import copy from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import CrossEntropyLoss from transformers.models.t5 import modeling_t5 from transformers.modeling_outputs import CausalLMOutputWithPast from transformers.utils import ( add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from decoder_only_t5.config import DecoderOnlyT5Config logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DecoderOnlyT5Config" class DecoderOnlyT5LayerFF(modeling_t5.T5LayerFF): def __init__(self, config: DecoderOnlyT5Config): super(modeling_t5.T5LayerFF, self).__init__() if config.is_gated_act: self.DenseReluDense = modeling_t5.T5DenseGatedActDense(config) else: self.DenseReluDense = modeling_t5.T5DenseActDense(config) if not config.parallel_layers: self.layer_norm = modeling_t5.T5LayerNorm( config.d_model, eps=config.layer_norm_epsilon ) else: self.layer_norm = nn.Identity() self.dropout = nn.Dropout(config.dropout_rate) # LlamaRotaryEmbedding class T5DecoderOnlyRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / ( self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) ) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(), ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange( self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype ) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed # https://github.com/huggingface/transformers/blob/7ee995fd9c692761c4601ddbffa2ac2ec9f27b0b/src/transformers/models/llama/modeling_llama.py#L263 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand( batch, num_key_value_heads, n_rep, slen, head_dim ) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class DecoderOnlyT5Attention(modeling_t5.T5Attention): """ Supports both multi-head and multi-query attention. https://arxiv.org/abs/1911.02150 https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/components/attention/dense_attention.py#L292 """ def __init__(self, config: DecoderOnlyT5Config, has_relative_attention_bias=False): super(modeling_t5.T5Attention, self).__init__() self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.relative_attention_num_buckets = config.relative_attention_num_buckets self.relative_attention_max_distance = config.relative_attention_max_distance self.d_model = config.d_model self.key_value_proj_dim = config.d_kv self.n_heads = config.num_heads self.n_kv_heads = 1 if config.multi_query_attention else self.n_heads self.n_kv_groups = self.n_heads // self.n_kv_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.key_value_proj_dim self.kv_inner_dim = self.n_kv_heads * self.key_value_proj_dim if config.use_rotary_embedding: self.rotary_embedding = T5DecoderOnlyRotaryEmbedding( self.key_value_proj_dim, max_position_embeddings=config.relative_attention_max_distance, base=config.rotary_embedding_max_timescale, ) else: self.rotary_embedding = None # Mesh TensorFlow initialization to avoid scaling before softmax self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) self.k = nn.Linear(self.d_model, self.kv_inner_dim, bias=False) self.v = nn.Linear(self.d_model, self.kv_inner_dim, bias=False) self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding( self.relative_attention_num_buckets, self.n_heads ) self.pruned_heads = set() self.gradient_checkpointing = False def forward( self, hidden_states, mask=None, key_value_states=None, position_bias=None, position_ids=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False, ): """ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). """ # Input is (batch_size, seq_length, dim) # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) # past_key_value[0] is (batch_size, n_kv_heads, q_len - 1, dim_per_head) batch_size, seq_length = hidden_states.shape[:2] real_seq_length = seq_length if past_key_value is not None: if len(past_key_value) != 2: raise ValueError( f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states" ) real_seq_length += ( past_key_value[0].shape[2] if query_length is None else query_length ) key_length = ( real_seq_length if key_value_states is None else key_value_states.shape[1] ) def shape(states, n_heads): """projection""" return states.view( batch_size, -1, n_heads, self.key_value_proj_dim ).transpose(1, 2) def unshape(states): """reshape""" return ( states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) ) def project(hidden_states, proj_layer, key_value_states, past_key_value): """projects hidden states correctly to key/query states""" if key_value_states is None: # self-attn # (batch_size, n_kv_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(hidden_states), self.n_kv_heads) elif past_key_value is None: # cross-attn # (batch_size, n_kv_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states), self.n_kv_heads) return hidden_states def concat_past_key_value(hidden_states, past_key_value, key_value_states): if key_value_states is None: # self-attn # (batch_size, n_kv_heads, key_length, dim_per_head) hidden_states = torch.cat([past_key_value, hidden_states], dim=2) elif past_key_value.shape[2] != key_value_states.shape[1]: # checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning # cross-attn # (batch_size, n_kv_heads, seq_length, dim_per_head) raise NotImplementedError( "cross attention with RoPE and past KV is not implemented" ) # hidden_states = shape(proj_layer(key_value_states), self.n_kv_heads) else: # cross-attn hidden_states = past_key_value return hidden_states # get query states query_states = shape( self.q(hidden_states), self.n_heads ) # (batch_size, n_heads, seq_length, dim_per_head) # get key/value states key_states = project(hidden_states, self.k, key_value_states, past_key_value) value_states = project(hidden_states, self.v, key_value_states, past_key_value) # RoPE if self.rotary_embedding is not None: kv_seq_len = key_states.shape[-2] if past_key_value: kv_seq_len += past_key_value[0].shape[-2] cos, sin = self.rotary_embedding(query_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb( query_states, key_states, cos, sin, position_ids ) # concat past if past_key_value is not None: key_states = concat_past_key_value( key_states, past_key_value[0], key_value_states, ) value_states = concat_past_key_value( value_states, past_key_value[1], key_value_states, ) # MultiQueryDotProductAttention key_states = repeat_kv(key_states, self.n_kv_groups) value_states = repeat_kv(value_states, self.n_kv_groups) # compute scores scores = torch.matmul( query_states, key_states.transpose(3, 2) ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 if position_bias is None: if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype, ) if self.gradient_checkpointing and self.training: position_bias.requires_grad = True else: position_bias = self.compute_bias( real_seq_length, key_length, device=scores.device ) # if key and values are already calculated # we want only the last query position bias if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :] if mask is not None: position_bias = ( position_bias + mask ) # (batch_size, n_heads, seq_length, key_length) if self.pruned_heads: mask = torch.ones(position_bias.shape[1]) mask[list(self.pruned_heads)] = 0 position_bias_masked = position_bias[:, mask.bool()] else: position_bias_masked = position_bias scores += position_bias_masked attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as( scores ) # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.dropout( attn_weights, p=self.dropout, training=self.training ) # (batch_size, n_heads, seq_length, key_length) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = unshape( torch.matmul(attn_weights, value_states) ) # (batch_size, seq_length, dim) attn_output = self.o(attn_output) present_key_value_state = ( (key_states, value_states) if (self.is_decoder and use_cache) else None ) outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) if output_attentions: outputs = outputs + (attn_weights,) return outputs class DecoderOnlyT5LayerSelfAttention(modeling_t5.T5LayerSelfAttention): def __init__(self, config, has_relative_attention_bias=False): super(modeling_t5.T5LayerSelfAttention, self).__init__() self.SelfAttention = DecoderOnlyT5Attention( config, has_relative_attention_bias=has_relative_attention_bias ) self.layer_norm = modeling_t5.T5LayerNorm( config.d_model, eps=config.layer_norm_epsilon ) self.dropout = nn.Dropout(config.dropout_rate) self.parallel_layers = config.parallel_layers def forward( self, hidden_states, attention_mask=None, position_bias=None, position_ids=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): if not self.parallel_layers: x = self.layer_norm(hidden_states) else: x = hidden_states attention_output = self.SelfAttention( x, mask=attention_mask, position_bias=position_bias, position_ids=position_ids, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) if not self.parallel_layers: # When parallel_layers is True, the residual connection is applied # in the decoder block instead of here. hidden_states = hidden_states + self.dropout(attention_output[0]) else: hidden_states = attention_output[0] outputs = (hidden_states,) + attention_output[ 1: ] # add attentions if we output them return outputs class DecoderOnlyT5Block(modeling_t5.T5Block): def __init__(self, config, has_relative_attention_bias=False): super(modeling_t5.T5Block, self).__init__() self.is_decoder = config.is_decoder self.is_decoder_only = config.is_decoder_only self.layer = nn.ModuleList() self.layer.append( DecoderOnlyT5LayerSelfAttention( config, has_relative_attention_bias=has_relative_attention_bias ) ) if self.is_decoder: if config.is_decoder_only: self.layer.append(nn.Identity()) else: self.layer.append(modeling_t5.T5LayerCrossAttention(config)) self.parallel_layers = config.parallel_layers self.layer.append(DecoderOnlyT5LayerFF(config)) def forward( self, hidden_states, attention_mask=None, position_bias=None, position_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True, ): if past_key_value is not None: if not self.is_decoder: logger.warning( "`past_key_values` is passed to the encoder. Please make sure this is intended." ) expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 if len(past_key_value) != expected_num_past_key_values: raise ValueError( f"There should be {expected_num_past_key_values} past states. " f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}" f"Got {len(past_key_value)} past key / value states" ) self_attn_past_key_value = past_key_value[:2] cross_attn_past_key_value = past_key_value[2:] else: self_attn_past_key_value, cross_attn_past_key_value = None, None ff_layer = self.layer[-1] if self.parallel_layers: # https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/architectures/t5/t5_architecture.py#L563-L568 x = self.layer[0].layer_norm(hidden_states) ff_output = ff_layer(x) else: x = hidden_states self_attention_outputs = self.layer[0]( x, attention_mask=attention_mask, position_bias=position_bias, position_ids=position_ids, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) x, present_key_value_state = self_attention_outputs[:2] attention_outputs = self_attention_outputs[ 2: ] # Keep self-attention outputs and relative position weights # clamp inf values to enable fp16 training if x.dtype == torch.float16: clamp_value = torch.where( torch.isinf(x).any(), torch.finfo(x.dtype).max - 1000, torch.finfo(x.dtype).max, ) x = torch.clamp(x, min=-clamp_value, max=clamp_value) do_cross_attention = ( self.is_decoder and not self.is_decoder_only and encoder_hidden_states is not None ) if do_cross_attention: # the actual query length is unknown for cross attention # if using past key value states. Need to inject it here if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2] else: query_length = None cross_attention_outputs = self.layer[1]( x, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, # position_ids ? layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions, ) x = cross_attention_outputs[0] # clamp inf values to enable fp16 training if x.dtype == torch.float16: clamp_value = torch.where( torch.isinf(x).any(), torch.finfo(x.dtype).max - 1000, torch.finfo(x.dtype).max, ) x = torch.clamp(x, min=-clamp_value, max=clamp_value) # Combine self attn and cross attn key value states if present_key_value_state is not None: present_key_value_state = ( present_key_value_state + cross_attention_outputs[1] ) # Keep cross-attention outputs and relative position weights attention_outputs = attention_outputs + cross_attention_outputs[2:] if self.parallel_layers: # https://github.com/google/flaxformer/blob/ea17eb012a1d340ddff017b7a534c2162aaec34c/flaxformer/architectures/t5/t5_architecture.py#L534-L578 x = x + ff_output x *= 2**-0.5 hidden_states = hidden_states + self.layer[0].dropout(x) else: hidden_states = ff_layer(x) # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp( hidden_states, min=-clamp_value, max=clamp_value ) outputs = (hidden_states,) if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs else: outputs = outputs + attention_outputs return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) class DecoderOnlyT5Stack(modeling_t5.T5Stack): def __init__(self, config, embed_tokens=None): super(modeling_t5.T5Stack, self).__init__(config) self.embed_tokens = embed_tokens self.is_decoder = config.is_decoder self.block = nn.ModuleList( [ DecoderOnlyT5Block( config, has_relative_attention_bias=( config.has_relative_attention_bias and bool(i == 0) ), ) for i in range(config.num_layers) ] ) if not config.parallel_layers: self.final_layer_norm = modeling_t5.T5LayerNorm( config.d_model, eps=config.layer_norm_epsilon ) else: self.final_layer_norm = nn.Identity() self.dropout = nn.Dropout(config.dropout_rate) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False def forward( self, input_ids=None, position_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): # Model parallel if self.model_parallel: torch.cuda.set_device(self.first_device) self.embed_tokens = self.embed_tokens.to(self.first_device) use_cache = use_cache if use_cache is not None else self.config.use_cache output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds" ) if position_ids is None: seq_length = input_ids.shape[1] past_key_values_length = ( 0 if past_key_values is None else past_key_values[0][0].shape[2] ) device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device, ) position_ids = position_ids.unsqueeze(0) if inputs_embeds is None: if self.embed_tokens is None: raise ValueError( "You have to initialize the model with valid token embeddings" ) inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape # required mask seq length can be calculated via length of past mask_seq_length = ( past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length ) if use_cache is True: if not self.is_decoder: raise ValueError( f"`use_cache` can only be set to `True` if {self} is used as a decoder" ) if attention_mask is None: attention_mask = torch.ones( batch_size, mask_seq_length, device=inputs_embeds.device ) if ( self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None ): encoder_seq_length = encoder_hidden_states.shape[1] encoder_attention_mask = torch.ones( batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long, ) # initialize past_key_values with `None` if past does not exist if past_key_values is None: past_key_values = [None] * len(self.block) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask = self.get_extended_attention_mask( attention_mask, input_shape ) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.is_decoder and encoder_hidden_states is not None: ( encoder_batch_size, encoder_sequence_length, _, ) = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones( encoder_hidden_shape, device=inputs_embeds.device ) encoder_extended_attention_mask = self.invert_attention_mask( encoder_attention_mask ) else: encoder_extended_attention_mask = None if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.num_layers) cross_attn_head_mask = self.get_head_mask( cross_attn_head_mask, self.config.num_layers ) present_key_value_states = () if use_cache else None all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if (output_attentions and self.is_decoder) else None position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(inputs_embeds) for i, (layer_module, past_key_value) in enumerate( zip(self.block, past_key_values) ): layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) # Ensure that attention_mask is always on the same device as hidden_states if attention_mask is not None: attention_mask = attention_mask.to(hidden_states.device) if position_bias is not None: position_bias = position_bias.to(hidden_states.device) if encoder_hidden_states is not None: encoder_hidden_states = encoder_hidden_states.to( hidden_states.device ) if encoder_extended_attention_mask is not None: encoder_extended_attention_mask = ( encoder_extended_attention_mask.to(hidden_states.device) ) if encoder_decoder_position_bias is not None: encoder_decoder_position_bias = encoder_decoder_position_bias.to( hidden_states.device ) if layer_head_mask is not None: layer_head_mask = layer_head_mask.to(hidden_states.device) if cross_attn_layer_head_mask is not None: cross_attn_layer_head_mask = cross_attn_layer_head_mask.to( hidden_states.device ) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.forward, hidden_states, extended_attention_mask, position_bias, encoder_hidden_states, encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, # past_key_value is always None with gradient checkpointing use_cache, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, position_ids=position_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask, cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) # layer_outputs is a tuple with: # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, present_key_value_state = layer_outputs[:2] # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[ 4 if output_attentions else 3 ] # append next layer key value states if use_cache: present_key_value_states = present_key_value_states + ( present_key_value_state, ) if output_attentions: all_attentions = all_attentions + (layer_outputs[3],) if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],) # Model Parallel: If it's the last layer for that device, put things on the next device if self.model_parallel: for k, v in self.device_map.items(): if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1)) hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions, ] if v is not None ) return modeling_t5.BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) class DecoderOnlyT5Model(modeling_t5.T5ForConditionalGeneration): def __init__(self, config: DecoderOnlyT5Config): super(modeling_t5.T5ForConditionalGeneration, self).__init__(config) self.model_dim = config.d_model self.shared = nn.Embedding(config.vocab_size, config.d_model) assert ( self.config.num_layers == 0 ), "Decoder only model cannot have encoder layers" self.encoder = None decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = DecoderOnlyT5Stack(decoder_config, self.shared) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None def _tie_weights(self): if not self.config.tie_word_embeddings: return if self.encoder: self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared) if self.decoder: self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared) @add_start_docstrings_to_model_forward(modeling_t5.T5_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC ) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: Examples: ```""" use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) if input_ids is not None: input_ids = input_ids.to(self.decoder.first_device) if attention_mask is not None: attention_mask = attention_mask.to(self.decoder.first_device) # Decode outputs = self.decoder( input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_head_mask=None, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) self.lm_head = self.lm_head.to(self.decoder.first_device) sequence_output = sequence_output.to(self.lm_head.weight.device) if self.config.tie_word_embeddings: # Rescale output before projecting on vocab # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 sequence_output = sequence_output * (self.model_dim**-0.5) lm_logits = self.lm_head(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-100) # move labels to correct device to enable PP labels = labels.to(lm_logits.device) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666 if not return_dict: output = (lm_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )