846 lines
37 KiB
Python
846 lines
37 KiB
Python
# coding=utf-8
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# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch OpenAI GPT model."""
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import json
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import math
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import os
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from dataclasses import dataclass
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from typing import Any, Callable, Optional, Union
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import gelu_new, get_activation, silu
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from ...generation import GenerationMixin
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from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
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from ...utils import (
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ModelOutput,
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auto_docstring,
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logging,
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)
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from .configuration_openai import OpenAIGPTConfig
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logger = logging.get_logger(__name__)
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def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
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"""Load tf pre-trained weights in a pytorch model (from NumPy arrays here)"""
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import re
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import numpy as np
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if ".ckpt" in openai_checkpoint_folder_path:
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openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)
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logger.info(f"Loading weights from {openai_checkpoint_folder_path}")
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with open(openai_checkpoint_folder_path + "/parameters_names.json", "r", encoding="utf-8") as names_handle:
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names = json.load(names_handle)
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with open(openai_checkpoint_folder_path + "/params_shapes.json", "r", encoding="utf-8") as shapes_handle:
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shapes = json.load(shapes_handle)
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offsets = np.cumsum([np.prod(shape) for shape in shapes])
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init_params = [np.load(openai_checkpoint_folder_path + f"/params_{n}.npy") for n in range(10)]
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init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
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init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
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# This was used when we had a single embedding matrix for positions and tokens
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# init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
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# del init_params[1]
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init_params = [arr.squeeze() for arr in init_params]
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# Check that the token and position embeddings weight dimensions map those of the init parameters.
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if model.tokens_embed.weight.shape != init_params[1].shape:
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raise ValueError(
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f"tokens_embed.weight.shape: {model.tokens_embed.weight.shape} does not match init_param[1].shape:"
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f" {init_params[1].shape}"
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)
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if model.positions_embed.weight.shape != init_params[0].shape:
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raise ValueError(
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f"positions_embed.weight.shape: {model.positions_embed.weight.shape} does not match init_param[0].shape:"
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f" {init_params[0].shape}"
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)
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model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
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model.positions_embed.weight.data = torch.from_numpy(init_params[0])
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names.pop(0)
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# Pop position and token embedding arrays
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init_params.pop(0)
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init_params.pop(0)
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for name, array in zip(names, init_params): # names[1:n_transfer], init_params[1:n_transfer]):
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name = name[6:] # skip "model/"
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if name[-2:] != ":0":
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raise ValueError(f"Layer {name} does not end with :0")
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name = name[:-2]
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name = name.split("/")
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pointer = model
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for m_name in name:
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if re.fullmatch(r"[A-Za-z]+\d+", m_name):
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scope_names = re.split(r"(\d+)", m_name)
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else:
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scope_names = [m_name]
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if scope_names[0] == "g":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "b":
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pointer = getattr(pointer, "bias")
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elif scope_names[0] == "w":
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pointer = getattr(pointer, "weight")
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else:
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pointer = getattr(pointer, scope_names[0])
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if len(scope_names) >= 2:
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num = int(scope_names[1])
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pointer = pointer[num]
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# Ensure that the pointer and array have compatible shapes.
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if pointer.shape != array.shape:
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raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
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logger.info(f"Initialize PyTorch weight {name}")
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pointer.data = torch.from_numpy(array)
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return model
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ACT_FNS = {"relu": nn.ReLU(), "silu": silu, "gelu": gelu_new, "swish": silu}
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class Attention(nn.Module):
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def __init__(self, nx, n_positions, config, scale=False):
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super().__init__()
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n_state = nx # in Attention: n_state=768 (nx=n_embd)
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# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
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if n_state % config.n_head != 0:
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raise ValueError(f"Attention n_state shape: {n_state} must be divisible by config.n_head {config.n_head}")
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self.register_buffer(
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"bias",
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torch.tril(torch.ones(n_positions, n_positions)).view(1, 1, n_positions, n_positions),
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persistent=False,
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)
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self.n_head = config.n_head
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self.split_size = n_state
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self.scale = scale
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self.c_attn = Conv1D(n_state * 3, nx)
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self.c_proj = Conv1D(n_state, nx)
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self.attn_dropout = nn.Dropout(config.attn_pdrop)
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self.resid_dropout = nn.Dropout(config.resid_pdrop)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.n_head, self.split_size // self.n_head, self.pruned_heads
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)
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index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
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# Prune conv1d layers
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self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
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self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
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# Update hyper params
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self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
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self.n_head = self.n_head - len(heads)
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self.pruned_heads = self.pruned_heads.union(heads)
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def _attn(self, q, k, v, attention_mask=None, head_mask=None, output_attentions=False):
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w = torch.matmul(q, k)
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if self.scale:
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w = w / math.sqrt(v.size(-1))
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# w = w * self.bias + -1e9 * (1 - self.bias) # TF implementation method: mask_attn_weights
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# XD: self.b may be larger than w, so we need to crop it
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b = self.bias[:, :, : w.size(-2), : w.size(-1)]
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w = w * b + -1e4 * (1 - b)
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if attention_mask is not None:
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# Apply the attention mask
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w = w + attention_mask
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w = nn.functional.softmax(w, dim=-1)
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w = self.attn_dropout(w)
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# Mask heads if we want to
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if head_mask is not None:
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w = w * head_mask
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outputs = [torch.matmul(w, v)]
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if output_attentions:
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outputs.append(w)
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return outputs
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def merge_heads(self, x):
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x = x.permute(0, 2, 1, 3).contiguous()
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new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
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return x.view(*new_x_shape) # in Tensorflow implementation: fct merge_states
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def split_heads(self, x, k=False):
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new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
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x = x.view(*new_x_shape) # in Tensorflow implementation: fct split_states
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if k:
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return x.permute(0, 2, 3, 1)
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else:
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return x.permute(0, 2, 1, 3)
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def forward(self, x, attention_mask=None, head_mask=None, output_attentions=False):
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x = self.c_attn(x)
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query, key, value = x.split(self.split_size, dim=2)
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query = self.split_heads(query)
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key = self.split_heads(key, k=True)
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value = self.split_heads(value)
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attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions)
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a = attn_outputs[0]
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a = self.merge_heads(a)
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a = self.c_proj(a)
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a = self.resid_dropout(a)
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outputs = [a] + attn_outputs[1:]
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return outputs # a, (attentions)
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class MLP(nn.Module):
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def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
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super().__init__()
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nx = config.n_embd
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self.c_fc = Conv1D(n_state, nx)
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self.c_proj = Conv1D(nx, n_state)
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self.act = ACT_FNS[config.afn]
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self.dropout = nn.Dropout(config.resid_pdrop)
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def forward(self, x):
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h = self.act(self.c_fc(x))
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h2 = self.c_proj(h)
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return self.dropout(h2)
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class Block(nn.Module):
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def __init__(self, n_positions, config, scale=False):
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super().__init__()
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nx = config.n_embd
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self.attn = Attention(nx, n_positions, config, scale)
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self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
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self.mlp = MLP(4 * nx, config)
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self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
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def forward(self, x, attention_mask=None, head_mask=None, output_attentions=False):
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attn_outputs = self.attn(
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x,
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attention_mask=attention_mask,
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head_mask=head_mask,
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output_attentions=output_attentions,
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)
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a = attn_outputs[0]
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n = self.ln_1(x + a)
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m = self.mlp(n)
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h = self.ln_2(n + m)
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outputs = [h] + attn_outputs[1:]
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return outputs
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# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->OpenAIGPT
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class OpenAIGPTSequenceSummary(nn.Module):
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r"""
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Compute a single vector summary of a sequence hidden states.
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Args:
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config ([`OpenAIGPTConfig`]):
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The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
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config class of your model for the default values it uses):
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- **summary_type** (`str`) -- The method to use to make this summary. Accepted values are:
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- `"last"` -- Take the last token hidden state (like XLNet)
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- `"first"` -- Take the first token hidden state (like Bert)
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- `"mean"` -- Take the mean of all tokens hidden states
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- `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
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- `"attn"` -- Not implemented now, use multi-head attention
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- **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
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- **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
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(otherwise to `config.hidden_size`).
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- **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
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another string or `None` will add no activation.
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- **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
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- **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
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"""
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def __init__(self, config: OpenAIGPTConfig):
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super().__init__()
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self.summary_type = getattr(config, "summary_type", "last")
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if self.summary_type == "attn":
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# We should use a standard multi-head attention module with absolute positional embedding for that.
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# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
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# We can probably just use the multi-head attention module of PyTorch >=1.1.0
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raise NotImplementedError
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self.summary = nn.Identity()
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if hasattr(config, "summary_use_proj") and config.summary_use_proj:
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if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
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num_classes = config.num_labels
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else:
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num_classes = config.hidden_size
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self.summary = nn.Linear(config.hidden_size, num_classes)
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activation_string = getattr(config, "summary_activation", None)
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self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity()
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self.first_dropout = nn.Identity()
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if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
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self.first_dropout = nn.Dropout(config.summary_first_dropout)
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self.last_dropout = nn.Identity()
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if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
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self.last_dropout = nn.Dropout(config.summary_last_dropout)
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def forward(
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self, hidden_states: torch.FloatTensor, cls_index: Optional[torch.LongTensor] = None
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) -> torch.FloatTensor:
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"""
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Compute a single vector summary of a sequence hidden states.
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Args:
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hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`):
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The hidden states of the last layer.
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cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
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Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token.
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Returns:
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`torch.FloatTensor`: The summary of the sequence hidden states.
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"""
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if self.summary_type == "last":
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output = hidden_states[:, -1]
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elif self.summary_type == "first":
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output = hidden_states[:, 0]
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elif self.summary_type == "mean":
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output = hidden_states.mean(dim=1)
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elif self.summary_type == "cls_index":
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if cls_index is None:
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cls_index = torch.full_like(
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hidden_states[..., :1, :],
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hidden_states.shape[-2] - 1,
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dtype=torch.long,
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)
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else:
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cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
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cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
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# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
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output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
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elif self.summary_type == "attn":
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raise NotImplementedError
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output = self.first_dropout(output)
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output = self.summary(output)
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output = self.activation(output)
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output = self.last_dropout(output)
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return output
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@auto_docstring
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class OpenAIGPTPreTrainedModel(PreTrainedModel):
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config: OpenAIGPTConfig
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load_tf_weights = load_tf_weights_in_openai_gpt
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base_model_prefix = "transformer"
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def _init_weights(self, module):
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"""Initialize the weights."""
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if isinstance(module, (nn.Linear, Conv1D)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for outputs of models predicting if two sentences are consecutive or not.
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"""
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)
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class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
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r"""
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Language modeling loss.
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mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided):
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Multiple choice classification loss.
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logits (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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mc_logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
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Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
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"""
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loss: Optional[torch.FloatTensor] = None
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mc_loss: Optional[torch.FloatTensor] = None
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logits: Optional[torch.FloatTensor] = None
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mc_logits: Optional[torch.FloatTensor] = None
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hidden_states: Optional[tuple[torch.FloatTensor]] = None
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attentions: Optional[tuple[torch.FloatTensor]] = None
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@auto_docstring
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class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
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self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
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self.drop = nn.Dropout(config.embd_pdrop)
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self.h = nn.ModuleList([Block(config.n_positions, config, scale=True) for _ in range(config.n_layer)])
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self.register_buffer("position_ids", torch.arange(config.n_positions), persistent=False)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.tokens_embed
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def set_input_embeddings(self, new_embeddings):
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self.tokens_embed = new_embeddings
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def _prune_heads(self, heads_to_prune):
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"""
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Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.h[layer].attn.prune_heads(heads)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutput]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
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:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
if position_ids is None:
|
|
# Code is different from when we had a single embedding matrix from position and token embeddings
|
|
position_ids = self.position_ids[None, : input_shape[-1]]
|
|
|
|
# Attention mask.
|
|
if attention_mask is not None:
|
|
# We create a 3D attention mask from a 2D tensor mask.
|
|
# Sizes are [batch_size, 1, 1, to_seq_length]
|
|
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
|
|
# this attention mask is more simple than the triangular masking of causal attention
|
|
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
|
|
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
|
|
|
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
|
# masked positions, this operation will create a tensor which is 0.0 for
|
|
# positions we want to attend and the dtype's smallest value for masked positions.
|
|
# Since we are adding it to the raw scores before the softmax, this is
|
|
# effectively the same as removing these entirely.
|
|
attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
|
|
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
|
|
|
|
# Prepare head mask if needed
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.tokens_embed(input_ids)
|
|
position_embeds = self.positions_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.tokens_embed(token_type_ids)
|
|
else:
|
|
token_type_embeds = 0
|
|
hidden_states = inputs_embeds + position_embeds + token_type_embeds
|
|
hidden_states = self.drop(hidden_states)
|
|
|
|
output_shape = input_shape + (hidden_states.size(-1),)
|
|
|
|
all_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
for i, block in enumerate(self.h):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
outputs = block(hidden_states, attention_mask, head_mask[i], output_attentions=output_attentions)
|
|
hidden_states = outputs[0]
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (outputs[1],)
|
|
|
|
hidden_states = hidden_states.view(*output_shape)
|
|
# Add last layer
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
|
embeddings).
|
|
"""
|
|
)
|
|
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = OpenAIGPTModel(config)
|
|
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[tuple[torch.Tensor], CausalLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
lm_logits = self.lm_head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Flatten the tokens
|
|
loss = self.loss_function(
|
|
lm_logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutput(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> dict[str, Any]:
|
|
# Overwritten -- old model with reduced inputs
|
|
return {"input_ids": input_ids}
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
|
|
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
|
|
input embeddings, the classification head takes as input the input of a specified classification token index in the
|
|
input sequence).
|
|
"""
|
|
)
|
|
class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
config.num_labels = 1
|
|
self.transformer = OpenAIGPTModel(config)
|
|
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
|
|
self.multiple_choice_head = OpenAIGPTSequenceSummary(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
mc_token_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
mc_labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], OpenAIGPTDoubleHeadsModelOutput]:
|
|
r"""
|
|
mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
|
|
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
|
|
1]`.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-1, 0, ..., config.vocab_size]` All labels set to `-100` are
|
|
ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
|
|
where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, OpenAIGPTDoubleHeadsModel
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
|
|
>>> model = OpenAIGPTDoubleHeadsModel.from_pretrained("openai-community/openai-gpt")
|
|
>>> tokenizer.add_special_tokens(
|
|
... {"cls_token": "[CLS]"}
|
|
... ) # Add a [CLS] to the vocabulary (we should train it also!)
|
|
>>> model.resize_token_embeddings(len(tokenizer))
|
|
|
|
>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
|
|
>>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
|
|
>>> mc_token_ids = torch.tensor([input_ids.size(-1) - 1, input_ids.size(-1) - 1]).unsqueeze(0) # Batch size 1
|
|
|
|
>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
|
|
>>> lm_logits = outputs.logits
|
|
>>> mc_logits = outputs.mc_logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
lm_logits = self.lm_head(hidden_states)
|
|
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
|
|
|
|
lm_loss, mc_loss = None, None
|
|
if mc_labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
|
|
if labels is not None:
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
loss_fct = CrossEntropyLoss()
|
|
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (lm_logits, mc_logits) + transformer_outputs[1:]
|
|
if mc_loss is not None:
|
|
output = (mc_loss,) + output
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
return OpenAIGPTDoubleHeadsModelOutput(
|
|
loss=lm_loss,
|
|
mc_loss=mc_loss,
|
|
logits=lm_logits,
|
|
mc_logits=mc_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Original OpenAI GPT Model transformer with a sequence classification head on top (linear layer).
|
|
[`OpenAIGPTForSequenceClassification`] uses the last token in order to do the classification, as other causal
|
|
models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the
|
|
last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding
|
|
token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since
|
|
it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take
|
|
the last value in each row of the batch).
|
|
"""
|
|
)
|
|
class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = OpenAIGPTModel(config)
|
|
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size, sequence_length = input_ids.shape[:2]
|
|
else:
|
|
batch_size, sequence_length = inputs_embeds.shape[:2]
|
|
|
|
# Ensure the batch size is > 1 if there is no padding.
|
|
if self.config.pad_token_id is None and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.pad_token_id is None:
|
|
last_non_pad_token = -1
|
|
elif input_ids is not None:
|
|
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
else:
|
|
last_non_pad_token = -1
|
|
logger.warning_once(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
if not return_dict:
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"OpenAIGPTDoubleHeadsModel",
|
|
"OpenAIGPTForSequenceClassification",
|
|
"OpenAIGPTLMHeadModel",
|
|
"OpenAIGPTModel",
|
|
"OpenAIGPTPreTrainedModel",
|
|
"load_tf_weights_in_openai_gpt",
|
|
]
|