235 lines
10 KiB
Python
Vendored
235 lines
10 KiB
Python
Vendored
# -*- coding: utf-8 -*-
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from __future__ import annotations
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import warnings
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from typing import 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, repeat
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from transformers.cache_utils import Cache
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from fla.modules import (FusedRMSNormSwishGateLinear, RMSNormLinear,
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RotaryEmbedding, ShortConvolution)
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from fla.modules.activations import ACT2FN, swiglu_linear, swish
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from fla.ops.abc.chunk_gate import chunk_gated_abc
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class GatedABCAttention(nn.Module):
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def __init__(
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self,
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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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share_conv_kernel: bool = True,
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num_slots: Optional[int] = None,
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elementwise_affine: Optional[bool] = True,
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norm_eps: float = 1e-5,
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gate_low_rank_dim: Optional[int] = None,
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gate_logit_normalizer: int = 16,
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feature_map: str = 'swish',
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use_rope: bool = False,
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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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**kwargs
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) -> GatedABCAttention:
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super().__init__()
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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.share_conv_kernel = share_conv_kernel
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if gate_low_rank_dim is None:
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gate_low_rank_dim = self.hidden_size // 16
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self.gate_low_rank_dim = gate_low_rank_dim
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self.gate_logit_normalizer = gate_logit_normalizer
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self.feature_map = feature_map
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self.use_rope = use_rope
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self.use_output_gate = use_output_gate
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self.use_norm = use_norm
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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.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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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_output_gate:
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self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
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if use_short_conv:
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self.conv_size = conv_size
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if share_conv_kernel:
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self.h_conv1d = ShortConvolution(hidden_size, conv_size, activation='silu')
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else:
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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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if self.use_norm:
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if self.use_output_gate:
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self.g_norm = FusedRMSNormSwishGateLinear(self.hidden_size, elementwise_affine, norm_eps)
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else:
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self.g_norm = RMSNormLinear(self.hidden_size, elementwise_affine, norm_eps)
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self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
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if self.use_rope:
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self.rotary = RotaryEmbedding(self.head_k_dim)
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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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lower_bound: Optional[torch.Tensor] = None,
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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
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last_state = past_key_values[self.layer_idx] if use_cache else None
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if self.use_short_conv:
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conv_state = last_state[0] if use_cache else None
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if self.share_conv_kernel:
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# conv state is updated inplace
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hidden_states = self.h_conv1d(hidden_states, attention_mask, conv_state)
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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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else:
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conv_state_q = last_state[0] if use_cache else None
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conv_state_k = last_state[1] if use_cache else None
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conv_state_v = last_state[2] if use_cache else None
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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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q = self.q_conv1d(q, attention_mask, conv_state_q)
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k = self.k_conv1d(k, attention_mask, conv_state_k)
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v = self.v_conv1d(v, attention_mask, conv_state_v)
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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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if self.use_rope:
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q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads)
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k = rearrange(k, '... (h d) -> ... h d', h=self.num_kv_heads)
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seqlen_offset = 0
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if past_key_values is not None:
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seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
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q, k = self.rotary(q, k, seqlen_offset)
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q = rearrange(q, 'b n h d -> b h n d', h=self.num_heads)
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k = rearrange(k, 'b n h d -> b h n d', h=self.num_kv_heads)
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else:
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q = rearrange(q, 'b n (h d) -> b h n d', h=self.num_heads)
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if self.num_kv_groups > 1:
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k = repeat(k, 'b n (h d) -> b (h g) n d', h=self.num_kv_heads, g=self.num_kv_groups)
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else:
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k = rearrange(k, 'b n (h d) -> b h n d', h=self.num_kv_heads)
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if self.num_kv_groups > 1:
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v = repeat(v, 'b n (h d) -> b (h g) n d', h=self.num_kv_heads, g=self.num_kv_groups)
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f = repeat(f, 'b n (h m) -> b (h g) n m', h=self.num_kv_heads, g=self.num_kv_groups)
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else:
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v = rearrange(v, 'b n (h d) -> b h n d', h=self.num_kv_heads)
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f = rearrange(f, 'b n (h m) -> b h n m', h=self.num_kv_heads)
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if self.feature_map is not None:
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q, k, v = map(lambda x: ACT2FN[self.feature_map](x), (q, k, 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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# dealing with left-padding
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if attention_mask is not None:
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s = s.mul_(attention_mask.view(attention_mask.shape[0], 1, -1, 1))
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v = v.mul_(attention_mask.view(attention_mask.shape[0], 1, -1, 1))
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recurrent_state = last_state[-2:] if use_cache else None
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o, recurrent_state = chunk_gated_abc(q, k, v, s, f,
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initial_state=recurrent_state,
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output_final_state=use_cache)
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if past_key_values is not None:
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if self.use_short_conv:
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if self.share_conv_kernel:
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last_state = (conv_state,) + recurrent_state
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else:
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last_state = (conv_state_q, conv_state_k, conv_state_v) + recurrent_state
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else:
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last_state = recurrent_state
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past_key_values.update(last_state, self.layer_idx, q.shape[2])
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o = rearrange(o, 'b h t d -> b t (h d)')
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if self.use_norm and not self.use_output_gate:
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o = swish(o)
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o = self.g_norm(o, self.o_proj.weight, self.o_proj.bias)
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elif self.use_output_gate and not self.use_norm:
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o = swiglu_linear(self.g_proj(hidden_states), o, self.o_proj.weight, self.o_proj.bias)
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elif self.use_output_gate and self.use_norm:
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o = self.g_norm(o, self.g_proj(hidden_states), self.o_proj.weight, self.o_proj.bias)
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else:
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o = self.o_proj(o)
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return o, None, past_key_values
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def init_state(self, batch_size: int) -> Tuple[torch.Tensor]:
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param = next(self.parameters())
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state = tuple()
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if self.use_short_conv:
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if self.share_conv_kernel:
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state += (param.new_zeros(batch_size, self.hidden_size, self.conv_size),)
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else:
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state += (param.new_zeros(batch_size, self.key_dim, self.conv_size),
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param.new_zeros(batch_size, self.key_dim, self.conv_size),
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param.new_zeros(batch_size, self.value_dim, self.conv_size))
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state += (param.new_zeros(batch_size, self.num_heads, self.head_k_dim, self.num_slots),
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param.new_zeros(batch_size, self.num_heads, self.num_slots, self.head_v_dim))
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return state
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def state_size(self, sequence_length: int = 2048):
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return self.num_heads * self.key_dim * self.head_v_dim
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