# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # 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 Transformer XL model. Adapted from https://github.com/kimiyoung/transformer-xl. In particular https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py """ import warnings from dataclasses import dataclass from typing import List, Optional, Tuple, Union import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_transfo_xl import TransfoXLConfig from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "transfo-xl-wt103" _CONFIG_FOR_DOC = "TransfoXLConfig" TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [ "transfo-xl-wt103", # See all Transformer XL models at https://huggingface.co/models?filter=transfo-xl ] def build_tf_to_pytorch_map(model, config): """ A map of modules from TF to PyTorch. This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible. """ tf_to_pt_map = {} if hasattr(model, "transformer"): # We are loading in a TransfoXLLMHeadModel => we will load also the Adaptive Softmax tf_to_pt_map.update( { "transformer/adaptive_softmax/cutoff_0/cluster_W": model.crit.cluster_weight, "transformer/adaptive_softmax/cutoff_0/cluster_b": model.crit.cluster_bias, } ) for i, (out_l, proj_l, tie_proj) in enumerate( zip(model.crit.out_layers, model.crit.out_projs, config.tie_projs) ): layer_str = f"transformer/adaptive_softmax/cutoff_{i}/" if config.tie_word_embeddings: tf_to_pt_map.update({layer_str + "b": out_l.bias}) else: raise NotImplementedError # I don't think this is implemented in the TF code tf_to_pt_map.update({layer_str + "lookup_table": out_l.weight, layer_str + "b": out_l.bias}) if not tie_proj: tf_to_pt_map.update({layer_str + "proj": proj_l}) # Now load the rest of the transformer model = model.transformer # Embeddings for i, (embed_l, proj_l) in enumerate(zip(model.word_emb.emb_layers, model.word_emb.emb_projs)): layer_str = f"transformer/adaptive_embed/cutoff_{i}/" tf_to_pt_map.update({layer_str + "lookup_table": embed_l.weight, layer_str + "proj_W": proj_l}) # Transformer blocks for i, b in enumerate(model.layers): layer_str = f"transformer/layer_{i}/" tf_to_pt_map.update( { layer_str + "rel_attn/LayerNorm/gamma": b.dec_attn.layer_norm.weight, layer_str + "rel_attn/LayerNorm/beta": b.dec_attn.layer_norm.bias, layer_str + "rel_attn/o/kernel": b.dec_attn.o_net.weight, layer_str + "rel_attn/qkv/kernel": b.dec_attn.qkv_net.weight, layer_str + "rel_attn/r/kernel": b.dec_attn.r_net.weight, layer_str + "ff/LayerNorm/gamma": b.pos_ff.layer_norm.weight, layer_str + "ff/LayerNorm/beta": b.pos_ff.layer_norm.bias, layer_str + "ff/layer_1/kernel": b.pos_ff.CoreNet[0].weight, layer_str + "ff/layer_1/bias": b.pos_ff.CoreNet[0].bias, layer_str + "ff/layer_2/kernel": b.pos_ff.CoreNet[3].weight, layer_str + "ff/layer_2/bias": b.pos_ff.CoreNet[3].bias, } ) # Relative positioning biases if config.untie_r: r_r_list = [] r_w_list = [] for b in model.layers: r_r_list.append(b.dec_attn.r_r_bias) r_w_list.append(b.dec_attn.r_w_bias) else: r_r_list = [model.r_r_bias] r_w_list = [model.r_w_bias] tf_to_pt_map.update({"transformer/r_r_bias": r_r_list, "transformer/r_w_bias": r_w_list}) return tf_to_pt_map def load_tf_weights_in_transfo_xl(model, config, tf_path): """Load tf checkpoints in a pytorch model""" try: import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise # Build TF to PyTorch weights loading map tf_to_pt_map = build_tf_to_pytorch_map(model, config) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) tf_weights = {} for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) tf_weights[name] = array for name, pointer in tf_to_pt_map.items(): assert name in tf_weights array = tf_weights[name] # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if "kernel" in name or "proj" in name: array = np.transpose(array) if ("r_r_bias" in name or "r_w_bias" in name) and len(pointer) > 1: # Here we will split the TF weights assert len(pointer) == array.shape[0] for i, p_i in enumerate(pointer): arr_i = array[i, ...] try: assert p_i.shape == arr_i.shape except AssertionError as e: e.args += (p_i.shape, arr_i.shape) raise logger.info(f"Initialize PyTorch weight {name} for layer {i}") p_i.data = torch.from_numpy(arr_i) else: 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(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) tf_weights.pop(name, None) tf_weights.pop(name + "/Adam", None) tf_weights.pop(name + "/Adam_1", None) logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}") return model class PositionalEmbedding(nn.Module): def __init__(self, demb): super().__init__() self.demb = demb inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb)) self.register_buffer("inv_freq", inv_freq) def forward(self, pos_seq, bsz=None): sinusoid_inp = torch.outer(pos_seq, self.inv_freq) pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1) if bsz is not None: return pos_emb[:, None, :].expand(-1, bsz, -1) else: return pos_emb[:, None, :] class PositionwiseFF(nn.Module): def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5): super().__init__() self.d_model = d_model self.d_inner = d_inner self.dropout = dropout self.CoreNet = nn.Sequential( nn.Linear(d_model, d_inner), nn.ReLU(inplace=True), nn.Dropout(dropout), nn.Linear(d_inner, d_model), nn.Dropout(dropout), ) self.layer_norm = nn.LayerNorm(d_model, eps=layer_norm_epsilon) self.pre_lnorm = pre_lnorm def forward(self, inp): if self.pre_lnorm: # layer normalization + positionwise feed-forward core_out = self.CoreNet(self.layer_norm(inp)) # residual connection output = core_out + inp else: # positionwise feed-forward core_out = self.CoreNet(inp) # residual connection + layer normalization output = self.layer_norm(inp + core_out) return output class RelPartialLearnableMultiHeadAttn(nn.Module): def __init__( self, n_head, d_model, d_head, dropout, dropatt=0, pre_lnorm=False, r_r_bias=None, r_w_bias=None, layer_norm_epsilon=1e-5, ): super().__init__() self.n_head = n_head self.d_model = d_model self.d_head = d_head self.dropout = dropout self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head, bias=False) self.drop = nn.Dropout(dropout) self.dropatt = nn.Dropout(dropatt) self.o_net = nn.Linear(n_head * d_head, d_model, bias=False) self.layer_norm = nn.LayerNorm(d_model, eps=layer_norm_epsilon) self.scale = 1 / (d_head**0.5) self.pre_lnorm = pre_lnorm if r_r_bias is None or r_w_bias is None: # Biases are not shared self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head)) self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head)) else: self.r_r_bias = r_r_bias self.r_w_bias = r_w_bias self.r_net = nn.Linear(self.d_model, self.n_head * self.d_head, bias=False) def _rel_shift(self, x): zero_pad_shape = (x.size(0), 1) + x.size()[2:] zero_pad = torch.zeros(zero_pad_shape, device=x.device, dtype=x.dtype) x_padded = torch.cat([zero_pad, x], dim=1) x_padded_shape = (x.size(1) + 1, x.size(0)) + x.size()[2:] x_padded = x_padded.view(*x_padded_shape) x = x_padded[1:].view_as(x) return x def forward(self, w, r, attn_mask=None, mems=None, head_mask=None, output_attentions=False): qlen, rlen, bsz = w.size(0), r.size(0), w.size(1) if mems is not None: cat = torch.cat([mems, w], 0) if self.pre_lnorm: w_heads = self.qkv_net(self.layer_norm(cat)) else: w_heads = self.qkv_net(cat) r_head_k = self.r_net(r) w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1) w_head_q = w_head_q[-qlen:] else: if self.pre_lnorm: w_heads = self.qkv_net(self.layer_norm(w)) else: w_heads = self.qkv_net(w) r_head_k = self.r_net(r) w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1) klen = w_head_k.size(0) w_head_q = w_head_q.view(qlen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head w_head_k = w_head_k.view(klen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head w_head_v = w_head_v.view(klen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head r_head_k = r_head_k.view(rlen, self.n_head, self.d_head) # qlen x n_head x d_head # compute attention score rw_head_q = w_head_q + self.r_w_bias # qlen x bsz x n_head x d_head AC = torch.einsum("ibnd,jbnd->ijbn", (rw_head_q, w_head_k)) # qlen x klen x bsz x n_head rr_head_q = w_head_q + self.r_r_bias BD = torch.einsum("ibnd,jnd->ijbn", (rr_head_q, r_head_k)) # qlen x klen x bsz x n_head BD = self._rel_shift(BD) # [qlen x klen x bsz x n_head] attn_score = AC + BD attn_score.mul_(self.scale) mask_value = torch.finfo(attn_score.dtype).min # compute attention probability if attn_mask is not None and torch.sum(attn_mask).item(): attn_mask = attn_mask == 1 # Switch to bool if attn_mask.dim() == 2: attn_score = ( attn_score.float().masked_fill(attn_mask[None, :, :, None], mask_value).type_as(attn_score) ) elif attn_mask.dim() == 3: attn_score = attn_score.float().masked_fill(attn_mask[:, :, :, None], mask_value).type_as(attn_score) # [qlen x klen x bsz x n_head] attn_prob = nn.functional.softmax(attn_score, dim=1) attn_prob = self.dropatt(attn_prob) # Mask heads if we want to if head_mask is not None: attn_prob = attn_prob * head_mask # compute attention vector attn_vec = torch.einsum("ijbn,jbnd->ibnd", (attn_prob, w_head_v)) # [qlen x bsz x n_head x d_head] attn_vec = attn_vec.contiguous().view(attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head) # linear projection attn_out = self.o_net(attn_vec) attn_out = self.drop(attn_out) if self.pre_lnorm: # residual connection outputs = [w + attn_out] else: # residual connection + layer normalization outputs = [self.layer_norm(w + attn_out)] if output_attentions: outputs.append(attn_prob) return outputs class RelPartialLearnableDecoderLayer(nn.Module): def __init__(self, n_head, d_model, d_head, d_inner, dropout, layer_norm_epsilon=1e-5, **kwargs): super().__init__() self.dec_attn = RelPartialLearnableMultiHeadAttn( n_head, d_model, d_head, dropout, layer_norm_epsilon=layer_norm_epsilon, **kwargs ) self.pos_ff = PositionwiseFF( d_model, d_inner, dropout, pre_lnorm=kwargs.get("pre_lnorm"), layer_norm_epsilon=layer_norm_epsilon ) def forward(self, dec_inp, r, dec_attn_mask=None, mems=None, head_mask=None, output_attentions=False): attn_outputs = self.dec_attn( dec_inp, r, attn_mask=dec_attn_mask, mems=mems, head_mask=head_mask, output_attentions=output_attentions, ) ff_output = self.pos_ff(attn_outputs[0]) outputs = [ff_output] + attn_outputs[1:] return outputs class AdaptiveEmbedding(nn.Module): def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, sample_softmax=False): super().__init__() self.n_token = n_token self.d_embed = d_embed self.cutoffs = cutoffs + [n_token] self.div_val = div_val self.d_proj = d_proj self.emb_scale = d_proj**0.5 self.cutoff_ends = [0] + self.cutoffs self.emb_layers = nn.ModuleList() self.emb_projs = nn.ParameterList() if div_val == 1: self.emb_layers.append(nn.Embedding(n_token, d_embed, sparse=sample_softmax > 0)) if d_proj != d_embed: self.emb_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed))) else: for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] d_emb_i = d_embed // (div_val**i) self.emb_layers.append(nn.Embedding(r_idx - l_idx, d_emb_i)) self.emb_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i))) def forward(self, inp): if self.div_val == 1: embed = self.emb_layers[0](inp) if self.d_proj != self.d_embed: embed = nn.functional.linear(embed, self.emb_projs[0]) else: param = next(self.parameters()) inp_flat = inp.view(-1) emb_flat = torch.zeros([inp_flat.size(0), self.d_proj], dtype=param.dtype, device=param.device) for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx) indices_i = mask_i.nonzero().squeeze() if indices_i.numel() == 0: continue inp_i = inp_flat.index_select(0, indices_i) - l_idx emb_i = self.emb_layers[i](inp_i) emb_i = nn.functional.linear(emb_i, self.emb_projs[i]) emb_flat.index_copy_(0, indices_i, emb_i) embed_shape = inp.size() + (self.d_proj,) embed = emb_flat.view(embed_shape) embed.mul_(self.emb_scale) return embed class TransfoXLPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = TransfoXLConfig load_tf_weights = load_tf_weights_in_transfo_xl base_model_prefix = "transformer" def _init_weight(self, weight): if self.config.init == "uniform": nn.init.uniform_(weight, -self.config.init_range, self.config.init_range) elif self.config.init == "normal": nn.init.normal_(weight, 0.0, self.config.init_std) def _init_bias(self, bias): nn.init.constant_(bias, 0.0) def _init_weights(self, m): """Initialize the weights.""" classname = m.__class__.__name__ if classname.find("Linear") != -1: if hasattr(m, "weight") and m.weight is not None: self._init_weight(m.weight) if hasattr(m, "bias") and m.bias is not None: self._init_bias(m.bias) elif classname.find("AdaptiveEmbedding") != -1: if hasattr(m, "emb_projs"): for i in range(len(m.emb_projs)): if m.emb_projs[i] is not None: nn.init.normal_(m.emb_projs[i], 0.0, self.config.proj_init_std) elif classname.find("Embedding") != -1: if hasattr(m, "weight"): self._init_weight(m.weight) elif classname.find("ProjectedAdaptiveLogSoftmax") != -1: if hasattr(m, "cluster_weight") and m.cluster_weight is not None: self._init_weight(m.cluster_weight) if hasattr(m, "cluster_bias") and m.cluster_bias is not None: self._init_bias(m.cluster_bias) if hasattr(m, "out_projs"): for i in range(len(m.out_projs)): if m.out_projs[i] is not None: nn.init.normal_(m.out_projs[i], 0.0, self.config.proj_init_std) elif classname.find("LayerNorm") != -1: if hasattr(m, "weight"): nn.init.normal_(m.weight, 1.0, self.config.init_std) if hasattr(m, "bias") and m.bias is not None: self._init_bias(m.bias) else: if hasattr(m, "r_emb"): self._init_weight(m.r_emb) if hasattr(m, "r_w_bias"): self._init_weight(m.r_w_bias) if hasattr(m, "r_r_bias"): self._init_weight(m.r_r_bias) if hasattr(m, "r_bias"): self._init_bias(m.r_bias) def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, layer: Optional[int] = -1): """ Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size. Take care of tying weights embeddings afterwards if the model class has a *tie_weights()* method. Arguments: new_num_tokens: (*optional*) int: New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None: does nothing and just returns a pointer to the input tokens `torch.nn.Embeddings` Module of the model. layer: (*optional*) int: Layer of the *AdaptiveEmbedding* where the resizing should be done. Per default the last layer will be resized. Be aware that when resizing other than the last layer, you have to ensure that the new token(s) in the tokenizer are at the corresponding position. Return: `torch.nn.Embeddings` Pointer to the input tokens Embeddings Module of the model """ base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed if new_num_tokens is None: return self.get_input_embeddings() new_num_tokens_layer, layer = self._get_new_num_tokens_layer(new_num_tokens, layer) assert new_num_tokens_layer > 0, "The size of the new embedding layer cannot be 0 or less" model_embeds = base_model._resize_token_embeddings(new_num_tokens_layer, layer) # Update base model and current model config self.config.vocab_size = new_num_tokens base_model.vocab_size = new_num_tokens base_model.n_token = new_num_tokens new_embedding_shapes = self._get_embedding_shapes() self._resize_cutoffs(new_num_tokens, new_num_tokens_layer, new_embedding_shapes, layer) # Tie weights again if needed self.tie_weights() return model_embeds def _get_new_num_tokens_layer(self, new_num_tokens, layer): embeddings = self.get_input_embeddings() if layer == -1: layer = len(embeddings.emb_layers) - 1 assert 0 <= layer <= len(embeddings.emb_layers) - 1 new_num_tokens_layer = ( new_num_tokens - sum([emb.weight.shape[0] for emb in embeddings.emb_layers[:layer]]) - sum([emb.weight.shape[0] for emb in embeddings.emb_layers[layer + 1 :]]) ) return new_num_tokens_layer, layer def _get_embedding_shapes(self): embeddings = self.get_input_embeddings() return [emb.weight.shape[0] for emb in embeddings.emb_layers] def _resize_token_embeddings(self, new_num_tokens, layer=-1): embeddings = self.get_input_embeddings() if new_num_tokens is None: return embeddings new_embeddings_layer = self._get_resized_embeddings(embeddings.emb_layers[layer], new_num_tokens) embeddings.emb_layers[layer] = new_embeddings_layer self.set_input_embeddings(embeddings) return self.get_input_embeddings() def _resize_cutoffs(self, new_num_tokens, new_emb_size, new_embedding_shapes, layer): embeddings = self.get_input_embeddings() for i in range(layer, len(embeddings.cutoffs)): embeddings.cutoffs[i] = sum(new_embedding_shapes[: i + 1]) embeddings.cutoff_ends = [0] + embeddings.cutoffs embeddings.n_token = new_num_tokens self.config.cutoffs = embeddings.cutoffs[:-1] return embeddings.cutoffs @dataclass class TransfoXLModelOutput(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: 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. mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ last_hidden_state: torch.FloatTensor mems: List[torch.FloatTensor] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class TransfoXLSequenceClassifierOutputWithPast(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None mems: List[torch.FloatTensor] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class TransfoXLLMHeadModelOutput(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: losses (`torch.FloatTensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided): Language modeling losses (not reduced). 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 after SoftMax). mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) 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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple 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. loss (`torch.FloatTensor` of shape `()`, *optional*, returned when `labels` is provided) Reduced language modeling loss. """ losses: Optional[torch.FloatTensor] = None prediction_scores: torch.FloatTensor = None mems: List[torch.FloatTensor] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None loss: Optional[torch.FloatTensor] = None @property def logits(self): # prediction scores are the output of the adaptive softmax, see # the file `modeling_transfo_xl_utilities`. Since the adaptive # softmax returns the log softmax value, `self.prediction_scores` # are strictly speaking not exactly `logits`, but behave the same # way logits do. return self.prediction_scores TRANSFO_XL_START_DOCSTRING = r""" This model inherits from [`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](https://pytorch.org/docs/stable/nn.html#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 ([`TransfoXLConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ TRANSFO_XL_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) mems (`List[torch.FloatTensor]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `mems` output below). Can be used to speed up sequential decoding. The token ids which have their mems given to this model should not be passed as `input_ids` as they have already been computed. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(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 (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`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 (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.", TRANSFO_XL_START_DOCSTRING, ) class TransfoXLModel(TransfoXLPreTrainedModel): def __init__(self, config): super().__init__(config) self.n_token = config.vocab_size self.d_embed = config.d_embed self.d_model = config.d_model self.n_head = config.n_head self.d_head = config.d_head self.word_emb = AdaptiveEmbedding( config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val ) self.drop = nn.Dropout(config.dropout) self.n_layer = config.n_layer self.mem_len = config.mem_len self.attn_type = config.attn_type if not config.untie_r: self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head)) self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head)) self.layers = nn.ModuleList() if config.attn_type == 0: # the default attention for i in range(config.n_layer): self.layers.append( RelPartialLearnableDecoderLayer( config.n_head, config.d_model, config.d_head, config.d_inner, config.dropout, dropatt=config.dropatt, pre_lnorm=config.pre_lnorm, r_w_bias=None if config.untie_r else self.r_w_bias, r_r_bias=None if config.untie_r else self.r_r_bias, layer_norm_epsilon=config.layer_norm_epsilon, ) ) else: # learnable embeddings and absolute embeddings are not used in our pretrained checkpoints raise NotImplementedError # Removed them to avoid maintaining dead code self.same_length = config.same_length self.clamp_len = config.clamp_len if self.attn_type == 0: # default attention self.pos_emb = PositionalEmbedding(self.d_model) else: # learnable embeddings and absolute embeddings raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.word_emb def set_input_embeddings(self, new_embeddings): self.word_emb = new_embeddings def backward_compatible(self): self.sample_softmax = -1 def reset_memory_length(self, mem_len): self.mem_len = mem_len def _prune_heads(self, heads): logger.info("Head pruning is not implemented for Transformer-XL model") pass def init_mems(self, bsz): if self.mem_len > 0: mems = [] param = next(self.parameters()) for i in range(self.n_layer): empty = torch.zeros(self.mem_len, bsz, self.config.d_model, dtype=param.dtype, device=param.device) mems.append(empty) return mems else: return None def _update_mems(self, hids, mems, mlen, qlen): # does not deal with None if mems is None: return None # mems is not None assert len(hids) == len(mems), "len(hids) != len(mems)" # There are `mlen + qlen` steps that can be cached into mems with torch.no_grad(): new_mems = [] end_idx = mlen + max(0, qlen) beg_idx = max(0, end_idx - self.mem_len) for i in range(len(hids)): cat = torch.cat([mems[i], hids[i]], dim=0) new_mems.append(cat[beg_idx:end_idx].detach()) return new_mems @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TransfoXLModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, mems: Optional[List[torch.FloatTensor]] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TransfoXLModelOutput]: 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 # the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library # so we transpose here from shape [bsz, len] to shape [len, bsz] if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_ids = input_ids.transpose(0, 1).contiguous() qlen, bsz = input_ids.size() elif inputs_embeds is not None: inputs_embeds = inputs_embeds.transpose(0, 1).contiguous() qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if mems is None: mems = self.init_mems(bsz) # 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] (a head_mask for each layer) # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head] if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0) head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1) head_mask = head_mask.to( dtype=next(self.parameters()).dtype ) # switch to float if need + fp16 compatibility else: head_mask = [None] * self.n_layer if inputs_embeds is not None: word_emb = inputs_embeds else: word_emb = self.word_emb(input_ids) mlen = mems[0].size(0) if mems is not None else 0 klen = mlen + qlen if self.same_length: all_ones = word_emb.new_ones((qlen, klen), dtype=torch.bool) mask_len = klen - self.mem_len if mask_len > 0: mask_shift_len = qlen - mask_len else: mask_shift_len = qlen dec_attn_mask = (torch.triu(all_ones, 1 + mlen) + torch.tril(all_ones, -mask_shift_len))[:, :, None] # -1 else: dec_attn_mask = torch.triu(word_emb.new_ones((qlen, klen), dtype=torch.bool), diagonal=1 + mlen)[ :, :, None ] hids = [] attentions = [] if output_attentions else None if self.attn_type == 0: # default pos_seq = torch.arange(klen - 1, -1, -1.0, device=word_emb.device, dtype=word_emb.dtype) if self.clamp_len > 0: pos_seq.clamp_(max=self.clamp_len) pos_emb = self.pos_emb(pos_seq) core_out = self.drop(word_emb) pos_emb = self.drop(pos_emb) for i, layer in enumerate(self.layers): hids.append(core_out) mems_i = None if mems is None else mems[i] layer_outputs = layer( core_out, pos_emb, dec_attn_mask=dec_attn_mask, mems=mems_i, head_mask=head_mask[i], output_attentions=output_attentions, ) core_out = layer_outputs[0] if output_attentions: attentions.append(layer_outputs[1]) else: # learnable embeddings and absolute embeddings raise NotImplementedError # Removed these to avoid maintaining dead code - They are not used in our pretrained checkpoint core_out = self.drop(core_out) new_mems = self._update_mems(hids, mems, mlen, qlen) if output_hidden_states: # Add last layer and transpose to library standard shape [bsz, len, hidden_dim] hids.append(core_out) hids = tuple(t.transpose(0, 1).contiguous() for t in hids) else: hids = None if output_attentions: # Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len] attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions) # We transpose back here to shape [bsz, len, hidden_dim] core_out = core_out.transpose(0, 1).contiguous() if not return_dict: return tuple(v for v in [core_out, new_mems, hids, attentions] if v is not None) return TransfoXLModelOutput( last_hidden_state=core_out, mems=new_mems, hidden_states=hids, attentions=attentions, ) @add_start_docstrings( """ The Transformer-XL Model with a language modeling head on top (adaptive softmax with weights tied to the adaptive input embeddings) """, TRANSFO_XL_START_DOCSTRING, ) class TransfoXLLMHeadModel(TransfoXLPreTrainedModel): _tied_weights_keys = [r"crit\.out_projs\.\d+", r"crit\.out_layers\.\d+\.weight"] def __init__(self, config): super().__init__(config) self.transformer = TransfoXLModel(config) self.sample_softmax = config.sample_softmax self.trainer_compatible = getattr(config, "trainer_compatible", False) if not self.trainer_compatible: warnings.warn( "The output of TransfoXL will be updated in v5 to support a single loss as first argument. In order" "to use that updated output, please specify `trainer_compatible=True` as your configuration" " attribute.", DeprecationWarning, ) assert self.sample_softmax <= 0, ( "Sampling from the softmax is not implemented yet. Please look at issue: #3310:" " https://github.com/huggingface/transformers/issues/3310" ) self.crit = ProjectedAdaptiveLogSoftmax( config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val ) # Initialize weights and apply final processing self.post_init() def tie_weights(self): """ Run this to be sure output and input (adaptive) softmax weights are tied """ if self.config.tie_word_embeddings: for i in range(len(self.crit.out_layers)): self._tie_or_clone_weights(self.crit.out_layers[i], self.transformer.word_emb.emb_layers[i]) if self.config.tie_projs: for i, tie_proj in enumerate(self.config.tie_projs): if tie_proj and self.config.div_val == 1 and self.config.d_model != self.config.d_embed: if self.config.torchscript: self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[0].clone()) else: self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[0] elif tie_proj and self.config.div_val != 1: if self.config.torchscript: self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[i].clone()) else: self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[i] def reset_memory_length(self, mem_len): self.transformer.reset_memory_length(mem_len) def init_mems(self, bsz): return self.transformer.init_mems(bsz) @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TransfoXLLMHeadModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, mems: Optional[List[torch.FloatTensor]] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TransfoXLLMHeadModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None: bsz, tgt_len = input_ids.size(0), input_ids.size(1) elif inputs_embeds is not None: bsz, tgt_len = inputs_embeds.size(0), inputs_embeds.size(1) else: raise ValueError("You have to specify either input_ids or inputs_embeds") transformer_outputs = self.transformer( input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden = transformer_outputs[0] pred_hid = last_hidden[:, -tgt_len:] if labels is not None: # Prevents all labels being -100 and throwing an error # when backwarding the loss miss_valid_label = labels[0, 1:].sum() == (labels.size(1) - 1) * -100 if miss_valid_label: # Sets an token, just to prevent loss from being NaN labels[0, 1] = self.config.eos_token_id softmax_output = self.crit(pred_hid, labels) prediction_scores = softmax_output.view(bsz, tgt_len, -1) if labels is None else () if labels is not None: losses = softmax_output.view(bsz, tgt_len - 1) # Avoids from incorporating padding (-100) tokens into loss value loss = losses[losses != 0].mean() else: losses, loss = None, None if not return_dict: if self.trainer_compatible: output = (prediction_scores, losses) if losses is not None else (prediction_scores,) output += transformer_outputs[1:] return ((loss,) + output) if loss is not None else output else: output = (prediction_scores, *transformer_outputs[1:]) output = ((losses,) + output) if losses is not None else output return (output + (loss,)) if loss is not None else output return TransfoXLLMHeadModelOutput( loss=loss, prediction_scores=prediction_scores, losses=losses, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def get_output_embeddings(self): """Double-check if you are using adaptive softmax.""" if self.sample_softmax > 0: return self.out_layer else: return self.crit.out_layers[-1] def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **model_kwargs): inputs = {} # if past is defined in model kwargs then use it for faster decoding if past_key_values: inputs["mems"] = past_key_values inputs["input_ids"] = input_ids[:, -1].unsqueeze(-1) else: inputs["input_ids"] = input_ids return inputs def _resize_cutoffs(self, new_num_tokens, new_emb_size, new_embedding_shapes, layer): new_cutoffs = super()._resize_cutoffs(new_num_tokens, new_emb_size, new_embedding_shapes, layer) self.crit.cutoffs = new_cutoffs self.crit.cutoff_ends = [0] + new_cutoffs self.crit.n_token = new_num_tokens @staticmethod def _reorder_cache(mems: List[torch.Tensor], beam_idx: torch.Tensor) -> List[torch.Tensor]: """ This function is used to re-order the `mems` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `mems` with the correct beam_idx at every generation step. """ return [layer_past.index_select(1, beam_idx.to(layer_past.device)) for layer_past in mems] @add_start_docstrings( """ The Transformer-XL Model transformer with a sequence classification head on top (linear layer). [`TransfoXLForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, TRANSFO_XL_START_DOCSTRING, ) class TransfoXLForSequenceClassification(TransfoXLPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = TransfoXLModel(config) self.score = nn.Linear(config.d_embed, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TransfoXLSequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, mems: Optional[List[torch.FloatTensor]] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TransfoXLSequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] assert ( self.config.pad_token_id is not None or batch_size == 1 ), "Cannot handle batch sizes > 1 if no padding token is defined." if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to( logits.device ) else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[range(batch_size), sequence_lengths] loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TransfoXLSequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, mems=transformer_outputs.mems, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )