added tests + fixed losses

pytorch_pretrained_bert/modeling.py

@@ -549,7 +549,7 @@ class BertPreTrainedModel(nn.Module):
                 model.__class__.__name__, unexpected_keys))
         if len(error_msgs) > 0:
             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
-                               self.__class__.__name__, "\n\t".join(error_msgs)))
+                               model.__class__.__name__, "\n\t".join(error_msgs)))
         if tempdir:
             # Clean up temp dir
             shutil.rmtree(tempdir)

pytorch_pretrained_bert/modeling_openai.py

@@ -48,12 +48,10 @@ class OpenAIGPTConfig(object):
                  n_embd=768,
                  n_layer=12,
                  n_head=12,
-                 intermediate_size=3072,
                  afn="gelu",
                  resid_pdrop=0.1,
                  embd_pdrop=0.1,
                  attn_pdrop=0.1,
-                 type_vocab_size=2,
                  initializer_range=0.02):
         """Constructs OpenAIGPTConfig.
 
@@ -65,8 +63,6 @@ class OpenAIGPTConfig(object):
             n_layer: Number of hidden layers in the Transformer encoder.
             n_head: Number of attention heads for each attention layer in
                 the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
             afn: The non-linear activation function (function or string) in the
                 encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
             resid_pdrop: The dropout probabilitiy for all fully connected
@@ -74,8 +70,6 @@ class OpenAIGPTConfig(object):
             attn_pdrop: The dropout ratio for the attention
                 probabilities.
             embd_pdrop: The dropout ratio for the embeddings.
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `OpenAIGPTModel`.
             initializer_range: The sttdev of the truncated_normal_initializer for
                 initializing all weight matrices.
         """
@@ -92,11 +86,9 @@ class OpenAIGPTConfig(object):
             self.n_layer = n_layer
             self.n_head = n_head
             self.afn = afn
-            self.intermediate_size = intermediate_size
             self.resid_pdrop = resid_pdrop
             self.embd_pdrop = embd_pdrop
             self.attn_pdrop = attn_pdrop
-            self.type_vocab_size = type_vocab_size
             self.initializer_range = initializer_range
         else:
             raise ValueError("First argument must be either a vocabulary size (int)"
@@ -133,6 +125,167 @@ class OpenAIGPTConfig(object):
         """Serializes this instance to a JSON string."""
         return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
+class Conv1D(nn.Module):
+    def __init__(self, nf, rf, nx):
+        super(Conv1D, self).__init__()
+        self.rf = rf
+        self.nf = nf
+        if rf == 1:  # faster 1x1 conv
+            w = torch.empty(nx, nf)
+            nn.init.normal_(w, std=0.02)
+            self.weight = Parameter(w)
+            self.bias = Parameter(torch.zeros(nf))
+        else:  # was used to train LM
+            raise NotImplementedError
+
+    def forward(self, x):
+        if self.rf == 1:
+            size_out = x.size()[:-1] + (self.nf,)
+            x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
+            x = x.view(*size_out)
+        else:
+            raise NotImplementedError
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, nx, n_ctx, cfg, scale=False):
+        super(Attention, self).__init__()
+        n_state = nx  # in Attention: n_state=768 (nx=n_embd)
+        # [switch nx => n_state from Block to Attention to keep identical to TF implem]
+        assert n_state % cfg.n_head == 0
+        self.register_buffer('b', torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
+        self.n_head = cfg.n_head
+        self.split_size = n_state
+        self.scale = scale
+        self.c_attn = Conv1D(n_state * 3, 1, nx)
+        self.c_proj = Conv1D(n_state, 1, nx)
+        self.attn_dropout = nn.Dropout(cfg.attn_pdrop)
+        self.resid_dropout = nn.Dropout(cfg.resid_pdrop)
+
+    def _attn(self, q, k, v):
+        w = torch.matmul(q, k)
+        if self.scale:
+            w = w / math.sqrt(v.size(-1))
+        # w = w * self.b + -1e9 * (1 - self.b)  # TF implem method: mask_attn_weights
+        # XD: self.b may be larger than w, so we need to crop it
+        b = self.b[:, :, :w.size(-2), :w.size(-1)]
+        w = w * b + -1e9 * (1 - b)
+
+        w = nn.Softmax(dim=-1)(w)
+        w = self.attn_dropout(w)
+        return torch.matmul(w, v)
+
+    def merge_heads(self, x):
+        x = x.permute(0, 2, 1, 3).contiguous()
+        new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
+        return x.view(*new_x_shape)  # in Tensorflow implem: fct merge_states
+
+    def split_heads(self, x, k=False):
+        new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
+        x = x.view(*new_x_shape)  # in Tensorflow implem: fct split_states
+        if k:
+            return x.permute(0, 2, 3, 1)
+        else:
+            return x.permute(0, 2, 1, 3)
+
+    def forward(self, x):
+        x = self.c_attn(x)
+        query, key, value = x.split(self.split_size, dim=2)
+        query = self.split_heads(query)
+        key = self.split_heads(key, k=True)
+        value = self.split_heads(value)
+        a = self._attn(query, key, value)
+        a = self.merge_heads(a)
+        a = self.c_proj(a)
+        a = self.resid_dropout(a)
+        return a
+
+
+class MLP(nn.Module):
+    def __init__(self, n_state, cfg):  # in MLP: n_state=3072 (4 * n_embd)
+        super(MLP, self).__init__()
+        nx = cfg.n_embd
+        self.c_fc = Conv1D(n_state, 1, nx)
+        self.c_proj = Conv1D(nx, 1, n_state)
+        self.act = ACT_FNS[cfg.afn]
+        self.dropout = nn.Dropout(cfg.resid_pdrop)
+
+    def forward(self, x):
+        h = self.act(self.c_fc(x))
+        h2 = self.c_proj(h)
+        return self.dropout(h2)
+
+
+class Block(nn.Module):
+    def __init__(self, n_ctx, cfg, scale=False):
+        super(Block, self).__init__()
+        nx = cfg.n_embd
+        self.attn = Attention(nx, n_ctx, cfg, scale)
+        self.ln_1 = LayerNorm(nx)
+        self.mlp = MLP(4 * nx, cfg)
+        self.ln_2 = LayerNorm(nx)
+
+    def forward(self, x):
+        a = self.attn(x)
+        n = self.ln_1(x + a)
+        m = self.mlp(n)
+        h = self.ln_2(n + m)
+        return h
+
+
+class OpenAIGPTLMHead(nn.Module):
+    """ Language Model Head for the transformer """
+
+    def __init__(self, model_embeddings_weights, cfg):
+        super(OpenAIGPTLMHead, self).__init__()
+        self.n_embd = cfg.n_embd
+        self.set_embeddings_weights(model_embeddings_weights)
+
+    def set_embeddings_weights(self, model_embeddings_weights):
+        embed_shape = model_embeddings_weights.shape
+        self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
+        self.decoder.weight = model_embeddings_weights # Tied weights
+
+    def forward(self, hidden_state):
+        # Truncated Language modeling logits (we remove the last token)
+        # h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd)
+        lm_logits = self.decoder(hidden_state)
+        return lm_logits
+
+
+class OpenAIGPTMultipleChoiceHead(nn.Module):
+    """ Classifier Head for the transformer """
+
+    def __init__(self, cfg):
+        super(OpenAIGPTMultipleChoiceHead, self).__init__()
+        self.n_embd = cfg.n_embd
+        # self.multiple_choice_token = multiple_choice_token
+        self.dropout = nn.Dropout2d(cfg.resid_pdrop)  # To reproduce the noise_shape parameter of TF implementation
+        self.linear = nn.Linear(cfg.n_embd, 1)
+
+        nn.init.normal_(self.linear.weight, std = 0.02)
+        nn.init.normal_(self.linear.bias, 0)
+
+    def forward(self, hidden_states, classification_token_mask):
+        # Classification logits
+        # hidden_states = hidden_states.view(-1, self.n_embd)
+        # classification_token_mask = classification_token_mask.view(-1, 1).expand_as(hidden_states)
+        multiple_choice_h = hidden_states * classification_token_mask.unsqueeze(-1)
+        multiple_choice_h = multiple_choice_h.sum(dim=-2)
+        # flat = x[..., 0].contiguous().view(-1)
+        # multiple_choice_h = multiple_choice_h[flat == self.multiple_choice_token, :]
+        # multiple_choice_h = multiple_choice_h.view(-1, x.size(1), self.n_embd, 1)
+        # # This double transposition is there to replicate the behavior
+        # # of the noise_shape argument in the tensorflow
+        # # implementation.  For more details, see
+        # # https://github.com/huggingface/pytorch-openai-transformer-lm/issues/11
+        # multiple_choice_h = self.dropout(multiple_choice_h.transpose(1, 2)).transpose(1, 2)
+        # multiple_choice_h = multiple_choice_h.contiguous().view(-1, self.n_embd)
+        multiple_choice_logits = self.linear(multiple_choice_h).squeeze(-1)
+        return multiple_choice_logits
+
+
 class OpenAIGPTPreTrainedModel(nn.Module):
     """ An abstract class to handle weights initialization and
         a simple interface for dowloading and loading pretrained models.
@@ -142,7 +295,7 @@ class OpenAIGPTPreTrainedModel(nn.Module):
         if not isinstance(config, OpenAIGPTConfig):
             raise ValueError(
                 "Parameter config in `{}(config)` should be an instance of class `OpenAIGPTConfig`. "
-                "To create a model from a Google pretrained model use "
+                "To create a model from a pretrained model use "
                 "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
                     self.__class__.__name__, self.__class__.__name__
                 ))
@@ -161,11 +314,12 @@ class OpenAIGPTPreTrainedModel(nn.Module):
         if isinstance(module, nn.Linear) and module.bias is not None:
             module.bias.data.zero_()
 
-    def post_loading(self):
+    def set_num_special_tokens(self, num_special_tokens):
         pass
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None, *inputs, **kwargs):
+    def from_pretrained(cls, pretrained_model_name, num_special_tokens=0, state_dict=None, cache_dir=None,
+                        *inputs, **kwargs):
         """
         Instantiate a OpenAIGPTPreTrainedModel from a pre-trained model file or a pytorch state dict.
         Download and cache the pre-trained model file if needed.
@@ -178,7 +332,7 @@ class OpenAIGPTPreTrainedModel(nn.Module):
                     . `openai_gpt_config.json` a configuration file for the model
                     . `pytorch_model.bin` a PyTorch dump of a OpenAIGPTModel instance
             cache_dir: an optional path to a folder in which the pre-trained models will be cached.
-            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
             *inputs, **kwargs: additional input for the specific Bert class
                 (ex: num_labels for BertForSequenceClassification)
         """
@@ -263,167 +417,15 @@ class OpenAIGPTPreTrainedModel(nn.Module):
                 model.__class__.__name__, unexpected_keys))
         if len(error_msgs) > 0:
             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
-                               self.__class__.__name__, "\n\t".join(error_msgs)))
-        model.post_loading()
+                               model.__class__.__name__, "\n\t".join(error_msgs)))
+        # Add additional embeddings for special tokens if needed
+        if num_special_tokens != config.n_special:
+            model.set_num_special_tokens(num_special_tokens)
         if tempdir:
             # Clean up temp dir
             shutil.rmtree(tempdir)
         return model
 
-class Conv1D(nn.Module):
-    def __init__(self, nf, rf, nx):
-        super(Conv1D, self).__init__()
-        self.rf = rf
-        self.nf = nf
-        if rf == 1:  # faster 1x1 conv
-            w = torch.empty(nx, nf)
-            nn.init.normal_(w, std=0.02)
-            self.weight = Parameter(w)
-            self.bias = Parameter(torch.zeros(nf))
-        else:  # was used to train LM
-            raise NotImplementedError
-
-    def forward(self, x):
-        if self.rf == 1:
-            size_out = x.size()[:-1] + (self.nf,)
-            x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
-            x = x.view(*size_out)
-        else:
-            raise NotImplementedError
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, nx, n_ctx, cfg, scale=False):
-        super(Attention, self).__init__()
-        n_state = nx  # in Attention: n_state=768 (nx=n_embd)
-        # [switch nx => n_state from Block to Attention to keep identical to TF implem]
-        assert n_state % cfg.n_head == 0
-        self.register_buffer('b', torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
-        self.n_head = cfg.n_head
-        self.split_size = n_state
-        self.scale = scale
-        self.c_attn = Conv1D(n_state * 3, 1, nx)
-        self.c_proj = Conv1D(n_state, 1, nx)
-        self.attn_dropout = nn.Dropout(cfg.attn_pdrop)
-        self.resid_dropout = nn.Dropout(cfg.resid_pdrop)
-
-    def _attn(self, q, k, v):
-        w = torch.matmul(q, k)
-        if self.scale:
-            w = w / math.sqrt(v.size(-1))
-        w = w * self.b + -1e9 * (1 - self.b)  # TF implem method: mask_attn_weights
-        w = nn.Softmax(dim=-1)(w)
-        w = self.attn_dropout(w)
-        return torch.matmul(w, v)
-
-    def merge_heads(self, x):
-        x = x.permute(0, 2, 1, 3).contiguous()
-        new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
-        return x.view(*new_x_shape)  # in Tensorflow implem: fct merge_states
-
-    def split_heads(self, x, k=False):
-        new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
-        x = x.view(*new_x_shape)  # in Tensorflow implem: fct split_states
-        if k:
-            return x.permute(0, 2, 3, 1)
-        else:
-            return x.permute(0, 2, 1, 3)
-
-    def forward(self, x):
-        x = self.c_attn(x)
-        query, key, value = x.split(self.split_size, dim=2)
-        query = self.split_heads(query)
-        key = self.split_heads(key, k=True)
-        value = self.split_heads(value)
-        a = self._attn(query, key, value)
-        a = self.merge_heads(a)
-        a = self.c_proj(a)
-        a = self.resid_dropout(a)
-        return a
-
-
-class MLP(nn.Module):
-    def __init__(self, n_state, cfg):  # in MLP: n_state=3072 (4 * n_embd)
-        super(MLP, self).__init__()
-        nx = cfg.n_embd
-        self.c_fc = Conv1D(n_state, 1, nx)
-        self.c_proj = Conv1D(nx, 1, n_state)
-        self.act = ACT_FNS[cfg.afn]
-        self.dropout = nn.Dropout(cfg.resid_pdrop)
-
-    def forward(self, x):
-        h = self.act(self.c_fc(x))
-        h2 = self.c_proj(h)
-        return self.dropout(h2)
-
-
-class Block(nn.Module):
-    def __init__(self, n_ctx, cfg, scale=False):
-        super(Block, self).__init__()
-        nx = cfg.n_embd
-        self.attn = Attention(nx, n_ctx, cfg, scale)
-        self.ln_1 = LayerNorm(nx)
-        self.mlp = MLP(4 * nx, cfg)
-        self.ln_2 = LayerNorm(nx)
-
-    def forward(self, x):
-        a = self.attn(x)
-        n = self.ln_1(x + a)
-        m = self.mlp(n)
-        h = self.ln_2(n + m)
-        return h
-
-
-class OpenAIGPTLMHead(nn.Module):
-    """ Language Model Head for the transformer """
-
-    def __init__(self, model_embeddings_weights, cfg):
-        super(OpenAIGPTLMHead, self).__init__()
-        self.n_embd = cfg.n_embd
-        self.set_embeddings_weights(model_embeddings_weights)
-
-    def set_embeddings_weights(self, model_embeddings_weights):
-        embed_shape = model_embeddings_weights.shape
-        self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
-        self.decoder.weight = model_embeddings_weights # Tied weights
-
-    def forward(self, h):
-        # Truncated Language modeling logits (we remove the last token)
-        h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd)
-        lm_logits = self.decoder(h_trunc)
-        return lm_logits
-
-
-class OpenAIGPTClfHead(nn.Module):
-    """ Classifier Head for the transformer """
-
-    def __init__(self, clf_token, cfg):
-        super(OpenAIGPTClfHead, self).__init__()
-        self.n_embd = cfg.n_embd
-        self.clf_token = clf_token
-        self.dropout = nn.Dropout2d(cfg.resid_pdrop)  # To reproduce the noise_shape parameter of TF implementation
-        self.linear = nn.Linear(cfg.n_embd, 1)
-
-        nn.init.normal_(self.linear.weight, std = 0.02)
-        nn.init.normal_(self.linear.bias, 0)
-
-    def forward(self, h, x):
-        # Classification logits
-        clf_h = h.view(-1, self.n_embd)
-        flat = x[..., 0].contiguous().view(-1)
-        clf_h = clf_h[flat == self.clf_token, :]
-        clf_h = clf_h.view(-1, x.size(1), self.n_embd, 1)
-        # This double transposition is there to replicate the behavior
-        # of the noise_shape argument in the tensorflow
-        # implementation.  For more details, see
-        # https://github.com/huggingface/pytorch-openai-transformer-lm/issues/11
-        clf_h = self.dropout(clf_h.transpose(1, 2)).transpose(1, 2)
-        clf_h = clf_h.contiguous().view(-1, self.n_embd)
-        clf_logits = self.linear(clf_h)
-
-        return clf_logits.view(-1, x.size(1))
-
 
 class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
     """ OpenAI GPT model """
@@ -440,6 +442,7 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
         # nn.init.normal_(self.embed.weight, std=0.02)
 
     def set_num_special_tokens(self, num_special_tokens):
+        " Update input embeddings with new embedding matrice "
         # Update config
         self.config.n_special = num_special_tokens
         # # Build new embeddings and initialize
@@ -451,45 +454,83 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
         self.embed.weight.data[:self.config.vocab_size, :] = old_embed.weight.data[:self.config.vocab_size, :]
         self.embed.weight.data[-self.config.n_ctx:, :] = old_embed.weight.data[-self.config.n_ctx:, :]
 
-    def forward(self, x):
-        x = x.view(-1, x.size(-2), x.size(-1))
-        e = self.embed(x)
+    def forward(self, input_ids, position_ids=None, token_type_ids=None):
+        if position_ids is None:
+            start = self.config.vocab_size + self.config.n_special
+            end = start + input_ids.size(-1)
+            position_ids = torch.arange(start, end, dtype=torch.long, device=input_ids.device)
+            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_ids.size(-1))
+        position_ids = position_ids.view(-1, position_ids.size(-1))
+
+        inputs_embeds = self.embed(input_ids)
+        position_embeds = self.embed(position_ids)
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
+            token_type_embeds = self.embed(token_type_ids)
+        else:
+            token_type_embeds = 0
         # Add the position information to the input embeddings
-        h = e.sum(dim=2)
+        # h = e.sum(dim=2)
+        hidden_states = inputs_embeds + position_embeds + token_type_embeds
         for block in self.h:
-            h = block(h)
-        return h
+            hidden_states = block(hidden_states)
+        return hidden_states.view(*input_shape, hidden_states.size(-1))
 
-
-class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
+class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
     """ OpenAI GPT model with language model and classification heads """
-    def __init__(self, cfg, clf_token='[CLS]'):
-        super(OpenAIGPTDoubleHeadsModel, self).__init__(cfg)
+    def __init__(self, cfg):
+        super(OpenAIGPTLMHeadModel, self).__init__(cfg)
         self.transformer = OpenAIGPTModel(cfg)
         self.lm_head = OpenAIGPTLMHead(self.transformer.embed.weight, cfg)
-        self.clf_head = OpenAIGPTClfHead(clf_token, cfg)
         self.apply(self.init_weights)
 
-    def post_loading(self):
-        " Set the number of special tokens to 1 (for the [CLS] token) "
-        self.set_num_special_tokens(1)
-
     def set_num_special_tokens(self, num_special_tokens):
         " Update input and output embeddings with new embedding matrice "
         self.transformer.set_num_special_tokens(num_special_tokens)
         self.lm_head.set_embeddings_weights(self.transformer.embed.weight)
 
-    def forward(self, x, lm_labels=None, clf_labels=None):
-        h = self.transformer(x)
-        lm_logits = self.lm_head(h)
-        clf_logits = self.clf_head(h, x)
-        losses = []
+    def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None):
+        hidden_states = self.transformer(input_ids, position_ids, token_type_ids)
+        lm_logits = self.lm_head(hidden_states)
         if lm_labels is not None:
             loss_fct = CrossEntropyLoss()
-            losses.append(loss_fct(lm_logits, lm_labels))
-        if clf_labels is not None:
+            loss = loss_fct(lm_logits, lm_labels)
+            return loss
+        return lm_logits
+
+class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
+    """ OpenAI GPT model with language model and classification heads """
+    def __init__(self, cfg):
+        super(OpenAIGPTDoubleHeadsModel, self).__init__(cfg)
+        self.transformer = OpenAIGPTModel(cfg)
+        self.lm_head = OpenAIGPTLMHead(self.transformer.embed.weight, cfg)
+        self.multiple_choice_head = OpenAIGPTMultipleChoiceHead(cfg)
+        self.apply(self.init_weights)
+
+    def set_num_special_tokens(self, num_special_tokens):
+        " Update input and output embeddings with new embedding matrice "
+        self.transformer.set_num_special_tokens(num_special_tokens)
+        self.lm_head.set_embeddings_weights(self.transformer.embed.weight)
+
+    def forward(self, input_ids, classification_token_mask, position_ids=None, token_type_ids=None,
+                lm_labels=None, multiple_choice_labels=None):
+        """ 
+            input_ids as to be of shape B x C x S
+            lm_labels can be masked using the -1 value
+        """
+        hidden_states = self.transformer(input_ids, position_ids, token_type_ids)
+        lm_logits = self.lm_head(hidden_states)
+        multiple_choice_logits = self.multiple_choice_head(hidden_states, classification_token_mask)
+        losses = []
+        if lm_labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            losses.append(loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1)))
+        if multiple_choice_labels is not None:
             loss_fct = CrossEntropyLoss()
-            losses.append(loss_fct(clf_logits, clf_labels))
+            losses.append(loss_fct(multiple_choice_logits, multiple_choice_labels.view(-1)))
         if losses:
             return losses
-        return lm_logits, clf_logits
+        return lm_logits, multiple_choice_logits

pytorch_pretrained_bert/tokenization_openai.py

@@ -67,19 +67,17 @@ class OpenAIGPTTokenizer(object):
     mostly a wrapper for a public python bpe tokenizer
     """
     @classmethod
-    def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
+    def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
         """
         Instantiate a PreTrainedBertModel from a pre-trained model file.
         Download and cache the pre-trained model file if needed.
         """
-        if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
-            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
-            merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name]
+        if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
+            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
+            merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
         else:
-            vocab_file = pretrained_model_name
-        if os.path.isdir(vocab_file):
-            vocab_file = os.path.join(vocab_file, VOCAB_NAME)
-            merges_file = os.path.join(vocab_file, MERGES_NAME)
+            vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
+            merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
         # redirect to the cache, if necessary
         try:
             resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
@@ -87,11 +85,12 @@ class OpenAIGPTTokenizer(object):
         except FileNotFoundError:
             logger.error(
                 "Model name '{}' was not found in model name list ({}). "
-                "We assumed '{}' was a path or url but couldn't find any file "
-                "associated to this path or url.".format(
-                    pretrained_model_name,
+                "We assumed '{}' was a path or url but couldn't find files {} and {} "
+                "at this path or url.".format(
+                    pretrained_model_name_or_path,
                     ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
-                    vocab_file))
+                    pretrained_model_name_or_path,
+                    vocab_file, merges_file))
             return None
         if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
             logger.info("loading vocabulary file {}".format(vocab_file))
@@ -101,29 +100,38 @@ class OpenAIGPTTokenizer(object):
                 vocab_file, resolved_vocab_file))
             logger.info("loading merges file {} from cache at {}".format(
                 merges_file, resolved_merges_file))
-        if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
+        if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
             # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
             # than the number of positional embeddings
-            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
+            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
             kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
         # Instantiate tokenizer.
         tokenizer = cls(resolved_vocab_file, resolved_merges_file, *inputs, **kwargs)
         return tokenizer
 
-    def __init__(self, vocab_file, merges_file):
+    def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None):
         try:
             import ftfy
             import spacy
         except ImportError:
             raise ImportError("Please install ftfy and spacy to use OpenAI GPT tokenizer.")
 
+        self.max_len = max_len if max_len is not None else int(1e12)
         self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
+        self.fix_text = ftfy.fix_text
         self.encoder = json.load(open(vocab_file))
         self.decoder = {v:k for k,v in self.encoder.items()}
         merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
         merges = [tuple(merge.split()) for merge in merges]
         self.bpe_ranks = dict(zip(merges, range(len(merges))))
         self.cache = {}
+        if not special_tokens:
+            self.special_tokens = {}
+        else:
+            self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
+
+    def set_special_tokens(self, special_tokens):
+        self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
 
     def bpe(self, token):
         word = tuple(token[:-1]) + ( token[-1] + '</w>',)
@@ -168,20 +176,38 @@ class OpenAIGPTTokenizer(object):
         self.cache[token] = word
         return word
 
-    def tokenize(self, texts, verbose=True):
-        texts_tokens = []
-        if verbose:
-            for text in tqdm(texts, ncols=80, leave=False):
-                text = self.nlp(text_standardize(ftfy.fix_text(text)))
-                text_tokens = []
-                for token in text:
-                    text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
-                texts_tokens.append(text_tokens)
-        else:
-            for text in texts:
-                text = self.nlp(text_standardize(ftfy.fix_text(text)))
-                text_tokens = []
-                for token in text:
-                    text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
-                texts_tokens.append(text_tokens)
-        return texts_tokens
+    def tokenize(self, text):
+        split_tokens = []
+        text = self.nlp(text_standardize(self.fix_text(text)))
+        for token in text:
+            split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        """Converts a sequence of tokens into ids using the vocab."""
+        ids = []
+        for token in tokens:
+            if token in self.special_tokens:
+                ids.append(self.special_tokens[token])
+            else:
+                ids.append(self.encoder.get(token, 0))
+        if len(ids) > self.max_len:
+            raise ValueError(
+                "Token indices sequence length is longer than the specified maximum "
+                " sequence length for this BERT model ({} > {}). Running this"
+                " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
+            )
+        return ids
+
+    def convert_ids_to_tokens(self, ids):
+        """Converts a sequence of ids in BPE tokens using the vocab."""
+        tokens = []
+        for i in ids:
+            tokens.append(self.decoder[i])
+        return tokens
+
+    def decode(self, ids):
+        """Converts a sequence of ids in a string."""
+        tokens = self.convert_ids_to_tokens(ids)
+        out_string = ''.join(tokens).replace('</w>', ' ')
+        return out_string

tests/modeling_openai_test.py

@@ -0,0 +1,192 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# 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.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import unittest
+import json
+import random
+
+import torch
+
+from pytorch_pretrained_bert import (OpenAIGPTConfig, OpenAIGPTModel, OpenAIGPTDoubleHeadsModel)
+
+
+class OpenAIGPTModelTest(unittest.TestCase):
+    class OpenAIGPTModelTester(object):
+
+        def __init__(self,
+                     parent,
+                     batch_size=13,
+                     seq_length=7,
+                     is_training=True,
+                     use_position_ids=True,
+                     use_token_type_ids=True,
+                     use_labels=True,
+                     vocab_size=99,
+                     n_special=1,
+                     n_ctx=33,
+                     n_embd=32,
+                     n_layer=5,
+                     n_head=4,
+                     n_choices=3,
+                     afn="gelu",
+                     resid_pdrop=0.1,
+                     attn_pdrop=0.1,
+                     embd_pdrop=0.1,
+                     type_sequence_label_size=2,
+                     initializer_range=0.02,
+                     num_labels=3,
+                     scope=None):
+            self.parent = parent
+            self.batch_size = batch_size
+            self.seq_length = seq_length
+            self.is_training = is_training
+            self.use_position_ids = use_position_ids
+            self.use_token_type_ids = use_token_type_ids
+            self.use_labels = use_labels
+            self.vocab_size = vocab_size
+            self.n_special = n_special
+            self.n_ctx = n_ctx
+            self.n_embd = n_embd
+            self.n_layer = n_layer
+            self.n_head = n_head
+            self.afn = afn
+            self.n_choices = n_choices
+            self.resid_pdrop = resid_pdrop
+            self.attn_pdrop = attn_pdrop
+            self.embd_pdrop = embd_pdrop
+            self.type_sequence_label_size = type_sequence_label_size
+            self.initializer_range = initializer_range
+            self.num_labels = num_labels
+            self.scope = scope
+
+        def prepare_config_and_inputs(self):
+            input_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.vocab_size)
+
+            position_ids = None
+            if self.use_position_ids:
+                position_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_ctx)
+                position_ids = position_ids + self.n_special + self.vocab_size
+
+            token_type_ids = None
+            if self.use_token_type_ids:
+                total_voc = self.n_ctx + self.n_special + self.vocab_size
+                token_type_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
+
+            multiple_choice_labels = None
+            lm_labels = None
+            classification_token_mask = None
+            if self.use_labels:
+                multiple_choice_labels = OpenAIGPTModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
+                lm_labels = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
+                classification_token_mask = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], 2).float()
+
+            config = OpenAIGPTConfig(
+                vocab_size_or_config_json_file=self.vocab_size,
+                n_ctx=self.n_ctx,
+                n_special=self.n_special,
+                n_embd=self.n_embd,
+                n_layer=self.n_layer,
+                n_head=self.n_head,
+                afn=self.afn,
+                resid_pdrop=self.resid_pdrop,
+                attn_pdrop=self.attn_pdrop,
+                embd_pdrop=self.embd_pdrop,
+                initializer_range=self.initializer_range)
+
+            return (config, input_ids, token_type_ids, position_ids,
+                    multiple_choice_labels, lm_labels, classification_token_mask)
+
+        def create_openai_model(self, config, input_ids, token_type_ids, position_ids,
+                                multiple_choice_labels, lm_labels, classification_token_mask):
+            model = OpenAIGPTModel(config)
+            hidden_states = model(input_ids, position_ids, token_type_ids)
+            outputs = {
+                "hidden_states": hidden_states,
+            }
+            return outputs
+
+        def check_openai_model_output(self, result):
+            self.parent.assertListEqual(
+                list(result["hidden_states"].size()),
+                [self.batch_size, self.n_choices, self.seq_length, self.n_embd])
+
+
+        def create_openai_double_heads(self, config, input_ids, token_type_ids, position_ids,
+                                       multiple_choice_labels, lm_labels, classification_token_mask):
+            model = OpenAIGPTDoubleHeadsModel(config)
+            loss = model(input_ids, classification_token_mask, position_ids,
+                         token_type_ids, lm_labels, multiple_choice_labels)
+            lm_logits, multiple_choice_logits = model(input_ids, classification_token_mask, position_ids, token_type_ids)
+            outputs = {
+                "loss": loss,
+                "lm_logits": lm_logits,
+                "multiple_choice_logits": multiple_choice_logits,
+            }
+            return outputs
+
+        def check_openai_double_heads_output(self, result):
+            total_voc = self.n_ctx + self.n_special + self.vocab_size
+            self.parent.assertListEqual(
+                list(result["lm_logits"].size()),
+                [self.batch_size, self.n_choices, self.seq_length, total_voc])
+            self.parent.assertListEqual(
+                list(result["multiple_choice_logits"].size()),
+                [self.batch_size, self.n_choices])
+
+        def check_openai_double_heads_loss_output(self, result):
+            self.parent.assertListEqual(
+                [list(l.size()) for l in result["loss"]],
+                [[], []])
+
+    def test_default(self):
+        self.run_tester(OpenAIGPTModelTest.OpenAIGPTModelTester(self))
+
+    def test_config_to_json_string(self):
+        config = OpenAIGPTConfig(vocab_size_or_config_json_file=99, n_embd=37)
+        obj = json.loads(config.to_json_string())
+        self.assertEqual(obj["vocab_size"], 99)
+        self.assertEqual(obj["n_embd"], 37)
+
+    def run_tester(self, tester):
+        config_and_inputs = tester.prepare_config_and_inputs()
+        output_result = tester.create_openai_model(*config_and_inputs)
+        tester.check_openai_model_output(output_result)
+
+        output_result = tester.create_openai_double_heads(*config_and_inputs)
+        tester.check_openai_double_heads_output(output_result)
+        tester.check_openai_double_heads_loss_output(output_result)
+
+    @classmethod
+    def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
+        """Creates a random int32 tensor of the shape within the vocab size."""
+        if rng is None:
+            rng = random.Random()
+
+        total_dims = 1
+        for dim in shape:
+            total_dims *= dim
+
+        values = []
+        for _ in range(total_dims):
+            values.append(rng.randint(0, vocab_size - 1))
+
+        return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
+
+
+if __name__ == "__main__":
+    unittest.main()