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| # coding=utf-8 | |
| # Copyright 2020 Microsoft and the Hugging Face Inc. team. | |
| # | |
| # 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 DeBERTa-v2 model.""" | |
| import math | |
| from collections.abc import Sequence | |
| from typing import Optional, Tuple, Union | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss | |
| from transformers.activations import ACT2FN | |
| from transformers.modeling_outputs import ( | |
| BaseModelOutput, | |
| MaskedLMOutput, | |
| MultipleChoiceModelOutput, | |
| QuestionAnsweringModelOutput, | |
| SequenceClassifierOutput, | |
| TokenClassifierOutput, | |
| ) | |
| from transformers.modeling_utils import PreTrainedModel | |
| from transformers.pytorch_utils import softmax_backward_data | |
| from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging | |
| from transformers import DebertaV2Config | |
| logger = logging.get_logger(__name__) | |
| _CONFIG_FOR_DOC = "DebertaV2Config" | |
| _TOKENIZER_FOR_DOC = "DebertaV2Tokenizer" | |
| _CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge" | |
| DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
| "microsoft/deberta-v2-xlarge", | |
| "microsoft/deberta-v2-xxlarge", | |
| "microsoft/deberta-v2-xlarge-mnli", | |
| "microsoft/deberta-v2-xxlarge-mnli", | |
| ] | |
| # Copied from transformers.models.deberta.modeling_deberta.ContextPooler | |
| class ContextPooler(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size) | |
| self.dropout = StableDropout(config.pooler_dropout) | |
| self.config = config | |
| def forward(self, hidden_states): | |
| # We "pool" the model by simply taking the hidden state corresponding | |
| # to the first token. | |
| context_token = hidden_states[:, 0] | |
| context_token = self.dropout(context_token) | |
| pooled_output = self.dense(context_token) | |
| pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output) | |
| return pooled_output | |
| def output_dim(self): | |
| return self.config.hidden_size | |
| # Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2 | |
| class XSoftmax(torch.autograd.Function): | |
| """ | |
| Masked Softmax which is optimized for saving memory | |
| Args: | |
| input (`torch.tensor`): The input tensor that will apply softmax. | |
| mask (`torch.IntTensor`): | |
| The mask matrix where 0 indicate that element will be ignored in the softmax calculation. | |
| dim (int): The dimension that will apply softmax | |
| Example: | |
| ```python | |
| >>> import torch | |
| >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax | |
| >>> # Make a tensor | |
| >>> x = torch.randn([4, 20, 100]) | |
| >>> # Create a mask | |
| >>> mask = (x > 0).int() | |
| >>> # Specify the dimension to apply softmax | |
| >>> dim = -1 | |
| >>> y = XSoftmax.apply(x, mask, dim) | |
| ```""" | |
| def forward(self, input, mask, dim): | |
| self.dim = dim | |
| rmask = ~(mask.to(torch.bool)) | |
| output = input.masked_fill(rmask, torch.tensor(torch.finfo(input.dtype).min)) | |
| output = torch.softmax(output, self.dim) | |
| output.masked_fill_(rmask, 0) | |
| self.save_for_backward(output) | |
| return output | |
| def backward(self, grad_output): | |
| (output,) = self.saved_tensors | |
| inputGrad = softmax_backward_data(self, grad_output, output, self.dim, output) | |
| return inputGrad, None, None | |
| def symbolic(g, self, mask, dim): | |
| import torch.onnx.symbolic_helper as sym_help | |
| from torch.onnx.symbolic_opset9 import masked_fill, softmax | |
| mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"]) | |
| r_mask = g.op( | |
| "Cast", | |
| g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value), | |
| to_i=sym_help.cast_pytorch_to_onnx["Byte"], | |
| ) | |
| output = masked_fill(g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.dtype).min))) | |
| output = softmax(g, output, dim) | |
| return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.uint8))) | |
| # Copied from transformers.models.deberta.modeling_deberta.DropoutContext | |
| class DropoutContext(object): | |
| def __init__(self): | |
| self.dropout = 0 | |
| self.mask = None | |
| self.scale = 1 | |
| self.reuse_mask = True | |
| # Copied from transformers.models.deberta.modeling_deberta.get_mask | |
| def get_mask(input, local_context): | |
| if not isinstance(local_context, DropoutContext): | |
| dropout = local_context | |
| mask = None | |
| else: | |
| dropout = local_context.dropout | |
| dropout *= local_context.scale | |
| mask = local_context.mask if local_context.reuse_mask else None | |
| if dropout > 0 and mask is None: | |
| mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool) | |
| if isinstance(local_context, DropoutContext): | |
| if local_context.mask is None: | |
| local_context.mask = mask | |
| return mask, dropout | |
| # Copied from transformers.models.deberta.modeling_deberta.XDropout | |
| class XDropout(torch.autograd.Function): | |
| """Optimized dropout function to save computation and memory by using mask operation instead of multiplication.""" | |
| def forward(ctx, input, local_ctx): | |
| mask, dropout = get_mask(input, local_ctx) | |
| ctx.scale = 1.0 / (1 - dropout) | |
| if dropout > 0: | |
| ctx.save_for_backward(mask) | |
| return input.masked_fill(mask, 0) * ctx.scale | |
| else: | |
| return input | |
| def backward(ctx, grad_output): | |
| if ctx.scale > 1: | |
| (mask,) = ctx.saved_tensors | |
| return grad_output.masked_fill(mask, 0) * ctx.scale, None | |
| else: | |
| return grad_output, None | |
| # Copied from transformers.models.deberta.modeling_deberta.StableDropout | |
| class StableDropout(nn.Module): | |
| """ | |
| Optimized dropout module for stabilizing the training | |
| Args: | |
| drop_prob (float): the dropout probabilities | |
| """ | |
| def __init__(self, drop_prob): | |
| super().__init__() | |
| self.drop_prob = drop_prob | |
| self.count = 0 | |
| self.context_stack = None | |
| def forward(self, x): | |
| """ | |
| Call the module | |
| Args: | |
| x (`torch.tensor`): The input tensor to apply dropout | |
| """ | |
| if self.training and self.drop_prob > 0: | |
| return XDropout.apply(x, self.get_context()) | |
| return x | |
| def clear_context(self): | |
| self.count = 0 | |
| self.context_stack = None | |
| def init_context(self, reuse_mask=True, scale=1): | |
| if self.context_stack is None: | |
| self.context_stack = [] | |
| self.count = 0 | |
| for c in self.context_stack: | |
| c.reuse_mask = reuse_mask | |
| c.scale = scale | |
| def get_context(self): | |
| if self.context_stack is not None: | |
| if self.count >= len(self.context_stack): | |
| self.context_stack.append(DropoutContext()) | |
| ctx = self.context_stack[self.count] | |
| ctx.dropout = self.drop_prob | |
| self.count += 1 | |
| return ctx | |
| else: | |
| return self.drop_prob | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm | |
| class DebertaV2SelfOutput(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.dense = nn.Linear(config.hidden_size, config.hidden_size) | |
| self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) | |
| self.dropout = StableDropout(config.hidden_dropout_prob) | |
| def forward(self, hidden_states, input_tensor): | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.dropout(hidden_states) | |
| hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
| return hidden_states | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2 | |
| class DebertaV2Attention(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.self = DisentangledSelfAttention(config) | |
| self.output = DebertaV2SelfOutput(config) | |
| self.config = config | |
| def forward( | |
| self, | |
| hidden_states, | |
| attention_mask, | |
| output_attentions=False, | |
| query_states=None, | |
| relative_pos=None, | |
| rel_embeddings=None, | |
| ): | |
| self_output = self.self( | |
| hidden_states, | |
| attention_mask, | |
| output_attentions, | |
| query_states=query_states, | |
| relative_pos=relative_pos, | |
| rel_embeddings=rel_embeddings, | |
| ) | |
| if output_attentions: | |
| self_output, att_matrix = self_output | |
| if query_states is None: | |
| query_states = hidden_states | |
| attention_output = self.output(self_output, query_states) | |
| if output_attentions: | |
| return (attention_output, att_matrix) | |
| else: | |
| return attention_output | |
| # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2 | |
| class DebertaV2Intermediate(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.dense = nn.Linear(config.hidden_size, config.intermediate_size) | |
| if isinstance(config.hidden_act, str): | |
| self.intermediate_act_fn = ACT2FN[config.hidden_act] | |
| else: | |
| self.intermediate_act_fn = config.hidden_act | |
| def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.intermediate_act_fn(hidden_states) | |
| return hidden_states | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm | |
| class DebertaV2Output(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.dense = nn.Linear(config.intermediate_size, config.hidden_size) | |
| self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) | |
| self.dropout = StableDropout(config.hidden_dropout_prob) | |
| self.config = config | |
| def forward(self, hidden_states, input_tensor): | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.dropout(hidden_states) | |
| hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
| return hidden_states | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2 | |
| class DebertaV2Layer(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.attention = DebertaV2Attention(config) | |
| self.intermediate = DebertaV2Intermediate(config) | |
| self.output = DebertaV2Output(config) | |
| def forward( | |
| self, | |
| hidden_states, | |
| attention_mask, | |
| query_states=None, | |
| relative_pos=None, | |
| rel_embeddings=None, | |
| output_attentions=False, | |
| ): | |
| attention_output = self.attention( | |
| hidden_states, | |
| attention_mask, | |
| output_attentions=output_attentions, | |
| query_states=query_states, | |
| relative_pos=relative_pos, | |
| rel_embeddings=rel_embeddings, | |
| ) | |
| if output_attentions: | |
| attention_output, att_matrix = attention_output | |
| intermediate_output = self.intermediate(attention_output) | |
| layer_output = self.output(intermediate_output, attention_output) | |
| if output_attentions: | |
| return (layer_output, att_matrix) | |
| else: | |
| return layer_output | |
| class ConvLayer(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| kernel_size = getattr(config, "conv_kernel_size", 3) | |
| groups = getattr(config, "conv_groups", 1) | |
| self.conv_act = getattr(config, "conv_act", "tanh") | |
| self.conv = nn.Conv1d( | |
| config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups | |
| ) | |
| self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) | |
| self.dropout = StableDropout(config.hidden_dropout_prob) | |
| self.config = config | |
| def forward(self, hidden_states, residual_states, input_mask): | |
| out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous() | |
| rmask = (1 - input_mask).bool() | |
| out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0) | |
| out = ACT2FN[self.conv_act](self.dropout(out)) | |
| layer_norm_input = residual_states + out | |
| output = self.LayerNorm(layer_norm_input).to(layer_norm_input) | |
| if input_mask is None: | |
| output_states = output | |
| else: | |
| if input_mask.dim() != layer_norm_input.dim(): | |
| if input_mask.dim() == 4: | |
| input_mask = input_mask.squeeze(1).squeeze(1) | |
| input_mask = input_mask.unsqueeze(2) | |
| input_mask = input_mask.to(output.dtype) | |
| output_states = output * input_mask | |
| return output_states | |
| class DebertaV2Encoder(nn.Module): | |
| """Modified BertEncoder with relative position bias support""" | |
| def __init__(self, config): | |
| super().__init__() | |
| self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)]) | |
| self.relative_attention = getattr(config, "relative_attention", False) | |
| if self.relative_attention: | |
| self.max_relative_positions = getattr(config, "max_relative_positions", -1) | |
| if self.max_relative_positions < 1: | |
| self.max_relative_positions = config.max_position_embeddings | |
| self.position_buckets = getattr(config, "position_buckets", -1) | |
| pos_ebd_size = self.max_relative_positions * 2 | |
| if self.position_buckets > 0: | |
| pos_ebd_size = self.position_buckets * 2 | |
| self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size) | |
| self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")] | |
| if "layer_norm" in self.norm_rel_ebd: | |
| self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True) | |
| self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None | |
| self.gradient_checkpointing = False | |
| def get_rel_embedding(self): | |
| rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None | |
| if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd): | |
| rel_embeddings = self.LayerNorm(rel_embeddings) | |
| return rel_embeddings | |
| def get_attention_mask(self, attention_mask): | |
| if attention_mask.dim() <= 2: | |
| extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) | |
| attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1) | |
| attention_mask = attention_mask.byte() | |
| elif attention_mask.dim() == 3: | |
| attention_mask = attention_mask.unsqueeze(1) | |
| return attention_mask | |
| def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None): | |
| if self.relative_attention and relative_pos is None: | |
| q = query_states.size(-2) if query_states is not None else hidden_states.size(-2) | |
| relative_pos = build_relative_position( | |
| q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions | |
| ) | |
| return relative_pos | |
| def forward( | |
| self, | |
| hidden_states, | |
| attention_mask, | |
| output_hidden_states=True, | |
| output_attentions=False, | |
| query_states=None, | |
| relative_pos=None, | |
| return_dict=True, | |
| ): | |
| if attention_mask.dim() <= 2: | |
| input_mask = attention_mask | |
| else: | |
| input_mask = (attention_mask.sum(-2) > 0).byte() | |
| attention_mask = self.get_attention_mask(attention_mask) | |
| relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos) | |
| all_hidden_states = () if output_hidden_states else None | |
| all_attentions = () if output_attentions else None | |
| if isinstance(hidden_states, Sequence): | |
| next_kv = hidden_states[0] | |
| else: | |
| next_kv = hidden_states | |
| rel_embeddings = self.get_rel_embedding() | |
| output_states = next_kv | |
| for i, layer_module in enumerate(self.layer): | |
| if output_hidden_states: | |
| all_hidden_states = all_hidden_states + (output_states,) | |
| if self.gradient_checkpointing and self.training: | |
| def create_custom_forward(module): | |
| def custom_forward(*inputs): | |
| return module(*inputs, output_attentions) | |
| return custom_forward | |
| output_states = torch.utils.checkpoint.checkpoint( | |
| create_custom_forward(layer_module), | |
| next_kv, | |
| attention_mask, | |
| query_states, | |
| relative_pos, | |
| rel_embeddings, | |
| ) | |
| else: | |
| output_states = layer_module( | |
| next_kv, | |
| attention_mask, | |
| query_states=query_states, | |
| relative_pos=relative_pos, | |
| rel_embeddings=rel_embeddings, | |
| output_attentions=output_attentions, | |
| ) | |
| if output_attentions: | |
| output_states, att_m = output_states | |
| if i == 0 and self.conv is not None: | |
| output_states = self.conv(hidden_states, output_states, input_mask) | |
| if query_states is not None: | |
| query_states = output_states | |
| if isinstance(hidden_states, Sequence): | |
| next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None | |
| else: | |
| next_kv = output_states | |
| if output_attentions: | |
| all_attentions = all_attentions + (att_m,) | |
| if output_hidden_states: | |
| all_hidden_states = all_hidden_states + (output_states,) | |
| if not return_dict: | |
| return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None) | |
| return BaseModelOutput( | |
| last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions | |
| ) | |
| def make_log_bucket_position(relative_pos, bucket_size, max_position): | |
| sign = np.sign(relative_pos) | |
| mid = bucket_size // 2 | |
| abs_pos = np.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos)) | |
| log_pos = np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid | |
| bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int) | |
| return bucket_pos | |
| def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1): | |
| """ | |
| Build relative position according to the query and key | |
| We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key | |
| \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - | |
| P_k\\) | |
| Args: | |
| query_size (int): the length of query | |
| key_size (int): the length of key | |
| bucket_size (int): the size of position bucket | |
| max_position (int): the maximum allowed absolute position | |
| Return: | |
| `torch.LongTensor`: A tensor with shape [1, query_size, key_size] | |
| """ | |
| q_ids = np.arange(0, query_size) | |
| k_ids = np.arange(0, key_size) | |
| rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1)) | |
| if bucket_size > 0 and max_position > 0: | |
| rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position) | |
| rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long) | |
| rel_pos_ids = rel_pos_ids[:query_size, :] | |
| rel_pos_ids = rel_pos_ids.unsqueeze(0) | |
| return rel_pos_ids | |
| # Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand | |
| def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos): | |
| return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]) | |
| # Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand | |
| def p2c_dynamic_expand(c2p_pos, query_layer, key_layer): | |
| return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)]) | |
| # Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand | |
| def pos_dynamic_expand(pos_index, p2c_att, key_layer): | |
| return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))) | |
| class DisentangledSelfAttention(nn.Module): | |
| """ | |
| Disentangled self-attention module | |
| Parameters: | |
| config (`DebertaV2Config`): | |
| A model config class instance with the configuration to build a new model. The schema is similar to | |
| *BertConfig*, for more details, please refer [`DebertaV2Config`] | |
| """ | |
| def __init__(self, config): | |
| super().__init__() | |
| if config.hidden_size % config.num_attention_heads != 0: | |
| raise ValueError( | |
| f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " | |
| f"heads ({config.num_attention_heads})" | |
| ) | |
| self.num_attention_heads = config.num_attention_heads | |
| _attention_head_size = config.hidden_size // config.num_attention_heads | |
| self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size) | |
| self.all_head_size = self.num_attention_heads * self.attention_head_size | |
| self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) | |
| self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) | |
| self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) | |
| self.share_att_key = getattr(config, "share_att_key", False) | |
| self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else [] | |
| self.relative_attention = getattr(config, "relative_attention", False) | |
| if self.relative_attention: | |
| self.position_buckets = getattr(config, "position_buckets", -1) | |
| self.max_relative_positions = getattr(config, "max_relative_positions", -1) | |
| if self.max_relative_positions < 1: | |
| self.max_relative_positions = config.max_position_embeddings | |
| self.pos_ebd_size = self.max_relative_positions | |
| if self.position_buckets > 0: | |
| self.pos_ebd_size = self.position_buckets | |
| self.pos_dropout = StableDropout(config.hidden_dropout_prob) | |
| if not self.share_att_key: | |
| if "c2p" in self.pos_att_type: | |
| self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) | |
| if "p2c" in self.pos_att_type: | |
| self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size) | |
| self.dropout = StableDropout(config.attention_probs_dropout_prob) | |
| def transpose_for_scores(self, x, attention_heads): | |
| new_x_shape = x.size()[:-1] + (attention_heads, -1) | |
| x = x.view(new_x_shape) | |
| return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1)) | |
| def forward( | |
| self, | |
| hidden_states, | |
| attention_mask, | |
| output_attentions=False, | |
| query_states=None, | |
| relative_pos=None, | |
| rel_embeddings=None, | |
| ): | |
| """ | |
| Call the module | |
| Args: | |
| hidden_states (`torch.FloatTensor`): | |
| Input states to the module usually the output from previous layer, it will be the Q,K and V in | |
| *Attention(Q,K,V)* | |
| attention_mask (`torch.ByteTensor`): | |
| An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum | |
| sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j* | |
| th token. | |
| output_attentions (`bool`, optional): | |
| Whether return the attention matrix. | |
| query_states (`torch.FloatTensor`, optional): | |
| The *Q* state in *Attention(Q,K,V)*. | |
| relative_pos (`torch.LongTensor`): | |
| The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with | |
| values ranging in [*-max_relative_positions*, *max_relative_positions*]. | |
| rel_embeddings (`torch.FloatTensor`): | |
| The embedding of relative distances. It's a tensor of shape [\\(2 \\times | |
| \\text{max_relative_positions}\\), *hidden_size*]. | |
| """ | |
| if query_states is None: | |
| query_states = hidden_states | |
| query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads) | |
| key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads) | |
| value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads) | |
| rel_att = None | |
| # Take the dot product between "query" and "key" to get the raw attention scores. | |
| scale_factor = 1 | |
| if "c2p" in self.pos_att_type: | |
| scale_factor += 1 | |
| if "p2c" in self.pos_att_type: | |
| scale_factor += 1 | |
| scale = math.sqrt(query_layer.size(-1) * scale_factor) | |
| attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale | |
| if self.relative_attention: | |
| rel_embeddings = self.pos_dropout(rel_embeddings) | |
| rel_att = self.disentangled_attention_bias( | |
| query_layer, key_layer, relative_pos, rel_embeddings, scale_factor | |
| ) | |
| if rel_att is not None: | |
| attention_scores = attention_scores + rel_att | |
| attention_scores = attention_scores | |
| attention_scores = attention_scores.view( | |
| -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1) | |
| ) | |
| # bsz x height x length x dimension | |
| attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) | |
| attention_probs = self.dropout(attention_probs) | |
| context_layer = torch.bmm( | |
| attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer | |
| ) | |
| context_layer = ( | |
| context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1)) | |
| .permute(0, 2, 1, 3) | |
| .contiguous() | |
| ) | |
| new_context_layer_shape = context_layer.size()[:-2] + (-1,) | |
| context_layer = context_layer.view(new_context_layer_shape) | |
| if output_attentions: | |
| return (context_layer, attention_probs) | |
| else: | |
| return context_layer | |
| def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor): | |
| if relative_pos is None: | |
| q = query_layer.size(-2) | |
| relative_pos = build_relative_position( | |
| q, key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions | |
| ) | |
| if relative_pos.dim() == 2: | |
| relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) | |
| elif relative_pos.dim() == 3: | |
| relative_pos = relative_pos.unsqueeze(1) | |
| # bsz x height x query x key | |
| elif relative_pos.dim() != 4: | |
| raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}") | |
| att_span = self.pos_ebd_size | |
| relative_pos = relative_pos.long().to(query_layer.device) | |
| rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0) | |
| if self.share_att_key: | |
| pos_query_layer = self.transpose_for_scores( | |
| self.query_proj(rel_embeddings), self.num_attention_heads | |
| ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) | |
| pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat( | |
| query_layer.size(0) // self.num_attention_heads, 1, 1 | |
| ) | |
| else: | |
| if "c2p" in self.pos_att_type: | |
| pos_key_layer = self.transpose_for_scores( | |
| self.pos_key_proj(rel_embeddings), self.num_attention_heads | |
| ).repeat( | |
| query_layer.size(0) // self.num_attention_heads, 1, 1 | |
| ) # .split(self.all_head_size, dim=-1) | |
| if "p2c" in self.pos_att_type: | |
| pos_query_layer = self.transpose_for_scores( | |
| self.pos_query_proj(rel_embeddings), self.num_attention_heads | |
| ).repeat( | |
| query_layer.size(0) // self.num_attention_heads, 1, 1 | |
| ) # .split(self.all_head_size, dim=-1) | |
| score = 0 | |
| # content->position | |
| if "c2p" in self.pos_att_type: | |
| scale = math.sqrt(pos_key_layer.size(-1) * scale_factor) | |
| c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2)) | |
| c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1) | |
| c2p_att = torch.gather( | |
| c2p_att, | |
| dim=-1, | |
| index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]), | |
| ) | |
| score += c2p_att / scale | |
| # position->content | |
| if "p2c" in self.pos_att_type: | |
| scale = math.sqrt(pos_query_layer.size(-1) * scale_factor) | |
| if key_layer.size(-2) != query_layer.size(-2): | |
| r_pos = build_relative_position( | |
| key_layer.size(-2), | |
| key_layer.size(-2), | |
| bucket_size=self.position_buckets, | |
| max_position=self.max_relative_positions, | |
| ).to(query_layer.device) | |
| r_pos = r_pos.unsqueeze(0) | |
| else: | |
| r_pos = relative_pos | |
| p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) | |
| p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2)) | |
| p2c_att = torch.gather( | |
| p2c_att, | |
| dim=-1, | |
| index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]), | |
| ).transpose(-1, -2) | |
| score += p2c_att / scale | |
| return score | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm | |
| class DebertaV2Embeddings(nn.Module): | |
| """Construct the embeddings from word, position and token_type embeddings.""" | |
| def __init__(self, config): | |
| super().__init__() | |
| pad_token_id = getattr(config, "pad_token_id", 0) | |
| self.embedding_size = getattr(config, "embedding_size", config.hidden_size) | |
| self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id) | |
| self.position_biased_input = getattr(config, "position_biased_input", True) | |
| if not self.position_biased_input: | |
| self.position_embeddings = None | |
| else: | |
| self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size) | |
| if config.type_vocab_size > 0: | |
| self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size) | |
| if self.embedding_size != config.hidden_size: | |
| self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False) | |
| self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) | |
| self.dropout = StableDropout(config.hidden_dropout_prob) | |
| self.config = config | |
| # position_ids (1, len position emb) is contiguous in memory and exported when serialized | |
| self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) | |
| def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None): | |
| if input_ids is not None: | |
| input_shape = input_ids.size() | |
| else: | |
| input_shape = inputs_embeds.size()[:-1] | |
| seq_length = input_shape[1] | |
| if position_ids is None: | |
| position_ids = self.position_ids[:, :seq_length] | |
| if token_type_ids is None: | |
| token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) | |
| if inputs_embeds is None: | |
| inputs_embeds = self.word_embeddings(input_ids) | |
| if self.position_embeddings is not None: | |
| position_embeddings = self.position_embeddings(position_ids.long()) | |
| else: | |
| position_embeddings = torch.zeros_like(inputs_embeds) | |
| embeddings = inputs_embeds | |
| if self.position_biased_input: | |
| embeddings += position_embeddings | |
| if self.config.type_vocab_size > 0: | |
| token_type_embeddings = self.token_type_embeddings(token_type_ids) | |
| embeddings += token_type_embeddings | |
| if self.embedding_size != self.config.hidden_size: | |
| embeddings = self.embed_proj(embeddings) | |
| embeddings = self.LayerNorm(embeddings) | |
| # if mask is not None: | |
| # if mask.dim() != embeddings.dim(): | |
| # if mask.dim() == 4: | |
| # mask = mask.squeeze(1).squeeze(1) | |
| # mask = mask.unsqueeze(2) | |
| # mask = mask.to(embeddings.dtype) | |
| # embeddings = embeddings * mask | |
| embeddings = self.dropout(embeddings) | |
| return embeddings | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2 | |
| class DebertaV2PreTrainedModel(PreTrainedModel): | |
| """ | |
| An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
| models. | |
| """ | |
| config_class = DebertaV2Config | |
| base_model_prefix = "deberta" | |
| _keys_to_ignore_on_load_missing = ["position_ids"] | |
| _keys_to_ignore_on_load_unexpected = ["position_embeddings"] | |
| supports_gradient_checkpointing = True | |
| def _init_weights(self, module): | |
| """Initialize the weights.""" | |
| if isinstance(module, nn.Linear): | |
| # 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_() | |
| def _set_gradient_checkpointing(self, module, value=False): | |
| if isinstance(module, DebertaV2Encoder): | |
| module.gradient_checkpointing = value | |
| DEBERTA_START_DOCSTRING = r""" | |
| The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled | |
| Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build | |
| on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two | |
| improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. | |
| 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 ([`DebertaV2Config`]): 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. | |
| """ | |
| DEBERTA_INPUTS_DOCSTRING = r""" | |
| Args: | |
| input_ids (`torch.LongTensor` of shape `({0})`): | |
| Indices of input sequence tokens in the vocabulary. | |
| Indices can be obtained using [`DebertaV2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
| [`PreTrainedTokenizer.__call__`] for details. | |
| [What are input IDs?](../glossary#input-ids) | |
| attention_mask (`torch.FloatTensor` of shape `({0})`, *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**. | |
| [What are attention masks?](../glossary#attention-mask) | |
| token_type_ids (`torch.LongTensor` of shape `({0})`, *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 `({0})`, *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) | |
| inputs_embeds (`torch.FloatTensor` of shape `({0}, 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. | |
| 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. | |
| """ | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2 | |
| class DebertaV2Model(DebertaV2PreTrainedModel): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.embeddings = DebertaV2Embeddings(config) | |
| self.encoder = DebertaV2Encoder(config) | |
| self.z_steps = 2 | |
| self.config = config | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def get_input_embeddings(self): | |
| return self.embeddings.word_embeddings | |
| def set_input_embeddings(self, new_embeddings): | |
| self.embeddings.word_embeddings = 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} See base | |
| class PreTrainedModel | |
| """ | |
| raise NotImplementedError("The prune function is not implemented in DeBERTa model.") | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| token_type_ids: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| ) -> Union[Tuple, 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: | |
| input_shape = input_ids.size() | |
| 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") | |
| device = input_ids.device if input_ids is not None else inputs_embeds.device | |
| if attention_mask is None: | |
| attention_mask = torch.ones(input_shape, device=device) | |
| if token_type_ids is None: | |
| token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) | |
| embedding_output = self.embeddings( | |
| input_ids=input_ids, | |
| token_type_ids=token_type_ids, | |
| position_ids=position_ids, | |
| mask=attention_mask, | |
| inputs_embeds=inputs_embeds, | |
| ) | |
| encoder_outputs = self.encoder( | |
| embedding_output, | |
| attention_mask, | |
| output_hidden_states=True, | |
| output_attentions=output_attentions, | |
| return_dict=return_dict, | |
| ) | |
| encoded_layers = encoder_outputs[1] | |
| if self.z_steps > 1: | |
| hidden_states = encoded_layers[-2] | |
| layers = [self.encoder.layer[-1] for _ in range(self.z_steps)] | |
| query_states = encoded_layers[-1] | |
| rel_embeddings = self.encoder.get_rel_embedding() | |
| attention_mask = self.encoder.get_attention_mask(attention_mask) | |
| rel_pos = self.encoder.get_rel_pos(embedding_output) | |
| for layer in layers[1:]: | |
| query_states = layer( | |
| hidden_states, | |
| attention_mask, | |
| output_attentions=False, | |
| query_states=query_states, | |
| relative_pos=rel_pos, | |
| rel_embeddings=rel_embeddings, | |
| ) | |
| encoded_layers = encoded_layers + (query_states,) | |
| sequence_output = encoded_layers[-1] | |
| if not return_dict: | |
| return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :] | |
| return BaseModelOutput( | |
| last_hidden_state=sequence_output, | |
| hidden_states=encoder_outputs.hidden_states if output_hidden_states else None, | |
| attentions=encoder_outputs.attentions, | |
| ) | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2 | |
| class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel): | |
| _keys_to_ignore_on_load_unexpected = [r"pooler"] | |
| _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.deberta = DebertaV2Model(config) | |
| self.cls = DebertaV2OnlyMLMHead(config) | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def get_output_embeddings(self): | |
| return self.cls.predictions.decoder | |
| def set_output_embeddings(self, new_embeddings): | |
| self.cls.predictions.decoder = new_embeddings | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| token_type_ids: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| ) -> Union[Tuple, MaskedLMOutput]: | |
| r""" | |
| labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
| Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., | |
| config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the | |
| loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` | |
| """ | |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
| outputs = self.deberta( | |
| input_ids, | |
| attention_mask=attention_mask, | |
| token_type_ids=token_type_ids, | |
| position_ids=position_ids, | |
| inputs_embeds=inputs_embeds, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| sequence_output = outputs[0] | |
| prediction_scores = self.cls(sequence_output) | |
| masked_lm_loss = None | |
| if labels is not None: | |
| loss_fct = CrossEntropyLoss() # -100 index = padding token | |
| masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) | |
| if not return_dict: | |
| output = (prediction_scores,) + outputs[1:] | |
| return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output | |
| return MaskedLMOutput( | |
| loss=masked_lm_loss, | |
| logits=prediction_scores, | |
| hidden_states=outputs.hidden_states, | |
| attentions=outputs.attentions, | |
| ) | |
| # copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta | |
| class DebertaV2PredictionHeadTransform(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.dense = nn.Linear(config.hidden_size, config.hidden_size) | |
| if isinstance(config.hidden_act, str): | |
| self.transform_act_fn = ACT2FN[config.hidden_act] | |
| else: | |
| self.transform_act_fn = config.hidden_act | |
| self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
| def forward(self, hidden_states): | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.transform_act_fn(hidden_states) | |
| hidden_states = self.LayerNorm(hidden_states) | |
| return hidden_states | |
| # copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta | |
| class DebertaV2LMPredictionHead(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.transform = DebertaV2PredictionHeadTransform(config) | |
| # The output weights are the same as the input embeddings, but there is | |
| # an output-only bias for each token. | |
| self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) | |
| self.bias = nn.Parameter(torch.zeros(config.vocab_size)) | |
| # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` | |
| self.decoder.bias = self.bias | |
| def forward(self, hidden_states): | |
| hidden_states = self.transform(hidden_states) | |
| hidden_states = self.decoder(hidden_states) | |
| return hidden_states | |
| # copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta | |
| class DebertaV2OnlyMLMHead(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.predictions = DebertaV2LMPredictionHead(config) | |
| def forward(self, sequence_output): | |
| prediction_scores = self.predictions(sequence_output) | |
| return prediction_scores | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2 | |
| class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| num_labels = getattr(config, "num_labels", 2) | |
| self.num_labels = num_labels | |
| self.deberta = DebertaV2Model(config) | |
| self.pooler = ContextPooler(config) | |
| output_dim = self.pooler.output_dim | |
| self.classifier = nn.Linear(output_dim, num_labels) | |
| drop_out = getattr(config, "cls_dropout", None) | |
| drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out | |
| self.dropout = StableDropout(drop_out) | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def get_input_embeddings(self): | |
| return self.deberta.get_input_embeddings() | |
| def set_input_embeddings(self, new_embeddings): | |
| self.deberta.set_input_embeddings(new_embeddings) | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| token_type_ids: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| ) -> Union[Tuple, 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 | |
| outputs = self.deberta( | |
| input_ids, | |
| token_type_ids=token_type_ids, | |
| attention_mask=attention_mask, | |
| position_ids=position_ids, | |
| inputs_embeds=inputs_embeds, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| encoder_layer = outputs[0] | |
| pooled_output = self.pooler(encoder_layer) | |
| pooled_output = self.dropout(pooled_output) | |
| logits = self.classifier(pooled_output) | |
| loss = None | |
| if labels is not None: | |
| if self.config.problem_type is None: | |
| if self.num_labels == 1: | |
| # regression task | |
| loss_fn = nn.MSELoss() | |
| logits = logits.view(-1).to(labels.dtype) | |
| loss = loss_fn(logits, labels.view(-1)) | |
| elif labels.dim() == 1 or labels.size(-1) == 1: | |
| label_index = (labels >= 0).nonzero() | |
| labels = labels.long() | |
| if label_index.size(0) > 0: | |
| labeled_logits = torch.gather( | |
| logits, 0, label_index.expand(label_index.size(0), logits.size(1)) | |
| ) | |
| labels = torch.gather(labels, 0, label_index.view(-1)) | |
| loss_fct = CrossEntropyLoss() | |
| loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1)) | |
| else: | |
| loss = torch.tensor(0).to(logits) | |
| else: | |
| log_softmax = nn.LogSoftmax(-1) | |
| loss = -((log_softmax(logits) * labels).sum(-1)).mean() | |
| elif self.config.problem_type == "regression": | |
| loss_fct = MSELoss() | |
| if self.num_labels == 1: | |
| loss = loss_fct(logits.squeeze(), labels.squeeze()) | |
| else: | |
| loss = loss_fct(logits, labels) | |
| elif self.config.problem_type == "single_label_classification": | |
| loss_fct = CrossEntropyLoss() | |
| loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) | |
| elif self.config.problem_type == "multi_label_classification": | |
| loss_fct = BCEWithLogitsLoss() | |
| loss = loss_fct(logits, labels) | |
| if not return_dict: | |
| output = (logits,) + outputs[1:] | |
| return ((loss,) + output) if loss is not None else output | |
| return SequenceClassifierOutput( | |
| loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions | |
| ) | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2 | |
| class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel): | |
| _keys_to_ignore_on_load_unexpected = [r"pooler"] | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.num_labels = config.num_labels | |
| self.deberta = DebertaV2Model(config) | |
| self.dropout = nn.Dropout(config.hidden_dropout_prob) | |
| self.classifier = nn.Linear(config.hidden_size, config.num_labels) | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| token_type_ids: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = 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 token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. | |
| """ | |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
| outputs = self.deberta( | |
| input_ids, | |
| attention_mask=attention_mask, | |
| token_type_ids=token_type_ids, | |
| position_ids=position_ids, | |
| inputs_embeds=inputs_embeds, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| sequence_output = outputs[0] | |
| sequence_output = self.dropout(sequence_output) | |
| logits = self.classifier(sequence_output) | |
| loss = None | |
| if labels is not None: | |
| loss_fct = CrossEntropyLoss() | |
| loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) | |
| if not return_dict: | |
| output = (logits,) + outputs[1:] | |
| return ((loss,) + output) if loss is not None else output | |
| return TokenClassifierOutput( | |
| loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions | |
| ) | |
| # Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering with Deberta->DebertaV2 | |
| class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel): | |
| _keys_to_ignore_on_load_unexpected = [r"pooler"] | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.num_labels = config.num_labels | |
| self.deberta = DebertaV2Model(config) | |
| self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| token_type_ids: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| start_positions: Optional[torch.Tensor] = None, | |
| end_positions: Optional[torch.Tensor] = 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.deberta( | |
| input_ids, | |
| attention_mask=attention_mask, | |
| token_type_ids=token_type_ids, | |
| position_ids=position_ids, | |
| 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) | |
| if len(end_positions.size()) > 1: | |
| end_positions = end_positions.squeeze(-1) | |
| # 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[1:] | |
| 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, | |
| ) | |
| class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| num_labels = getattr(config, "num_labels", 2) | |
| self.num_labels = num_labels | |
| self.deberta = DebertaV2Model(config) | |
| self.pooler = ContextPooler(config) | |
| output_dim = self.pooler.output_dim | |
| self.classifier = nn.Linear(output_dim, 1) | |
| drop_out = getattr(config, "cls_dropout", None) | |
| drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out | |
| self.dropout = StableDropout(drop_out) | |
| self.init_weights() | |
| def get_input_embeddings(self): | |
| return self.deberta.get_input_embeddings() | |
| def set_input_embeddings(self, new_embeddings): | |
| self.deberta.set_input_embeddings(new_embeddings) | |
| def forward( | |
| self, | |
| input_ids=None, | |
| attention_mask=None, | |
| token_type_ids=None, | |
| position_ids=None, | |
| inputs_embeds=None, | |
| labels=None, | |
| output_attentions=None, | |
| output_hidden_states=None, | |
| return_dict=None, | |
| ): | |
| r""" | |
| labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): | |
| Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., | |
| num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See | |
| `input_ids` above) | |
| """ | |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
| num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] | |
| flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None | |
| flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None | |
| flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None | |
| flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None | |
| flat_inputs_embeds = ( | |
| inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) | |
| if inputs_embeds is not None | |
| else None | |
| ) | |
| outputs = self.deberta( | |
| flat_input_ids, | |
| position_ids=flat_position_ids, | |
| token_type_ids=flat_token_type_ids, | |
| attention_mask=flat_attention_mask, | |
| inputs_embeds=flat_inputs_embeds, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| encoder_layer = outputs[0] | |
| pooled_output = self.pooler(encoder_layer) | |
| pooled_output = self.dropout(pooled_output) | |
| logits = self.classifier(pooled_output) | |
| reshaped_logits = logits.view(-1, num_choices) | |
| loss = None | |
| if labels is not None: | |
| loss_fct = CrossEntropyLoss() | |
| loss = loss_fct(reshaped_logits, labels) | |
| if not return_dict: | |
| output = (reshaped_logits,) + outputs[1:] | |
| return ((loss,) + output) if loss is not None else output | |
| return MultipleChoiceModelOutput( | |
| loss=loss, | |
| logits=reshaped_logits, | |
| hidden_states=outputs.hidden_states, | |
| attentions=outputs.attentions, | |
| ) | |