# coding=utf-8 # Copyright 2018 The OpenAI Team Authors and 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 OpenAI GPT-2 model.""" import math import os import warnings from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.cuda.amp import autocast from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from transformers.modeling_utils import PreTrainedModel, SequenceSummary from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer from transformers.utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, ) from transformers.utils.model_parallel_utils import assert_device_map, get_device_map from .configuration_gpt2 import GPT2Config if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "openai-community/gpt2" _CONFIG_FOR_DOC = "GPT2Config" # Copied from transformers.models.llama.modeling_llama._get_unpad_data def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path): """Load tf checkpoints in a pytorch model""" try: import re import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(gpt2_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array.squeeze()) for name, array in zip(names, arrays): name = name[6:] # skip "model/" name = name.split("/") pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+\d+", m_name): scope_names = re.split(r"(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "w" or scope_names[0] == "g": pointer = getattr(pointer, "weight") elif scope_names[0] == "b": pointer = getattr(pointer, "bias") elif scope_names[0] == "wpe" or scope_names[0] == "wte": pointer = getattr(pointer, scope_names[0]) pointer = getattr(pointer, "weight") else: pointer = getattr(pointer, scope_names[0]) if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] try: if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") except ValueError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class GPT2Attention(nn.Module): def __init__(self, config, is_cross_attention=False, layer_idx=None): super().__init__() self.config = config max_positions = config.max_position_embeddings self.register_buffer( "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( 1, 1, max_positions, max_positions ), persistent=False, ) self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads self.split_size = self.embed_dim if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale_attn_weights = config.scale_attn_weights self.is_cross_attention = is_cross_attention # Layer-wise attention scaling, reordering, and upcasting self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx self.layer_idx = layer_idx self.reorder_and_upcast_attn = config.reorder_and_upcast_attn if self.is_cross_attention: self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim) self.q_attn = Conv1D(self.embed_dim, self.embed_dim) else: self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim) self.c_proj = Conv1D(self.embed_dim, self.embed_dim) # rhys101 do attention in float32 if model in bfloat16 ? if self.config.torch_dtype == torch.bfloat16: self.c_attn = self.c_attn.to(torch.float32) self.c_proj = self.c_proj.to(torch.float32) self.attn_dropout = nn.Dropout(config.attn_pdrop) self.resid_dropout = nn.Dropout(config.resid_pdrop) self.is_causal = True self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) # Prune conv1d layers self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) # Update hyper params self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) self.num_heads = self.num_heads - len(heads) self.pruned_heads = self.pruned_heads.union(heads) def _attn(self, query, key, value, attention_mask=None, head_mask=None): attn_weights = torch.matmul(query, key.transpose(-1, -2)) if self.scale_attn_weights: attn_weights = attn_weights / torch.full( [], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device ) # Layer-wise attention scaling if self.scale_attn_by_inverse_layer_idx: attn_weights = attn_weights / float(self.layer_idx + 1) if not self.is_cross_attention: # if only "normal" attention layer implements causal mask query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.full([], mask_value, dtype=attn_weights.dtype, device=attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value) if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None): # Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM) bsz, num_heads, q_seq_len, dk = query.size() _, _, k_seq_len, _ = key.size() # Preallocate attn_weights for `baddbmm` attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device) # Compute Scale Factor scale_factor = 1.0 if self.scale_attn_weights: scale_factor /= float(value.size(-1)) ** 0.5 if self.scale_attn_by_inverse_layer_idx: scale_factor /= float(self.layer_idx + 1) # Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk)) with autocast(enabled=False): q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len) attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor) attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) if not self.is_cross_attention: # if only "normal" attention layer implements causal mask query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights, mask_value) if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise if attn_weights.dtype != torch.float32: raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32") attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def _split_heads(self, tensor, num_heads, attn_head_size): """ Splits hidden_size dim into attn_head_size and num_heads """ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) tensor = tensor.view(new_shape) return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) def _merge_heads(self, tensor, num_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden_size """ tensor = tensor.permute(0, 2, 1, 3).contiguous() new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) return tensor.view(new_shape) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: # rhys101 do attention in float32 if model in bfloat16 ? if self.config.torch_dtype == torch.bfloat16: hidden_states = hidden_states.to(torch.float32) if encoder_hidden_states is not None: if not hasattr(self, "q_attn"): raise ValueError( "If class is used as cross attention, the weights `q_attn` have to be defined. " "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." ) query = self.q_attn(hidden_states) key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) attention_mask = encoder_attention_mask else: query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: past_key, past_value = layer_past key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) if use_cache is True: present = (key, value) else: present = None if self.reorder_and_upcast_attn: attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask) else: attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) attn_output = self.c_proj(attn_output) attn_output = self.resid_dropout(attn_output) # rhys101 revert back to our model dtype outputs = (attn_output.to(self.config.torch_dtype), present) if output_attentions: outputs += (attn_weights,) return outputs # a, present, (attentions) class GPT2FlashAttention2(GPT2Attention): """ GPT2 flash attention module. This module inherits from `GPT2Attention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: bsz, _, _ = hidden_states.size() if encoder_hidden_states is not None: if not hasattr(self, "q_attn"): raise ValueError( "If class is used as cross attention, the weights `q_attn` have to be defined. " "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." ) query = self.q_attn(hidden_states) key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) attention_mask = encoder_attention_mask else: query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: past_key = layer_past[0] past_value = layer_past[1] key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) present = None if use_cache is True: present = (key, value) query_length = query.shape[2] tgt_len = key.shape[2] # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim query = query.transpose(1, 2).view(bsz, query_length, self.num_heads, self.head_dim) key = key.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim) value = value.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim) attn_dropout = self.attn_dropout.p if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) if query.dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.c_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query = query.to(target_dtype) key = key.to(target_dtype) value = value.to(target_dtype) attn_output = self._flash_attention_forward( query, key, value, attention_mask, query_length, dropout=attn_dropout ) attn_weights_reshaped = attn_output.reshape(bsz, query_length, self.num_heads * self.head_dim) attn_output = self.c_proj(attn_weights_reshaped) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights_reshaped,) return outputs # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`float`): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) """ if not self._flash_attn_uses_top_left_mask: causal = self.is_causal else: # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. causal = self.is_causal and query_length != 1 # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal ) return attn_output # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) class GPT2MLP(nn.Module): def __init__(self, intermediate_size, config): super().__init__() embed_dim = config.hidden_size self.c_fc = Conv1D(intermediate_size, embed_dim) self.c_proj = Conv1D(embed_dim, intermediate_size) self.act = ACT2FN[config.activation_function] self.dropout = nn.Dropout(config.resid_pdrop) def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor: hidden_states = self.c_fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.c_proj(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states GPT2_ATTENTION_CLASSES = { "eager": GPT2Attention, "flash_attention_2": GPT2FlashAttention2, } class GPT2Block(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() hidden_size = config.hidden_size inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size attention_class = GPT2_ATTENTION_CLASSES[config._attn_implementation] self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.attn = attention_class(config=config, layer_idx=layer_idx) self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) if config.add_cross_attention: self.crossattention = attention_class(config=config, is_cross_attention=True, layer_idx=layer_idx) self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.mlp = GPT2MLP(inner_dim, config) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs = self.attn( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] # residual connection hidden_states = attn_output + residual if encoder_hidden_states is not None: # add one self-attention block for cross-attention if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with " "cross-attention layers by setting `config.add_cross_attention=True`" ) residual = hidden_states hidden_states = self.ln_cross_attn(hidden_states) cross_attn_outputs = self.crossattention( hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, ) attn_output = cross_attn_outputs[0] # residual connection hidden_states = residual + attn_output outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights residual = hidden_states hidden_states = self.ln_2(hidden_states) feed_forward_hidden_states = self.mlp(hidden_states) # residual connection hidden_states = residual + feed_forward_hidden_states if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs # hidden_states, present, (attentions, cross_attentions) class GPT2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPT2Config load_tf_weights = load_tf_weights_in_gpt2 base_model_prefix = "transformer" is_parallelizable = True supports_gradient_checkpointing = True _no_split_modules = ["GPT2Block"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear, Conv1D)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. # > -- GPT-2 :: https://openai.com/blog/better-language-models/ # # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py for name, p in module.named_parameters(): if name == "c_proj.weight": # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))) @dataclass class GPT2DoubleHeadsModelOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss. mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided): Multiple choice classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). mc_logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). past_key_values (`Tuple[Tuple[torch.Tensor]]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of length `config.n_layers`, containing tuples of tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. GPT2Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None mc_loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None mc_logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None GPT2_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`GPT2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ GPT2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`): Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for `past_key_values`. In other words, the `attention_mask` always has to have the length: `len(past_key_values) + len(input_ids)` [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see `past_key_values`). use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ PARALLELIZE_DOCSTRING = r""" This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks across all devices. Args: device_map (`Dict[int, list]`, optional, defaults to None): A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always automatically mapped to the first device (for esoteric reasons). That means that the first device should have fewer attention modules mapped to it than other devices. For reference, the gpt2 models have the following number of attention modules: - openai-community/gpt2: 12 - openai-community/gpt2-medium: 24 - openai-community/gpt2-large: 36 - openai-community/gpt2-xl: 48 Example: ```python # Here is an example of a device map on a machine with 4 GPUs using gpt2-xl, which has a total of 48 attention modules: model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-xl") device_map = { 0: [0, 1, 2, 3, 4, 5, 6, 7, 8], 1: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], 2: [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], 3: [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], } model.parallelize(device_map) ``` """ DEPARALLELIZE_DOCSTRING = r""" Moves the model to cpu from a model parallel state. Example: ```python # On a 4 GPU machine with openai-community/gpt2-large: model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-large") device_map = { 0: [0, 1, 2, 3, 4, 5, 6, 7], 1: [8, 9, 10, 11, 12, 13, 14, 15], 2: [16, 17, 18, 19, 20, 21, 22, 23], 3: [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35], } model.parallelize(device_map) # Splits the model across several devices model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache() ``` """ @add_start_docstrings( "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.", GPT2_START_DOCSTRING, ) class GPT2Model(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.embed_dim = config.hidden_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) self.drop = nn.Dropout(config.embd_pdrop) self.h = nn.ModuleList([GPT2Block(config, layer_idx=i) for i in range(config.num_hidden_layers)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False self._attn_implementation = config._attn_implementation # Initialize weights and apply final processing self.post_init() @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): # Check validity of device_map warnings.warn( "`GPT2Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your" " model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own" " `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1," " ...}", FutureWarning, ) self.device_map = ( get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.h)) self.model_parallel = True self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) self.last_device = "cuda:" + str(max(self.device_map.keys())) self.wte = self.wte.to(self.first_device) self.wpe = self.wpe.to(self.first_device) # Load onto devices for k, v in self.device_map.items(): for block in v: cuda_device = "cuda:" + str(k) self.h[block] = self.h[block].to(cuda_device) # ln_f to last self.ln_f = self.ln_f.to(self.last_device) @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.model_parallel = False self.device_map = None self.first_device = "cpu" self.last_device = "cpu" self.wte = self.wte.to("cpu") self.wpe = self.wpe.to("cpu") for index in range(len(self.h)): self.h[index] = self.h[index].to("cpu") self.ln_f = self.ln_f.to("cpu") torch.cuda.empty_cache() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings def _prune_heads(self, heads_to_prune): """ 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) @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache 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]) batch_size = input_ids.shape[0] elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size = inputs_embeds.shape[0] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1]) if past_key_values is None: past_length = 0 past_key_values = tuple([None] * len(self.h)) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) position_ids = position_ids.unsqueeze(0) # Attention mask. if attention_mask is not None: attention_mask = attention_mask.view(batch_size, -1) if self._attn_implementation == "flash_attention_2": attention_mask = attention_mask if 0 in attention_mask else None else: # 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[:, None, None, :] # 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=self.dtype) # fp16 compatibility attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.add_cross_attention and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) if self._attn_implementation != "flash_attention_2": encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # head_mask has shape n_layer x batch x n_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.n_layer) if inputs_embeds is None: inputs_embeds = self.wte(input_ids) position_embeds = self.wpe(position_ids) hidden_states = inputs_embeds + position_embeds if token_type_ids is not None: token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) # Ensure layer_past is on same device as hidden_states (might not be correct) if layer_past is not None: layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) # Ensure that attention_mask is always on the same device as hidden_states if attention_mask is not None: attention_mask = attention_mask.to(hidden_states.device) if isinstance(head_mask, torch.Tensor): head_mask = head_mask.to(hidden_states.device) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func( block.__call__, hidden_states, None, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, ) else: outputs = block( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) # Model Parallel: If it's the last layer for that device, put things on the next device if self.model_parallel: for k, v in self.device_map.items(): if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1)) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) @add_start_docstrings( """ The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, GPT2_START_DOCSTRING, ) class GPT2LMHeadModel(GPT2PreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = GPT2Model(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): warnings.warn( "`GPT2LMHeadModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load" " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own" " `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':" " 0, 'transformer.h.1': 1, ...}", FutureWarning, ) self.device_map = ( get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.transformer.h)) self.transformer.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.transformer.first_device) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.transformer.deparallelize() self.transformer = self.transformer.to("cpu") self.lm_head = self.lm_head.to("cpu") self.model_parallel = False torch.cuda.empty_cache() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # Omit tokens covered by past_key_values if past_key_values: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] else: position_ids = None # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } ) return model_inputs @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: 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, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.transformer.first_device) hidden_states = hidden_states.to(self.lm_head.weight.device) lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, cross_attentions=transformer_outputs.cross_attentions, ) @staticmethod def _reorder_cache( past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor ) -> Tuple[Tuple[torch.Tensor]]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. """ return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values ) @add_start_docstrings( """ The GPT2 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). """, GPT2_START_DOCSTRING, ) class GPT2DoubleHeadsModel(GPT2PreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) config.num_labels = 1 self.transformer = GPT2Model(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.multiple_choice_head = SequenceSummary(config) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): warnings.warn( "`GPT2DoubleHeadsModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should" " load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your" " own `device_map` but it needs to be a dictionary module_name to device, so for instance" " {'transformer.h.0': 0, 'transformer.h.1': 1, ...}", FutureWarning, ) self.device_map = ( get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.transformer.h)) self.transformer.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.transformer.first_device) self.multiple_choice_head = self.multiple_choice_head.to(self.transformer.first_device) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.transformer.deparallelize() self.transformer = self.transformer.to("cpu") self.lm_head = self.lm_head.to("cpu") self.multiple_choice_head = self.multiple_choice_head.to("cpu") self.model_parallel = False torch.cuda.empty_cache() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # Omit tokens covered by past_key_values if past_key_values: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] else: position_ids = None return { "input_ids": input_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=GPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, GPT2DoubleHeadsModelOutput]: 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 `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]` 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) Return: Example: ```python >>> import torch >>> from transformers import AutoTokenizer, GPT2DoubleHeadsModel >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") >>> model = GPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2") >>> # Add a [CLS] to the vocabulary (we should train it also!) >>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"}) >>> # Update the model embeddings with the new vocabulary size >>> embedding_layer = model.resize_token_embeddings(len(tokenizer)) >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] >>> encoded_choices = [tokenizer.encode(s) for s in choices] >>> cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices] >>> input_ids = torch.tensor(encoded_choices).unsqueeze(0) # Batch size: 1, number of choices: 2 >>> mc_token_ids = torch.tensor([cls_token_location]) # 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, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.transformer.first_device) hidden_states = hidden_states.to(self.lm_head.weight.device) lm_logits = self.lm_head(hidden_states) mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1) mc_loss = 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)) lm_loss = None if labels is not None: labels = labels.to(lm_logits.device) 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 GPT2DoubleHeadsModelOutput( loss=lm_loss, mc_loss=mc_loss, logits=lm_logits, mc_logits=mc_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache( past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor ) -> Tuple[Tuple[torch.Tensor]]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. """ return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values ) @add_start_docstrings( """ The GPT2 Model transformer with a sequence classification head on top (linear layer). [`GPT2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) 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). """, GPT2_START_DOCSTRING, ) class GPT2ForSequenceClassification(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) self.score = nn.Linear(config.n_embd, self.num_labels, bias=False) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint="microsoft/DialogRPT-updown", output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutputWithPast]: 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, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, 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] assert ( self.config.pad_token_id is not None or batch_size == 1 ), "Cannot handle batch sizes > 1 if no padding token is defined." if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 logger.warning( 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), sequence_lengths] 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 SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ GPT2 Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, GPT2_START_DOCSTRING, ) class GPT2ForTokenClassification(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: classifier_dropout = config.hidden_dropout else: classifier_dropout = 0.1 self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING) # fmt: off @add_code_sample_docstrings( checkpoint="brad1141/gpt2-finetuned-comp2", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_loss=0.25, expected_output=[ "Lead", "Lead", "Lead", "Position", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", ], ) # fmt: on def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *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, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: labels = labels.to(logits.device) loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The GPT-2 Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, GPT2_START_DOCSTRING, ) class GPT2ForQuestionAnswering(GPT2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) self.qa_outputs = nn.Linear(config.hidden_size, 2) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, real_checkpoint=_CHECKPOINT_FOR_DOC, ) 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, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict 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, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1).to(start_logits.device) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1).to(end_logits.device) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )