1224 lines
47 KiB
Python
1224 lines
47 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
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# Copyright 2020 Microsoft and the Hugging Face Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch DeBERTa-v2 model."""
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from collections.abc import Sequence
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import LayerNorm
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from transformers.activations import ACT2FN
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from transformers.modeling_utils import PreTrainedModel
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from transformers.pytorch_utils import softmax_backward_data
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from modelscope.metainfo import Models
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from modelscope.models import Model, TorchModel
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from modelscope.models.builder import MODELS
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from modelscope.outputs import AttentionBackboneModelOutput
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from modelscope.utils import logger as logging
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from modelscope.utils.constant import Tasks
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from .configuration import DebertaV2Config
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logger = logging.get_logger()
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# Copied from transformers.models.deberta.modeling_deberta.ContextPooler
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class ContextPooler(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.pooler_hidden_size,
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config.pooler_hidden_size)
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self.dropout = StableDropout(config.pooler_dropout)
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self.config = config
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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context_token = hidden_states[:, 0]
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context_token = self.dropout(context_token)
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pooled_output = self.dense(context_token)
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pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
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return pooled_output
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@property
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def output_dim(self):
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return self.config.hidden_size
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# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2
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class XSoftmax(torch.autograd.Function):
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"""
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Masked Softmax which is optimized for saving memory
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Args:
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input (`torch.tensor`): The input tensor that will apply softmax.
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mask (`torch.IntTensor`):
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The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
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dim (int): The dimension that will apply softmax
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Example:
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>>> import torch
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>>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax
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>>> # Make a tensor
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>>> x = torch.randn([4, 20, 100])
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>>> # Create a mask
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>>> mask = (x > 0).int()
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>>> # Specify the dimension to apply softmax
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>>> dim = -1
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>>> y = XSoftmax.apply(x, mask, dim)
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"""
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@staticmethod
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def forward(self, input, mask, dim):
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self.dim = dim
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rmask = ~(mask.to(torch.bool))
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output = input.masked_fill(rmask,
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torch.tensor(torch.finfo(input.dtype).min))
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output = torch.softmax(output, self.dim)
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output.masked_fill_(rmask, 0)
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self.save_for_backward(output)
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return output
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@staticmethod
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def backward(self, grad_output):
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(output, ) = self.saved_tensors
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inputGrad = softmax_backward_data(self, grad_output, output, self.dim,
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output)
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return inputGrad, None, None
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@staticmethod
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def symbolic(g, self, mask, dim):
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import torch.onnx.symbolic_helper as sym_help
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from torch.onnx.symbolic_opset9 import masked_fill, softmax
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mask_cast_value = g.op(
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'Cast', mask, to_i=sym_help.cast_pytorch_to_onnx['Long'])
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r_mask = g.op(
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'Cast',
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g.op('Sub',
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g.op('Constant', value_t=torch.tensor(1, dtype=torch.int64)),
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mask_cast_value),
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to_i=sym_help.cast_pytorch_to_onnx['Byte'],
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)
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output = masked_fill(
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g, self, r_mask,
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g.op(
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'Constant',
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value_t=torch.tensor(torch.finfo(self.type().dtype()).min)))
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output = softmax(g, output, dim)
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return masked_fill(
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g, output, r_mask,
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g.op('Constant', value_t=torch.tensor(0, dtype=torch.uint8)))
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# Copied from transformers.models.deberta.modeling_deberta.DropoutContext
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class DropoutContext(object):
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def __init__(self):
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self.dropout = 0
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self.mask = None
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self.scale = 1
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self.reuse_mask = True
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# Copied from transformers.models.deberta.modeling_deberta.get_mask
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def get_mask(input, local_context):
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if not isinstance(local_context, DropoutContext):
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dropout = local_context
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mask = None
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else:
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dropout = local_context.dropout
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dropout *= local_context.scale
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mask = local_context.mask if local_context.reuse_mask else None
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if dropout > 0 and mask is None:
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mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(
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torch.bool)
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if isinstance(local_context, DropoutContext):
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if local_context.mask is None:
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local_context.mask = mask
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return mask, dropout
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# Copied from transformers.models.deberta.modeling_deberta.XDropout
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class XDropout(torch.autograd.Function):
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"""Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
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@staticmethod
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def forward(ctx, input, local_ctx):
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mask, dropout = get_mask(input, local_ctx)
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ctx.scale = 1.0 / (1 - dropout)
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if dropout > 0:
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ctx.save_for_backward(mask)
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return input.masked_fill(mask, 0) * ctx.scale
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else:
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return input
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@staticmethod
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def backward(ctx, grad_output):
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if ctx.scale > 1:
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(mask, ) = ctx.saved_tensors
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return grad_output.masked_fill(mask, 0) * ctx.scale, None
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else:
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return grad_output, None
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@staticmethod
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def symbolic(g: torch._C.Graph, input: torch._C.Value,
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local_ctx: Union[float, DropoutContext]) -> torch._C.Value:
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from torch.onnx import symbolic_opset12
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dropout_p = local_ctx
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if isinstance(local_ctx, DropoutContext):
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dropout_p = local_ctx.dropout
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# StableDropout only calls this function when training.
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train = True
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# TODO: We should check if the opset_version being used to export
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# is > 12 here, but there's no good way to do that. As-is, if the
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# opset_version < 12, export will fail with a CheckerError.
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# Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like:
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# if opset_version < 12:
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# return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train)
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return symbolic_opset12.dropout(g, input, dropout_p, train)
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# Copied from transformers.models.deberta.modeling_deberta.StableDropout
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class StableDropout(nn.Module):
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"""
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Optimized dropout module for stabilizing the training
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Args:
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drop_prob (float): the dropout probabilities
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"""
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def __init__(self, drop_prob):
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super().__init__()
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self.drop_prob = drop_prob
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self.count = 0
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self.context_stack = None
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def forward(self, x):
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"""
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Call the module
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Args:
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x (`torch.tensor`): The input tensor to apply dropout
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"""
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if self.training and self.drop_prob > 0:
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return XDropout.apply(x, self.get_context())
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return x
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def clear_context(self):
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self.count = 0
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self.context_stack = None
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def init_context(self, reuse_mask=True, scale=1):
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if self.context_stack is None:
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self.context_stack = []
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self.count = 0
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for c in self.context_stack:
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c.reuse_mask = reuse_mask
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c.scale = scale
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def get_context(self):
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if self.context_stack is not None:
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if self.count >= len(self.context_stack):
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self.context_stack.append(DropoutContext())
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ctx = self.context_stack[self.count]
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ctx.dropout = self.drop_prob
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self.count += 1
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return ctx
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else:
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return self.drop_prob
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# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm
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class DebertaV2SelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
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self.dropout = StableDropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2
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class DebertaV2Attention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = DisentangledSelfAttention(config)
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self.output = DebertaV2SelfOutput(config)
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self.config = config
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def forward(
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self,
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hidden_states,
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attention_mask,
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output_attentions=False,
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query_states=None,
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relative_pos=None,
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rel_embeddings=None,
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):
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self_output = self.self(
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hidden_states,
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attention_mask,
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output_attentions,
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query_states=query_states,
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relative_pos=relative_pos,
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rel_embeddings=rel_embeddings,
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)
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if output_attentions:
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self_output, att_matrix = self_output
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if query_states is None:
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query_states = hidden_states
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attention_output = self.output(self_output, query_states)
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if output_attentions:
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return (attention_output, att_matrix)
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else:
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return attention_output
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2
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class DebertaV2Intermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm
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class DebertaV2Output(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
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self.dropout = StableDropout(config.hidden_dropout_prob)
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self.config = config
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2
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class DebertaV2Layer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.attention = DebertaV2Attention(config)
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self.intermediate = DebertaV2Intermediate(config)
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self.output = DebertaV2Output(config)
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def forward(
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self,
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hidden_states,
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attention_mask,
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query_states=None,
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relative_pos=None,
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rel_embeddings=None,
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output_attentions=False,
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):
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attention_output = self.attention(
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hidden_states,
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attention_mask,
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output_attentions=output_attentions,
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query_states=query_states,
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relative_pos=relative_pos,
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rel_embeddings=rel_embeddings,
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)
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if output_attentions:
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attention_output, att_matrix = attention_output
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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if output_attentions:
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return (layer_output, att_matrix)
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else:
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return layer_output
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class ConvLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
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kernel_size = getattr(config, 'conv_kernel_size', 3)
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groups = getattr(config, 'conv_groups', 1)
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self.conv_act = getattr(config, 'conv_act', 'tanh')
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self.conv = nn.Conv1d(
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config.hidden_size,
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config.hidden_size,
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kernel_size,
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padding=(kernel_size - 1) // 2,
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groups=groups)
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self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
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self.dropout = StableDropout(config.hidden_dropout_prob)
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self.config = config
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def forward(self, hidden_states, residual_states, input_mask):
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out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(
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0, 2, 1).contiguous()
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rmask = (1 - input_mask).bool()
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out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)
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out = ACT2FN[self.conv_act](self.dropout(out))
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layer_norm_input = residual_states + out
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output = self.LayerNorm(layer_norm_input).to(layer_norm_input)
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if input_mask is None:
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output_states = output
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else:
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if input_mask.dim() != layer_norm_input.dim():
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if input_mask.dim() == 4:
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input_mask = input_mask.squeeze(1).squeeze(1)
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input_mask = input_mask.unsqueeze(2)
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input_mask = input_mask.to(output.dtype)
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output_states = output * input_mask
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return output_states
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class DebertaV2Encoder(nn.Module):
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"""Modified BertEncoder with relative position bias support"""
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def __init__(self, config):
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super().__init__()
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self.layer = nn.ModuleList(
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[DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])
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self.relative_attention = getattr(config, 'relative_attention', False)
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if self.relative_attention:
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self.max_relative_positions = getattr(config,
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'max_relative_positions', -1)
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if self.max_relative_positions < 1:
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self.max_relative_positions = config.max_position_embeddings
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self.position_buckets = getattr(config, 'position_buckets', -1)
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pos_ebd_size = self.max_relative_positions * 2
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if self.position_buckets > 0:
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pos_ebd_size = self.position_buckets * 2
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self.rel_embeddings = nn.Embedding(pos_ebd_size,
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config.hidden_size)
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self.norm_rel_ebd = [
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x.strip()
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for x in getattr(config, 'norm_rel_ebd', 'none').lower().split('|')
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]
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if 'layer_norm' in self.norm_rel_ebd:
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self.LayerNorm = LayerNorm(
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config.hidden_size,
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config.layer_norm_eps,
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elementwise_affine=True)
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self.conv = ConvLayer(config) if getattr(config, 'conv_kernel_size',
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0) > 0 else None
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self.gradient_checkpointing = False
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def get_rel_embedding(self):
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rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
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if rel_embeddings is not None and ('layer_norm' in self.norm_rel_ebd):
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rel_embeddings = self.LayerNorm(rel_embeddings)
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return rel_embeddings
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def get_attention_mask(self, attention_mask):
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if attention_mask.dim() <= 2:
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extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
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attention_mask = extended_attention_mask * extended_attention_mask.squeeze(
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-2).unsqueeze(-1)
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attention_mask = attention_mask.byte()
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elif attention_mask.dim() == 3:
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attention_mask = attention_mask.unsqueeze(1)
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return attention_mask
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def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
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if self.relative_attention and relative_pos is None:
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q = query_states.size(
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-2) if query_states is not None else hidden_states.size(-2)
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relative_pos = build_relative_position(
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q,
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hidden_states.size(-2),
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bucket_size=self.position_buckets,
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max_position=self.max_relative_positions)
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return relative_pos
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def forward(
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self,
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hidden_states,
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attention_mask,
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output_hidden_states=True,
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output_attentions=False,
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query_states=None,
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relative_pos=None,
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return_dict=True,
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):
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if attention_mask.dim() <= 2:
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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 AttentionBackboneModelOutput(
|
|
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 = torch.sign(relative_pos)
|
|
mid = bucket_size // 2
|
|
abs_pos = torch.where(
|
|
(relative_pos < mid) & (relative_pos > -mid),
|
|
torch.tensor(mid - 1).type_as(relative_pos),
|
|
torch.abs(relative_pos),
|
|
)
|
|
log_pos = (
|
|
torch.ceil(
|
|
torch.log(abs_pos / mid)
|
|
/ torch.log(torch.tensor(
|
|
(max_position - 1) / mid)) * (mid - 1)) + mid)
|
|
bucket_pos = torch.where(abs_pos <= mid, relative_pos.type_as(log_pos),
|
|
log_pos * sign)
|
|
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 = torch.arange(0, query_size)
|
|
k_ids = torch.arange(0, key_size)
|
|
rel_pos_ids = q_ids[:, None] - k_ids[None, :]
|
|
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 = rel_pos_ids.to(torch.long)
|
|
rel_pos_ids = rel_pos_ids[:query_size, :]
|
|
rel_pos_ids = rel_pos_ids.unsqueeze(0)
|
|
return rel_pos_ids
|
|
|
|
|
|
@torch.jit.script
|
|
# 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)
|
|
])
|
|
|
|
|
|
@torch.jit.script
|
|
# 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)
|
|
])
|
|
|
|
|
|
@torch.jit.script
|
|
# 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 = torch.sqrt(
|
|
torch.tensor(query_layer.size(-1), dtype=torch.float)
|
|
* scale_factor)
|
|
attention_scores = torch.bmm(query_layer, key_layer.transpose(
|
|
-1, -2)) / torch.tensor(
|
|
scale, dtype=query_layer.dtype)
|
|
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 = torch.sqrt(
|
|
torch.tensor(pos_key_layer.size(-1), dtype=torch.float)
|
|
* 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 / torch.tensor(scale, dtype=c2p_att.dtype)
|
|
|
|
# position->content
|
|
if 'p2c' in self.pos_att_type:
|
|
scale = torch.sqrt(
|
|
torch.tensor(pos_query_layer.size(-1), dtype=torch.float)
|
|
* 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 / torch.tensor(scale, dtype=p2c_att.dtype)
|
|
|
|
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(TorchModel, 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__(self, config, **kwargs):
|
|
super().__init__(config.name_or_path, **kwargs)
|
|
super(Model, self).__init__(config)
|
|
|
|
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
|
|
|
|
@classmethod
|
|
def _instantiate(cls, **kwargs):
|
|
model_dir = kwargs.pop('model_dir', None)
|
|
if model_dir is None:
|
|
ponet_config = DebertaV2Config(**kwargs)
|
|
model = cls(ponet_config)
|
|
else:
|
|
model = super(
|
|
Model,
|
|
cls).from_pretrained(pretrained_model_name_or_path=model_dir)
|
|
return model
|
|
|
|
|
|
@MODELS.register_module(Tasks.backbone, module_name=Models.deberta_v2)
|
|
# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2
|
|
class DebertaV2Model(DebertaV2PreTrainedModel):
|
|
"""The bare DeBERTa_v2 Model transformer outputting raw hidden-states without any specific head on top.
|
|
|
|
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.
|
|
"""
|
|
|
|
def __init__(self, config, **kwargs):
|
|
super().__init__(config)
|
|
|
|
self.embeddings = DebertaV2Embeddings(config)
|
|
self.encoder = DebertaV2Encoder(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, AttentionBackboneModelOutput]:
|
|
r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `('batch_size, sequence_length')`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `('batch_size, sequence_length')`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
token_type_ids (`torch.LongTensor` of shape `('batch_size, sequence_length')`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
position_ids (`torch.LongTensor` of shape `('batch_size, sequence_length')`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
|
|
`[0,config.max_position_embeddings - 1]`.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `('batch_size, sequence_length', hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert *input_ids* indices into associated
|
|
vectors than the model's internal embedding lookup matrix.
|
|
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 dataclass instead of a plain tuple.
|
|
|
|
Returns:
|
|
Returns `modelscope.outputs.AttentionBackboneModelOutput`
|
|
|
|
Examples:
|
|
>>> from modelscope.models import Model
|
|
>>> from modelscope.preprocessors import Preprocessor
|
|
>>> model = Model.from_pretrained('damo/nlp_debertav2_fill-mask_chinese-lite', task='backbone')
|
|
>>> preprocessor = Preprocessor.from_pretrained('damo/nlp_debertav2_fill-mask_chinese-lite')
|
|
>>> print(model(**preprocessor('这是个测试')))
|
|
"""
|
|
|
|
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.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 AttentionBackboneModelOutput(
|
|
last_hidden_state=sequence_output,
|
|
hidden_states=encoder_outputs.hidden_states
|
|
if output_hidden_states else None,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|