151 lines
5.2 KiB
Python
151 lines
5.2 KiB
Python
from typing import Any, 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 ModelConfig
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from astrai.inference.core.cache import KvcacheView
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from astrai.model.automodel import AutoModel
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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_tensor: Tensor,
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position_ids: Optional[Tensor],
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input_mask: Optional[Tensor] = None,
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is_causal: bool = False,
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) -> Optional[Tensor]:
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if position_ids is None:
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return None
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if input_mask is not None and input_mask.dim() > 2:
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return input_mask
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device = input_tensor.device
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dtype = input_tensor.dtype
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B, S = input_tensor.size()[:2]
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T = position_ids.max().item() + 1
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if input_mask is None:
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if position_ids.min().item() == 0 and is_causal:
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return None
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pad = torch.ones(B, T, dtype=torch.bool, device=device)
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else:
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pad = input_mask[:, :T].to(device=device, dtype=torch.bool)
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attend = pad.view(B, 1, T).expand(B, S, T).clone()
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if is_causal:
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attend &= position_ids.unsqueeze(-1) >= torch.arange(T, device=device)
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return torch.full(
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(B, 1, S, T), -torch.finfo(dtype).max / 2, dtype=dtype, device=device
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).masked_fill_(attend.unsqueeze(1), 0.0)
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@AutoModel.register("transformer")
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class Transformer(AutoModel):
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"""Transformer language model with paged KV cache."""
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def __init__(self, config: ModelConfig):
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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.dim // config.n_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(rope_dim, config.max_len, rope_base)
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self.embed_tokens = Embedding(config.vocab_size, config.dim)
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self.layers = nn.ModuleList(
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[
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DecoderBlock(
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config.dim,
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config.n_heads,
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config.dim_ffn,
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config.n_kv_heads,
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config.norm_eps,
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config.use_qk_norm,
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config.use_gated_attention,
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layer_id,
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attn_type=config.attn_type,
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ffn_type=config.ffn_type,
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n_routed_experts=config.n_routed_experts,
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n_shared_experts=config.n_shared_experts,
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n_activated_experts=config.n_activated_experts,
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topk_method=config.topk_method,
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kv_lora_rank=config.kv_lora_rank,
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qk_nope_head_dim=config.qk_nope_head_dim,
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qk_rope_head_dim=config.qk_rope_head_dim,
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)
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for layer_id in range(config.n_layers)
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]
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)
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self.norm = RMSNorm(config.dim, config.norm_eps)
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self.lm_head = Linear(config.dim, config.vocab_size)
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if self.config.tie_weight 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_weight 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_weight 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[KvcacheView] = None,
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position_ids: Optional[Tensor] = None,
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) -> 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(x, position_ids, input_mask, is_causal=True)
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for layer in self.layers:
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x = layer(x, rotary_emb, attn_mask, paged_cache)
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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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