add from_pretrained method to all configuration classes

pytorch_pretrained_bert/__init__.py

@@ -18,7 +18,7 @@ from .modeling_transfo_xl import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHe
 from .modeling_gpt2 import (GPT2Config, GPT2Model,
                             GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2MultipleChoiceHead,
                             load_tf_weights_in_gpt2)
-from .modeling_xlnet import (XLNetBaseConfig, XLNetConfig, XLNetRunConfig,
+from .modeling_xlnet import (XLNetConfig,
                              XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
                              XLNetForSequenceClassification, XLNetForQuestionAnswering,
                              load_tf_weights_in_xlnet)
@@ -26,5 +26,6 @@ from .modeling_xlnet import (XLNetBaseConfig, XLNetConfig, XLNetRunConfig,
 from .optimization import BertAdam
 from .optimization_openai import OpenAIAdam
 
-from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE, cached_path,
-                         WEIGHTS_NAME, CONFIG_NAME)
+from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE, cached_path)
+
+from .model_utils import (WEIGHTS_NAME, CONFIG_NAME, PretrainedConfig)

pytorch_pretrained_bert/convert_xlnet_checkpoint_to_pytorch.py

@@ -23,7 +23,7 @@ import argparse
 import torch
 
 from pytorch_pretrained_bert.modeling_xlnet import (CONFIG_NAME, WEIGHTS_NAME,
-                                                    XLNetConfig, XLNetRunConfig,
+                                                    XLNetConfig,
                                                     XLNetLMHeadModel, XLNetForQuestionAnswering,
                                                     XLNetForSequenceClassification,
                                                     load_tf_weights_in_xlnet)

pytorch_pretrained_bert/file_utils.py

@@ -44,9 +44,6 @@ except (AttributeError, ImportError):
     PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
                                               default_cache_path)
 
-CONFIG_NAME = "config.json"
-WEIGHTS_NAME = "pytorch_model.bin"
-
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
 

pytorch_pretrained_bert/model_utils.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team 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 BERT model."""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import json
+import copy
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss, MSELoss
+
+from .file_utils import cached_path
+
+logger = logging.getLogger(__name__)
+
+CONFIG_NAME = "config.json"
+WEIGHTS_NAME = "pytorch_model.bin"
+
+
+class PretrainedConfig(object):
+    """ An abstract class to handle dowloading a model pretrained config.
+    """
+    pretrained_config_archive_map = {}
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        """
+        Instantiate a PretrainedConfig from a pre-trained model configuration.
+
+        Params:
+            pretrained_model_name_or_path: either:
+                - a str with the name of a pre-trained model to load selected in the list of:
+                    . `xlnet-large-cased`
+                - a path or url to a pretrained model archive containing:
+                    . `config.json` a configuration file for the model
+            cache_dir: an optional path to a folder in which the pre-trained model configuration will be cached.
+        """
+        cache_dir = kwargs.get('cache_dir', None)
+        kwargs.pop('cache_dir', None)
+
+        if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
+            config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
+        else:
+            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+        # redirect to the cache, if necessary
+        try:
+            resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
+        except EnvironmentError:
+            if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
+                logger.error(
+                    "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
+                        config_file))
+            else:
+                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_or_path,
+                        ', '.join(cls.pretrained_config_archive_map.keys()),
+                        config_file))
+            return None
+        if resolved_config_file == config_file:
+            logger.info("loading configuration file {}".format(config_file))
+        else:
+            logger.info("loading configuration file {} from cache at {}".format(
+                config_file, resolved_config_file))
+
+        # Load config
+        config = cls.from_json_file(resolved_config_file)
+
+        # Update config with kwargs if needed
+        to_remove = []
+        for key, value in kwargs.items():
+            if hasattr(config, key):
+                setattr(config, key, value)
+                to_remove.append(key)
+        for key in to_remove:
+            kwargs.pop(key, None)
+
+        logger.info("Model config {}".format(config))
+        return config
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `Config` from a Python dictionary of parameters."""
+        config = cls(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())
+
+
+def prune_linear_layer(layer, index, dim=0):
+    """ Prune a linear layer (a model parameters) to keep only entries in index.
+        Return the pruned layer as a new layer with requires_grad=True.
+        Used to remove heads.
+    """
+    index = index.to(layer.weight.device)
+    W = layer.weight.index_select(dim, index).clone().detach()
+    if layer.bias is not None:
+        if dim == 1:
+            b = layer.bias.clone().detach()
+        else:
+            b = layer.bias[index].clone().detach()
+    new_size = list(layer.weight.size())
+    new_size[dim] = len(index)
+    new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
+    new_layer.weight.requires_grad = False
+    new_layer.weight.copy_(W.contiguous())
+    new_layer.weight.requires_grad = True
+    if layer.bias is not None:
+        new_layer.bias.requires_grad = False
+        new_layer.bias.copy_(b.contiguous())
+        new_layer.bias.requires_grad = True
+    return new_layer
+
+
+class Conv1D(nn.Module):
+    """ Conv1D layer as defined by Alec Radford for GPT (and also used in GPT-2)
+        Basically works like a Linear layer but the weights are transposed
+    """
+    def __init__(self, nf, nx):
+        super(Conv1D, self).__init__()
+        self.nf = nf
+        w = torch.empty(nx, nf)
+        nn.init.normal_(w, std=0.02)
+        self.weight = nn.Parameter(w)
+        self.bias = nn.Parameter(torch.zeros(nf))
+
+    def forward(self, x):
+        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)
+        return x
+
+
+def prune_conv1d_layer(layer, index, dim=1):
+    """ Prune a Conv1D layer (a model parameters) to keep only entries in index.
+        A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
+        Return the pruned layer as a new layer with requires_grad=True.
+        Used to remove heads.
+    """
+    index = index.to(layer.weight.device)
+    W = layer.weight.index_select(dim, index).clone().detach()
+    if dim == 0:
+        b = layer.bias.clone().detach()
+    else:
+        b = layer.bias[index].clone().detach()
+    new_size = list(layer.weight.size())
+    new_size[dim] = len(index)
+    new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
+    new_layer.weight.requires_grad = False
+    new_layer.weight.copy_(W.contiguous())
+    new_layer.weight.requires_grad = True
+    new_layer.bias.requires_grad = False
+    new_layer.bias.copy_(b.contiguous())
+    new_layer.bias.requires_grad = True
+    return new_layer

pytorch_pretrained_bert/modeling.py

@@ -29,7 +29,8 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .file_utils import cached_path, WEIGHTS_NAME, CONFIG_NAME
+from .file_utils import cached_path
+from .model_utils import WEIGHTS_NAME, CONFIG_NAME, PretrainedConfig, prune_linear_layer
 
 logger = logging.getLogger(__name__)
 
@@ -66,30 +67,6 @@ PRETRAINED_CONFIG_ARCHIVE_MAP = {
 BERT_CONFIG_NAME = 'bert_config.json'
 TF_WEIGHTS_NAME = 'model.ckpt'
 
-def prune_linear_layer(layer, index, dim=0):
-    """ Prune a linear layer (a model parameters) to keep only entries in index.
-        Return the pruned layer as a new layer with requires_grad=True.
-        Used to remove heads.
-    """
-    index = index.to(layer.weight.device)
-    W = layer.weight.index_select(dim, index).clone().detach()
-    if layer.bias is not None:
-        if dim == 1:
-            b = layer.bias.clone().detach()
-        else:
-            b = layer.bias[index].clone().detach()
-    new_size = list(layer.weight.size())
-    new_size[dim] = len(index)
-    new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
-    new_layer.weight.requires_grad = False
-    new_layer.weight.copy_(W.contiguous())
-    new_layer.weight.requires_grad = True
-    if layer.bias is not None:
-        new_layer.bias.requires_grad = False
-        new_layer.bias.copy_(b.contiguous())
-        new_layer.bias.requires_grad = True
-    return new_layer
-
 
 def load_tf_weights_in_bert(model, tf_checkpoint_path):
     """ Load tf checkpoints in a pytorch model
@@ -174,9 +151,11 @@ def swish(x):
 ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
 
 
-class BertConfig(object):
+class BertConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `BertModel`.
     """
+    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+
     def __init__(self,
                  vocab_size_or_config_json_file,
                  hidden_size=768,
@@ -238,37 +217,6 @@ class BertConfig(object):
             raise ValueError("First argument must be either a vocabulary size (int)"
                              "or the path to a pretrained model config file (str)")
 
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `BertConfig` from a Python dictionary of parameters."""
-        config = BertConfig(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `BertConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding='utf-8') as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
-        with open(json_file_path, "w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())
 
 try:
     from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm

pytorch_pretrained_bert/modeling_gpt2.py

@@ -31,7 +31,8 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .file_utils import cached_path, CONFIG_NAME, WEIGHTS_NAME
+from .file_utils import cached_path
+from .model_utils import Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, prune_conv1d_layer
 from .modeling import BertLayerNorm as LayerNorm
 
 logger = logging.getLogger(__name__)
@@ -41,30 +42,6 @@ PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.hugging
 PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
                                  "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json"}
 
-def prune_conv1d_layer(layer, index, dim=1):
-    """ Prune a Conv1D layer (a model parameters) to keep only entries in index.
-        A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
-        Return the pruned layer as a new layer with requires_grad=True.
-        Used to remove heads.
-    """
-    index = index.to(layer.weight.device)
-    W = layer.weight.index_select(dim, index).clone().detach()
-    if dim == 0:
-        b = layer.bias.clone().detach()
-    else:
-        b = layer.bias[index].clone().detach()
-    new_size = list(layer.weight.size())
-    new_size[dim] = len(index)
-    new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
-    new_layer.weight.requires_grad = False
-    new_layer.weight.copy_(W.contiguous())
-    new_layer.weight.requires_grad = True
-    new_layer.bias.requires_grad = False
-    new_layer.bias.copy_(b.contiguous())
-    new_layer.bias.requires_grad = True
-    return new_layer
-
-
 def load_tf_weights_in_gpt2(model, gpt2_checkpoint_path):
     """ Load tf checkpoints in a pytorch model
     """
@@ -123,9 +100,10 @@ def gelu(x):
     return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
 
 
-class GPT2Config(object):
+class GPT2Config(PretrainedConfig):
     """Configuration class to store the configuration of a `GPT2Model`.
     """
+    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(
         self,
@@ -194,54 +172,6 @@ class GPT2Config(object):
     def total_tokens_embeddings(self):
         return self.vocab_size + self.n_special
 
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `GPT2Config` from a Python dictionary of parameters."""
-        config = GPT2Config(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `GPT2Config` from a json file of parameters."""
-        with open(json_file, "r", encoding="utf-8") as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
-        with open(json_file_path, "w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())
-
-
-class Conv1D(nn.Module):
-    def __init__(self, nf, nx):
-        super(Conv1D, self).__init__()
-        self.nf = nf
-        w = torch.empty(nx, nf)
-        nn.init.normal_(w, std=0.02)
-        self.weight = Parameter(w)
-        self.bias = Parameter(torch.zeros(nf))
-
-    def forward(self, x):
-        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)
-        return x
-
 
 class Attention(nn.Module):
     def __init__(self, nx, n_ctx, config, scale=False, output_attentions=False, keep_multihead_output=False):

pytorch_pretrained_bert/modeling_openai.py

@@ -31,9 +31,9 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .file_utils import cached_path, CONFIG_NAME, WEIGHTS_NAME
+from .file_utils import cached_path
+from .model_utils import Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, prune_conv1d_layer
 from .modeling import BertLayerNorm as LayerNorm
-from .modeling_gpt2 import prune_conv1d_layer
 
 logger = logging.getLogger(__name__)
 
@@ -122,9 +122,10 @@ def swish(x):
 ACT_FNS = {"relu": nn.ReLU, "swish": swish, "gelu": gelu}
 
 
-class OpenAIGPTConfig(object):
+class OpenAIGPTConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `OpenAIGPTModel`.
     """
+    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(
         self,
@@ -197,61 +198,6 @@ class OpenAIGPTConfig(object):
     def total_tokens_embeddings(self):
         return self.vocab_size + self.n_special
 
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters."""
-        config = OpenAIGPTConfig(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `OpenAIGPTConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding="utf-8") as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
-        with open(json_file_path, "w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())
-
-
-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, config, scale=False, output_attentions=False, keep_multihead_output=False):
@@ -268,8 +214,8 @@ class Attention(nn.Module):
         self.keep_multihead_output = keep_multihead_output
         self.multihead_output = None
 
-        self.c_attn = Conv1D(n_state * 3, 1, nx)
-        self.c_proj = Conv1D(n_state, 1, nx)
+        self.c_attn = Conv1D(n_state * 3, nx)
+        self.c_proj = Conv1D(n_state, nx)
         self.attn_dropout = nn.Dropout(config.attn_pdrop)
         self.resid_dropout = nn.Dropout(config.resid_pdrop)
 
@@ -348,8 +294,8 @@ class MLP(nn.Module):
     def __init__(self, n_state, config):  # in MLP: n_state=3072 (4 * n_embd)
         super(MLP, self).__init__()
         nx = config.n_embd
-        self.c_fc = Conv1D(n_state, 1, nx)
-        self.c_proj = Conv1D(nx, 1, n_state)
+        self.c_fc = Conv1D(n_state, nx)
+        self.c_proj = Conv1D(nx, n_state)
         self.act = ACT_FNS[config.afn]
         self.dropout = nn.Dropout(config.resid_pdrop)
 

pytorch_pretrained_bert/modeling_transfo_xl.py

@@ -37,7 +37,8 @@ from torch.nn.parameter import Parameter
 
 from .modeling import BertLayerNorm as LayerNorm
 from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax, sample_logits
-from .file_utils import cached_path, CONFIG_NAME, WEIGHTS_NAME
+from .file_utils import cached_path
+from .model_utils import CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig
 
 logger = logging.getLogger(__name__)
 
@@ -178,9 +179,11 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
     return model
 
 
-class TransfoXLConfig(object):
+class TransfoXLConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `TransfoXLModel`.
     """
+    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+
     def __init__(self,
                  vocab_size_or_config_json_file=267735,
                  cutoffs=[20000, 40000, 200000],
@@ -285,38 +288,6 @@ class TransfoXLConfig(object):
             raise ValueError("First argument must be either a vocabulary size (int)"
                              "or the path to a pretrained model config file (str)")
 
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `TransfoXLConfig` from a Python dictionary of parameters."""
-        config = TransfoXLConfig(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `TransfoXLConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding='utf-8') as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
-        with open(json_file_path, "w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())
-
 
 class PositionalEmbedding(nn.Module):
     def __init__(self, demb):

pytorch_pretrained_bert/modeling_xlnet.py

@@ -32,7 +32,9 @@ from torch import nn
 from torch.nn import functional as F
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .file_utils import cached_path, WEIGHTS_NAME, CONFIG_NAME
+from .file_utils import cached_path
+from .model_utils import CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig
+
 
 logger = logging.getLogger(__name__)
 
@@ -192,48 +194,12 @@ def swish(x):
 
 ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
 
-class XLNetBaseConfig(object):
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `XLNetBaseConfig` from a Python dictionary of parameters."""
-        config = cls(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `XLNetBaseConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding='utf-8') as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def update(self, other):
-        dict_b = other.to_dict()
-        for key, value in dict_b.items():
-            self.__dict__[key] = value
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
-        with open(json_file_path, "w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())
-
 
-class XLNetConfig(XLNetBaseConfig):
+class XLNetConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `XLNetModel`.
     """
+    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+
     def __init__(self,
                  vocab_size_or_config_json_file,
                  d_model=1024,
@@ -337,53 +303,6 @@ class XLNetConfig(XLNetBaseConfig):
                              "or the path to a pretrained model config file (str)")
 
 
-class XLNetRunConfig(XLNetBaseConfig):
-    """XLNetRunConfig contains hyperparameters that could be different
-    between pretraining and finetuning.
-    These hyperparameters can also be changed from run to run.
-    We store them separately from XLNetConfig for flexibility.
-    """
-    def __init__(self, 
-                 dropout=0.1,
-                 dropatt=0.1,
-                 init="normal",
-                 init_range=0.1,
-                 init_std=0.02,
-                 mem_len=None,
-                 reuse_len=None,
-                 bi_data=False,
-                 clamp_len=-1,
-                 same_length=False):
-        """
-        Args:
-        dropout: float, dropout rate.
-        dropatt: float, dropout rate on attention probabilities.
-        init: str, the initialization scheme, either "normal" or "uniform".
-        init_range: float, initialize the parameters with a uniform distribution
-            in [-init_range, init_range]. Only effective when init="uniform".
-        init_std: float, initialize the parameters with a normal distribution
-            with mean 0 and stddev init_std. Only effective when init="normal".
-        mem_len: int, the number of tokens to cache.
-        reuse_len: int, the number of tokens in the currect batch to be cached
-            and reused in the future.
-        bi_data: bool, whether to use bidirectional input pipeline.
-            Usually set to True during pretraining and False during finetuning.
-        clamp_len: int, clamp all relative distances larger than clamp_len.
-            -1 means no clamping.
-        same_length: bool, whether to use the same attention length for each token.
-        """
-
-        self.init = init
-        self.init_range = init_range
-        self.init_std = init_std
-        self.dropout = dropout
-        self.dropatt = dropatt
-        self.mem_len = mem_len
-        self.reuse_len = reuse_len
-        self.bi_data = bi_data
-        self.clamp_len = clamp_len
-        self.same_length = same_length
-
 try:
     from apex.normalization.fused_layer_norm import FusedLayerNorm as XLNetLayerNorm
 except ImportError:
@@ -637,9 +556,9 @@ class XLNetPreTrainedModel(nn.Module):
     """
     def __init__(self, config, *inputs, **kwargs):
         super(XLNetPreTrainedModel, self).__init__()
-        if not isinstance(config, XLNetBaseConfig):
+        if not isinstance(config, XLNetConfig):
             raise ValueError(
-                "Parameter config in `{}(config)` should be an instance of class `XLNetBaseConfig`. "
+                "Parameter config in `{}(config)` should be an instance of class `XLNetConfig`. "
                 "To create a model from a Google pretrained model use "
                 "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
                     self.__class__.__name__, self.__class__.__name__

tests/modeling_xlnet_test.py

@@ -25,7 +25,7 @@ import pytest
 
 import torch
 
-from pytorch_pretrained_bert import (XLNetConfig, XLNetRunConfig, XLNetModel, XLNetLMHeadModel)
+from pytorch_pretrained_bert import (XLNetConfig, XLNetModel, XLNetLMHeadModel)
 from pytorch_pretrained_bert.modeling_xlnet import PRETRAINED_MODEL_ARCHIVE_MAP
 
 class XLNetModelTest(unittest.TestCase):
@@ -117,17 +117,13 @@ class XLNetModelTest(unittest.TestCase):
                 d_inner=self.d_inner,
                 n_layer=self.n_layer,
                 untie_r=self.untie_r,
-                max_position_embeddings=self.max_position_embeddings)
-
-            run_config = XLNetRunConfig(
+                max_position_embeddings=self.max_position_embeddings,
                 mem_len=self.mem_len,
                 clamp_len=self.clamp_len,
                 same_length=self.same_length,
                 reuse_len=self.reuse_len,
                 bi_data=self.bi_data)
 
-            config.update(run_config)
-
             return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, target_mapping, inp_q, segment_ids, lm_labels)
 
         def set_seed(self):