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from __future__ import annotations |
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import warnings |
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from typing import TYPE_CHECKING, Optional, Tuple |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from einops import rearrange |
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from fla.modules import RMSNorm, ShortConvolution |
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from fla.modules.activations import swish |
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from fla.modules.feature_map import (ReLUFeatureMap, SwishFeatureMap, |
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T2RFeatureMap) |
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from fla.modules.layernorm import rms_norm_linear |
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from fla.ops.gsa import chunk_gsa, fused_recurrent_gsa |
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if TYPE_CHECKING: |
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from fla.models.utils import Cache |
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class GatedSlotAttention(nn.Module): |
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def __init__( |
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self, |
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mode: str = 'chunk', |
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hidden_size: int = 1024, |
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expand_k: float = 1., |
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expand_v: float = 1., |
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num_heads: int = 4, |
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num_kv_heads: Optional[int] = None, |
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use_short_conv: bool = False, |
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conv_size: int = 4, |
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conv_bias: bool = False, |
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num_slots: Optional[int] = None, |
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elementwise_affine: Optional[bool] = True, |
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norm_first: bool = True, |
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norm_eps: float = 1e-5, |
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gate_logit_normalizer: int = 8, |
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feature_map: str = 'swish', |
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use_output_gate: bool = False, |
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use_norm: bool = True, |
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layer_idx: Optional[int] = None, |
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scale: Optional[float] = 1., |
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**kwargs |
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) -> GatedSlotAttention: |
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super().__init__() |
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self.mode = mode |
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self.hidden_size = hidden_size |
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self.expand_k = expand_k |
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self.expand_v = expand_v |
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self.num_heads = num_heads |
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self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads |
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self.num_kv_groups = self.num_heads // self.num_kv_heads |
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self.key_dim = int(hidden_size * expand_k) |
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self.value_dim = int(hidden_size * expand_v) |
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self.key_dim_per_group = self.key_dim // self.num_kv_groups |
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self.value_dim_per_group = self.value_dim // self.num_kv_groups |
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self.head_k_dim = self.key_dim // self.num_heads |
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self.head_v_dim = self.value_dim // self.num_heads |
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self.use_short_conv = use_short_conv |
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self.conv_size = conv_size |
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self.conv_bias = conv_bias |
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self.gate_logit_normalizer = gate_logit_normalizer |
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self.use_output_gate = use_output_gate |
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self.use_norm = use_norm |
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self.scale = scale |
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if num_slots is None: |
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num_slots = self.head_k_dim |
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self.num_slots = num_slots |
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self.norm_first = norm_first |
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self.layer_idx = layer_idx |
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if layer_idx is None: |
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warnings.warn( |
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f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " |
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"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " |
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"when creating this class." |
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) |
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if norm_first: |
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self.norm = RMSNorm(self.hidden_size, eps=norm_eps) |
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self.register_module('feature_map', None) |
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if feature_map == 'swish': |
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self.feature_map = SwishFeatureMap() |
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elif feature_map == 'relu': |
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self.feature_map = ReLUFeatureMap() |
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elif feature_map == 't2r': |
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self.feature_map = T2RFeatureMap(self.head_k_dim, self.head_k_dim) |
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else: |
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raise NotImplementedError(f"Feature map `{feature_map}` is not supported now.") |
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self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) |
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self.k_proj = nn.Linear(self.hidden_size, self.key_dim_per_group, bias=False) |
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self.v_proj = nn.Linear(self.hidden_size, self.value_dim_per_group, bias=False) |
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self.f_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.num_slots, bias=False) |
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if use_short_conv: |
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self.conv_size = conv_size |
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self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu') |
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self.k_conv1d = ShortConvolution(self.key_dim_per_group, conv_size, activation='silu') |
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self.v_conv1d = ShortConvolution(self.value_dim_per_group, conv_size, activation='silu') |
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self.g_norm = RMSNorm(self.hidden_size, elementwise_affine, eps=norm_eps) |
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self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) |
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self.apply(self._initialize_weights) |
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def _initialize_weights(self, module: nn.Module): |
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if getattr(module, "_is_hf_initialized", False): |
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return |
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if isinstance(module, nn.Linear): |
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nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5) |
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if module.bias is not None: |
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nn.init.zeros_(module.bias) |
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module._is_hf_initialized = True |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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attention_mask: Optional[torch.Tensor] = None, |
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past_key_values: Optional[Cache] = None, |
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use_cache: Optional[bool] = False, |
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output_attentions: Optional[bool] = False, |
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**kwargs |
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: |
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if attention_mask is not None: |
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assert len(attention_mask.shape) == 2, ( |
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"Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " |
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"for padding purposes (0 indicating padding). " |
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"Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." |
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) |
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mode = 'fused_recurrent' if hidden_states.shape[1] == 1 else self.mode |
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if self.norm_first: |
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hidden_states = self.norm(hidden_states) |
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last_state = None |
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if past_key_values is not None and len(past_key_values) > self.layer_idx: |
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last_state = past_key_values[self.layer_idx] |
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if self.use_short_conv: |
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conv_state_q, conv_state_k, conv_state_v = None, None, None |
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if last_state is not None: |
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conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] |
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conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None |
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q, conv_state_q = self.q_conv1d(x=self.q_proj(hidden_states), |
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mask=conv_mask, |
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cache=conv_state_q, |
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output_final_state=use_cache) |
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k, conv_state_k = self.k_conv1d(x=self.k_proj(hidden_states), |
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mask=conv_mask, |
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cache=conv_state_k, |
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output_final_state=use_cache) |
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v, conv_state_v = self.v_conv1d(x=self.v_proj(hidden_states), |
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mask=conv_mask, |
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cache=conv_state_v, |
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output_final_state=use_cache) |
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else: |
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q = self.q_proj(hidden_states) |
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k = self.k_proj(hidden_states) |
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v = self.v_proj(hidden_states) |
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f = self.f_proj(hidden_states) |
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q = rearrange(q, 'b t (h d) -> b t h d', h=self.num_heads) |
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k = rearrange(k, 'b t (h d) -> b t h d', h=self.num_kv_heads) |
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v = rearrange(v, 'b t (h d) -> b t h d', h=self.num_kv_heads) |
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f = rearrange(f, 'b t (h m) -> b t h m', h=self.num_kv_heads) |
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if self.feature_map is not None: |
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q, k = map(lambda x: self.feature_map(x), (q, k)) |
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v = swish(v) |
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f = F.logsigmoid(f) / self.gate_logit_normalizer |
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s = (1 - f.exp()).to(f.dtype) |
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if attention_mask is not None: |
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s = s.mul_(attention_mask[:, -s.shape[1]:, None, None]) |
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v = v.mul_(attention_mask[:, -v.shape[1]:, None, None]) |
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recurrent_state = last_state['recurrent_state'] if last_state is not None else None |
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if mode == 'fused_recurrent': |
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o, recurrent_state = fused_recurrent_gsa( |
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q=q, |
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k=k, |
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v=v, |
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s=s, |
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g=f, |
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initial_state=recurrent_state, |
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output_final_state=use_cache, |
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scale=self.scale, |
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head_first=False |
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) |
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elif mode == 'chunk': |
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o, recurrent_state = chunk_gsa( |
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q=q, |
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k=k, |
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v=v, |
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s=s, |
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g=f, |
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initial_state=recurrent_state, |
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output_final_state=use_cache, |
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scale=self.scale, |
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head_first=False |
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) |
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else: |
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raise NotImplementedError(f"Not supported mode `{mode}`.") |
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if past_key_values is not None: |
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past_key_values.update( |
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recurrent_state=recurrent_state, |
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conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, |
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layer_idx=self.layer_idx, |
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offset=q.shape[2] |
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) |
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o = rearrange(o, 'b t h d -> b t (h d)') |
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o = rms_norm_linear(swish(o), self.g_norm.weight, self.g_norm.bias, self.o_proj.weight, self.o_proj.bias) |
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return o, None, past_key_values |
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def state_size(self, *args, **kwargs) -> int: |
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return 2 * self.num_slots * self.hidden_size |
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