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from __future__ import annotations |
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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, repeat |
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from fla.modules import FusedRMSNormSwishGate, RMSNorm, ShortConvolution |
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from fla.modules.activations import ACT2FN |
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from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla |
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if TYPE_CHECKING: |
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from fla.models.utils import Cache |
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class GatedLinearAttention(nn.Module): |
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r""" |
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The layer implementaion for [Gated Linear Attention Transformers with Hardware-Efficient Training](https://arxiv.org/abs/2312.06635). # noqa |
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Args: |
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mode (str, Optional): |
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Which GLA kernel to use. |
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Currently available: `chunk`, `fused_recurrent`, and `fused_chunk`. |
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Default: `chunk`. |
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hidden_size (int, Optional): |
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The hidden size of the input. Default: 1024. |
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expand_k (float, Optional): |
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The expansion ratio for the key dim. Default: 0.5. |
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expand_v (float, Optional): |
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The expansion ratio for the value dim. Default: 1.0. |
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num_heads (int, Optional): |
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The number of heads. Default: 4. |
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num_kv_heads (int, Optional): |
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The number of key/value heads, used for MQA. Default: None. |
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feature_map (str, Optional): |
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Feature map function applied to queries/keys. Default: None. |
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use_short_conv (bool, Optional): |
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Whether to use short convolutions. Default: `False`. |
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conv_size (int, Optional): |
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The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4. |
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conv_bias (bool, Optional): |
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Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`. |
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use_output_gate (bool, Optional): |
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Whether to use output gate. Default: `True`. |
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gate_fn (str, Optional): |
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The activation function for the output gate. Default: `swish`. |
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elementwise_affine (bool, Optional): |
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If `True`, applies elementwise affine to LayerNorm with learnable parameters. Default: `True`. |
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norm_eps (float, Optional): |
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The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5. |
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gate_logit_normalizer (int, Optional): |
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The normalizer for the gate logits, appied after `logsigmoid`. Default: 16. |
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gate_low_rank_dim (int, Optional): |
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The low rank dim for the gate projection. Default: 16. |
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clamp_min (float, Optional): |
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The minimum value for the gate logits. Default: None. |
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fuse_norm (bool, Optional): |
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Whether to fuse the norm and the output gate for better memory footprint. Default: `True`. |
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layer_idx (int, Optional): |
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The index of the layer. Default: None. |
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""" |
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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 = 0.5, |
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expand_v: float = 1.0, |
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num_heads: int = 4, |
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num_kv_heads: Optional[int] = None, |
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feature_map: Optional[str] = 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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use_output_gate: bool = True, |
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gate_fn: str = 'swish', |
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elementwise_affine: Optional[bool] = True, |
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norm_eps: float = 1e-5, |
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gate_logit_normalizer: int = 16, |
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gate_low_rank_dim: int = 16, |
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clamp_min: Optional[float] = None, |
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fuse_norm: bool = True, |
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layer_idx: int = None, |
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) -> GatedLinearAttention: |
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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_kv_heads if num_kv_heads is not None else num_heads |
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self.num_kv_groups = self.num_heads // self.num_kv_heads |
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self.feature_map_fn = ACT2FN[feature_map] if feature_map is not None else None |
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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.use_output_gate = use_output_gate |
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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.clamp_min = clamp_min |
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self.layer_idx = layer_idx |
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assert mode in ['chunk', 'fused_recurrent', 'fused_chunk'], f"Not suppoerted mode `{mode}`." |
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assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" |
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assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" |
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self.head_qk_dim = self.key_dim // num_heads |
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self.head_v_dim = self.value_dim // num_heads |
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self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) |
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self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False) |
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self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False) |
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if self.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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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.gk_proj = nn.Sequential(nn.Linear(hidden_size, gate_low_rank_dim, bias=False), |
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nn.Linear(gate_low_rank_dim, self.key_dim_per_group, bias=True)) |
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self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) |
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if gate_fn == 'swish' and fuse_norm and use_output_gate: |
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self.g_norm_swish_gate = FusedRMSNormSwishGate(self.head_v_dim, elementwise_affine, norm_eps) |
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self.fuse_norm_and_gate = True |
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else: |
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self.fuse_norm_and_gate = False |
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self.g_norm = RMSNorm(hidden_size=self.head_v_dim, elementwise_affine=elementwise_affine, eps=norm_eps) |
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self.gate_fn = ACT2FN[gate_fn] |
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self.gate_logit_normalizer = gate_logit_normalizer |
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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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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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gk = self.gk_proj(hidden_states) |
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if self.feature_map_fn is not None: |
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q, k = map(self.feature_map_fn, (q, k)) |
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if attention_mask is not None: |
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v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) |
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q = rearrange(q, 'b t (h d) -> b t h d', h=self.num_heads) |
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if self.num_kv_groups > 1: |
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k, v, gk = (repeat(x, 'b t (h d) -> b t (h g) d', h=self.num_kv_heads, g=self.num_kv_groups) for x in (k, v, gk)) |
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else: |
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k, v, gk = (rearrange(x, 'b t (h d) -> b t h d', h=self.num_kv_heads) for x in (k, v, gk)) |
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gk = F.logsigmoid(gk) / self.gate_logit_normalizer |
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if self.clamp_min is not None: |
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gk = torch.clamp_min(gk, self.clamp_min) |
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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_gla( |
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q=q, |
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k=k, |
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v=v, |
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gk=gk, |
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initial_state=recurrent_state, |
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output_final_state=use_cache, |
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head_first=False |
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) |
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elif mode == 'fused_chunk': |
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o, recurrent_state = fused_chunk_gla( |
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q=q, |
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k=k, |
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v=v, |
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g=gk, |
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initial_state=recurrent_state, |
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output_final_state=use_cache, |
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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_gla( |
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q=q, |
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k=k, |
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v=v, |
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g=gk, |
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initial_state=recurrent_state, |
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output_final_state=use_cache, |
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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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if self.use_output_gate: |
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g = self.g_proj(hidden_states) |
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if self.fuse_norm_and_gate: |
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g = rearrange(g, 'b t (h d) -> b t h d', h=self.num_heads) |
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o = self.g_norm_swish_gate(o, g) |
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o = rearrange(o, 'b t h d -> b t (h d)') |
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else: |
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o = rearrange(self.g_norm(o), 'b t h d -> b t (h d)') |
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o = o * self.gate_fn(g) |
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else: |
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o = rearrange(self.g_norm(o), 'b t h d -> b t (h d)') |
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o = self.o_proj(o) |
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return o, None, past_key_values |
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def state_size(self, **kwargs) -> int: |
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state_size = self.key_dim * self.head_v_dim |
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for module in self.children(): |
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if isinstance(module, ShortConvolution): |
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state_size += module.state_size |
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return state_size |
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