add gitignore
This commit is contained in:
FelixChan
@ -389,85 +389,6 @@ class XtransformerCrossAttendDecoder(nn.Module):
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else:
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return self.transformer_decoder(seq, context=context)
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class XtransformerLargeCrossAttendDecoder(nn.Module):
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def __init__(
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self,
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dim:int,
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depth:int,
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heads:int,
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dropout:float
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):
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super().__init__()
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self._make_decoder_layer(dim, depth, heads, dropout)
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self.text_encoder = T5EncoderModel.from_pretrained('google/flan-t5-large')
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# frozen text encoder
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for param in self.text_encoder.parameters():
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param.requires_grad = False
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def _make_decoder_layer(self, dim, depth, heads, dropout):
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self.transformer_decoder = Decoder(
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dim = dim,
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depth = depth,
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heads = heads,
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attn_dropout = dropout,
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ff_dropout = dropout,
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attn_flash = True,
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cross_attend = True,
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only_cross = False)
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# add final dropout
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print('Applying Xavier Uniform Init to x-transformer following torch.Transformer')
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self._apply_xavier_init()
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print('Adding dropout after feedforward layer in x-transformer')
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self._add_dropout_after_ff(dropout)
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print('Adding dropout after attention layer in x-transformer')
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self._add_dropout_after_attn(dropout)
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def _add_dropout_after_attn(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'Attention' in str(type(layer[1])):
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if isinstance(layer[1].to_out, nn.Sequential): # if GLU
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layer[1].to_out.append(nn.Dropout(dropout))
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elif isinstance(layer[1].to_out, nn.Linear): # if simple linear
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layer[1].to_out = nn.Sequential(layer[1].to_out, nn.Dropout(dropout))
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else:
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raise ValueError('to_out should be either nn.Sequential or nn.Linear')
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def _add_dropout_after_ff(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'FeedForward' in str(type(layer[1])):
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layer[1].ff.append(nn.Dropout(dropout))
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def _apply_xavier_init(self):
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for name, param in self.transformer_decoder.named_parameters():
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if 'to_q' in name or 'to_k' in name or 'to_v' in name:
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torch.nn.init.xavier_uniform_(param, gain=0.5**0.5)
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def forward(self, seq, cache=None,train=False,context=None,context_embedding=None):
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assert context is not None or context_embedding is not None, 'context or context_embedding should be provided for prefix decoder'
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if context_embedding is None:
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input_ids = context['input_ids'].squeeze(1) if context['input_ids'].ndim == 3 else context['input_ids']
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attention_mask = context['attention_mask'].squeeze(1) if context['attention_mask'].ndim == 3 else context['attention_mask']
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assert input_ids is not None, 'input_ids should be provided for prefix decoder'
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assert attention_mask is not None, 'attention_mask should be provided for prefix decoder'
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assert input_ids.device == self.text_encoder.device, 'input_ids should be on the same device as text_encoder'
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context = self.text_encoder(
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input_ids=input_ids,
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attention_mask=attention_mask
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).last_hidden_state
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else:
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context = context_embedding
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if cache is not None: # implementing run_one_step in inference
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if cache.hiddens is None: cache = None
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hidden_vec, intermediates = self.transformer_decoder(seq, cache=cache, return_hiddens=True, context=context)
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return hidden_vec, intermediates
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else:
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if train:
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hidden_vec, intermediates = self.transformer_decoder(seq, context=context, return_hiddens=True)
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return hidden_vec, intermediates
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else:
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return self.transformer_decoder(seq, context=context)
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class NewCrossAttendDecoder(nn.Module):
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def __init__(
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@ -638,6 +559,75 @@ class NewCrossAttendwithRoPEDecoder(nn.Module):
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else:
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return self.transformer_decoder(seq, context=context)
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class RoPEDecoder(nn.Module):
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def __init__(
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self,
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dim:int,
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depth:int,
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heads:int,
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dropout:float
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):
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super().__init__()
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self._make_decoder_layer(dim, depth, heads, dropout)
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# self.text_encoder = T5EncoderModel.from_pretrained('google/flan-t5-base')
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# frozen text encoder
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def _make_decoder_layer(self, dim, depth, heads, dropout):
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self.transformer_decoder = Decoder(
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dim = dim,
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depth = depth,
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heads = heads,
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attn_dropout = dropout,
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ff_dropout = dropout,
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attn_flash = True,
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# cross_attend = True,
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only_cross = False,
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use_rmsnorm=True,
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rotary_pos_emb = True,
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ff_swish = True, # set this to True
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ff_glu = True, # set to true to use for all feedforwards
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)
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# add final dropout
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print('Applying Xavier Uniform Init to x-transformer following torch.Transformer')
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self._apply_xavier_init()
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print('Adding dropout after feedforward layer in x-transformer')
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self._add_dropout_after_ff(dropout)
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print('Adding dropout after attention layer in x-transformer')
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self._add_dropout_after_attn(dropout)
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def _add_dropout_after_attn(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'Attention' in str(type(layer[1])):
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if isinstance(layer[1].to_out, nn.Sequential): # if GLU
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layer[1].to_out.append(nn.Dropout(dropout))
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elif isinstance(layer[1].to_out, nn.Linear): # if simple linear
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layer[1].to_out = nn.Sequential(layer[1].to_out, nn.Dropout(dropout))
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else:
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raise ValueError('to_out should be either nn.Sequential or nn.Linear')
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def _add_dropout_after_ff(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'FeedForward' in str(type(layer[1])):
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layer[1].ff.append(nn.Dropout(dropout))
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def _apply_xavier_init(self):
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for name, param in self.transformer_decoder.named_parameters():
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if 'to_q' in name or 'to_k' in name or 'to_v' in name:
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torch.nn.init.xavier_uniform_(param, gain=0.5**0.5)
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def forward(self, seq, cache=None,train=False,context=None,context_embedding=None):
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if cache is not None: # implementing run_one_step in inference
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if cache.hiddens is None: cache = None
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hidden_vec, intermediates = self.transformer_decoder(seq, cache=cache, return_hiddens=True)
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return hidden_vec, intermediates
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else:
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if train:
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hidden_vec, intermediates = self.transformer_decoder(seq, return_hiddens=True)
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return hidden_vec, intermediates
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else:
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return self.transformer_decoder(seq)
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class XtransformerPrefixDecoder(nn.Module):
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def __init__(
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self,
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@ -711,7 +701,80 @@ class XtransformerPrefixDecoder(nn.Module):
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return hidden_vec, intermediates
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else:
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return self.transformer_decoder(seq)
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class XtransformerNewPretrainingDecoder(nn.Module):
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def __init__(
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self,
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dim:int,
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depth:int,
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heads:int,
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dropout:float
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):
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super().__init__()
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self._make_decoder_layer(dim, depth, heads, dropout)
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self.text_encoder = T5EncoderModel.from_pretrained('google/flan-t5-base')
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# frozen text encoder
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for param in self.text_encoder.parameters():
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param.requires_grad = False
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def _make_decoder_layer(self, dim, depth, heads, dropout):
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self.transformer_decoder = Decoder(
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dim = dim,
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depth = depth,
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heads = heads,
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attn_dropout = dropout,
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ff_dropout = dropout,
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attn_flash = True,
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use_rmsnorm=True,
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rotary_pos_emb = True,
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ff_swish = True, # set this to True
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ff_glu = True, # set to true to use for all feedforwards
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# shift_tokens = 1,
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# attn_qk_norm = True,
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# attn_qk_norm_dim_scale = True
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)
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# add final dropout
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print('Applying Xavier Uniform Init to x-transformer following torch.Transformer')
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self._apply_xavier_init()
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print('Adding dropout after feedforward layer in x-transformer')
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self._add_dropout_after_ff(dropout)
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print('Adding dropout after attention layer in x-transformer')
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self._add_dropout_after_attn(dropout)
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def _add_dropout_after_attn(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'Attention' in str(type(layer[1])):
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if isinstance(layer[1].to_out, nn.Sequential): # if GLU
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layer[1].to_out.append(nn.Dropout(dropout))
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elif isinstance(layer[1].to_out, nn.Linear): # if simple linear
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layer[1].to_out = nn.Sequential(layer[1].to_out, nn.Dropout(dropout))
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else:
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raise ValueError('to_out should be either nn.Sequential or nn.Linear')
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def _add_dropout_after_ff(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'FeedForward' in str(type(layer[1])):
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layer[1].ff.append(nn.Dropout(dropout))
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def _apply_xavier_init(self):
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for name, param in self.transformer_decoder.named_parameters():
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if 'to_q' in name or 'to_k' in name or 'to_v' in name:
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torch.nn.init.xavier_uniform_(param, gain=0.5**0.5)
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def forward(self, seq, cache=None,train=False,context=None, context_embedding=None):
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if cache is not None: # implementing run_one_step in inference
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if cache.hiddens is None: cache = None
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hidden_vec, intermediates = self.transformer_decoder(seq, cache=cache, return_hiddens=True)
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return hidden_vec, intermediates
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else:
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if train:
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hidden_vec, intermediates = self.transformer_decoder(seq, return_hiddens=True)
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return hidden_vec, intermediates
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else:
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return self.transformer_decoder(seq)
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class XtransformerPretrainingDecoder(nn.Module):
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def __init__(
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self,
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@ -827,92 +890,6 @@ class XtransformerFinetuningDecoder(nn.Module):
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if 'to_q' in name or 'to_k' in name or 'to_v' in name:
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torch.nn.init.xavier_uniform_(param, gain=0.5**0.5)
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def forward(self, seq, cache=None,train=False,context=None,context_embedding=None):
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assert context is not None or context_embedding is not None, 'context or context_embedding should be provided for prefix decoder'
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if context_embedding is None:
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input_ids = context['input_ids'].squeeze(1) if context['input_ids'].ndim == 3 else context['input_ids']
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attention_mask = context['attention_mask'].squeeze(1) if context['attention_mask'].ndim == 3 else context['attention_mask']
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assert input_ids is not None, 'input_ids should be provided for prefix decoder'
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assert attention_mask is not None, 'attention_mask should be provided for prefix decoder'
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assert input_ids.device == self.text_encoder.device, 'input_ids should be on the same device as text_encoder'
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context = self.text_encoder(
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input_ids=input_ids,
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attention_mask=attention_mask,
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).last_hidden_state
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else:
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context = context_embedding
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# concatenate context with seq
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seq = torch.cat([context, seq], dim=1) # B x (T+context_length) x emb_size
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if cache is not None: # implementing run_one_step in inference
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if cache.hiddens is None: cache = None
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hidden_vec, intermediates = self.transformer_decoder(seq, cache=cache, return_hiddens=True)
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# cut to only return the seq part
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return hidden_vec, intermediates
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else:
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if train:
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hidden_vec, intermediates = self.transformer_decoder(seq, return_hiddens=True)
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# cut to only return the seq part
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hidden_vec = hidden_vec[:, context.shape[1]:, :]
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return hidden_vec, intermediates
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else:
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# cut to only return the seq part
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hidden_vec = self.transformer_decoder(seq)
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hidden_vec = hidden_vec[:, context.shape[1]:, :]
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return hidden_vec
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class XtransformerLargeFinetuningDecoder(nn.Module):
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def __init__(
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self,
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dim:int,
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depth:int,
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heads:int,
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dropout:float
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):
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super().__init__()
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self._make_decoder_layer(dim, depth, heads, dropout)
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self.text_encoder = T5EncoderModel.from_pretrained('google/flan-t5-large')
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# frozen text encoder
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for param in self.text_encoder.parameters():
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param.requires_grad = False
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def _make_decoder_layer(self, dim, depth, heads, dropout):
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self.transformer_decoder = Decoder(
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dim = dim,
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depth = depth,
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heads = heads,
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attn_dropout = dropout,
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ff_dropout = dropout,
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attn_flash = True)
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# add final dropout
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print('Applying Xavier Uniform Init to x-transformer following torch.Transformer')
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self._apply_xavier_init()
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print('Adding dropout after feedforward layer in x-transformer')
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self._add_dropout_after_ff(dropout)
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print('Adding dropout after attention layer in x-transformer')
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self._add_dropout_after_attn(dropout)
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def _add_dropout_after_attn(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'Attention' in str(type(layer[1])):
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if isinstance(layer[1].to_out, nn.Sequential): # if GLU
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layer[1].to_out.append(nn.Dropout(dropout))
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elif isinstance(layer[1].to_out, nn.Linear): # if simple linear
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layer[1].to_out = nn.Sequential(layer[1].to_out, nn.Dropout(dropout))
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else:
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raise ValueError('to_out should be either nn.Sequential or nn.Linear')
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def _add_dropout_after_ff(self, dropout):
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for layer in self.transformer_decoder.layers:
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if 'FeedForward' in str(type(layer[1])):
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layer[1].ff.append(nn.Dropout(dropout))
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def _apply_xavier_init(self):
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for name, param in self.transformer_decoder.named_parameters():
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if 'to_q' in name or 'to_k' in name or 'to_v' in name:
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torch.nn.init.xavier_uniform_(param, gain=0.5**0.5)
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def forward(self, seq, cache=None,train=False,context=None,context_embedding=None):
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assert context is not None or context_embedding is not None, 'context or context_embedding should be provided for prefix decoder'
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if context_embedding is None:
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