746 lines
26 KiB
Python
746 lines
26 KiB
Python
# Copyright (c) 2022 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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import math
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from typing import List, Optional, Tuple, Union
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import numpy as np
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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 fairscale.nn import checkpoint_wrapper, wrap
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from timm.models.layers import drop_path
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from transformers.modeling_outputs import CausalLMOutputWithPast
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try:
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from apex.normalization import FusedLayerNorm as LayerNorm
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except ModuleNotFoundError:
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from torch.nn import LayerNorm
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def rotate_every_two(x):
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x1 = x[:, :, :, ::2]
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x2 = x[:, :, :, 1::2]
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x = torch.stack((-x2, x1), dim=-1)
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return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')\
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def theta_shift(x, sin, cos):
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return (x * cos) + (rotate_every_two(x) * sin)
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def get_activation_fn(activation):
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if activation == "relu":
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return F.relu
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elif activation == "gelu":
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return F.gelu
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elif activation == "swish":
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return F.silu
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else:
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raise NotImplementedError
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class RMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True):
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super().__init__()
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self.eps = eps
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self.elementwise_affine = elementwise_affine
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if self.elementwise_affine:
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self.weight = nn.Parameter(torch.ones(dim))
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else:
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self.register_parameter('weight', None)
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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output = self._norm(x.float()).type_as(x)
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if self.weight is not None:
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output = output * self.weight
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return output
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class RetNetRelPos(nn.Module):
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def __init__(self, config):
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super().__init__()
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num_heads = config.decoder_retention_heads
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angle = 1.0 / (10000**torch.linspace(0, 1, config.decoder_embed_dim // num_heads // 2))
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angle = angle.unsqueeze(-1).repeat(1, 2).flatten()
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if config.use_lm_decay:
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# NOTE: alternative way described in the paper
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s = torch.log(torch.tensor(1 / 32))
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e = torch.log(torch.tensor(1 / 512))
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decay = torch.log(1 - torch.exp(torch.linspace(s, e, num_heads))) # [h,]
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else:
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decay = torch.log(1 - 2**(-5 - torch.arange(num_heads, dtype=torch.float)))
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self.register_buffer("angle", angle)
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self.register_buffer("decay", decay)
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self.recurrent_chunk_size = config.recurrent_chunk_size
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def forward(self, slen, activate_recurrent=False, chunkwise_recurrent=False):
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if activate_recurrent:
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sin = torch.sin(self.angle * (slen - 1))
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cos = torch.cos(self.angle * (slen - 1))
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retention_rel_pos = ((sin, cos), self.decay.exp())
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elif chunkwise_recurrent:
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index = torch.arange(slen).to(self.decay)
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sin = torch.sin(index[:, None] * self.angle[None, :])
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cos = torch.cos(index[:, None] * self.angle[None, :])
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block_index = torch.arange(self.recurrent_chunk_size).to(self.decay)
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mask = torch.tril(torch.ones(self.recurrent_chunk_size,
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self.recurrent_chunk_size)).to(self.decay)
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mask = torch.masked_fill(block_index[:, None] - block_index[None, :], ~mask.bool(),
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float("inf"))
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mask = torch.exp(mask * self.decay[:, None, None])
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mask = torch.nan_to_num(mask)
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value_inner_decay = mask[:, -1] / mask[:, -1].sum(dim=-1, keepdim=True)
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value_inner_decay = value_inner_decay.unsqueeze(-1)
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scale = mask.sum(dim=-1, keepdim=True).sqrt()
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inner_mask = mask / scale
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cross_decay = torch.exp(self.decay * self.recurrent_chunk_size)
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query_inner_decay = torch.exp(self.decay[:, None] * (block_index + 1))
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query_inner_decay = query_inner_decay[:, :, None] / (
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scale / mask[:, -1].sum(dim=-1)[:, None, None])
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cross_decay = cross_decay[:, None, None]
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retention_rel_pos = ((sin, cos), (inner_mask, cross_decay, query_inner_decay,
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value_inner_decay))
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else:
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index = torch.arange(slen).to(self.decay)
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sin = torch.sin(index[:, None] * self.angle[None, :])
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cos = torch.cos(index[:, None] * self.angle[None, :])
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mask = torch.tril(torch.ones(slen, slen)).to(self.decay)
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mask = torch.masked_fill(index[:, None] - index[None, :], ~mask.bool(), float("inf"))
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mask = torch.exp(mask * self.decay[:, None, None])
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mask = torch.nan_to_num(mask)
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mask = mask / mask.sum(dim=-1, keepdim=True).sqrt()
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retention_rel_pos = ((sin, cos), mask)
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return retention_rel_pos
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class MultiScaleRetention(nn.Module):
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def __init__(
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self,
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config,
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embed_dim,
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value_dim,
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num_heads,
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gate_fn="swish",
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):
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super().__init__()
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self.config = config
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self.embed_dim = embed_dim
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self.value_dim = value_dim
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self.num_heads = num_heads
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self.head_dim = self.value_dim // num_heads
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self.key_dim = self.embed_dim // num_heads
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self.scaling = self.key_dim**-0.5
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self.gate_fn = get_activation_fn(activation=str(gate_fn))
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
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self.v_proj = nn.Linear(embed_dim, value_dim, bias=False)
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self.g_proj = nn.Linear(embed_dim, value_dim, bias=False)
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self.out_proj = nn.Linear(value_dim, embed_dim, bias=False)
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self.group_norm = RMSNorm(self.head_dim, eps=config.layernorm_eps, elementwise_affine=False)
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self.reset_parameters()
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def reset_parameters(self):
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nn.init.xavier_uniform_(self.q_proj.weight, gain=2**-2.5)
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nn.init.xavier_uniform_(self.k_proj.weight, gain=2**-2.5)
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nn.init.xavier_uniform_(self.v_proj.weight, gain=2**-2.5)
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nn.init.xavier_uniform_(self.g_proj.weight, gain=2**-2.5)
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nn.init.xavier_uniform_(self.out_proj.weight, gain=2**-1)
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def parallel_forward(self, qr, kr, v, mask):
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bsz, tgt_len, embed_dim = v.size()
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vr = v.view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2)
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qk_mat = qr @ kr.transpose(-1, -2) # bsz * m * tgt_len * tgt_len
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qk_mat = qk_mat * mask
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# invariant after normalization
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qk_mat = qk_mat / qk_mat.detach().abs().sum(dim=-1, keepdim=True).clamp(min=1, max=5e4)
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output = torch.matmul(qk_mat, vr)
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output = output.transpose(1, 2)
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return output
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def recurrent_forward(self, qr, kr, v, decay, incremental_state):
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bsz = v.size(0)
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v = v.view(bsz, self.num_heads, self.head_dim, 1)
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kv = kr * v
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if "prev_key_value" in incremental_state:
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prev_kv = incremental_state["prev_key_value"]
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prev_scale = incremental_state["scale"]
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scale = prev_scale * decay + 1
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kv = prev_kv * (prev_scale.sqrt() * decay / scale.sqrt()).view(
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self.num_heads, 1, 1) + kv / scale.sqrt().view(self.num_heads, 1, 1)
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# kv = prev_kv * decay.view(self.num_heads, 1, 1) + kv
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else:
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scale = torch.ones_like(decay)
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incremental_state["prev_key_value"] = kv
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incremental_state["scale"] = scale
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output = torch.sum(qr * kv, dim=3)
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return output
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def chunk_recurrent_forward(self, qr, kr, v, inner_mask):
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mask, cross_decay, query_inner_decay, value_inner_decay = inner_mask
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bsz, tgt_len, embed_dim = v.size()
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chunk_len = mask.size(1)
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num_chunks = tgt_len // chunk_len
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assert tgt_len % chunk_len == 0
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qr = qr.view(bsz, self.num_heads, num_chunks, chunk_len, self.key_dim).transpose(1, 2)
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kr = kr.view(bsz, self.num_heads, num_chunks, chunk_len, self.key_dim).transpose(1, 2)
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v = v.view(bsz, num_chunks, chunk_len, self.num_heads, self.head_dim).transpose(2, 3)
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kr_t = kr.transpose(-1, -2)
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qk_mat = qr @ kr_t # bsz * num_heads * chunk_len * chunk_len
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qk_mat = qk_mat * mask
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inner_scale = qk_mat.detach().abs().sum(dim=-1, keepdim=True).clamp(min=1)
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qk_mat = qk_mat / inner_scale
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inner_output = torch.matmul(qk_mat,
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v) # bsz * num_heads * num_value_heads * chunk_len * head_dim
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# reduce kv in one chunk
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kv = kr_t @ (v * value_inner_decay)
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kv_recurrent = []
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cross_scale = []
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kv_state = torch.zeros(bsz, self.num_heads, self.key_dim, self.head_dim).to(v)
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kv_scale = torch.ones(bsz, self.num_heads, 1, 1).to(v)
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# accumulate kv by loop
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for i in range(num_chunks):
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kv_recurrent.append(kv_state / kv_scale)
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cross_scale.append(kv_scale)
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kv_state = kv_state * cross_decay + kv[:, i]
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kv_scale = kv_state.detach().abs().sum(dim=-2, keepdim=True).max(
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dim=-1, keepdim=True).values.clamp(min=1)
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kv_recurrent = torch.stack(kv_recurrent, dim=1)
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cross_scale = torch.stack(cross_scale, dim=1)
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all_scale = torch.maximum(inner_scale, cross_scale)
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align_inner_scale = all_scale / inner_scale
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align_cross_scale = all_scale / cross_scale
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cross_output = (qr * query_inner_decay) @ kv_recurrent
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output = inner_output / align_inner_scale + cross_output / align_cross_scale
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# output = inner_output / cross_scale + cross_output / inner_scale
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output = output.transpose(2, 3)
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return output
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def forward(self, x, rel_pos, chunkwise_recurrent=False, incremental_state=None):
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bsz, tgt_len, _ = x.size()
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(sin, cos), inner_mask = rel_pos
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q = self.q_proj(x)
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k = self.k_proj(x)
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v = self.v_proj(x)
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g = self.g_proj(x)
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k *= self.scaling
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q = q.view(bsz, tgt_len, self.num_heads, self.key_dim).transpose(1, 2)
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k = k.view(bsz, tgt_len, self.num_heads, self.key_dim).transpose(1, 2)
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qr = theta_shift(q, sin, cos)
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kr = theta_shift(k, sin, cos)
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if incremental_state is not None:
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output = self.recurrent_forward(qr, kr, v, inner_mask, incremental_state)
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elif chunkwise_recurrent:
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output = self.chunk_recurrent_forward(qr, kr, v, inner_mask)
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else:
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output = self.parallel_forward(qr, kr, v, inner_mask)
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output = self.group_norm(output).reshape(bsz, tgt_len, self.head_dim * self.num_heads)
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output = self.gate_fn(g) * output
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output = self.out_proj(output)
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return output
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class FeedForwardNetwork(nn.Module):
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def __init__(
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self,
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embed_dim,
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ffn_dim,
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activation_fn,
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dropout,
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activation_dropout,
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layernorm_eps,
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subln=False,
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use_rms_norm=False,
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.activation_fn = get_activation_fn(activation=str(activation_fn))
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self.activation_dropout_module = torch.nn.Dropout(activation_dropout)
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self.dropout_module = torch.nn.Dropout(dropout)
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self.fc1 = nn.Linear(self.embed_dim, ffn_dim)
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self.fc2 = nn.Linear(ffn_dim, self.embed_dim)
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if subln:
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if use_rms_norm:
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self.ffn_layernorm = RMSNorm(self.embed_dim, eps=layernorm_eps)
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else:
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self.ffn_layernorm = LayerNorm(self.embed_dim, eps=layernorm_eps)
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else:
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self.ffn_layernorm = None
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def reset_parameters(self):
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self.fc1.reset_parameters()
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self.fc2.reset_parameters()
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if self.ffn_layernorm is not None:
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self.ffn_layernorm.reset_parameters()
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def forward(self, x):
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x_shape = x.shape
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x = x.reshape(-1, x.size(-1))
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x = self.fc1(x)
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x = self.activation_fn(x.float()).type_as(x)
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x = self.activation_dropout_module(x)
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if self.ffn_layernorm is not None:
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x = self.ffn_layernorm(x)
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x = self.fc2(x)
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x = x.view(x_shape)
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x = self.dropout_module(x)
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return x
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class GLU(nn.Module):
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def __init__(
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self,
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embed_dim,
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ffn_dim,
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activation_fn,
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dropout,
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activation_dropout,
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.activation_fn = get_activation_fn(activation=str(activation_fn))
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self.activation_dropout_module = torch.nn.Dropout(activation_dropout)
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self.dropout_module = torch.nn.Dropout(dropout)
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self.fc1 = nn.Linear(self.embed_dim, ffn_dim, bias=False)
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self.fc2 = nn.Linear(ffn_dim, self.embed_dim, bias=False)
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self.gate = nn.Linear(self.embed_dim, ffn_dim, bias=False)
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def reset_parameters(self):
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self.fc1.reset_parameters()
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self.fc2.reset_parameters()
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self.gate.reset_parameters()
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def forward(self, x):
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x_shape = x.shape
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x = x.reshape(-1, x.size(-1))
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g = self.gate(x)
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x = self.fc1(x)
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x = self.activation_fn(x.float()).type_as(x) * g
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x = self.activation_dropout_module(x)
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x = self.fc2(x)
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x = x.view(x_shape)
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x = self.dropout_module(x)
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return x
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class DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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def extra_repr(self):
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return "p={}".format(self.drop_prob)
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class RetNetDecoderLayer(nn.Module):
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def __init__(
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self,
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config,
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depth,
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):
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super().__init__()
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self.config = config
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self.embed_dim = config.decoder_embed_dim
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self.dropout_module = torch.nn.Dropout(config.dropout)
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if config.drop_path_rate > 0:
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drop_path_prob = np.linspace(0, config.drop_path_rate, config.decoder_layers)[depth]
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self.drop_path = DropPath(drop_path_prob)
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else:
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self.drop_path = None
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self.retention = MultiScaleRetention(
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config,
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self.embed_dim,
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config.decoder_value_embed_dim,
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config.decoder_retention_heads,
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)
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self.normalize_before = config.decoder_normalize_before
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self.retention_layer_norm = RMSNorm(self.embed_dim, eps=config.layernorm_eps)
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self.ffn_dim = config.decoder_ffn_embed_dim
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self.ffn = self.build_ffn()
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self.final_layer_norm = RMSNorm(self.embed_dim, eps=config.layernorm_eps)
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if config.deepnorm:
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self.alpha = math.pow(2.0 * config.decoder_layers, 0.25)
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else:
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self.alpha = 1.0
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def build_ffn(self):
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if self.config.use_glu:
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return GLU(
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self.embed_dim,
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self.ffn_dim,
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self.config.activation_fn,
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self.config.dropout,
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self.config.activation_dropout,
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)
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else:
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return FeedForwardNetwork(
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self.embed_dim,
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self.ffn_dim,
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self.config.activation_fn,
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self.config.dropout,
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self.config.activation_dropout,
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self.config.layernorm_eps,
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self.config.subln,
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self.config.use_ffn_rms_norm,
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)
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def residual_connection(self, x, residual):
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return residual * self.alpha + x
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def forward(
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self,
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x,
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incremental_state=None,
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chunkwise_recurrent=False,
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retention_rel_pos=None,
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):
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residual = x
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if self.normalize_before:
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x = self.retention_layer_norm(x)
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x = self.retention(
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x,
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incremental_state=incremental_state,
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rel_pos=retention_rel_pos,
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chunkwise_recurrent=chunkwise_recurrent,
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)
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x = self.dropout_module(x)
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|
|
|
if self.drop_path is not None:
|
|
x = self.drop_path(x)
|
|
|
|
x = self.residual_connection(x, residual)
|
|
if not self.normalize_before:
|
|
x = self.retention_layer_norm(x)
|
|
|
|
residual = x
|
|
if self.normalize_before:
|
|
x = self.final_layer_norm(x)
|
|
|
|
x = self.ffn(x)
|
|
|
|
if self.drop_path is not None:
|
|
x = self.drop_path(x)
|
|
|
|
x = self.residual_connection(x, residual)
|
|
if not self.normalize_before:
|
|
x = self.final_layer_norm(x)
|
|
|
|
return x
|
|
|
|
|
|
class RetNetModel(nn.Module):
|
|
|
|
def __init__(self, config, embed_tokens=None, output_projection=None, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.config = config
|
|
|
|
self.dropout_module = torch.nn.Dropout(config.dropout)
|
|
|
|
embed_dim = config.decoder_embed_dim
|
|
self.embed_dim = embed_dim
|
|
self.embed_scale = 1.0 if config.no_scale_embedding else math.sqrt(embed_dim)
|
|
|
|
self.embed_tokens = embed_tokens
|
|
|
|
if (output_projection is None and not config.no_output_layer and config.vocab_size > 0):
|
|
self.output_projection = self.build_output_projection(config)
|
|
else:
|
|
self.output_projection = output_projection
|
|
|
|
if config.layernorm_embedding:
|
|
self.layernorm_embedding = RMSNorm(embed_dim, eps=config.layernorm_eps)
|
|
else:
|
|
self.layernorm_embedding = None
|
|
|
|
self.layers = nn.ModuleList([])
|
|
|
|
for i in range(config.decoder_layers):
|
|
self.layers.append(self.build_decoder_layer(
|
|
config,
|
|
depth=i,
|
|
))
|
|
|
|
self.num_layers = len(self.layers)
|
|
|
|
if config.decoder_normalize_before:
|
|
self.layer_norm = RMSNorm(embed_dim, eps=config.layernorm_eps)
|
|
else:
|
|
self.layer_norm = None
|
|
|
|
self.retnet_rel_pos = RetNetRelPos(config)
|
|
self.chunkwise_recurrent = config.chunkwise_recurrent
|
|
self.recurrent_chunk_size = config.recurrent_chunk_size
|
|
|
|
if config.deepnorm:
|
|
init_scale = math.pow(8.0 * config.decoder_layers, 0.25)
|
|
for name, p in self.named_parameters():
|
|
if ("fc1" in name or "fc2" in name or "out_proj" in name or "v_proj" in name):
|
|
p.data.div_(init_scale)
|
|
|
|
if config.subln and not config.use_glu:
|
|
init_scale = math.sqrt(math.log(config.decoder_layers * 2))
|
|
for name, p in self.named_parameters():
|
|
if ("fc1" in name or "fc2" in name or "out_proj" in name or "v_proj" in name):
|
|
p.data.mul_(init_scale)
|
|
|
|
def build_output_projection(
|
|
self,
|
|
config,
|
|
):
|
|
if config.share_decoder_input_output_embed:
|
|
output_projection = torch.nn.Linear(
|
|
self.embed_tokens.weight.shape[1],
|
|
self.embed_tokens.weight.shape[0],
|
|
bias=False,
|
|
)
|
|
output_projection.weight = self.embed_tokens.weight
|
|
else:
|
|
output_projection = torch.nn.Linear(config.decoder_embed_dim,
|
|
config.vocab_size,
|
|
bias=False)
|
|
torch.nn.init.normal_(output_projection.weight,
|
|
mean=0,
|
|
std=config.decoder_embed_dim**-0.5)
|
|
return output_projection
|
|
|
|
def build_decoder_layer(self, config, depth):
|
|
layer = RetNetDecoderLayer(
|
|
config,
|
|
depth,
|
|
)
|
|
# if config.checkpoint_activations:
|
|
# layer = checkpoint_wrapper(layer)
|
|
# if config.fsdp:
|
|
# layer = wrap(layer)
|
|
return layer
|
|
|
|
def forward_embedding(
|
|
self,
|
|
tokens,
|
|
token_embedding=None,
|
|
incremental_state=None,
|
|
):
|
|
if incremental_state is not None and not self.is_first_step(incremental_state):
|
|
tokens = tokens[:, -1:]
|
|
|
|
if token_embedding is None:
|
|
token_embedding = self.embed_tokens(tokens)
|
|
|
|
x = embed = self.embed_scale * token_embedding
|
|
|
|
if self.layernorm_embedding is not None:
|
|
x = self.layernorm_embedding(x)
|
|
|
|
x = self.dropout_module(x)
|
|
|
|
return x, embed
|
|
|
|
def is_first_step(self, incremental_state):
|
|
if incremental_state is None:
|
|
return False
|
|
return incremental_state.get("is_first_step", False)
|
|
|
|
def forward(self,
|
|
prev_output_tokens,
|
|
incremental_state=None,
|
|
features_only=False,
|
|
token_embeddings=None):
|
|
# embed tokens
|
|
x, _ = self.forward_embedding(prev_output_tokens, token_embeddings, incremental_state)
|
|
is_first_step = self.is_first_step(incremental_state)
|
|
|
|
if self.chunkwise_recurrent and prev_output_tokens.size(1) % self.recurrent_chunk_size != 0:
|
|
padding_len = self.recurrent_chunk_size - prev_output_tokens.size(
|
|
1) % self.recurrent_chunk_size
|
|
slen = prev_output_tokens.size(1) + padding_len
|
|
x = F.pad(x, (0, 0, 0, padding_len))
|
|
else:
|
|
slen = prev_output_tokens.size(1)
|
|
# relative position
|
|
retention_rel_pos = self.retnet_rel_pos(slen,
|
|
incremental_state is not None and not is_first_step,
|
|
chunkwise_recurrent=self.chunkwise_recurrent)
|
|
|
|
# decoder layers
|
|
inner_states = [x]
|
|
|
|
for idx, layer in enumerate(self.layers):
|
|
if incremental_state is None or is_first_step:
|
|
if is_first_step and incremental_state is not None:
|
|
if idx not in incremental_state:
|
|
incremental_state[idx] = {}
|
|
else:
|
|
if idx not in incremental_state:
|
|
incremental_state[idx] = {}
|
|
|
|
x = layer(
|
|
x,
|
|
incremental_state[idx] if incremental_state is not None else None,
|
|
retention_rel_pos=retention_rel_pos,
|
|
chunkwise_recurrent=self.chunkwise_recurrent,
|
|
)
|
|
inner_states.append(x)
|
|
|
|
if self.chunkwise_recurrent and prev_output_tokens.size(1) % self.recurrent_chunk_size != 0:
|
|
x = x[:, :prev_output_tokens.size(1), :]
|
|
|
|
if self.layer_norm is not None:
|
|
x = self.layer_norm(x)
|
|
|
|
if not features_only:
|
|
x = self.output_layer(x)
|
|
|
|
return x, {
|
|
"inner_states": inner_states,
|
|
"attn": None,
|
|
}
|
|
|
|
def output_layer(self, features):
|
|
return self.output_projection(features)
|
|
|
|
|
|
class RetNetForCausalLM(nn.Module):
|
|
|
|
def __init__(self, config, embed_tokens=None, output_projection=None, **kwargs):
|
|
super().__init__(**kwargs)
|
|
assert config.vocab_size > 0, "you must specify vocab size"
|
|
if output_projection is None:
|
|
config.no_output_layer = False
|
|
if embed_tokens is None:
|
|
embed_tokens = nn.Embedding(config.vocab_size, config.decoder_embed_dim,
|
|
config.pad_token_id)
|
|
|
|
self.config = config
|
|
self.model = RetNetModel(config,
|
|
embed_tokens=embed_tokens,
|
|
output_projection=output_projection,
|
|
**kwargs)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.model.output_projection
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.model.output_projection = new_embeddings
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
retention_mask: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_retentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
recurrent_chunk_size: Optional[int] = None,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
|
|
outputs = self.model(
|
|
input_ids,
|
|
incremental_state=past_key_values,
|
|
features_only=False,
|
|
token_embeddings=inputs_embeds,
|
|
)
|
|
|
|
logits, inner_hidden_states = outputs[0], outputs[1]['inner_states']
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
|
shift_labels = shift_labels.view(-1)
|
|
# Enable model parallelism
|
|
shift_labels = shift_labels.to(shift_logits.device)
|
|
loss = loss_fct(shift_logits, shift_labels)
|
|
|
|
if self.config.z_loss_coeff > 0:
|
|
# z_loss from PaLM paper
|
|
# z_loss = 1e-4 * log(log(z)), where z = sum(exp(logits))
|
|
z_loss = torch.logsumexp(shift_logits, dim=-1).log().mean()
|
|
loss += self.config.z_loss_coeff * z_loss
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=past_key_values,
|
|
hidden_states=inner_hidden_states,
|
|
attentions=None,
|
|
) |