mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-25 01:41:19 +08:00
225 lines
7.4 KiB
Python
225 lines
7.4 KiB
Python
from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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from .base import BaseModelArgs
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from .layers import RMSNorm
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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hidden_size: int
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num_hidden_layers: int
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intermediate_size: int
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num_attention_heads: int
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rms_norm_eps: float
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vocab_size: int
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n_shared_head: int = (8,)
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rope_theta: float = 10000
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rope_traditional: bool = False
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class Attention(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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head_dim = self.hidden_size // config.num_attention_heads
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self.q_num_heads = config.num_attention_heads
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self.qk_dim = self.v_dim = head_dim
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self.k_num_heads = self.v_num_heads = int(
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np.ceil(self.q_num_heads / config.n_shared_head)
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)
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self.scale = head_dim**-0.5
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self.q_proj = nn.Linear(
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self.hidden_size, self.q_num_heads * self.qk_dim, bias=False
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)
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self.k_proj = nn.Linear(
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self.hidden_size, self.k_num_heads * self.qk_dim, bias=False
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)
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self.v_proj = nn.Linear(
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self.hidden_size, self.v_num_heads * self.v_dim, bias=False
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)
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self.o_proj = nn.Linear(
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self.q_num_heads * self.v_dim, self.hidden_size, bias=False
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)
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self.rotary_emb = nn.RoPE(
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head_dim,
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traditional=config.rope_traditional,
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base=config.rope_theta,
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scale=1.0,
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)
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def __call__(
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self,
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hidden_states: mx.array,
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attention_mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
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bsz, q_len, _ = hidden_states.shape
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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# Prepare the queries, keys and values for the attention computation
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query_states = query_states.reshape(
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bsz, q_len, self.q_num_heads, self.qk_dim
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).transpose(0, 2, 1, 3)
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key_states = key_states.reshape(
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bsz, q_len, self.k_num_heads, self.qk_dim
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).transpose(0, 2, 1, 3)
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value_states = value_states.reshape(
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bsz, q_len, self.v_num_heads, self.v_dim
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).transpose(0, 2, 1, 3)
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def _expand_kv(a: mx.array) -> mx.array:
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a = mx.concatenate(
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[mx.expand_dims(a, 1)] * self.config.n_shared_head, axis=1
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)
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return a.reshape([bsz, self.q_num_heads, q_len, -1])
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# expand shared kv
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assert self.k_num_heads == self.v_num_heads
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key_states = _expand_kv(key_states)
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value_states = _expand_kv(value_states)
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kv_seq_len = 0
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if cache is not None:
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kv_seq_len += cache[0].shape[-2]
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query_states = self.rotary_emb(query_states, offset=kv_seq_len)
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key_states = self.rotary_emb(key_states, offset=kv_seq_len)
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if cache is not None:
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# reuse k, v, self_attention
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key_states = mx.concatenate([cache[0], key_states], axis=2)
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value_states = mx.concatenate([cache[1], value_states], axis=2)
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scores = (query_states * self.scale) @ key_states.transpose(0, 1, 3, 2)
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if attention_mask is not None:
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scores += attention_mask
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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output = (scores @ value_states).transpose(0, 2, 1, 3).reshape(bsz, q_len, -1)
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return self.o_proj(output), (key_states, value_states)
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class MLP(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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def __call__(self, x: mx.array) -> mx.array:
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return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x)) # type: ignore
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class PlamoDecoderLayer(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.self_attn = Attention(config)
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self.mlp = MLP(config)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def __call__(
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self,
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hidden_states: mx.array,
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attention_mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[Any, ...]:
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# from LlamaDecoder
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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# Self Attention
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hidden_states_sa, cache = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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cache=cache,
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)
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# Fully Connected
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hidden_states_mlp = self.mlp(hidden_states)
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hidden_states = residual + hidden_states_sa + hidden_states_mlp
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return hidden_states, cache
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class PlamoDecoder(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.layers = [
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PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)
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]
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class PlamoModel(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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self.layers = PlamoDecoder(config) # type: ignore
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def __call__(
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self,
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inputs: mx.array,
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cache: Optional[List[Union[Tuple[mx.array, mx.array], None]]] = None,
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) -> Tuple[mx.array, Optional[List[Union[Tuple[mx.array, mx.array], None]]]]:
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h = self.embed_tokens(inputs)
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mask = None
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if h.shape[1] > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
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mask = mask.astype(self.embed_tokens.weight.dtype)
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if cache is None:
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past_key_values_length = 0
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cache = [None for _ in range(len(self.layers.layers))]
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else:
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if cache[0] is not None:
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past_key_values_length = cache[0][0].shape[2]
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for e, layer in enumerate(self.layers.layers):
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h, c = layer(h, mask, cache[e])
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if cache is not None:
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cache[e] = c
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else:
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cache.append(c)
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return self.norm(h), cache
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class Model(nn.Module):
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def __init__(self, args: ModelArgs) -> None:
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super().__init__()
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self.model_type = args.model_type
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self.model = PlamoModel(args)
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self.lm_head: nn.Module = nn.Linear(
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args.hidden_size, args.vocab_size, bias=False
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)
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def __call__(
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self,
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inputs: mx.array,
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cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
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) -> Tuple[mx.array, mx.array]:
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out, cache = self.model(inputs, cache)
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return self.lm_head(out), cache
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