166 lines
6.7 KiB
Python
Vendored
166 lines
6.7 KiB
Python
Vendored
# -*- coding: utf-8 -*-
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# "Hierarchically Gated Recurrent Neural Network for Sequence Modeling" [https://arxiv.org/abs/2311.04823]
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from __future__ import annotations
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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
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from transformers.cache_utils import Cache
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from fla.modules import FusedRMSNormSwishGate, ShortConvolution
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from fla.modules.activations import swiglu
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from fla.ops.hgrn import chunk_hgrn, fused_recurrent_hgrn
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class HGRNAttention(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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num_heads: Optional[int] = None,
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expand_ratio: Optional[int] = 1,
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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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elementwise_affine: Optional[bool] = True,
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norm_eps: float = 1e-5,
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layer_idx: int = None
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) -> HGRNAttention:
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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.num_heads = num_heads
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self.expand_ratio = expand_ratio
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self.input_dim = int(hidden_size * expand_ratio)
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self.head_dim = self.input_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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self.layer_idx = layer_idx
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assert mode in ['chunk', 'fused_recurrent'], f"Not suppoerted mode `{mode}`."
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assert self.hidden_size % num_heads == 0, f"hidden size must be divisible by num_heads of {num_heads}"
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self.i_proj = nn.Linear(hidden_size, self.input_dim, bias=False)
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self.f_proj = nn.Linear(hidden_size, self.input_dim, bias=False)
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self.g_proj = nn.Linear(hidden_size, self.input_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.input_dim, conv_size, activation='silu')
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self.f_conv1d = ShortConvolution(self.input_dim, conv_size, activation='silu')
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self.i_conv1d = ShortConvolution(self.input_dim, conv_size, activation='silu')
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self.g_norm = FusedRMSNormSwishGate(self.input_dim, elementwise_affine, norm_eps)
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self.o_proj = nn.Linear(self.input_dim, 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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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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# launching the triton kernel for just one token will actually be slower
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mode = 'fused_recurrent' if hidden_states.shape[1] == 1 else self.mode
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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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i = self.i_proj(hidden_states)
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f = self.f_proj(hidden_states)
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else:
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conv_state_i = last_state[2] if use_cache else None
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conv_state_f = last_state[1] if use_cache else None
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i = self.i_conv1d(self.i_proj(hidden_states), attention_mask, conv_state_i)
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f = self.f_conv1d(self.f_proj(hidden_states), attention_mask, conv_state_f)
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else:
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i = self.i_proj(hidden_states)
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f = self.f_proj(hidden_states)
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# the lower bound for the first layer is zero
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if lower_bound is None or self.layer_idx == 0:
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i, f = swiglu(i, 1 - f.sigmoid()), F.logsigmoid(f)
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else:
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g = lower_bound + (1 - lower_bound) * f.sigmoid()
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i, f = swiglu(i, 1 - g), g.log()
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# dealing with left-padding
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if attention_mask is not None:
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i = i.mul_(attention_mask.unsqueeze(-1))
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i, f = map(lambda x: rearrange(x, 'b l (h d) -> b h l d', h=self.num_heads), (i, f))
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recurrent_state = last_state[-1] if use_cache else None
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if mode == 'chunk':
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o, recurrent_state = chunk_hgrn(i, f, initial_state=recurrent_state, output_final_state=use_cache)
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elif mode == 'fused_recurrent':
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o, recurrent_state = fused_recurrent_hgrn(i, f, initial_state=recurrent_state, output_final_state=use_cache)
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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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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_i, conv_state_f, 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, i.shape[2])
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o = self.g_norm(self.g_proj(hidden_states), rearrange(o, 'b h l d -> b l (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 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.hidden_size, self.conv_size),
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param.new_zeros(batch_size, self.hidden_size, self.conv_size),
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param.new_zeros(batch_size, self.hidden_size, self.conv_size))
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state += (param.new_zeros(batch_size, self.num_heads, self.head_dim),)
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return state
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def state_size(self, **kwargs) -> int:
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state_size = self.hidden_size
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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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