1105 lines
46 KiB
Python
1105 lines
46 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/sew/modular_sew.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_sew.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2021 ASAPP Inc. and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import warnings
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from typing import Callable, Optional, Union
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ...activations import ACT2FN
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from ...integrations.deepspeed import is_deepspeed_zero3_enabled
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from ...integrations.fsdp import is_fsdp_managed_module
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import auto_docstring, logging
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from ...utils.deprecation import deprecate_kwarg
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from .configuration_sew import SEWConfig
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logger = logging.get_logger(__name__)
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class SEWNoLayerNormConvLayer(GradientCheckpointingLayer):
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def __init__(self, config, layer_id=0):
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super().__init__()
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self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
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self.out_conv_dim = config.conv_dim[layer_id]
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self.conv = nn.Conv1d(
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self.in_conv_dim,
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self.out_conv_dim,
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kernel_size=config.conv_kernel[layer_id],
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stride=config.conv_stride[layer_id],
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bias=config.conv_bias,
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)
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self.activation = ACT2FN[config.feat_extract_activation]
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def forward(self, hidden_states):
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hidden_states = self.conv(hidden_states)
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hidden_states = self.activation(hidden_states)
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return hidden_states
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class SEWLayerNormConvLayer(GradientCheckpointingLayer):
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def __init__(self, config, layer_id=0):
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super().__init__()
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self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
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self.out_conv_dim = config.conv_dim[layer_id]
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self.conv = nn.Conv1d(
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self.in_conv_dim,
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self.out_conv_dim,
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kernel_size=config.conv_kernel[layer_id],
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stride=config.conv_stride[layer_id],
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bias=config.conv_bias,
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)
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self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True)
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self.activation = ACT2FN[config.feat_extract_activation]
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def forward(self, hidden_states):
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hidden_states = self.conv(hidden_states)
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hidden_states = hidden_states.transpose(-2, -1)
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hidden_states = self.layer_norm(hidden_states)
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hidden_states = hidden_states.transpose(-2, -1)
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hidden_states = self.activation(hidden_states)
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return hidden_states
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class SEWGroupNormConvLayer(GradientCheckpointingLayer):
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def __init__(self, config, layer_id=0):
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super().__init__()
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self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
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self.out_conv_dim = config.conv_dim[layer_id]
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self.conv = nn.Conv1d(
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self.in_conv_dim,
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self.out_conv_dim,
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kernel_size=config.conv_kernel[layer_id],
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stride=config.conv_stride[layer_id],
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bias=config.conv_bias,
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)
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self.activation = ACT2FN[config.feat_extract_activation]
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self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True)
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def forward(self, hidden_states):
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hidden_states = self.conv(hidden_states)
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hidden_states = self.layer_norm(hidden_states)
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hidden_states = self.activation(hidden_states)
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return hidden_states
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class SEWPositionalConvEmbedding(nn.Module):
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def __init__(self, config):
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super().__init__()
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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=config.num_conv_pos_embeddings,
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padding=config.num_conv_pos_embeddings // 2,
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groups=config.num_conv_pos_embedding_groups,
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stride=config.squeeze_factor,
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)
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weight_norm = nn.utils.weight_norm
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if hasattr(nn.utils.parametrizations, "weight_norm"):
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weight_norm = nn.utils.parametrizations.weight_norm
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if is_deepspeed_zero3_enabled():
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import deepspeed
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with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0):
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self.conv = weight_norm(self.conv, name="weight", dim=2)
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if hasattr(self.conv, "parametrizations"):
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weight_g = self.conv.parametrizations.weight.original0
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weight_v = self.conv.parametrizations.weight.original1
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else:
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weight_g = self.conv.weight_g
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weight_v = self.conv.weight_v
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deepspeed.zero.register_external_parameter(self, weight_v)
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deepspeed.zero.register_external_parameter(self, weight_g)
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else:
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self.conv = weight_norm(self.conv, name="weight", dim=2)
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self.padding = SEWSamePadLayer(config.num_conv_pos_embeddings)
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self.activation = ACT2FN[config.feat_extract_activation]
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def forward(self, hidden_states):
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hidden_states = self.conv(hidden_states)
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hidden_states = self.padding(hidden_states)
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hidden_states = self.activation(hidden_states)
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return hidden_states
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class SEWSamePadLayer(nn.Module):
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def __init__(self, num_conv_pos_embeddings):
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super().__init__()
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self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
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def forward(self, hidden_states):
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if self.num_pad_remove > 0:
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hidden_states = hidden_states[:, :, : -self.num_pad_remove]
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return hidden_states
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class SEWUpsampling(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.projection = nn.Linear(config.hidden_size, config.hidden_size * config.squeeze_factor)
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self.activation = ACT2FN[config.feat_extract_activation]
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self.squeeze_factor = config.squeeze_factor
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def forward(self, hidden_states):
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hidden_states = self.projection(hidden_states)
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hidden_states = self.activation(hidden_states)
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if self.squeeze_factor > 1:
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# transform embedding channels to sequence length
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bsz, src_len, src_embed_dim = hidden_states.size()
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tgt_len = src_len * self.squeeze_factor
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tgt_embed_dim = src_embed_dim // self.squeeze_factor
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hidden_states = hidden_states.reshape(bsz, src_len, self.squeeze_factor, tgt_embed_dim)
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hidden_states = hidden_states.reshape(bsz, tgt_len, tgt_embed_dim)
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return hidden_states
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class SEWFeatureEncoder(nn.Module):
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"""Construct the features from raw audio waveform"""
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def __init__(self, config):
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super().__init__()
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if config.feat_extract_norm == "group":
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conv_layers = [SEWGroupNormConvLayer(config, layer_id=0)] + [
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SEWNoLayerNormConvLayer(config, layer_id=i + 1) for i in range(config.num_feat_extract_layers - 1)
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]
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elif config.feat_extract_norm == "layer":
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conv_layers = [SEWLayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)]
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else:
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raise ValueError(
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f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']"
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)
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self.conv_layers = nn.ModuleList(conv_layers)
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self.gradient_checkpointing = False
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self._requires_grad = True
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def _freeze_parameters(self):
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for param in self.parameters():
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param.requires_grad = False
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self._requires_grad = False
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def forward(self, input_values):
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hidden_states = input_values[:, None]
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# make sure hidden_states require grad for gradient_checkpointing
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if self._requires_grad and self.training:
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hidden_states.requires_grad = True
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for conv_layer in self.conv_layers:
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hidden_states = conv_layer(hidden_states)
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return hidden_states
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: Optional[float] = None,
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dropout: float = 0.0,
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head_mask: Optional[torch.Tensor] = None,
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**kwargs,
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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if head_mask is not None:
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attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class SEWAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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dropout: float = 0.0,
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is_decoder: bool = False,
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bias: bool = True,
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is_causal: bool = False,
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config: Optional[SEWConfig] = None,
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.dropout = dropout
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self.head_dim = embed_dim // num_heads
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self.config = config
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if (self.head_dim * num_heads) != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads})."
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)
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self.scaling = self.head_dim**-0.5
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self.is_decoder = is_decoder
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self.is_causal = is_causal
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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@deprecate_kwarg("past_key_value", version="4.54.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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key_value_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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layer_head_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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# TODO: we need a refactor so that the different attention modules can get their specific kwargs
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# ATM, we have mixed things encoder, decoder, and encoder-decoder attn
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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# if key_value_states are provided this layer is used as a cross-attention layer
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# for the decoder
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is_cross_attention = key_value_states is not None
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# determine input shapes
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bsz, tgt_len = hidden_states.shape[:-1]
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src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
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q_input_shape = (bsz, tgt_len, -1, self.head_dim)
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kv_input_shape = (bsz, src_len, -1, self.head_dim)
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# get query proj
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query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
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current_states = key_value_states if is_cross_attention else hidden_states
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key_states = self.k_proj(current_states).view(*kv_input_shape).transpose(1, 2)
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value_states = self.v_proj(current_states).view(*kv_input_shape).transpose(1, 2)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout,
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scaling=self.scaling,
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output_attentions=output_attentions,
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head_mask=layer_head_mask,
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**kwargs,
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)
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attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights, None
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class SEWFeedForward(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.intermediate_dropout = nn.Dropout(config.activation_dropout)
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self.intermediate_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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self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.output_dropout = nn.Dropout(config.hidden_dropout)
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def forward(self, hidden_states):
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hidden_states = self.intermediate_dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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hidden_states = self.intermediate_dropout(hidden_states)
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hidden_states = self.output_dense(hidden_states)
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hidden_states = self.output_dropout(hidden_states)
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return hidden_states
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class SEWEncoderLayer(GradientCheckpointingLayer):
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def __init__(self, config):
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super().__init__()
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self.attention = SEWAttention(
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embed_dim=config.hidden_size,
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num_heads=config.num_attention_heads,
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dropout=config.attention_dropout,
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is_decoder=False,
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config=config,
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)
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self.dropout = nn.Dropout(config.hidden_dropout)
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self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.feed_forward = SEWFeedForward(config)
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self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states, attention_mask=None, output_attentions=False):
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attn_residual = hidden_states
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hidden_states, attn_weights, _ = self.attention(
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hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
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)
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hidden_states = self.dropout(hidden_states)
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hidden_states = attn_residual + hidden_states
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hidden_states = self.layer_norm(hidden_states)
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hidden_states = hidden_states + self.feed_forward(hidden_states)
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hidden_states = self.final_layer_norm(hidden_states)
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs
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class SEWEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.pos_conv_embed = SEWPositionalConvEmbedding(config)
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self.pool = nn.AvgPool1d(config.squeeze_factor, config.squeeze_factor)
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self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout)
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self.layers = nn.ModuleList([SEWEncoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.upsample = SEWUpsampling(config)
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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output_attentions=False,
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output_hidden_states=False,
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return_dict=True,
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):
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all_hidden_states = () if output_hidden_states else None
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all_self_attentions = () if output_attentions else None
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if attention_mask is not None:
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expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
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if self.config._attn_implementation == "flash_attention_2":
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# make sure padded tokens output 0
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hidden_states[~expand_attention_mask] = 0.0
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# 2d mask is passed through the layers
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attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
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else:
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# make sure padded tokens output 0
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hidden_states[~expand_attention_mask] = 0.0
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input_lengths = (attention_mask.long()).sum(-1)
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# apply pooling formula to get real output_lengths
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output_lengths = input_lengths // self.config.squeeze_factor
|
|
max_encoder_length = hidden_states.shape[1] // self.config.squeeze_factor
|
|
attention_ids = (
|
|
torch.arange(0, max_encoder_length, device=output_lengths.device)
|
|
.view(1, -1)
|
|
.expand(output_lengths.shape[0], -1)
|
|
)
|
|
attention_mask = (attention_ids < output_lengths.view(-1, 1)).long()
|
|
|
|
# extend attention_mask
|
|
attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
|
|
attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
|
|
attention_mask = attention_mask.expand(
|
|
attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
|
|
)
|
|
|
|
n_input_timesteps = hidden_states.shape[1]
|
|
|
|
hidden_states = hidden_states.transpose(1, 2)
|
|
position_embeddings = self.pos_conv_embed(hidden_states)
|
|
pooled_hidden_states = self.pool(hidden_states)
|
|
min_length = min(position_embeddings.size(-1), pooled_hidden_states.size(-1))
|
|
hidden_states = pooled_hidden_states[..., :min_length] + position_embeddings[..., :min_length]
|
|
hidden_states = hidden_states.transpose(1, 2)
|
|
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
|
|
|
|
for layer in self.layers:
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
|
|
dropout_probability = torch.rand([])
|
|
|
|
skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
|
|
if not skip_the_layer or synced_gpus:
|
|
# under fsdp or deepspeed zero3 all gpus must run in sync
|
|
layer_outputs = layer(
|
|
hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if skip_the_layer:
|
|
layer_outputs = (None, None)
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
hidden_states = self.upsample(hidden_states)
|
|
if hidden_states.shape[1] < n_input_timesteps:
|
|
hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, n_input_timesteps - hidden_states.shape[1]))
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class SEWPreTrainedModel(PreTrainedModel):
|
|
config: SEWConfig
|
|
base_model_prefix = "sew"
|
|
main_input_name = "input_values"
|
|
supports_gradient_checkpointing = True
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = False # needs a proper look into the mask creation
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, SEWPositionalConvEmbedding):
|
|
nn.init.normal_(
|
|
module.conv.weight,
|
|
mean=0,
|
|
std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)),
|
|
)
|
|
nn.init.constant_(module.conv.bias, 0)
|
|
elif 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)
|
|
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, nn.Conv1d):
|
|
if is_deepspeed_zero3_enabled():
|
|
import deepspeed
|
|
|
|
if hasattr(module, "weight_v") and hasattr(module, "weight_g"):
|
|
with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0):
|
|
nn.init.kaiming_normal_(module.weight.data)
|
|
else:
|
|
with deepspeed.zero.GatheredParameters(module.weight, modifier_rank=0):
|
|
nn.init.kaiming_normal_(module.weight.data)
|
|
else:
|
|
nn.init.kaiming_normal_(module.weight.data)
|
|
|
|
if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
|
|
"""
|
|
Computes the output length of the convolutional layers
|
|
"""
|
|
|
|
def _conv_out_length(input_length, kernel_size, stride):
|
|
# 1D convolutional layer output length formula taken
|
|
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
|
|
return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1
|
|
|
|
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
|
|
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
|
|
|
|
return input_lengths
|
|
|
|
def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
|
|
output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
|
|
batch_size = attention_mask.shape[0]
|
|
|
|
attention_mask = torch.zeros(
|
|
(batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
|
|
)
|
|
# these two operations makes sure that all values before the output lengths idxs are attended to
|
|
attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
|
|
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
|
|
return attention_mask
|
|
|
|
|
|
def _compute_mask_indices(
|
|
shape: tuple[int, int],
|
|
mask_prob: float,
|
|
mask_length: int,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
min_masks: int = 0,
|
|
) -> np.ndarray:
|
|
"""
|
|
Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
|
|
ASR](https://huggingface.co/papers/1904.08779). Note that this method is not optimized to run on TPU and should be run on
|
|
CPU as part of the preprocessing during training.
|
|
|
|
Args:
|
|
shape: The shape for which to compute masks. This should be of a tuple of size 2 where
|
|
the first element is the batch size and the second element is the length of the axis to span.
|
|
mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of
|
|
independently generated mask spans of length `mask_length` is computed by
|
|
`mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
|
|
actual percentage will be smaller.
|
|
mask_length: size of the mask
|
|
min_masks: minimum number of masked spans
|
|
attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
|
|
each batch dimension.
|
|
"""
|
|
batch_size, sequence_length = shape
|
|
|
|
if mask_length < 1:
|
|
raise ValueError("`mask_length` has to be bigger than 0.")
|
|
|
|
if mask_length > sequence_length:
|
|
raise ValueError(
|
|
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
|
|
f" and `sequence_length`: {sequence_length}`"
|
|
)
|
|
|
|
# epsilon is used for probabilistic rounding
|
|
epsilon = np.random.rand(1).item()
|
|
|
|
def compute_num_masked_span(input_length):
|
|
"""Given input length, compute how many spans should be masked"""
|
|
num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
|
|
num_masked_span = max(num_masked_span, min_masks)
|
|
|
|
# make sure num masked span <= sequence_length
|
|
if num_masked_span * mask_length > sequence_length:
|
|
num_masked_span = sequence_length // mask_length
|
|
|
|
# make sure num_masked span is also <= input_length - (mask_length - 1)
|
|
if input_length - (mask_length - 1) < num_masked_span:
|
|
num_masked_span = max(input_length - (mask_length - 1), 0)
|
|
|
|
return num_masked_span
|
|
|
|
# compute number of masked spans in batch
|
|
input_lengths = (
|
|
attention_mask.detach().sum(-1).tolist()
|
|
if attention_mask is not None
|
|
else [sequence_length for _ in range(batch_size)]
|
|
)
|
|
|
|
# SpecAugment mask to fill
|
|
spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
|
|
spec_aug_mask_idxs = []
|
|
|
|
max_num_masked_span = compute_num_masked_span(sequence_length)
|
|
|
|
if max_num_masked_span == 0:
|
|
return spec_aug_mask
|
|
|
|
for input_length in input_lengths:
|
|
# compute num of masked spans for this input
|
|
num_masked_span = compute_num_masked_span(input_length)
|
|
|
|
# get random indices to mask
|
|
spec_aug_mask_idx = np.random.choice(
|
|
np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
|
|
)
|
|
|
|
# pick first sampled index that will serve as a dummy index to pad vector
|
|
# to ensure same dimension for all batches due to probabilistic rounding
|
|
# Picking first sample just pads those vectors twice.
|
|
if len(spec_aug_mask_idx) == 0:
|
|
# this case can only happen if `input_length` is strictly smaller then
|
|
# `sequence_length` in which case the last token has to be a padding
|
|
# token which we can use as a dummy mask id
|
|
dummy_mask_idx = sequence_length - 1
|
|
else:
|
|
dummy_mask_idx = spec_aug_mask_idx[0]
|
|
|
|
spec_aug_mask_idx = np.concatenate(
|
|
[spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
|
|
)
|
|
spec_aug_mask_idxs.append(spec_aug_mask_idx)
|
|
|
|
spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)
|
|
|
|
# expand masked indices to masked spans
|
|
spec_aug_mask_idxs = np.broadcast_to(
|
|
spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
|
|
)
|
|
spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)
|
|
|
|
# add offset to the starting indexes so that indexes now create a span
|
|
offsets = np.arange(mask_length)[None, None, :]
|
|
offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
|
|
batch_size, max_num_masked_span * mask_length
|
|
)
|
|
spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
|
|
|
|
# ensure that we cannot have indices larger than sequence_length
|
|
if spec_aug_mask_idxs.max() > sequence_length - 1:
|
|
spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
|
|
|
|
# scatter indices to mask
|
|
np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
|
|
|
|
return spec_aug_mask
|
|
|
|
|
|
@auto_docstring
|
|
class SEWModel(SEWPreTrainedModel):
|
|
def __init__(self, config: SEWConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.feature_extractor = SEWFeatureEncoder(config)
|
|
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
|
|
|
|
self.project_features = config.conv_dim[-1] != config.hidden_size
|
|
if self.project_features:
|
|
self.feature_projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
|
|
self.feature_dropout = nn.Dropout(config.feat_proj_dropout)
|
|
|
|
if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
|
|
self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_())
|
|
|
|
self.encoder = SEWEncoder(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states
|
|
def _mask_hidden_states(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
mask_time_indices: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
):
|
|
"""
|
|
Masks extracted features along time axis and/or along feature axis according to
|
|
[SpecAugment](https://huggingface.co/papers/1904.08779).
|
|
"""
|
|
|
|
# `config.apply_spec_augment` can set masking to False
|
|
if not getattr(self.config, "apply_spec_augment", True):
|
|
return hidden_states
|
|
|
|
# generate indices & apply SpecAugment along time axis
|
|
batch_size, sequence_length, hidden_size = hidden_states.size()
|
|
|
|
if mask_time_indices is not None:
|
|
# apply SpecAugment along time axis with given mask_time_indices
|
|
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
|
|
elif self.config.mask_time_prob > 0 and self.training:
|
|
mask_time_indices = _compute_mask_indices(
|
|
(batch_size, sequence_length),
|
|
mask_prob=self.config.mask_time_prob,
|
|
mask_length=self.config.mask_time_length,
|
|
attention_mask=attention_mask,
|
|
min_masks=self.config.mask_time_min_masks,
|
|
)
|
|
mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool)
|
|
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
|
|
|
|
if self.config.mask_feature_prob > 0 and self.training:
|
|
# generate indices & apply SpecAugment along feature axis
|
|
mask_feature_indices = _compute_mask_indices(
|
|
(batch_size, hidden_size),
|
|
mask_prob=self.config.mask_feature_prob,
|
|
mask_length=self.config.mask_feature_length,
|
|
min_masks=self.config.mask_feature_min_masks,
|
|
)
|
|
mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool)
|
|
mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
|
|
hidden_states[mask_feature_indices] = 0
|
|
|
|
return hidden_states
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_values: Optional[torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
mask_time_indices: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
r"""
|
|
mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
|
|
masked extracted features in *config.proj_codevector_dim* space.
|
|
"""
|
|
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
|
|
|
|
extract_features = self.feature_extractor(input_values)
|
|
extract_features = extract_features.transpose(1, 2)
|
|
extract_features = self.layer_norm(extract_features)
|
|
|
|
if self.project_features:
|
|
extract_features = self.feature_projection(extract_features)
|
|
hidden_states = self.feature_dropout(extract_features)
|
|
|
|
if attention_mask is not None:
|
|
# compute reduced attention_mask corresponding to feature vectors
|
|
attention_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
|
|
|
|
hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)
|
|
|
|
encoder_outputs = self.encoder(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = encoder_outputs[0]
|
|
|
|
if not return_dict:
|
|
return (hidden_states,) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
_HIDDEN_STATES_START_POSITION = 1
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
SEW Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
|
|
"""
|
|
)
|
|
class SEWForCTC(SEWPreTrainedModel):
|
|
def __init__(self, config, target_lang: Optional[str] = None):
|
|
r"""
|
|
target_lang (`str`, *optional*):
|
|
Language id of adapter weights. Adapter weights are stored in the format adapter.<lang>.safetensors or
|
|
adapter.<lang>.bin. Only relevant when using an instance of [`SEWForCTC`] with adapters. Uses 'eng' by
|
|
default.
|
|
"""
|
|
super().__init__(config)
|
|
|
|
self.sew = SEWModel(config)
|
|
self.dropout = nn.Dropout(config.final_dropout)
|
|
|
|
self.target_lang = target_lang
|
|
|
|
if config.vocab_size is None:
|
|
raise ValueError(
|
|
f"You are trying to instantiate {self.__class__} with a configuration that "
|
|
"does not define the vocabulary size of the language model head. Please "
|
|
"instantiate the model as follows: `SEWForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
|
|
"or define `vocab_size` of your model's configuration."
|
|
)
|
|
output_hidden_size = (
|
|
config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size
|
|
)
|
|
self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def tie_weights(self):
|
|
"""
|
|
This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
|
|
passing `target_lang=...` to `from_pretrained(...)`.
|
|
|
|
This method is **not** supposed to be called by the user and is prone to be changed in the future.
|
|
"""
|
|
|
|
# Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
|
|
# correctly load adapter layers for SEW so that we do not have to introduce a new API to
|
|
# [`PreTrainedModel`]. While slightly hacky, SEW never has to tie input and output embeddings, so that it is
|
|
# ok to repurpose this function here.
|
|
target_lang = self.target_lang
|
|
|
|
if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
|
|
raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
|
|
elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
|
|
logger.info("By default `target_lang` is set to 'eng'.")
|
|
elif target_lang is not None:
|
|
self.load_adapter(target_lang, force_load=True)
|
|
|
|
def freeze_feature_extractor(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
|
|
not be updated during training.
|
|
"""
|
|
warnings.warn(
|
|
"The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. "
|
|
"Please use the equivalent `freeze_feature_encoder` method instead.",
|
|
FutureWarning,
|
|
)
|
|
self.freeze_feature_encoder()
|
|
|
|
def freeze_feature_encoder(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
|
|
not be updated during training.
|
|
"""
|
|
self.sew.feature_extractor._freeze_parameters()
|
|
|
|
def freeze_base_model(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the base model so that its parameters will not
|
|
be updated during training. Only the classification head will be updated.
|
|
"""
|
|
for param in self.sew.parameters():
|
|
param.requires_grad = False
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_values: Optional[torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
) -> Union[tuple, CausalLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
|
|
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
|
|
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
|
|
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
|
|
config.vocab_size - 1]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if labels is not None and labels.max() >= self.config.vocab_size:
|
|
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
|
|
|
|
outputs = self.sew(
|
|
input_values,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
logits = self.lm_head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# retrieve loss input_lengths from attention_mask
|
|
attention_mask = (
|
|
attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
|
|
)
|
|
input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
|
|
|
|
# assuming that padded tokens are filled with -100
|
|
# when not being attended to
|
|
labels_mask = labels >= 0
|
|
target_lengths = labels_mask.sum(-1)
|
|
flattened_targets = labels.masked_select(labels_mask)
|
|
|
|
# ctc_loss doesn't support fp16
|
|
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
|
|
|
|
with torch.backends.cudnn.flags(enabled=False):
|
|
loss = nn.functional.ctc_loss(
|
|
log_probs,
|
|
flattened_targets,
|
|
input_lengths,
|
|
target_lengths,
|
|
blank=self.config.pad_token_id,
|
|
reduction=self.config.ctc_loss_reduction,
|
|
zero_infinity=self.config.ctc_zero_infinity,
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutput(
|
|
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
SEW Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
|
|
SUPERB Keyword Spotting.
|
|
"""
|
|
)
|
|
class SEWForSequenceClassification(SEWPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
if hasattr(config, "add_adapter") and config.add_adapter:
|
|
raise ValueError(
|
|
"Sequence classification does not support the use of SEW adapters (config.add_adapter=True)"
|
|
)
|
|
self.sew = SEWModel(config)
|
|
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
|
|
if config.use_weighted_layer_sum:
|
|
self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
|
|
self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
|
|
self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def freeze_feature_extractor(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
|
|
not be updated during training.
|
|
"""
|
|
warnings.warn(
|
|
"The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. "
|
|
"Please use the equivalent `freeze_feature_encoder` method instead.",
|
|
FutureWarning,
|
|
)
|
|
self.freeze_feature_encoder()
|
|
|
|
def freeze_feature_encoder(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
|
|
not be updated during training.
|
|
"""
|
|
self.sew.feature_extractor._freeze_parameters()
|
|
|
|
def freeze_base_model(self):
|
|
"""
|
|
Calling this function will disable the gradient computation for the base model so that its parameters will not
|
|
be updated during training. Only the classification head will be updated.
|
|
"""
|
|
for param in self.sew.parameters():
|
|
param.requires_grad = False
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_values: Optional[torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
) -> Union[tuple, SequenceClassifierOutput]:
|
|
r"""
|
|
input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
|
|
Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
|
|
into an array of type `list[float]`, a `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec library
|
|
(`pip install torchcodec`) or the soundfile library (`pip install soundfile`).
|
|
To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and conversion
|
|
into a tensor of type `torch.FloatTensor`. See [`SEWProcessor.__call__`] for details.
|
|
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
|
|
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
|
|
|
|
outputs = self.sew(
|
|
input_values,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if self.config.use_weighted_layer_sum:
|
|
hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
|
|
hidden_states = torch.stack(hidden_states, dim=1)
|
|
norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
|
|
hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
|
|
else:
|
|
hidden_states = outputs[0]
|
|
|
|
hidden_states = self.projector(hidden_states)
|
|
if attention_mask is None:
|
|
pooled_output = hidden_states.mean(dim=1)
|
|
else:
|
|
padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
|
|
expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
|
|
hidden_states[~expand_padding_mask] = 0.0
|
|
pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
|
|
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = ["SEWForCTC", "SEWForSequenceClassification", "SEWModel", "SEWPreTrainedModel"]
|