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| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from math import sqrt | |
| from utils.masking import TriangularCausalMask, ProbMask | |
| from reformer_pytorch import LSHSelfAttention | |
| from einops import rearrange, repeat | |
| class DSAttention(nn.Module): | |
| '''De-stationary Attention''' | |
| def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): | |
| super(DSAttention, self).__init__() | |
| self.scale = scale | |
| self.mask_flag = mask_flag | |
| self.output_attention = output_attention | |
| self.dropout = nn.Dropout(attention_dropout) | |
| def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): | |
| B, L, H, E = queries.shape | |
| _, S, _, D = values.shape | |
| scale = self.scale or 1. / sqrt(E) | |
| tau = 1.0 if tau is None else tau.unsqueeze( | |
| 1).unsqueeze(1) # B x 1 x 1 x 1 | |
| delta = 0.0 if delta is None else delta.unsqueeze( | |
| 1).unsqueeze(1) # B x 1 x 1 x S | |
| # De-stationary Attention, rescaling pre-softmax score with learned de-stationary factors | |
| scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta | |
| if self.mask_flag: | |
| if attn_mask is None: | |
| attn_mask = TriangularCausalMask(B, L, device=queries.device) | |
| scores.masked_fill_(attn_mask.mask, -np.inf) | |
| A = self.dropout(torch.softmax(scale * scores, dim=-1)) | |
| V = torch.einsum("bhls,bshd->blhd", A, values) | |
| if self.output_attention: | |
| return V.contiguous(), A | |
| else: | |
| return V.contiguous(), None | |
| class FullAttention(nn.Module): | |
| def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): | |
| super(FullAttention, self).__init__() | |
| self.scale = scale | |
| self.mask_flag = mask_flag | |
| self.output_attention = output_attention | |
| self.dropout = nn.Dropout(attention_dropout) | |
| def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): | |
| B, L, H, E = queries.shape | |
| _, S, _, D = values.shape | |
| scale = self.scale or 1. / sqrt(E) | |
| scores = torch.einsum("blhe,bshe->bhls", queries, keys) | |
| if self.mask_flag: | |
| if attn_mask is None: | |
| attn_mask = TriangularCausalMask(B, L, device=queries.device) | |
| scores.masked_fill_(attn_mask.mask, -np.inf) | |
| A = self.dropout(torch.softmax(scale * scores, dim=-1)) | |
| V = torch.einsum("bhls,bshd->blhd", A, values) | |
| if self.output_attention: | |
| return V.contiguous(), A | |
| else: | |
| return V.contiguous(), None | |
| class ProbAttention(nn.Module): | |
| def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): | |
| super(ProbAttention, self).__init__() | |
| self.factor = factor | |
| self.scale = scale | |
| self.mask_flag = mask_flag | |
| self.output_attention = output_attention | |
| self.dropout = nn.Dropout(attention_dropout) | |
| def _prob_QK(self, Q, K, sample_k, n_top): # n_top: c*ln(L_q) | |
| # Q [B, H, L, D] | |
| B, H, L_K, E = K.shape | |
| _, _, L_Q, _ = Q.shape | |
| # calculate the sampled Q_K | |
| K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E) | |
| # real U = U_part(factor*ln(L_k))*L_q | |
| index_sample = torch.randint(L_K, (L_Q, sample_k)) | |
| K_sample = K_expand[:, :, torch.arange( | |
| L_Q).unsqueeze(1), index_sample, :] | |
| Q_K_sample = torch.matmul( | |
| Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze() | |
| # find the Top_k query with sparisty measurement | |
| M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K) | |
| M_top = M.topk(n_top, sorted=False)[1] | |
| # use the reduced Q to calculate Q_K | |
| Q_reduce = Q[torch.arange(B)[:, None, None], | |
| torch.arange(H)[None, :, None], | |
| M_top, :] # factor*ln(L_q) | |
| Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # factor*ln(L_q)*L_k | |
| return Q_K, M_top | |
| def _get_initial_context(self, V, L_Q): | |
| B, H, L_V, D = V.shape | |
| if not self.mask_flag: | |
| # V_sum = V.sum(dim=-2) | |
| V_sum = V.mean(dim=-2) | |
| contex = V_sum.unsqueeze(-2).expand(B, H, | |
| L_Q, V_sum.shape[-1]).clone() | |
| else: # use mask | |
| # requires that L_Q == L_V, i.e. for self-attention only | |
| assert (L_Q == L_V) | |
| contex = V.cumsum(dim=-2) | |
| return contex | |
| def _update_context(self, context_in, V, scores, index, L_Q, attn_mask): | |
| B, H, L_V, D = V.shape | |
| if self.mask_flag: | |
| attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device) | |
| scores.masked_fill_(attn_mask.mask, -np.inf) | |
| attn = torch.softmax(scores, dim=-1) # nn.Softmax(dim=-1)(scores) | |
| context_in[torch.arange(B)[:, None, None], | |
| torch.arange(H)[None, :, None], | |
| index, :] = torch.matmul(attn, V).type_as(context_in) | |
| if self.output_attention: | |
| attns = (torch.ones([B, H, L_V, L_V]) / | |
| L_V).type_as(attn).to(attn.device) | |
| attns[torch.arange(B)[:, None, None], torch.arange(H)[ | |
| None, :, None], index, :] = attn | |
| return context_in, attns | |
| else: | |
| return context_in, None | |
| def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): | |
| B, L_Q, H, D = queries.shape | |
| _, L_K, _, _ = keys.shape | |
| queries = queries.transpose(2, 1) | |
| keys = keys.transpose(2, 1) | |
| values = values.transpose(2, 1) | |
| U_part = self.factor * \ | |
| np.ceil(np.log(L_K)).astype('int').item() # c*ln(L_k) | |
| u = self.factor * \ | |
| np.ceil(np.log(L_Q)).astype('int').item() # c*ln(L_q) | |
| U_part = U_part if U_part < L_K else L_K | |
| u = u if u < L_Q else L_Q | |
| scores_top, index = self._prob_QK( | |
| queries, keys, sample_k=U_part, n_top=u) | |
| # add scale factor | |
| scale = self.scale or 1. / sqrt(D) | |
| if scale is not None: | |
| scores_top = scores_top * scale | |
| # get the context | |
| context = self._get_initial_context(values, L_Q) | |
| # update the context with selected top_k queries | |
| context, attn = self._update_context( | |
| context, values, scores_top, index, L_Q, attn_mask) | |
| return context.contiguous(), attn | |
| class AttentionLayer(nn.Module): | |
| def __init__(self, attention, d_model, n_heads, d_keys=None, | |
| d_values=None): | |
| super(AttentionLayer, self).__init__() | |
| d_keys = d_keys or (d_model // n_heads) | |
| d_values = d_values or (d_model // n_heads) | |
| self.inner_attention = attention | |
| self.query_projection = nn.Linear(d_model, d_keys * n_heads) | |
| self.key_projection = nn.Linear(d_model, d_keys * n_heads) | |
| self.value_projection = nn.Linear(d_model, d_values * n_heads) | |
| self.out_projection = nn.Linear(d_values * n_heads, d_model) | |
| self.n_heads = n_heads | |
| def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): | |
| B, L, _ = queries.shape | |
| _, S, _ = keys.shape | |
| H = self.n_heads | |
| queries = self.query_projection(queries).view(B, L, H, -1) | |
| keys = self.key_projection(keys).view(B, S, H, -1) | |
| values = self.value_projection(values).view(B, S, H, -1) | |
| out, attn = self.inner_attention( | |
| queries, | |
| keys, | |
| values, | |
| attn_mask, | |
| tau=tau, | |
| delta=delta | |
| ) | |
| out = out.view(B, L, -1) | |
| return self.out_projection(out), attn | |
| class ReformerLayer(nn.Module): | |
| def __init__(self, attention, d_model, n_heads, d_keys=None, | |
| d_values=None, causal=False, bucket_size=4, n_hashes=4): | |
| super().__init__() | |
| self.bucket_size = bucket_size | |
| self.attn = LSHSelfAttention( | |
| dim=d_model, | |
| heads=n_heads, | |
| bucket_size=bucket_size, | |
| n_hashes=n_hashes, | |
| causal=causal | |
| ) | |
| def fit_length(self, queries): | |
| # inside reformer: assert N % (bucket_size * 2) == 0 | |
| B, N, C = queries.shape | |
| if N % (self.bucket_size * 2) == 0: | |
| return queries | |
| else: | |
| # fill the time series | |
| fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2)) | |
| return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1) | |
| def forward(self, queries, keys, values, attn_mask, tau, delta): | |
| # in Reformer: defalut queries=keys | |
| B, N, C = queries.shape | |
| queries = self.attn(self.fit_length(queries))[:, :N, :] | |
| return queries, None | |
| class TwoStageAttentionLayer(nn.Module): | |
| ''' | |
| The Two Stage Attention (TSA) Layer | |
| input/output shape: [batch_size, Data_dim(D), Seg_num(L), d_model] | |
| ''' | |
| def __init__(self, configs, | |
| seg_num, factor, d_model, n_heads, d_ff=None, dropout=0.1): | |
| super(TwoStageAttentionLayer, self).__init__() | |
| d_ff = d_ff or 4 * d_model | |
| self.time_attention = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), d_model, n_heads) | |
| self.dim_sender = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), d_model, n_heads) | |
| self.dim_receiver = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), d_model, n_heads) | |
| self.router = nn.Parameter(torch.randn(seg_num, factor, d_model)) | |
| self.dropout = nn.Dropout(dropout) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.norm3 = nn.LayerNorm(d_model) | |
| self.norm4 = nn.LayerNorm(d_model) | |
| self.MLP1 = nn.Sequential(nn.Linear(d_model, d_ff), | |
| nn.GELU(), | |
| nn.Linear(d_ff, d_model)) | |
| self.MLP2 = nn.Sequential(nn.Linear(d_model, d_ff), | |
| nn.GELU(), | |
| nn.Linear(d_ff, d_model)) | |
| def forward(self, x, attn_mask=None, tau=None, delta=None): | |
| # Cross Time Stage: Directly apply MSA to each dimension | |
| batch = x.shape[0] | |
| time_in = rearrange(x, 'b ts_d seg_num d_model -> (b ts_d) seg_num d_model') | |
| time_enc, attn = self.time_attention( | |
| time_in, time_in, time_in, attn_mask=None, tau=None, delta=None | |
| ) | |
| dim_in = time_in + self.dropout(time_enc) | |
| dim_in = self.norm1(dim_in) | |
| dim_in = dim_in + self.dropout(self.MLP1(dim_in)) | |
| dim_in = self.norm2(dim_in) | |
| # Cross Dimension Stage: use a small set of learnable vectors to aggregate and distribute messages to build the D-to-D connection | |
| dim_send = rearrange(dim_in, '(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model', b=batch) | |
| batch_router = repeat(self.router, 'seg_num factor d_model -> (repeat seg_num) factor d_model', repeat=batch) | |
| dim_buffer, attn = self.dim_sender(batch_router, dim_send, dim_send, attn_mask=None, tau=None, delta=None) | |
| dim_receive, attn = self.dim_receiver(dim_send, dim_buffer, dim_buffer, attn_mask=None, tau=None, delta=None) | |
| dim_enc = dim_send + self.dropout(dim_receive) | |
| dim_enc = self.norm3(dim_enc) | |
| dim_enc = dim_enc + self.dropout(self.MLP2(dim_enc)) | |
| dim_enc = self.norm4(dim_enc) | |
| final_out = rearrange(dim_enc, '(b seg_num) ts_d d_model -> b ts_d seg_num d_model', b=batch) | |
| return final_out | |