fix tests

README.md

@@ -930,12 +930,12 @@ all_hidden_states = lower_hidden_states + [hidden_states]
 `TransfoXLLMHeadModel` includes the `TransfoXLModel` Transformer followed by an (adaptive) softmax head with weights tied to the input embeddings.
 
 *Inputs* are the same as the inputs of the [`TransfoXLModel`](#-12.-`TransfoXLModel`) class plus optional labels:
-- `target`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the target token indices selected in the range [0, self.config.n_token[
+- `labels`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the labels token indices selected in the range [0, self.config.n_token[
 
 *Outputs* a tuple of (last_hidden_state, new_mems)
 - `softmax_output`: output of the (adaptive) softmax:
-  - if target is None: log probabilities of tokens, shape [batch_size, sequence_length, n_tokens] 
-  - else: Negative log likelihood of target tokens with shape [batch_size, sequence_length]
+  - if labels is None: log probabilities of tokens, shape [batch_size, sequence_length, n_tokens] 
+  - else: Negative log likelihood of labels tokens with shape [batch_size, sequence_length]
 - `new_mems`: list (num layers) of updated mem states at the entry of each layer each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]. Note that the first two dimensions are transposed in `mems` with regards to `input_ids`.
 
 #### 14. `GPT2Model`

pytorch_pretrained_bert/modeling_transfo_xl.py

@@ -1025,14 +1025,14 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
         `mems`: optional memomry of hidden states from previous forward passes
             as a list (num layers) of hidden states at the entry of each layer
             each hidden states has shape [self.config.mem_len, bsz, self.config.d_model]
-            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `target`
+            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `labels`
     Outputs:
         A tuple of (last_hidden_state, new_mems)
         `last_hidden_state`: the encoded-hidden-states at the top of the model
             as a torch.FloatTensor of size [batch_size, sequence_length, self.config.d_model]
         `new_mems`: list (num layers) of updated mem states at the entry of each layer
             each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]
-            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `target`
+            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `labels`
 
     Example usage:
     ```python
@@ -1265,7 +1265,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
                 mems :: optional mems from previous forwar passes (or init_mems)
                     list (num layers) of mem states at the entry of each layer
                         shape :: [self.config.mem_len, bsz, self.config.d_model]
-                    Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `target`
+                    Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `labels`
             Returns:
                 tuple (last_hidden, new_mems) where:
                     new_mems: list (num layers) of mem states at the entry of each layer
@@ -1303,23 +1303,23 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
     Inputs:
         `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
             with the token indices selected in the range [0, self.config.n_token[
-        `target`: an optional torch.LongTensor of shape [batch_size, sequence_length]
-            with the target token indices selected in the range [0, self.config.n_token[
+        `labels`: an optional torch.LongTensor of shape [batch_size, sequence_length]
+            with the labels token indices selected in the range [0, self.config.n_token[
         `mems`: an optional memory of hidden states from previous forward passes
             as a list (num layers) of hidden states at the entry of each layer
             each hidden states has shape [self.config.mem_len, bsz, self.config.d_model]
-            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `target`
+            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `labels`
 
     Outputs:
         A tuple of (last_hidden_state, new_mems)
         `softmax_output`: output of the (adaptive) softmax:
-            if target is None:
+            if labels is None:
                 Negative log likelihood of shape [batch_size, sequence_length] 
             else:
                 log probabilities of tokens, shape [batch_size, sequence_length, n_tokens]
         `new_mems`: list (num layers) of updated mem states at the entry of each layer
             each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]
-            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `target`
+            Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `labels`
 
     Example usage:
     ```python
@@ -1375,16 +1375,16 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
     def init_mems(self, data):
         return self.transformer.init_mems(data)
 
-    def forward(self, input_ids, target=None, mems=None):
+    def forward(self, input_ids, labels=None, mems=None):
         """ Params:
                 input_ids :: [bsz, len]
-                target :: [bsz, len]
+                labels :: [bsz, len]
             Returns:
                 tuple(softmax_output, new_mems) where:
                     new_mems: list (num layers) of hidden states at the entry of each layer
                         shape :: [mem_len, bsz, self.config.d_model] :: Warning: shapes are transposed here w. regards to input_ids
                     softmax_output: output of the (adaptive) softmax:
-                        if target is None:
+                        if labels is None:
                             Negative log likelihood of shape :: [bsz, len] 
                         else:
                             log probabilities of tokens, shape :: [bsz, len, n_tokens]
@@ -1397,11 +1397,11 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
         pred_hid = last_hidden[:, -tgt_len:]
         if self.sample_softmax > 0 and self.training:
             assert self.config.tie_weight
-            logit = sample_logits(self.transformer.word_emb, self.out_layer.bias, target, pred_hid, self.sampler)
+            logit = sample_logits(self.transformer.word_emb, self.out_layer.bias, labels, pred_hid, self.sampler)
             softmax_output = -F.log_softmax(logit, -1)[:, :, 0]
         else:
-            softmax_output = self.crit(pred_hid.view(-1, pred_hid.size(-1)), target)
-            if target is None:
+            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)
             else:
                 softmax_output = softmax_output.view(bsz, tgt_len)

pytorch_pretrained_bert/modeling_transfo_xl_utilities.py

@@ -89,13 +89,13 @@ class ProjectedAdaptiveLogSoftmax(nn.Module):
 
         return logit
 
-    def forward(self, hidden, target=None, keep_order=False):
+    def forward(self, hidden, labels=None, keep_order=False):
         '''
             Params:
                 hidden :: [len*bsz x d_proj]
-                target :: [len*bsz]
+                labels :: [len*bsz]
             Return:
-                if target is None:
+                if labels is None:
                     out :: [len*bsz] Negative log likelihood
                 else:
                     out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary
@@ -104,18 +104,18 @@ class ProjectedAdaptiveLogSoftmax(nn.Module):
             here: https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138
         '''
 
-        if target is not None:
-            target = target.view(-1)
-            if hidden.size(0) != target.size(0):
-                raise RuntimeError('Input and target should have the same size '
+        if labels is not None:
+            labels = labels.view(-1)
+            if hidden.size(0) != labels.size(0):
+                raise RuntimeError('Input and labels should have the same size '
                                 'in the batch dimension.')
 
         if self.n_clusters == 0:
             logit = self._compute_logit(hidden, self.out_layers[0].weight,
                                         self.out_layers[0].bias, self.out_projs[0])
-            if target is not None:
+            if labels is not None:
                 out = -F.log_softmax(logit, dim=-1) \
-                        .gather(1, target.unsqueeze(1)).squeeze(1)
+                        .gather(1, labels.unsqueeze(1)).squeeze(1)
             else:
                 out = F.log_softmax(logit, dim=-1)
         else:
@@ -144,31 +144,31 @@ class ProjectedAdaptiveLogSoftmax(nn.Module):
             head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
             head_logprob = F.log_softmax(head_logit, dim=1)
 
-            if target is None:
+            if labels is None:
                 out = hidden.new_empty((head_logit.size(0), self.n_token))
             else:
-                out = torch.zeros_like(target, dtype=hidden.dtype, device=hidden.device)
+                out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device)
 
             offset = 0
             cutoff_values = [0] + self.cutoffs
             for i in range(len(cutoff_values) - 1):
                 l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]
 
-                if target is not None:
-                    mask_i = (target >= l_idx) & (target < r_idx)
+                if labels is not None:
+                    mask_i = (labels >= l_idx) & (labels < r_idx)
                     indices_i = mask_i.nonzero().squeeze()
 
                     if indices_i.numel() == 0:
                         continue
 
-                    target_i = target.index_select(0, indices_i) - l_idx
+                    target_i = labels.index_select(0, indices_i) - l_idx
                     head_logprob_i = head_logprob.index_select(0, indices_i)
                     hidden_i = hidden.index_select(0, indices_i)
                 else:
                     hidden_i = hidden
 
                 if i == 0:
-                    if target is not None:
+                    if labels is not None:
                         logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
                     else:
                         out[:, :self.cutoffs[0]] = head_logprob[:, :self.cutoffs[0]]
@@ -178,14 +178,14 @@ class ProjectedAdaptiveLogSoftmax(nn.Module):
                     tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
                     tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
                     cluster_prob_idx = self.cutoffs[0] + i - 1  # No probability for the head cluster
-                    if target is not None:
+                    if labels is not None:
                         logprob_i = head_logprob_i[:, cluster_prob_idx] \
                                 + tail_logprob_i.gather(1, target_i[:, None]).squeeze(1)
                     else:
                         logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                         out[:, l_idx:r_idx] = logprob_i
 
-                if target is not None:
+                if labels is not None:
                     if (hasattr(self, 'keep_order') and self.keep_order) or keep_order:
                         out.index_copy_(0, indices_i, -logprob_i)
                     else:

pytorch_pretrained_bert/modeling_xlnet_utilities.py

-# 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.
-""" Utilities for PyTorch XLNet model.
-"""
-
-from collections import defaultdict
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-special_symbols = {
-    "<unk>"  : 0,
-    "<s>"    : 1,
-    "</s>"   : 2,
-    "<cls>"  : 3,
-    "<sep>"  : 4,
-    "<pad>"  : 5,
-    "<mask>" : 6,
-    "<eod>"  : 7,
-    "<eop>"  : 8,
-}
-
-VOCAB_SIZE = 32000
-UNK_ID = special_symbols["<unk>"]
-CLS_ID = special_symbols["<cls>"]
-SEP_ID = special_symbols["<sep>"]
-MASK_ID = special_symbols["<mask>"]
-EOD_ID = special_symbols["<eod>"]
-
-
-def permutation_mask(inputs, targets, is_masked, perm_size, seq_len):
-    """
-    Sample a permutation of the factorization order, and create an
-    attention mask accordingly.
-    Args:
-        inputs: int64 Tensor in shape [seq_len], input ids.
-        targets: int64 Tensor in shape [seq_len], target ids.
-        is_masked: bool Tensor in shape [seq_len]. True means being selected
-            for partial prediction.
-        perm_size: the length of longest permutation. Could be set to be reuse_len.
-            Should not be larger than reuse_len or there will be data leaks.
-        seq_len: int, sequence length.
-    """
-
-    # Generate permutation indices
-    index = np.arange(10)
-    index = np.transpose(np.reshape(index, [-1, perm_size]))
-    index = np.random.shuffle(index)
-    index = np.reshape(np.transpose(index), [-1])
-
-    # `perm_mask` and `target_mask`
-    # non-functional tokens
-    non_func_tokens = tf.logical_not(tf.logical_or(
-        tf.equal(inputs, SEP_ID),
-        tf.equal(inputs, CLS_ID)))
-
-    non_mask_tokens = tf.logical_and(tf.logical_not(is_masked), non_func_tokens)
-    masked_or_func_tokens = tf.logical_not(non_mask_tokens)
-
-    # Set the permutation indices of non-masked (& non-funcional) tokens to the
-    # smallest index (-1):
-    # (1) they can be seen by all other positions
-    # (2) they cannot see masked positions, so there won"t be information leak
-    smallest_index = -tf.ones([seq_len], dtype=tf.int64)
-    rev_index = tf.where(non_mask_tokens, smallest_index, index)
-
-    # Create `target_mask`: non-funcional and maksed tokens
-    # 1: use mask as input and have loss
-    # 0: use token (or [SEP], [CLS]) as input and do not have loss
-    target_tokens = tf.logical_and(masked_or_func_tokens, non_func_tokens)
-    target_mask = tf.cast(target_tokens, tf.float32)
-
-    # Create `perm_mask`
-    # `target_tokens` cannot see themselves
-    self_rev_index = tf.where(target_tokens, rev_index, rev_index + 1)
-
-    # 1: cannot attend if i <= j and j is not non-masked (masked_or_func_tokens)
-    # 0: can attend if i > j or j is non-masked
-    perm_mask = tf.logical_and(
-        self_rev_index[:, None] <= rev_index[None, :],
-        masked_or_func_tokens)
-    perm_mask = tf.cast(perm_mask, tf.float32)
-
-    # new target: [next token] for LM and [curr token] (self) for PLM
-    new_targets = tf.concat([inputs[0: 1], targets[: -1]],
-                            axis=0)
-
-    # construct inputs_k
-    inputs_k = inputs
-
-    # construct inputs_q
-    inputs_q = target_mask
-
-    return perm_mask, new_targets, target_mask, inputs_k, inputs_q
-

tests/modeling_transfo_xl_test.py

@@ -129,10 +129,10 @@ class TransfoXLModelTest(unittest.TestCase):
             model = TransfoXLLMHeadModel(config)
             model.eval()
 
-            loss_1, mems_1a = model(input_ids_1, target=lm_labels)
+            loss_1, mems_1a = model(input_ids_1, labels=lm_labels)
             lm_logits_1, mems_1b = model(input_ids_1)
 
-            loss_2, mems_2a = model(input_ids_2, target=lm_labels, mems=mems_1a)
+            loss_2, mems_2a = model(input_ids_2, labels=lm_labels, mems=mems_1a)
             lm_logits_2, mems_2b = model(input_ids_2, mems=mems_1b)
 
             outputs = {

tests/modeling_xlnet_test.py

@@ -138,10 +138,10 @@ class XLNetModelTest(unittest.TestCase):
             model = XLNetLMHeadModel(config)
             model.eval()
 
-            loss_1, mems_1a = model(input_ids_1, token_type_ids=segment_ids, target=lm_labels)
+            loss_1, mems_1a = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
             all_logits_1, mems_1b = model(input_ids_1, token_type_ids=segment_ids)
 
-            loss_2, mems_2a = model(input_ids_2, token_type_ids=segment_ids, target=lm_labels, mems=mems_1a)
+            loss_2, mems_2a = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=mems_1a)
             all_logits_2, mems_2b = model(input_ids_2, token_type_ids=segment_ids, mems=mems_1b)
 
             logits, _ = model(input_ids_q,