mirror of
https://github.com/KimMeen/Time-LLM.git
synced 2024-11-21 03:13:47 +08:00
243 lines
8.7 KiB
Python
243 lines
8.7 KiB
Python
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import torch
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import torch.nn as nn
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import numpy as np
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from math import sqrt
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from utils.masking import TriangularCausalMask, ProbMask
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from reformer_pytorch import LSHSelfAttention
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class DSAttention(nn.Module):
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'''De-stationary Attention'''
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def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
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super(DSAttention, self).__init__()
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self.scale = scale
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self.mask_flag = mask_flag
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self.output_attention = output_attention
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self.dropout = nn.Dropout(attention_dropout)
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
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B, L, H, E = queries.shape
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_, S, _, D = values.shape
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scale = self.scale or 1. / sqrt(E)
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tau = 1.0 if tau is None else tau.unsqueeze(
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1).unsqueeze(1) # B x 1 x 1 x 1
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delta = 0.0 if delta is None else delta.unsqueeze(
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1).unsqueeze(1) # B x 1 x 1 x S
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# De-stationary Attention, rescaling pre-softmax score with learned de-stationary factors
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scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta
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if self.mask_flag:
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if attn_mask is None:
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attn_mask = TriangularCausalMask(B, L, device=queries.device)
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scores.masked_fill_(attn_mask.mask, -np.inf)
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A = self.dropout(torch.softmax(scale * scores, dim=-1))
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V = torch.einsum("bhls,bshd->blhd", A, values)
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if self.output_attention:
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return (V.contiguous(), A)
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else:
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return (V.contiguous(), None)
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class FullAttention(nn.Module):
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def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
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super(FullAttention, self).__init__()
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self.scale = scale
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self.mask_flag = mask_flag
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self.output_attention = output_attention
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self.dropout = nn.Dropout(attention_dropout)
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
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B, L, H, E = queries.shape
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_, S, _, D = values.shape
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scale = self.scale or 1. / sqrt(E)
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scores = torch.einsum("blhe,bshe->bhls", queries, keys)
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if self.mask_flag:
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if attn_mask is None:
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attn_mask = TriangularCausalMask(B, L, device=queries.device)
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scores.masked_fill_(attn_mask.mask, -np.inf)
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A = self.dropout(torch.softmax(scale * scores, dim=-1))
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V = torch.einsum("bhls,bshd->blhd", A, values)
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if self.output_attention:
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return (V.contiguous(), A)
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else:
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return (V.contiguous(), None)
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class ProbAttention(nn.Module):
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def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
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super(ProbAttention, self).__init__()
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self.factor = factor
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self.scale = scale
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self.mask_flag = mask_flag
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self.output_attention = output_attention
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self.dropout = nn.Dropout(attention_dropout)
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def _prob_QK(self, Q, K, sample_k, n_top): # n_top: c*ln(L_q)
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# Q [B, H, L, D]
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B, H, L_K, E = K.shape
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_, _, L_Q, _ = Q.shape
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# calculate the sampled Q_K
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K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
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# real U = U_part(factor*ln(L_k))*L_q
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index_sample = torch.randint(L_K, (L_Q, sample_k))
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K_sample = K_expand[:, :, torch.arange(
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L_Q).unsqueeze(1), index_sample, :]
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Q_K_sample = torch.matmul(
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Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze()
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# find the Top_k query with sparisty measurement
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M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
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M_top = M.topk(n_top, sorted=False)[1]
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# use the reduced Q to calculate Q_K
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Q_reduce = Q[torch.arange(B)[:, None, None],
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torch.arange(H)[None, :, None],
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M_top, :] # factor*ln(L_q)
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Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # factor*ln(L_q)*L_k
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return Q_K, M_top
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def _get_initial_context(self, V, L_Q):
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B, H, L_V, D = V.shape
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if not self.mask_flag:
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# V_sum = V.sum(dim=-2)
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V_sum = V.mean(dim=-2)
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contex = V_sum.unsqueeze(-2).expand(B, H,
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L_Q, V_sum.shape[-1]).clone()
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else: # use mask
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# requires that L_Q == L_V, i.e. for self-attention only
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assert (L_Q == L_V)
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contex = V.cumsum(dim=-2)
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return contex
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def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
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B, H, L_V, D = V.shape
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if self.mask_flag:
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attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
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scores.masked_fill_(attn_mask.mask, -np.inf)
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attn = torch.softmax(scores, dim=-1) # nn.Softmax(dim=-1)(scores)
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context_in[torch.arange(B)[:, None, None],
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torch.arange(H)[None, :, None],
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index, :] = torch.matmul(attn, V).type_as(context_in)
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if self.output_attention:
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attns = (torch.ones([B, H, L_V, L_V]) /
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L_V).type_as(attn).to(attn.device)
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attns[torch.arange(B)[:, None, None], torch.arange(H)[
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None, :, None], index, :] = attn
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return (context_in, attns)
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else:
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return (context_in, None)
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
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B, L_Q, H, D = queries.shape
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_, L_K, _, _ = keys.shape
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queries = queries.transpose(2, 1)
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keys = keys.transpose(2, 1)
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values = values.transpose(2, 1)
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U_part = self.factor * \
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np.ceil(np.log(L_K)).astype('int').item() # c*ln(L_k)
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u = self.factor * \
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np.ceil(np.log(L_Q)).astype('int').item() # c*ln(L_q)
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U_part = U_part if U_part < L_K else L_K
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u = u if u < L_Q else L_Q
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scores_top, index = self._prob_QK(
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queries, keys, sample_k=U_part, n_top=u)
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# add scale factor
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scale = self.scale or 1. / sqrt(D)
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if scale is not None:
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scores_top = scores_top * scale
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# get the context
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context = self._get_initial_context(values, L_Q)
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# update the context with selected top_k queries
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context, attn = self._update_context(
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context, values, scores_top, index, L_Q, attn_mask)
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return context.contiguous(), attn
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class AttentionLayer(nn.Module):
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def __init__(self, attention, d_model, n_heads, d_keys=None,
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d_values=None):
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super(AttentionLayer, self).__init__()
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d_keys = d_keys or (d_model // n_heads)
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d_values = d_values or (d_model // n_heads)
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self.inner_attention = attention
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self.query_projection = nn.Linear(d_model, d_keys * n_heads)
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self.key_projection = nn.Linear(d_model, d_keys * n_heads)
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self.value_projection = nn.Linear(d_model, d_values * n_heads)
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self.out_projection = nn.Linear(d_values * n_heads, d_model)
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self.n_heads = n_heads
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def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
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B, L, _ = queries.shape
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_, S, _ = keys.shape
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H = self.n_heads
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queries = self.query_projection(queries).view(B, L, H, -1)
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keys = self.key_projection(keys).view(B, S, H, -1)
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values = self.value_projection(values).view(B, S, H, -1)
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out, attn = self.inner_attention(
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queries,
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keys,
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values,
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attn_mask,
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tau=tau,
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delta=delta
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)
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out = out.view(B, L, -1)
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return self.out_projection(out), attn
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class ReformerLayer(nn.Module):
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def __init__(self, attention, d_model, n_heads, d_keys=None,
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d_values=None, causal=False, bucket_size=4, n_hashes=4):
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super().__init__()
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self.bucket_size = bucket_size
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self.attn = LSHSelfAttention(
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dim=d_model,
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heads=n_heads,
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bucket_size=bucket_size,
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n_hashes=n_hashes,
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causal=causal
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)
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def fit_length(self, queries):
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# inside reformer: assert N % (bucket_size * 2) == 0
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B, N, C = queries.shape
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if N % (self.bucket_size * 2) == 0:
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return queries
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else:
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# fill the time series
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fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2))
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return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1)
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def forward(self, queries, keys, values, attn_mask, tau, delta):
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# in Reformer: defalut queries=keys
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B, N, C = queries.shape
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queries = self.attn(self.fit_length(queries))[:, :N, :]
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return queries, None
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