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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 model.""" | |
from collections.abc import Sequence | |
from typing import Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss | |
from ...activations import ACT2FN | |
from ...modeling_outputs import ( | |
BaseModelOutput, | |
MaskedLMOutput, | |
QuestionAnsweringModelOutput, | |
SequenceClassifierOutput, | |
TokenClassifierOutput, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...pytorch_utils import softmax_backward_data | |
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging | |
from .configuration_deberta import DebertaConfig | |
logger = logging.get_logger(__name__) | |
_CONFIG_FOR_DOC = "DebertaConfig" | |
_CHECKPOINT_FOR_DOC = "microsoft/deberta-base" | |
# Masked LM docstring | |
_CHECKPOINT_FOR_MASKED_LM = "lsanochkin/deberta-large-feedback" | |
_MASKED_LM_EXPECTED_OUTPUT = "' Paris'" | |
_MASKED_LM_EXPECTED_LOSS = "0.54" | |
# QuestionAnswering docstring | |
_CHECKPOINT_FOR_QA = "Palak/microsoft_deberta-large_squad" | |
_QA_EXPECTED_OUTPUT = "' a nice puppet'" | |
_QA_EXPECTED_LOSS = 0.14 | |
_QA_TARGET_START_INDEX = 12 | |
_QA_TARGET_END_INDEX = 14 | |
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"microsoft/deberta-base", | |
"microsoft/deberta-large", | |
"microsoft/deberta-xlarge", | |
"microsoft/deberta-base-mnli", | |
"microsoft/deberta-large-mnli", | |
"microsoft/deberta-xlarge-mnli", | |
] | |
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 | |
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.modeling_deberta 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["Bool"], | |
) | |
output = masked_fill( | |
g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min)) | |
) | |
output = softmax(g, output, dim) | |
return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.bool))) | |
class DropoutContext(object): | |
def __init__(self): | |
self.dropout = 0 | |
self.mask = None | |
self.scale = 1 | |
self.reuse_mask = True | |
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 | |
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 | |
def symbolic(g: torch._C.Graph, input: torch._C.Value, local_ctx: Union[float, DropoutContext]) -> torch._C.Value: | |
from torch.onnx import symbolic_opset12 | |
dropout_p = local_ctx | |
if isinstance(local_ctx, DropoutContext): | |
dropout_p = local_ctx.dropout | |
# StableDropout only calls this function when training. | |
train = True | |
# TODO: We should check if the opset_version being used to export | |
# is > 12 here, but there's no good way to do that. As-is, if the | |
# opset_version < 12, export will fail with a CheckerError. | |
# Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like: | |
# if opset_version < 12: | |
# return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train) | |
return symbolic_opset12.dropout(g, input, dropout_p, train) | |
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 | |
class DebertaLayerNorm(nn.Module): | |
"""LayerNorm module in the TF style (epsilon inside the square root).""" | |
def __init__(self, size, eps=1e-12): | |
super().__init__() | |
self.weight = nn.Parameter(torch.ones(size)) | |
self.bias = nn.Parameter(torch.zeros(size)) | |
self.variance_epsilon = eps | |
def forward(self, hidden_states): | |
input_type = hidden_states.dtype | |
hidden_states = hidden_states.float() | |
mean = hidden_states.mean(-1, keepdim=True) | |
variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True) | |
hidden_states = (hidden_states - mean) / torch.sqrt(variance + self.variance_epsilon) | |
hidden_states = hidden_states.to(input_type) | |
y = self.weight * hidden_states + self.bias | |
return y | |
class DebertaSelfOutput(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.dense = nn.Linear(config.hidden_size, config.hidden_size) | |
self.LayerNorm = DebertaLayerNorm(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 | |
class DebertaAttention(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.self = DisentangledSelfAttention(config) | |
self.output = DebertaSelfOutput(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->Deberta | |
class DebertaIntermediate(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 | |
class DebertaOutput(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.dense = nn.Linear(config.intermediate_size, config.hidden_size) | |
self.LayerNorm = DebertaLayerNorm(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 | |
class DebertaLayer(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.attention = DebertaAttention(config) | |
self.intermediate = DebertaIntermediate(config) | |
self.output = DebertaOutput(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 DebertaEncoder(nn.Module): | |
"""Modified BertEncoder with relative position bias support""" | |
def __init__(self, config): | |
super().__init__() | |
self.layer = nn.ModuleList([DebertaLayer(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.rel_embeddings = nn.Embedding(self.max_relative_positions * 2, config.hidden_size) | |
self.gradient_checkpointing = False | |
def get_rel_embedding(self): | |
rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None | |
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) | |
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), hidden_states.device) | |
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, | |
): | |
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() | |
for i, layer_module in enumerate(self.layer): | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_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 | |
hidden_states = torch.utils.checkpoint.checkpoint( | |
create_custom_forward(layer_module), | |
next_kv, | |
attention_mask, | |
query_states, | |
relative_pos, | |
rel_embeddings, | |
) | |
else: | |
hidden_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: | |
hidden_states, att_m = hidden_states | |
if query_states is not None: | |
query_states = hidden_states | |
if isinstance(hidden_states, Sequence): | |
next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None | |
else: | |
next_kv = hidden_states | |
if output_attentions: | |
all_attentions = all_attentions + (att_m,) | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
if not return_dict: | |
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) | |
return BaseModelOutput( | |
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions | |
) | |
def build_relative_position(query_size, key_size, device): | |
""" | |
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 | |
Return: | |
`torch.LongTensor`: A tensor with shape [1, query_size, key_size] | |
""" | |
q_ids = torch.arange(query_size, dtype=torch.long, device=device) | |
k_ids = torch.arange(key_size, dtype=torch.long, device=device) | |
rel_pos_ids = q_ids[:, None] - k_ids.view(1, -1).repeat(query_size, 1) | |
rel_pos_ids = rel_pos_ids[:query_size, :] | |
rel_pos_ids = rel_pos_ids.unsqueeze(0) | |
return rel_pos_ids | |
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)]) | |
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)]) | |
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 (`str`): | |
A model config class instance with the configuration to build a new model. The schema is similar to | |
*BertConfig*, for more details, please refer [`DebertaConfig`] | |
""" | |
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 | |
self.attention_head_size = int(config.hidden_size / config.num_attention_heads) | |
self.all_head_size = self.num_attention_heads * self.attention_head_size | |
self.in_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=False) | |
self.q_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float)) | |
self.v_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float)) | |
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) | |
self.talking_head = getattr(config, "talking_head", False) | |
if self.talking_head: | |
self.head_logits_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias=False) | |
self.head_weights_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias=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.pos_dropout = StableDropout(config.hidden_dropout_prob) | |
if "c2p" in self.pos_att_type: | |
self.pos_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=False) | |
if "p2c" in self.pos_att_type: | |
self.pos_q_proj = nn.Linear(config.hidden_size, self.all_head_size) | |
self.dropout = StableDropout(config.attention_probs_dropout_prob) | |
def transpose_for_scores(self, x): | |
new_x_shape = x.size()[:-1] + (self.num_attention_heads, -1) | |
x = x.view(new_x_shape) | |
return x.permute(0, 2, 1, 3) | |
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.BoolTensor`): | |
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: | |
qp = self.in_proj(hidden_states) # .split(self.all_head_size, dim=-1) | |
query_layer, key_layer, value_layer = self.transpose_for_scores(qp).chunk(3, dim=-1) | |
else: | |
def linear(w, b, x): | |
if b is not None: | |
return torch.matmul(x, w.t()) + b.t() | |
else: | |
return torch.matmul(x, w.t()) # + b.t() | |
ws = self.in_proj.weight.chunk(self.num_attention_heads * 3, dim=0) | |
qkvw = [torch.cat([ws[i * 3 + k] for i in range(self.num_attention_heads)], dim=0) for k in range(3)] | |
qkvb = [None] * 3 | |
q = linear(qkvw[0], qkvb[0], query_states.to(dtype=qkvw[0].dtype)) | |
k, v = [linear(qkvw[i], qkvb[i], hidden_states.to(dtype=qkvw[i].dtype)) for i in range(1, 3)] | |
query_layer, key_layer, value_layer = [self.transpose_for_scores(x) for x in [q, k, v]] | |
query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :]) | |
value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :]) | |
rel_att = None | |
# Take the dot product between "query" and "key" to get the raw attention scores. | |
scale_factor = 1 + len(self.pos_att_type) | |
scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor) | |
query_layer = query_layer / scale.to(dtype=query_layer.dtype) | |
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) | |
if self.relative_attention: | |
rel_embeddings = self.pos_dropout(rel_embeddings) | |
rel_att = self.disentangled_att_bias(query_layer, key_layer, relative_pos, rel_embeddings, scale_factor) | |
if rel_att is not None: | |
attention_scores = attention_scores + rel_att | |
# bxhxlxd | |
if self.talking_head: | |
attention_scores = self.head_logits_proj(attention_scores.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) | |
attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) | |
attention_probs = self.dropout(attention_probs) | |
if self.talking_head: | |
attention_probs = self.head_weights_proj(attention_probs.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) | |
context_layer = torch.matmul(attention_probs, value_layer) | |
context_layer = context_layer.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_att_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), query_layer.device) | |
if relative_pos.dim() == 2: | |
relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) | |
elif relative_pos.dim() == 3: | |
relative_pos = relative_pos.unsqueeze(1) | |
# bxhxqxk | |
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 = min(max(query_layer.size(-2), key_layer.size(-2)), self.max_relative_positions) | |
relative_pos = relative_pos.long().to(query_layer.device) | |
rel_embeddings = rel_embeddings[ | |
self.max_relative_positions - att_span : self.max_relative_positions + att_span, : | |
].unsqueeze(0) | |
score = 0 | |
# content->position | |
if "c2p" in self.pos_att_type: | |
pos_key_layer = self.pos_proj(rel_embeddings) | |
pos_key_layer = self.transpose_for_scores(pos_key_layer) | |
c2p_att = torch.matmul(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_dynamic_expand(c2p_pos, query_layer, relative_pos)) | |
score += c2p_att | |
# position->content | |
if "p2c" in self.pos_att_type: | |
pos_query_layer = self.pos_q_proj(rel_embeddings) | |
pos_query_layer = self.transpose_for_scores(pos_query_layer) | |
pos_query_layer /= torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor) | |
if query_layer.size(-2) != key_layer.size(-2): | |
r_pos = build_relative_position(key_layer.size(-2), key_layer.size(-2), query_layer.device) | |
else: | |
r_pos = relative_pos | |
p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) | |
p2c_att = torch.matmul(key_layer, pos_query_layer.transpose(-1, -2).to(dtype=key_layer.dtype)) | |
p2c_att = torch.gather( | |
p2c_att, dim=-1, index=p2c_dynamic_expand(p2c_pos, query_layer, key_layer) | |
).transpose(-1, -2) | |
if query_layer.size(-2) != key_layer.size(-2): | |
pos_index = relative_pos[:, :, :, 0].unsqueeze(-1) | |
p2c_att = torch.gather(p2c_att, dim=-2, index=pos_dynamic_expand(pos_index, p2c_att, key_layer)) | |
score += p2c_att | |
return score | |
class DebertaEmbeddings(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 = DebertaLayerNorm(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)), persistent=False | |
) | |
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 | |
class DebertaPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = DebertaConfig | |
base_model_prefix = "deberta" | |
_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, DebertaEncoder): | |
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 ([`DebertaConfig`]): 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 [`AutoTokenizer`]. 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. | |
""" | |
class DebertaModel(DebertaPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.embeddings = DebertaEmbeddings(config) | |
self.encoder = DebertaEncoder(config) | |
self.z_steps = 0 | |
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: | |
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) | |
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.append(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, | |
) | |
class DebertaForMaskedLM(DebertaPreTrainedModel): | |
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"] | |
def __init__(self, config): | |
super().__init__(config) | |
self.deberta = DebertaModel(config) | |
self.cls = DebertaOnlyMLMHead(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, | |
) | |
class DebertaPredictionHeadTransform(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.embedding_size = getattr(config, "embedding_size", config.hidden_size) | |
self.dense = nn.Linear(config.hidden_size, self.embedding_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(self.embedding_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 | |
class DebertaLMPredictionHead(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.transform = DebertaPredictionHeadTransform(config) | |
self.embedding_size = getattr(config, "embedding_size", config.hidden_size) | |
# The output weights are the same as the input embeddings, but there is | |
# an output-only bias for each token. | |
self.decoder = nn.Linear(self.embedding_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 DebertaOnlyMLMHead(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.predictions = DebertaLMPredictionHead(config) | |
def forward(self, sequence_output): | |
prediction_scores = self.predictions(sequence_output) | |
return prediction_scores | |
class DebertaForSequenceClassification(DebertaPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
num_labels = getattr(config, "num_labels", 2) | |
self.num_labels = num_labels | |
self.deberta = DebertaModel(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 | |
) | |
class DebertaForTokenClassification(DebertaPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.deberta = DebertaModel(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 | |
) | |
class DebertaForQuestionAnswering(DebertaPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.deberta = DebertaModel(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, | |
) | |