# coding=utf-8 # Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch T5 model. """ import copy import math import os import warnings import torch import torch.nn as nn import torch.nn.functional as F from transformers import logging from transformers.modeling_utils import ( PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer, ) from transformers.file_utils import ( DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from .glen_t5_config import T5Config from .glen_t5_outputs import BaseModelOutputWithPast, Seq2SeqModelOutput logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "T5Config" #################################################### # This dict contrains shortcut names and associated url # for the pretrained weights provided with the models #################################################### T5_PRETRAINED_MODEL_ARCHIVE_LIST = [ "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b", # See all T5 models at https://huggingface.co/models?filter=t5 ] #################################################### # This is a conversion method from TF 1.0 to PyTorch # More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28 #################################################### def load_tf_weights_in_t5(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info("Converting TensorFlow checkpoint from {}".format(tf_path)) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] tf_weights = {} for name, shape in init_vars: logger.info("Loading TF weight {} with shape {}".format(name, shape)) array = tf.train.load_variable(tf_path, name) names.append(name) tf_weights[name] = array for txt_name in names: name = txt_name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in [ "adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", ] for n in name ): logger.info("Skipping {}".format("/".join(name))) tf_weights.pop(txt_name, None) continue if "_slot_" in name[-1]: logger.info("Skipping {}".format("/".join(name))) tf_weights.pop(txt_name, None) continue pointer = model array = tf_weights[txt_name] for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight") # elif scope_names[0] == 'scale': # pointer = getattr(pointer, 'weight') # elif scope_names[0] == 'output_bias' or scope_names[0] == 'beta': # pointer = getattr(pointer, 'bias') # elif scope_names[0] == 'squad': # pointer = getattr(pointer, 'classifier') else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info("Skipping {}".format("/".join(name))) continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if scope_names[0] not in ["kernel", "scale", "embedding"]: pointer = getattr(pointer, "weight") if scope_names[0] != "embedding": logger.info( "Transposing numpy weight of shape {} for {}".format(array.shape, name) ) array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info("Initialize PyTorch weight {}".format(name)) pointer.data = torch.from_numpy(array.astype(np.float32)) tf_weights.pop(txt_name, None) logger.info( "Weights not copied to PyTorch model: {}".format(", ".join(tf_weights.keys())) ) # logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys()))) return model #################################################### # PyTorch Models are constructed by sub-classing # - torch.nn.Module for the layers and # - PreTrainedModel for the models (it-self a sub-class of torch.nn.Module) #################################################### class T5LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """Construct a layernorm module in the T5 style No bias and no substraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, x): # layer norm should always be calculated in float32 variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True) x = x / torch.sqrt(variance + self.variance_epsilon) if self.weight.dtype == torch.float16: x = x.to(torch.float16) return self.weight * x class T5DenseReluDense(nn.Module): def __init__(self, config): super().__init__() self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states): h = self.wi(hidden_states) h = F.relu(h) h = self.dropout(h) h = self.wo(h) return h class T5LayerFF(nn.Module): def __init__(self, config): super().__init__() self.DenseReluDense = T5DenseReluDense(config) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states): norm_x = self.layer_norm(hidden_states) y = self.DenseReluDense(norm_x) layer_output = hidden_states + self.dropout(y) return layer_output class T5Attention(nn.Module): def __init__( self, config: T5Config, has_relative_attention_bias=False, is_bidirectional=False, ): super().__init__() self.is_bidirectional = is_bidirectional 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.d_model = config.d_model self.d_kv = config.d_kv self.n_heads = config.num_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.d_kv # 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.inner_dim, bias=False) self.v = nn.Linear(self.d_model, self.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() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.n_heads, self.d_kv, self.pruned_heads ) # Prune linear layers self.q = prune_linear_layer(self.q, index) self.k = prune_linear_layer(self.k, index) self.v = prune_linear_layer(self.v, index) self.o = prune_linear_layer(self.o, index, dim=1) # Update hyper params self.n_heads = self.n_heads - len(heads) self.inner_dim = self.d_kv * self.n_heads self.pruned_heads = self.pruned_heads.union(heads) @staticmethod def _relative_position_bucket( relative_position, bidirectional=True, num_buckets=32, max_distance=128 ): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on. Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ ret = 0 n = -relative_position if bidirectional: num_buckets //= 2 ret += (n < 0).to( torch.long ) * num_buckets # mtf.to_int32(mtf.less(n, 0)) * num_buckets n = torch.abs(n) else: n = torch.max(n, torch.zeros_like(n)) # now n is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = n < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance val_if_large = max_exact + ( torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) val_if_large = torch.min( val_if_large, torch.full_like(val_if_large, num_buckets - 1) ) ret += torch.where(is_small, n, val_if_large) return ret def compute_bias(self, qlen, klen): """Compute binned relative position bias""" context_position = torch.arange(qlen, dtype=torch.long)[:, None] memory_position = torch.arange(klen, dtype=torch.long)[None, :] relative_position = memory_position - context_position # shape (qlen, klen) rp_bucket = self._relative_position_bucket( relative_position, # shape (qlen, klen) bidirectional=self.is_bidirectional, num_buckets=self.relative_attention_num_buckets, ) rp_bucket = rp_bucket.to(self.relative_attention_bias.weight.device) values = self.relative_attention_bias( rp_bucket ) # shape (qlen, klen, num_heads) values = values.permute([2, 0, 1]).unsqueeze( 0 ) # shape (1, num_heads, qlen, klen) return values def forward( self, input, mask=None, kv=None, position_bias=None, past_key_value=None, head_mask=None, query_length=None, use_cache=False, output_attentions=False, ): """ Self-attention (if kv is None) or attention over source sentence (provided by kv). """ # Input is (bs, qlen, dim) # Mask is (bs, klen) (non-causal) or (bs, klen, klen) # past_key_value[0] is (bs, n_heads, q_len - 1, dim_per_head) bs, qlen, dim = input.size() if past_key_value is not None: assert self.is_decoder is True, "Encoder cannot cache past key value states" assert ( len(past_key_value) == 2 ), "past_key_value should have 2 past states: keys and values. Got {} past states".format( len(past_key_value) ) real_qlen = ( qlen + past_key_value[0].shape[2] if query_length is None else query_length ) else: real_qlen = qlen if kv is None: klen = real_qlen else: klen = kv.size(1) def shape(x): """projection""" return x.view(bs, -1, self.n_heads, self.d_kv).transpose(1, 2) def unshape(x): """compute context""" return x.transpose(1, 2).contiguous().view(bs, -1, self.inner_dim) q = shape(self.q(input)) # (bs, n_heads, qlen, dim_per_head) if kv is None: k = shape(self.k(input)) # (bs, n_heads, qlen, dim_per_head) v = shape(self.v(input)) # (bs, n_heads, qlen, dim_per_head) elif past_key_value is None: k = v = kv k = shape(self.k(k)) # (bs, n_heads, qlen, dim_per_head) v = shape(self.v(v)) # (bs, n_heads, qlen, dim_per_head) if past_key_value is not None: if kv is None: k_, v_ = past_key_value k = torch.cat([k_, k], dim=2) # (bs, n_heads, klen, dim_per_head) v = torch.cat([v_, v], dim=2) # (bs, n_heads, klen, dim_per_head) else: k, v = past_key_value if self.is_decoder and use_cache is True: present_key_value_state = ((k, v),) else: present_key_value_state = (None,) # (bs, n_heads, qlen, klen) scores = torch.matmul( q, k.transpose(3, 2) ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", q, k), compatible with onnx op>9 if position_bias is None: if not self.has_relative_attention_bias: raise ValueError( "No position_bias provided and no weights to compute position_bias" ) position_bias = self.compute_bias(real_qlen, klen) # 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[:, :, -qlen:, :] if mask is not None: position_bias = position_bias + mask # (bs, n_heads, qlen, klen) scores += position_bias weights = F.softmax(scores.float(), dim=-1).type_as( scores ) # (bs, n_heads, qlen, klen) weights = F.dropout( weights, p=self.dropout, training=self.training ) # (bs, n_heads, qlen, klen) # Mask heads if we want to if head_mask is not None: weights = weights * head_mask context = torch.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head) context = unshape(context) # (bs, qlen, dim) context = self.o(context) outputs = (context,) + present_key_value_state if output_attentions: outputs = outputs + (weights,) if self.has_relative_attention_bias: outputs = outputs + (position_bias,) return outputs class T5LayerSelfAttention(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.SelfAttention = T5Attention( config, has_relative_attention_bias=has_relative_attention_bias, is_bidirectional=not config.is_decoder, ) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, attention_mask=None, position_bias=None, head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): norm_x = self.layer_norm(hidden_states) attention_output = self.SelfAttention( norm_x, mask=attention_mask, position_bias=position_bias, head_mask=head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) y = attention_output[0] layer_output = hidden_states + self.dropout(y) outputs = (layer_output,) + attention_output[ 1: ] # add attentions if we output them return outputs class T5LayerCrossAttention(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.EncDecAttention = T5Attention( config, has_relative_attention_bias=has_relative_attention_bias, is_bidirectional=True, ) self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, kv, attention_mask=None, position_bias=None, head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False, ): norm_x = self.layer_norm(hidden_states) attention_output = self.EncDecAttention( norm_x, mask=attention_mask, kv=kv, position_bias=position_bias, head_mask=head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions, ) y = attention_output[0] layer_output = hidden_states + self.dropout(y) outputs = (layer_output,) + attention_output[ 1: ] # add attentions if we output them return outputs class T5Block(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.layer = nn.ModuleList() self.layer.append( T5LayerSelfAttention( config, has_relative_attention_bias=has_relative_attention_bias ) ) if self.is_decoder: self.layer.append( T5LayerCrossAttention( config, has_relative_attention_bias=has_relative_attention_bias ) ) self.layer.append(T5LayerFF(config)) def forward( self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): if past_key_value is not None: assert self.is_decoder, "Only decoder can use `past_key_values`" expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format( expected_num_past_key_values, "2 (past / key) for cross attention" if expected_num_past_key_values == 4 else "", len(past_key_value), ) assert len(past_key_value) == expected_num_past_key_values, error_message 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 self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, head_mask=head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present_key_value_state = self_attention_outputs[:2] attention_outputs = self_attention_outputs[ 2: ] # Keep self-attention outputs and relative position weights if self.is_decoder and encoder_hidden_states is not None: # 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]( hidden_states, kv=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, head_mask=head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = cross_attention_outputs[0] # 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:] # Apply Feed Forward layer hidden_states = self.layer[-1](hidden_states) outputs = (hidden_states,) # Add attentions if we output them outputs = outputs + (present_key_value_state,) + attention_outputs return outputs # hidden-states, present_key_value_states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) class T5PreTrainedModel(PreTrainedModel): """An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = T5Config load_tf_weights = load_tf_weights_in_t5 base_model_prefix = "transformer" @property def dummy_inputs(self): input_ids = torch.tensor(DUMMY_INPUTS) input_mask = torch.tensor(DUMMY_MASK) dummy_inputs = { "decoder_input_ids": input_ids, "input_ids": input_ids, "decoder_attention_mask": input_mask, } return dummy_inputs def _init_weights(self, module): """Initialize the weights""" from tevatron.modeling import ( T5ForConditionalGeneration_GLEN as T5ForConditionalGeneration, ) factor = ( self.config.initializer_factor ) # Used for testing weights initialization if isinstance(module, T5LayerNorm): module.weight.data.fill_(factor * 1.0) elif isinstance(module, (T5Model, T5ForConditionalGeneration)): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, T5DenseReluDense): # Mesh TensorFlow FF initialization # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 module.wi.weight.data.normal_( mean=0.0, std=factor * ((self.config.d_model) ** -0.5) ) if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_() module.wo.weight.data.normal_( mean=0.0, std=factor * ((self.config.d_ff) ** -0.5) ) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, T5Attention): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model d_kv = self.config.d_kv n_heads = self.config.num_heads module.q.weight.data.normal_( mean=0.0, std=factor * ((d_model * d_kv) ** -0.5) ) module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.o.weight.data.normal_( mean=0.0, std=factor * ((n_heads * d_kv) ** -0.5) ) if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_( mean=0.0, std=factor * ((d_model) ** -0.5) ) def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id assert ( decoder_start_token_id is not None ), "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id. See T5 docs for more information" # shift inputs to the right shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id assert ( pad_token_id is not None ), "self.model.config.pad_token_id has to be defined." # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) assert torch.all( shifted_input_ids >= 0 ).item(), "Verify that `shifted_input_ids` has only positive values" return shifted_input_ids class T5Stack(T5PreTrainedModel): def __init__(self, config, embed_tokens=None): super().__init__(config) self.embed_tokens = embed_tokens self.is_decoder = config.is_decoder self.block = nn.ModuleList( [ T5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers) ] ) self.final_layer_norm = T5LayerNorm( config.d_model, eps=config.layer_norm_epsilon ) self.dropout = nn.Dropout(config.dropout_rate) self.init_weights() def get_input_embeddings(self): return self.embed_tokens def get_output_embeddings(self): return self.embed_tokens def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): 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}inputs 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}inputs or {err_msg_prefix}inputs_embeds" ) if inputs_embeds is None: assert ( self.embed_tokens is not None ), "You have to intialize the model with valid token embeddings" if self.training and self.is_decoder and len(input_ids) == 2: inputs_embeds = self.embed_tokens(input_ids[0]) else: 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: assert ( self.is_decoder ), ":obj:`use_cache` can only be set to `True` if {} is used as a decoder".format( self ) if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length).to( 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) # 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, inputs_embeds.device ) # print(f"extended_attention_mask: {extended_attention_mask.shape}") if self.is_decoder and encoder_attention_mask is not None: encoder_extended_attention_mask = self.invert_attention_mask( encoder_attention_mask ) else: encoder_extended_attention_mask = None # Prepare head mask if needed head_mask = self.get_head_mask(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 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) ): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = layer_module( hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, head_mask=head_mask[i], 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 weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) hidden_states, present_key_value_state = layer_outputs[:2] if i == 0: # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) position_bias = layer_outputs[3 if output_attentions else 2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[ 5 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[2], ) # We keep only self-attention weights for now 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, ] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, ) T5_START_DOCSTRING = r""" The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer `__ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting. This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch `torch.nn.Module `__ subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config (:class:`~transformers.T5Config`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. """ T5_INPUTS_DOCSTRING = r""" Args: input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using :class:`~transformers.T5Tokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for detail. To know more on how to prepare :obj:`input_ids` for pretraining take a look a `T5 Training <./t5.html#training>`__. attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. `What are attention masks? <../glossary.html#attention-mask>`__ decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`): Provide for sequence to sequence training. T5 uses the :obj:`pad_token_id` as the starting token for :obj:`decoder_input_ids` generation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_input_ids` have to be input (see :obj:`past_key_values`). To know more on how to prepare :obj:`decoder_input_ids` for pretraining take a look at `T5 Training <./t5.html#training>`__. If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_input_ids` takes the value of :obj:`input_ids`. decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`): Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will also be used by default. encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`): Tuple consists of (:obj:`last_hidden_state`, :obj:`optional`: `hidden_states`, :obj:`optional`: `attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`): Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert :obj:`input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`): Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds` have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert :obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds` takes the value of :obj:`inputs_embeds`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). output_attentions (:obj:`bool`, `optional`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. """ @add_start_docstrings( "The bare T5 Model transformer outputting raw hidden-states" "without any specific head on top.", T5_START_DOCSTRING, ) class T5Model(T5PreTrainedModel): def __init__(self, config: T5Config): super().__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers if self.multiple_decoder: self.decoder_list = [] for i in range(self.decoder_num): self.decoder_list.append(T5Stack(decoder_config, self.shared)) else: self.decoder = T5Stack(decoder_config, self.shared) self.init_weights() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def _prune_heads(self, heads_to_prune): """Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC ) def forward( self, input_ids=None, input_mask=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, only_encoder=False, past_key_values=None, head_mask=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs, ): r""" Returns: Example:: >>> from transformers import T5Tokenizer, T5Model >>> tokenizer = T5Tokenizer.from_pretrained('t5-small') >>> model = T5Model.from_pretrained('t5-small') >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids # Batch size 1 >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, return_dict=True) >>> last_hidden_states = outputs.last_hidden_state """ if "decoder_past_key_value_states" in kwargs: warnings.warn( "The `decoder_past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = kwargs.pop("decoder_past_key_value_states") if "decoder_past_key_values" in kwargs: warnings.warn( "The `decoder_past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = kwargs.pop("decoder_past_key_values") assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}." 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 ) # Encode if needed (training, first prediction pass) if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): # encoder_outputs = BaseModelOutput( # last_hidden_state=encoder_outputs[0], # hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, # attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, # ) if only_encoder: return encoder_outputs hidden_states = encoder_outputs[0] # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class HierarchicT5Stack(T5PreTrainedModel): def __init__(self, config, embed_tokens=None, depth=1): super().__init__(config) self.embed_tokens = embed_tokens self.depth = depth self.stacks = nn.ModuleList( [T5Stack(config, embed_tokens) for _ in range(depth)] ) for stack in self.stacks: stack.init_weights() def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): is_train = input_ids.shape[1] > 1 and past_key_values is None if is_train: assert input_ids.shape[1] <= self.depth max_depth = input_ids.shape[1] outputs = [ self.stacks[i]( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) for i in range(max_depth) ] final_output = outputs[max_depth - 1] for i in range(max_depth - 1): final_output[0][:, i, :] = outputs[i][0][:, i, :] else: cur_depth = 0 if past_key_values is not None: cur_depth = past_key_values[0][0].shape[2] # print(f"generate for {cur_depth}th token") final_output = self.stacks[cur_depth]( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) return final_output