig):
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super().__init__(config)
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self.config = config
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rope_dim = (
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config.qk_rope_head_dim
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if config.attn_type == "mla"
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else config.hidden_size // config.num_attention_heads
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)
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rope_base = config.rope_theta if config.rope_theta is not None else 10000
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self.rotary_embedding = RotaryEmbedding(
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rope_dim,
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config.max_position_embeddings,
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rope_base,
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rope_scaling=config.rope_scaling,
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)
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self.embed_tokens = Embedding(
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config.vocab_size,
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config.hidden_size,
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neftune_alpha=config.neftune_alpha,
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)
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self.layers = nn.ModuleList(
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[
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DecoderBlock(config, layer_id)
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for layer_id in range(config.num_hidden_layers)
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]
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)
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self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
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self.lm_head = Linear(config.hidden_size, config.vocab_size)
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if self.config.tie_word_embeddings is True:
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self.lm_head.weight = self.embed_tokens.weight
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self.apply(self._init_weights)
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def _init_weights(self, module):
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if hasattr(module, "reset_parameters"):
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module.reset_parameters()
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def load_state_dict(self, state_dict: Mapping[str, Any], strict=True, assign=False):
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lm_head_key = "lm_head.weight"
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embed_key = "embed_tokens.weight"
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state_dict = dict(state_dict)
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if self.config.tie_word_embeddings is True:
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# same tensor for embed and lm_head
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if embed_key in state_dict:
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state_dict[lm_head_key] = state_dict[embed_key]
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else:
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if lm_head_key not in state_dict and embed_key in state_dict:
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# clone to avoid sharing gradients
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state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
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return super().load_state_dict(state_dict, strict, assign)
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def state_dict(self, destination=None, prefix="", keep_vars=False):
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state_dict = super().state_dict(
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destination=destination, prefix=prefix, keep_vars=keep_vars
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)
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if self.config.tie_word_embeddings is True:
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lm_head_key = prefix + "lm_head.weight"
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if lm_head_key in state_dict:
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del state_dict[lm_head_key]
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return state_dict
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def forward(
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self,
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input_ids: Tensor,
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input_mask: Optional[Tensor] = None,
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paged_cache: Optional[CacheView] = None,
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position_ids: Optional[Tensor] = None,
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) -> Dict[str, Tensor]:
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assert input_ids.ndim == 2
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x = self.embed_tokens(input_ids)
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rotary_emb = self.rotary_embedding(x, position_ids)
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attn_mask = process_attention_mask(input_mask)
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use_sdpa_causal_mask = attn_mask is None
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for layer in self.layers:
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x = layer(x, rotary_emb, attn_mask, paged_cache, use_sdpa_causal_mask)
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hidden_states = self.norm(x)
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logits = self.lm_head(hidden_states)
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return {"logits": logits, "hidden_states": hidden_states}
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