# 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. """ from __future__ import absolute_import, division, print_function, unicode_literals import copy import itertools import json import logging import math import os import sys from io import open import torch import torch.nn.functional as F from torch import nn from torch.nn import CrossEntropyLoss, MSELoss from .configuration_t5 import T5Config from .file_utils import DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings from .modeling_utils import PreTrainedModel, prune_linear_layer logger = logging.getLogger(__name__) #################################################### # This dict contrains shortcut names and associated url # for the pretrained weights provided with the models #################################################### T5_PRETRAINED_MODEL_ARCHIVE_MAP = { "t5-small": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-pytorch_model.bin", "t5-base": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-pytorch_model.bin", "t5-large": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-pytorch_model.bin", "t5-3b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-pytorch_model.bin", "t5-11b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-pytorch_model.bin", } #################################################### # 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", "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 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(T5LayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, x): variance = x.pow(2).mean(-1, keepdim=True) x = x / torch.sqrt(variance + self.variance_epsilon) return self.weight * x class T5DenseReluDense(nn.Module): def __init__(self, config): super(T5DenseReluDense, self).__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(T5LayerFF, self).__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): NEW_ID = itertools.count() def __init__(self, config, has_relative_attention_bias=False): super(T5Attention, self).__init__() self.layer_id = next(T5Attention.NEW_ID) self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.output_attentions = config.output_attentions 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 mask = torch.ones(self.n_heads, self.d_kv) heads = set(heads) - self.pruned_heads for head in heads: head -= sum(1 if h < head else 0 for h in self.pruned_heads) mask[head] = 0 mask = mask.view(-1).contiguous().eq(1) index = torch.arange(len(mask))[mask].long() # 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=not self.is_decoder, num_buckets=self.relative_attention_num_buckets, ) 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, cache=None, head_mask=None): """ 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) bs, qlen, dim = input.size() if kv is None: klen = qlen if cache is None else cache["slen"] + 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 cache is None or self.layer_id not in cache: 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 cache is not None: if self.layer_id in cache: if kv is None: k_, v_ = cache[self.layer_id] 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 = cache[self.layer_id] cache[self.layer_id] = (k, v) # q = q / math.sqrt(dim_per_head) # No scaling in T5 scores = torch.einsum("bnqd,bnkd->bnqk", q, k) # (bs, n_heads, qlen, klen) 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(qlen, klen) 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,) if self.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(T5LayerSelfAttention, self).__init__() self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias) 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): norm_x = self.layer_norm(hidden_states) attention_output = self.SelfAttention( norm_x, mask=attention_mask, position_bias=position_bias, head_mask=head_mask ) 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(T5LayerCrossAttention, self).__init__() self.EncDecAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias) 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): 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 ) 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(T5Block, self).__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)) else: 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, ): self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, head_mask=head_mask ) hidden_states = self_attention_outputs[0] outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights if not self.is_decoder: hidden_states = self.layer[1](hidden_states) else: 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, ) hidden_states = cross_attention_outputs[0] outputs = ( outputs + cross_attention_outputs[1:] ) # Keep cross-attention outputs and relative position weights hidden_states = self.layer[2](hidden_states) outputs = (hidden_states,) + outputs # add attentions if we output them return outputs # hidden-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 dowloading and loading pretrained models. """ config_class = T5Config pretrained_model_archive_map = T5_PRETRAINED_MODEL_ARCHIVE_MAP 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, "encoder_input_ids": input_ids, "decoder_attention_mask": input_mask, } return dummy_inputs def _init_weights(self, module): """ Initialize the weights """ 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, T5WithLMHeadModel)): # 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)) class T5Stack(T5PreTrainedModel): def __init__(self, config): super(T5Stack, self).__init__(config) self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states 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 forward( self, hidden_states, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, ): batch_size, seq_length = hidden_states.shape[0], hidden_states.shape[1] if attention_mask is None: attention_mask = torch.ones(batch_size, seq_length).to(hidden_states.device) if self.is_decoder and encoder_attention_mask is None: encoder_seq_length = encoder_hidden_states.shape[1] encoder_attention_mask = torch.ones(batch_size, encoder_seq_length).to(hidden_states.device) # 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. if attention_mask.dim() == 3: extended_attention_mask = attention_mask[:, None, :, :] elif attention_mask.dim() == 2: # Provided a padding mask of dimensions [batch_size, seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder: seq_ids = torch.arange(seq_length, device=hidden_states.device) causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] causal_mask = causal_mask.to(attention_mask) extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] else: extended_attention_mask = attention_mask[:, None, None, :] # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -1e9 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # extended_attention_mask = (extended_attention_mask == extended_attention_mask.transpose(-1, -2)) extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -1e9 if self.is_decoder: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length] if encoder_attention_mask.dim() == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if encoder_attention_mask.dim() == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = (encoder_extended_attention_mask == encoder_extended_attention_mask.transpose(-1, -2)) encoder_extended_attention_mask = encoder_extended_attention_mask.to( dtype=next(self.parameters()).dtype ) # fp16 compatibility encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9 else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.expand(self.config.num_layers, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = ( head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) ) # We can specify head_mask for each layer head_mask = head_mask.to( dtype=next(self.parameters()).dtype ) # switch to fload if need + fp16 compatibility else: head_mask = [None] * self.config.num_layers all_hidden_states = () all_attentions = () position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(hidden_states) for i, layer_module in enumerate(self.block): if self.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], ) # layer_outputs is a tuple with: # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) hidden_states = layer_outputs[0] if i == 0: # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias) position_bias = layer_outputs[2 if self.output_attentions else 1] if self.is_decoder: encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2] if self.output_attentions: all_attentions = all_attentions + (layer_outputs[1],) # We keep only self-attention weights for now hidden_states = self.final_layer_norm(hidden_states) layer_output = self.dropout(hidden_states) # Add last layer if self.output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = (hidden_states,) if self.output_hidden_states: outputs = outputs + (all_hidden_states,) if self.output_attentions: outputs = outputs + (all_attentions,) return outputs # last-layer hidden state, (all hidden states), (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 is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`: https://arxiv.org/abs/1910.10683 .. _`torch.nn.Module`: https://pytorch.org/docs/stable/nn.html#module 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""" Inputs: **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Indices of input sequence tokens in the vocabulary. To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows: (a) For sequence pairs: ``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]`` (b) For single sequences: ``tokens: [CLS] the dog is hairy . [SEP]`` T5 is a model with relative position embeddings so you should be able to pad the inputs on the right or the left. Indices can be obtained using :class:`transformers.T5Tokenizer`. See :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``: Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``: 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**. """ @add_start_docstrings( "The bare T5 Model transformer outputting raw hidden-states" "without any specific head on top.", T5_START_DOCSTRING, T5_INPUTS_DOCSTRING, ) class T5Model(T5PreTrainedModel): r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)`` Sequence of hidden-states at the output of the last layer of the model. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: tokenizer = T5Tokenizer.from_pretrained('t5-small') model = T5Model.from_pretrained('t5-small') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 outputs = model(input_ids=input_ids) last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple """ def __init__(self, config): super(T5Model, self).__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) self.encoder = T5Stack(encoder_config) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True self.decoder = T5Stack(decoder_config) self.init_weights() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings 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) def forward(self, **kwargs): # keyword arguments come in 3 flavors: encoder-specific (prefixed by # `encoder_`), decoder-specific (prefixed by `decoder_`) and those # that apply to the model as whole. # We let the specific kwargs override the common ones in case of conflict. kwargs_common = dict( (k, v) for k, v in kwargs.items() if not k.startswith("encoder_") and not k.startswith("decoder_") ) kwargs_encoder = kwargs_common.copy() kwargs_decoder = kwargs_common.copy() kwargs_encoder.update(dict((k[len("encoder_") :], v) for k, v in kwargs.items() if k.startswith("encoder_"))) kwargs_decoder.update(dict((k[len("decoder_") :], v) for k, v in kwargs.items() if k.startswith("decoder_"))) # Encode if needed (training, first prediction pass) encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) encoder_attention_mask = kwargs_encoder.get("attention_mask", None) if encoder_hidden_states is None: # Convert encoder inputs in embeddings if needed hidden_states = kwargs_encoder.pop("inputs_embeds", None) if hidden_states is None: encoder_inputs_ids = kwargs_encoder.pop("input_ids") hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings if encoder_attention_mask is not None: # Apply masking encoder_attention_mask = (encoder_attention_mask != 0).to(hidden_states) hidden_states = hidden_states * encoder_attention_mask.unsqueeze(-1) encoder_outputs = self.encoder(hidden_states, **kwargs_encoder) encoder_hidden_states = encoder_outputs[0] else: encoder_outputs = () # Decode # Convert decoder inputs in embeddings if needed hidden_states = kwargs_decoder.pop("inputs_embeds", None) if hidden_states is None: decoder_inputs_ids = kwargs_decoder.pop("input_ids") hidden_states = self.shared(decoder_inputs_ids) kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states kwargs_decoder["encoder_attention_mask"] = encoder_attention_mask decoder_outputs = self.decoder(hidden_states, **kwargs_decoder) return decoder_outputs + encoder_outputs @add_start_docstrings("""T5 Model with a `language modeling` head on top. """, T5_START_DOCSTRING, T5_INPUTS_DOCSTRING) class T5WithLMHeadModel(T5PreTrainedModel): r""" **lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Labels for computing the masked language modeling loss. Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]`` Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Masked language modeling loss. **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``: Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: tokenizer = T5Tokenizer.from_pretrained('t5-small') model = T5WithLMHeadModel.from_pretrained('t5-small') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 outputs = model(input_ids=input_ids, lm_labels=input_ids) loss, prediction_scores = outputs[:2] """ def __init__(self, config): super(T5WithLMHeadModel, self).__init__(config) self.model_dim = config.d_model self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) self.encoder = T5Stack(encoder_config) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True self.decoder = T5Stack(decoder_config) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) self.init_weights() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings def get_output_embeddings(self): return self.lm_head def forward(self, **kwargs): # keyword arguments come in 3 flavors: encoder-specific (prefixed by # `encoder_`), decoder-specific (prefixed by `decoder_`) and those # that apply to the model as whole. # We let the specific kwargs override the common ones in case of conflict. lm_labels = kwargs.pop("decoder_lm_labels", None) kwargs_common = dict( (k, v) for k, v in kwargs.items() if not k.startswith("encoder_") and not k.startswith("decoder_") ) kwargs_encoder = kwargs_common.copy() kwargs_decoder = kwargs_common.copy() kwargs_encoder.update(dict((k[len("encoder_") :], v) for k, v in kwargs.items() if k.startswith("encoder_"))) kwargs_decoder.update(dict((k[len("decoder_") :], v) for k, v in kwargs.items() if k.startswith("decoder_"))) # Encode if needed (training, first prediction pass) encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) if encoder_hidden_states is None: # Convert encoder inputs in embeddings if needed hidden_states = kwargs_encoder.pop("inputs_embeds", None) if hidden_states is None: encoder_inputs_ids = kwargs_encoder.pop("input_ids") hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings encoder_outputs = self.encoder(hidden_states, **kwargs_encoder) encoder_hidden_states = encoder_outputs[0] else: encoder_outputs = () # Decode # Convert decoder inputs in embeddings if needed hidden_states = kwargs_decoder.pop("inputs_embeds", None) if hidden_states is None: decoder_inputs_ids = kwargs_decoder.pop("input_ids") hidden_states = self.shared(decoder_inputs_ids) kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None) decoder_outputs = self.decoder(hidden_states, **kwargs_decoder) sequence_output = decoder_outputs[0] # 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) decoder_outputs = (lm_logits,) + decoder_outputs[1:] # Add hidden states and attention if they are here if lm_labels is not None: shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = lm_labels[..., 1:].contiguous() loss_fct = CrossEntropyLoss(ignore_index=-1) loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) decoder_outputs = ( loss, ) + decoder_outputs # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666 return decoder_outputs + encoder_outputs