# 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 logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from .configuration_transfo_xl import TransfoXLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_transfo_xl_utilities import LogUniformSampler, ProjectedAdaptiveLogSoftmax, sample_logits
from .modeling_utils import PreTrainedModel
logger = logging.getLogger(__name__)
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP = {
"transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-pytorch_model.bin",
}
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 = "transformer/adaptive_softmax/cutoff_%d/" % i
if config.tie_weight:
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 = "transformer/adaptive_embed/cutoff_%d/" % 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 = "transformer/layer_%d/" % 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("Loading TF weight {} with shape {}".format(name, 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 weigths
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("Initialize PyTorch weight {} for layer {}".format(name, i))
p_i.data = torch.from_numpy(arr_i)
else:
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)
tf_weights.pop(name, None)
tf_weights.pop(name + "/Adam", None)
tf_weights.pop(name + "/Adam_1", None)
logger.info("Weights not copied to PyTorch model: {}".format(", ".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.ger(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,
tgt_len=None,
ext_len=None,
mem_len=None,
pre_lnorm=False,
r_r_bias=None,
r_w_bias=None,
output_attentions=False,
layer_norm_epsilon=1e-5,
):
super().__init__()
self.output_attentions = output_attentions
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):
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)
# 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:
if next(self.parameters()).dtype == torch.float16:
attn_score = (
attn_score.float().masked_fill(attn_mask[None, :, :, None], -65000).type_as(attn_score)
)
else:
attn_score = attn_score.float().masked_fill(attn_mask[None, :, :, None], -1e30).type_as(attn_score)
elif attn_mask.dim() == 3:
if next(self.parameters()).dtype == torch.float16:
attn_score = attn_score.float().masked_fill(attn_mask[:, :, :, None], -65000).type_as(attn_score)
else:
attn_score = attn_score.float().masked_fill(attn_mask[:, :, :, None], -1e30).type_as(attn_score)
# [qlen x klen x bsz x n_head]
attn_prob = F.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 self.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):
attn_outputs = self.dec_attn(dec_inp, r, attn_mask=dec_attn_mask, mems=mems, head_mask=head_mask)
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 = F.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 = F.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
pretrained_model_archive_map = TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
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)
TRANSFO_XL_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
TRANSFO_XL_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`transformers.TransfoXLTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
`What are input IDs? <../glossary.html#input-ids>`__
mems (:obj:`List[torch.FloatTensor]` of length :obj:`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 (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
input_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
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 `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
"""
[docs]@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.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
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.tgt_len = config.tgt_len
self.mem_len = config.mem_len
self.ext_len = config.ext_len
self.max_klen = config.tgt_len + config.ext_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,
tgt_len=config.tgt_len,
ext_len=config.ext_len,
mem_len=config.mem_len,
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,
output_attentions=self.output_attentions,
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
self.init_weights()
def backward_compatible(self):
self.sample_softmax = -1
def reset_length(self, tgt_len, ext_len, mem_len):
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_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, qlen, mlen):
# 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
# For the next step, the last `ext_len` of the `qlen` tokens
# will be used as the extended context. Hence, we only cache
# the tokens from `mlen + qlen - self.ext_len - self.mem_len`
# to `mlen + qlen - self.ext_len`.
with torch.no_grad():
new_mems = []
end_idx = mlen + max(0, qlen - 0 - self.ext_len)
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
[docs] @add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING)
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the last layer of the model.
mems (:obj:`List[torch.FloatTensor]` of length :obj:`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 (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(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::
from transformers import TransfoXLTokenizer, TransfoXLModel
import torch
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
model = TransfoXLModel.from_pretrained('transfo-xl-wt103')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states, mems = outputs[:2]
"""
# 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 fload 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.uint8)
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.uint8), diagonal=1 + mlen)[
:, :, None
]
hids = []
attentions = []
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]
)
core_out = layer_outputs[0]
if self.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)
# We transpose back here to shape [bsz, len, hidden_dim]
outputs = [core_out.transpose(0, 1).contiguous(), new_mems]
if self.output_hidden_states:
# Add last layer and transpose to library standard shape [bsz, len, hidden_dim]
hids.append(core_out)
hids = list(t.transpose(0, 1).contiguous() for t in hids)
outputs.append(hids)
if self.output_attentions:
# Transpose to library standard shape [bsz, n_heads, query_seq_len, key_seq_len]
attentions = list(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
outputs.append(attentions)
return outputs # last hidden state, new_mems, (all hidden states), (all attentions)
[docs]@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):
def __init__(self, config):
super().__init__(config)
self.transformer = TransfoXLModel(config)
self.sample_softmax = config.sample_softmax
# use sampled softmax
if config.sample_softmax > 0:
self.out_layer = nn.Linear(config.d_model, config.vocab_size)
self.sampler = LogUniformSampler(config.vocab_size, config.sample_softmax)
# use adaptive softmax (including standard softmax)
else:
self.crit = ProjectedAdaptiveLogSoftmax(
config.vocab_size, config.d_embed, config.d_model, config.cutoffs, div_val=config.div_val
)
self.init_weights()
[docs] def tie_weights(self):
"""
Run this to be sure output and input (adaptive) softmax weights are tied
"""
# sampled softmax
if self.sample_softmax > 0:
if self.config.tie_weight:
self.out_layer.weight = self.transformer.word_emb.weight
# adaptive softmax (including standard softmax)
else:
if self.config.tie_weight:
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_length(self, tgt_len, ext_len, mem_len):
self.transformer.reset_length(tgt_len, ext_len, mem_len)
def init_mems(self, bsz):
return self.transformer.init_mems(bsz)
[docs] @add_start_docstrings_to_callable(TRANSFO_XL_INPUTS_DOCSTRING)
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None, labels=None):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Labels for language modeling.
Note that the labels **are shifted** inside the model, i.e. you can set ``lm_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:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
Language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` 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 (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(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::
from transformers import TransfoXLTokenizer, TransfoXLLMHeadModel
import torch
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
model = TransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
prediction_scores, mems = outputs[:2]
"""
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)
last_hidden = transformer_outputs[0]
pred_hid = last_hidden[:, -tgt_len:]
outputs = transformer_outputs[1:]
if self.sample_softmax > 0 and self.training:
assert self.config.tie_weight
logit = sample_logits(self.transformer.word_emb, self.out_layer.bias, labels, pred_hid, self.sampler)
softmax_output = -F.log_softmax(logit, -1)[:, :, 0]
outputs = [softmax_output] + outputs
if labels is not None:
# TODO: This is not implemented
raise NotImplementedError
else:
softmax_output = self.crit(pred_hid.view(-1, pred_hid.size(-1)), labels)
if labels is None:
softmax_output = softmax_output.view(bsz, tgt_len, -1)
outputs = [softmax_output] + outputs
else:
softmax_output = softmax_output.view(bsz, tgt_len)
outputs = [softmax_output, None] + outputs
return outputs # (loss), logits or None if labels is not None (speed up adaptive softmax), new_mems, (all hidden states), (all attentions)
[docs] 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, **model_kwargs):
inputs = {"input_ids": input_ids}
# if past is defined in model kwargs then use it for faster decoding
if "past" in model_kwargs and model_kwargs["past"]:
inputs["mems"] = model_kwargs["past"]
return inputs