ViperEkura
1c7369f293
- Propagates MoE load-balancing loss through model outputs - Logs task, auxiliary, and weighted losses across strategies - Computes only explicitly requested callback metrics - Preserves tensor compute_loss API and adds regression tests
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
from dataclasses import asdict
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from typing import Optional, TypedDict
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import torch.nn as nn
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from torch import Tensor
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from astrai.inference.core.cache import KVCache
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from astrai.model.components.attention import AttnFactory
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from astrai.model.components.mlp import FFNFactory
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from astrai.model.components.norm import RMSNorm
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class DecoderOutput(TypedDict):
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hidden_states: Tensor
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aux_loss: Optional[Tensor]
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class DecoderBlock(nn.Module):
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def __init__(self, config, layer_id: int):
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super().__init__()
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cfg = asdict(config)
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cfg.update(
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dim=config.hidden_size,
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dim_ffn=config.intermediate_size,
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n_layers=config.num_hidden_layers,
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n_heads=config.num_attention_heads,
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n_kv_heads=config.num_key_value_heads,
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norm_eps=config.rms_norm_eps,
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down_init_std=0.02 / (2 * config.num_hidden_layers) ** 0.5,
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)
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self.attention = AttnFactory.create(config.attn_type, **cfg, layer_id=layer_id)
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self.input_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
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self.post_attention_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
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ffn_type = self._resolve_ffn_type(config, layer_id)
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self.mlp = FFNFactory.create(ffn_type, **cfg)
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@staticmethod
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def _resolve_ffn_type(config, layer_id: int) -> str:
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if config.ffn_type != "moe":
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return config.ffn_type
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mlp_only = config.mlp_only_layers or []
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if layer_id in mlp_only:
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return "mlp"
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if config.decoder_sparse_step > 1:
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if (layer_id + 1) % config.decoder_sparse_step != 0:
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return "mlp"
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return "moe"
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def forward(
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self,
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x: Tensor,
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rotary_emb: Tensor,
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attention_mask: Optional[Tensor] = None,
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kv_cache: Optional[KVCache] = None,
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is_causal: bool = False,
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) -> DecoderOutput:
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attn_output = self.attention(
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self.input_norm(x),
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rotary_emb,
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attention_mask,
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kv_cache,
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is_causal,
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)
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x = attn_output + x
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normalized = self.post_attention_norm(x)
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mlp_output = self.mlp(normalized)
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x = mlp_output["hidden_states"] + x
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return {"hidden_states": x, "aux_loss": mlp_output["aux_loss"]}
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