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modeling_flax_wav2vec2.py
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modeling_flax_wav2vec2.py
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# coding=utf-8
# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flax Wav2Vec2 model."""
from functools import partial
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_wav2vec2 import Wav2Vec2Config
logger = logging.get_logger(__name__)
@flax.struct.dataclass
class FlaxWav2Vec2BaseModelOutput(ModelOutput):
"""
Output type of [`FlaxWav2Vec2BaseModelOutput`], with potential hidden states and attentions.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
extract_features (`jnp.ndarray` of shape `(batch_size, sequence_length, last_conv_dim)`):
Sequence of extracted feature vectors of the last convolutional layer of the model with `last_conv_dim`
being the dimension of the last convolutional layer.
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: jnp.ndarray = None
extract_features: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None
@flax.struct.dataclass
class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput):
"""
Output type of [`FlaxWav2Vec2ForPreTrainingOutput`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`):
Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
projected_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
projected quantized states.
projected_quantized_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
target vectors for contrastive loss.
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
projected_states: jnp.ndarray = None
projected_quantized_states: jnp.ndarray = None
codevector_perplexity: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None
def _compute_mask_indices(
shape: Tuple[int, int],
mask_prob: float,
mask_length: int,
attention_mask: Optional[np.ndarray] = 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://arxiv.org/abs/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.
should be of size 2 where first element is batch size and 2nd is timesteps
mask_prob:
probability for each token to be chosen as start of the span to be masked. this will be multiplied by
number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
however due to overlaps, the actual number will be smaller (unless no_overlap is True)
mask_length: size of the mask
min_masks: minimum number of masked spans
"""
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} and"
f" `sequence_length`: {sequence_length}`"
)
# compute number of masked spans in batch
num_masked_spans = int(mask_prob * sequence_length / mask_length + np.random.rand(1).item())
num_masked_spans = max(num_masked_spans, min_masks)
# make sure num masked indices <= sequence_length
if num_masked_spans * mask_length > sequence_length:
num_masked_spans = sequence_length // mask_length
# SpecAugment mask to fill
spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
# get random indices to mask
spec_aug_mask_idxs = np.array(
[
np.random.choice(np.arange(sequence_length - (mask_length - 1)), num_masked_spans, replace=False)
for _ in range(batch_size)
]
)
# expand masked indices to masked spans
spec_aug_mask_idxs = np.broadcast_to(spec_aug_mask_idxs[:, :, None], (batch_size, num_masked_spans, mask_length))
spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, num_masked_spans * mask_length)
offsets = np.arange(mask_length)[None, None, :]
offsets = np.broadcast_to(offsets, (batch_size, num_masked_spans, mask_length)).reshape(
batch_size, num_masked_spans * mask_length
)
spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
# scatter indices to mask
np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
if attention_mask is not None:
# make sure padded input ids cannot be masked
spec_aug_mask = np.where(attention_mask, spec_aug_mask, False)
return spec_aug_mask
def _sample_negative_indices(features_shape: Tuple, num_negatives: int, attention_mask: Optional[np.ndarray] = None):
"""
Sample `num_negatives` vectors from feature vectors.
"""
batch_size, sequence_length, hidden_size = features_shape
if sequence_length <= 1:
raise ValueError(
"`features should have `sequence_length` > 1, but are of shape "
f"(batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})."
)
# get `num_negatives` random vector indices from the same utterance
sampled_negative_indices = []
for batch_idx in range(batch_size):
high = attention_mask[batch_idx].sum() - 1 if attention_mask is not None else sequence_length - 1
sampled_indices_slice = np.random.randint(0, high, size=(num_negatives * sequence_length,))
sampled_negative_indices.append(sampled_indices_slice)
sampled_negative_indices = np.asarray(sampled_negative_indices, dtype=np.int32)
# generate indices of the positive vectors themselves, repeat them `num_negatives` times
feature_indices = np.broadcast_to(np.arange(sequence_length)[:, None], (sequence_length, num_negatives)).flatten()
# avoid sampling the same positive vector, but keep the distribution uniform
sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1
# correct for batch size
for batch_idx in range(1, batch_size):
sampled_negative_indices[batch_idx] += batch_idx * sequence_length
return sampled_negative_indices
WAV_2_VEC_2_START_DOCSTRING = r"""
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`Wav2Vec2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
WAV_2_VEC_2_INPUTS_DOCSTRING = r"""
Args:
input_values (`jnp.ndarray` 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]` or a `numpy.ndarray`, *e.g.* via 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 `jnp.ndarray`. See [`Wav2Vec2Processor.__call__`] for details.
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed
if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor
has `config.return_attention_mask == False`, such as
[wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
passed to avoid degraded performance when doing batched inference. For such models `input_values` should
simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
different results depending on whether `input_values` is padded or not.
mask_time_indices (`jnp.ndarray` 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 (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class FlaxWav2Vec2LayerNormConvLayer(nn.Module):
config: Wav2Vec2Config
layer_id: int = 0
dtype: jnp.dtype = jnp.float32
def setup(self):
self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1
self.out_conv_dim = self.config.conv_dim[self.layer_id]
self.conv = nn.Conv(
features=self.config.conv_dim[self.layer_id],
kernel_size=(self.config.conv_kernel[self.layer_id],),
strides=(self.config.conv_stride[self.layer_id],),
use_bias=self.config.conv_bias,
kernel_init=jax.nn.initializers.he_normal(),
padding="VALID",
dtype=self.dtype,
)
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.activation = ACT2FN[self.config.feat_extract_activation]
def __call__(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
class FlaxConvWithWeightNorm(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
features=self.config.hidden_size,
kernel_size=(self.config.num_conv_pos_embeddings,),
kernel_init=jax.nn.initializers.he_normal(),
padding="VALID",
feature_group_count=self.config.num_conv_pos_embedding_groups,
dtype=self.dtype,
)
weight_shape = (
self.conv.features,
self.conv.features // self.conv.feature_group_count,
self.conv.kernel_size[0],
)
self.weight_v = self.param("weight_v", jax.nn.initializers.he_normal(), weight_shape)
self.weight_g = self.param("weight_g", lambda _: jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :])
self.bias = self.param("bias", jax.nn.initializers.zeros, (self.conv.features,))
self.prev_padding = self.conv.kernel_size[0] // 2
def _get_normed_weights(self):
weight_v_norm = jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :]
normed_weight_v = jnp.divide(self.weight_v, weight_v_norm)
normed_kernel = jnp.multiply(normed_weight_v, self.weight_g)
return normed_kernel
def __call__(self, hidden_states):
kernel = self._get_normed_weights()
hidden_states = jnp.pad(hidden_states, ((0, 0), (self.prev_padding, self.prev_padding), (0, 0)))
hidden_states = self.conv.apply({"params": {"kernel": kernel.T, "bias": self.bias}}, hidden_states)
return hidden_states
class FlaxWav2Vec2PositionalConvEmbedding(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype)
self.activation = ACT2FN[self.config.feat_extract_activation]
self.num_pad_remove = 1 if self.config.num_conv_pos_embeddings % 2 == 0 else 0
def __call__(self, hidden_states):
hidden_states = hidden_states.transpose((0, 1, 2))
hidden_states = self.conv(hidden_states)
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, : -self.num_pad_remove, :]
hidden_states = self.activation(hidden_states)
hidden_states = hidden_states.transpose((0, 1, 2))
return hidden_states
class FlaxConvLayersCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
if self.config.feat_extract_norm == "layer":
self.layers = [
FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
for i in range(self.config.num_feat_extract_layers)
]
elif self.config.feat_extract_norm == "group":
raise NotImplementedError("At the moment only ``config.feat_extact_norm == 'layer'`` is supported")
else:
raise ValueError(
f"`config.feat_extract_norm` is {self.config.feat_extract_norm}, but has to be one of ['group',"
" 'layer']"
)
def __call__(self, hidden_states):
for i, conv_layer in enumerate(self.layers):
hidden_states = conv_layer(hidden_states)
return hidden_states
class FlaxWav2Vec2FeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype)
def __call__(self, input_values, freeze_feature_encoder=False):
hidden_states = input_values[:, :, None]
hidden_states = self.conv_layers(hidden_states)
if freeze_feature_encoder:
hidden_states = jax.lax.stop_gradient(hidden_states)
return hidden_states
class FlaxWav2Vec2FeatureProjection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.projection = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.feat_proj_dropout)
def __call__(self, hidden_states, deterministic=True):
norm_hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(norm_hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
return hidden_states, norm_hidden_states
class FlaxWav2Vec2Attention(nn.Module):
config: Wav2Vec2Config
embed_dim: int
num_heads: int
dropout: float = 0.0
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
dense = partial(
nn.Dense,
self.embed_dim,
use_bias=self.bias,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
self.out_proj = dense()
self.dropout_layer = nn.Dropout(rate=self.dropout)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"""
# get query proj
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
if attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class FlaxWav2Vec2FeedForward(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout)
self.intermediate_dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
if isinstance(self.config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[self.config.hidden_act]
else:
self.intermediate_act_fn = self.config.hidden_act
self.output_dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.output_dropout = nn.Dropout(rate=self.config.hidden_dropout)
def __call__(self, hidden_states, deterministic=True):
hidden_states = self.intermediate_dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.intermediate_dropout(hidden_states, deterministic=deterministic)
hidden_states = self.output_dense(hidden_states)
hidden_states = self.output_dropout(hidden_states, deterministic=deterministic)
return hidden_states
class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attention = FlaxWav2Vec2Attention(
config=self.config,
embed_dim=self.config.hidden_size,
num_heads=self.config.num_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout)
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.feed_forward = FlaxWav2Vec2FeedForward(self.config, dtype=self.dtype)
self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
def __call__(self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False):
attn_residual = hidden_states
hidden_states = self.layer_norm(hidden_states)
hidden_states, attn_weights = self.attention(
hidden_states, attention_mask=attention_mask, deterministic=deterministic
)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = attn_residual + hidden_states
hidden_states = hidden_states + self.feed_forward(
self.final_layer_norm(hidden_states), deterministic=deterministic
)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = [
FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_states,
attention_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(
hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class FlaxWav2Vec2StableLayerNormEncoder(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype)
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout)
self.layers = FlaxWav2Vec2EncoderLayerStableLayerNormCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_states,
attention_mask=None,
deterministic=True,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
if attention_mask is not None:
# make sure padded tokens are not attended to
hidden_states = jnp.where(
jnp.broadcast_to(attention_mask[:, :, None], hidden_states.shape), hidden_states, 0
)
position_embeddings = self.pos_conv_embed(hidden_states)
hidden_states = hidden_states + position_embeddings
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = self.layer_norm(outputs[0])
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
hidden_states = hidden_states[:-1] + (last_hidden_state,)
if not return_dict:
outputs = (last_hidden_state, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=outputs.attentions
)
class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.num_groups = self.config.num_codevector_groups
self.num_vars = self.config.num_codevectors_per_group
if self.config.codevector_dim % self.num_groups != 0:
raise ValueError(
f"`config.codevector_dim {self.config.codevector_dim} must be divisible by"
f" `config.num_codevector_groups` {self.num_groups} for concatenation"
)
# storage for codebook variables (codewords)
self.codevectors = self.param(
"codevectors",
jax.nn.initializers.uniform(),
(1, self.num_groups * self.num_vars, self.config.codevector_dim // self.num_groups),
)
self.weight_proj = nn.Dense(
self.num_groups * self.num_vars,
kernel_init=jax.nn.initializers.normal(1.0),
dtype=self.dtype,
)
@staticmethod
def _compute_perplexity(probs, mask=None):
if mask is not None:
mask_extended = jnp.broadcast_to(mask.flatten()[:, None, None], probs.shape)
probs = jnp.where(mask_extended, probs, jnp.zeros_like(probs))
marginal_probs = probs.sum(axis=0) / mask.sum()
else:
marginal_probs = probs.mean(axis=0)
perplexity = jnp.exp(-jnp.sum(marginal_probs * jnp.log(marginal_probs + 1e-7), axis=-1)).sum()
return perplexity
def __call__(self, hidden_states, mask_time_indices=None, deterministic=True, temperature=1):
batch_size, sequence_length, hidden_size = hidden_states.shape
# project to codevector dim
hidden_states = self.weight_proj(hidden_states)
hidden_states = hidden_states.reshape(batch_size * sequence_length * self.num_groups, -1)
if not deterministic:
# sample code vector probs via gumbel in differentiateable way
gumbel_rng = self.make_rng("gumbel")
gumbels = jax.random.gumbel(gumbel_rng, hidden_states.shape)
codevector_probs = nn.softmax((hidden_states + gumbels) / temperature)
# compute perplexity
codevector_soft_dist = nn.softmax(
hidden_states.reshape(batch_size * sequence_length, self.num_groups, -1), axis=-1
)
perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices)
else:
# take argmax in non-differentiable way
# comptute hard codevector distribution (one hot)
codevector_idx = hidden_states.argmax(axis=-1)
codevector_probs = jax.nn.one_hot(codevector_idx, hidden_states.shape[-1]) * 1.0
codevector_probs = codevector_probs.reshape(batch_size * sequence_length, self.num_groups, -1)
perplexity = self._compute_perplexity(codevector_probs, mask_time_indices)
codevector_probs = codevector_probs.reshape(batch_size * sequence_length, -1)
# use probs to retrieve codevectors
codevectors_per_group = jnp.expand_dims(codevector_probs, axis=-1) * self.codevectors
codevectors = codevectors_per_group.reshape(batch_size * sequence_length, self.num_groups, self.num_vars, -1)
codevectors = codevectors.sum(-2).reshape(batch_size, sequence_length, -1)
return codevectors, perplexity
class FlaxWav2Vec2Adapter(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
# hidden_states require down-projection if feature dims don't match
if self.config.output_hidden_size != self.config.hidden_size:
self.proj = nn.Dense(
self.config.output_hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.proj_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
else:
self.proj = self.proj_layer_norm = None
self.layers = FlaxWav2Vec2AdapterLayersCollection(self.config, dtype=self.dtype)
def __call__(self, hidden_states, deterministic=True):
# down-project hidden_states if required
if self.proj is not None and self.proj_layer_norm is not None:
hidden_states = self.proj(hidden_states)
hidden_states = self.proj_layer_norm(hidden_states)
hidden_states = self.layers(hidden_states)
return hidden_states
class FlaxWav2Vec2AdapterLayer(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
features=2 * self.config.output_hidden_size,
kernel_size=(self.config.adapter_kernel_size,),
strides=(self.config.adapter_stride,),
padding=((1, 1),),
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
def __call__(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = nn.glu(hidden_states, axis=2)
return hidden_states
class FlaxWav2Vec2AdapterLayersCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = [
FlaxWav2Vec2AdapterLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_adapter_layers)
]
def __call__(self, hidden_states):
for conv_layer in self.layers:
hidden_states = conv_layer(hidden_states)
return hidden_states
class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2Config
base_model_prefix: str = "wav2vec2"
main_input_name = "input_values"
module_class: nn.Module = None
def __init__(
self,
config: Wav2Vec2Config,
input_shape: Tuple = (1, 1024),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_values = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_values)
params_rng, dropout_rng = jax.random.split(rng, 2)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(rngs, input_values, attention_mask, return_dict=False)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
def __call__(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
freeze_feature_encoder: bool = False,
return_dict: Optional[bool] = None,
):
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.return_dict
batch_size, sequence_length = input_values.shape
if attention_mask is None:
attention_mask = jnp.ones((batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
return self.module.apply(
inputs,
jnp.array(input_values, dtype="f4"),
jnp.array(attention_mask, dtype="i4"),
mask_time_indices,
not train,
output_attentions,
output_hidden_states,
freeze_feature_encoder,
return_dict,
rngs=rngs,
)
def _get_feat_extract_output_lengths(
self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
):
return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter)
class FlaxWav2Vec2Module(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.feature_extractor = FlaxWav2Vec2FeatureEncoder(self.config, dtype=self.dtype)
self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype)
self.masked_spec_embed = self.param(
"masked_spec_embed", jax.nn.initializers.uniform(), (self.config.hidden_size,)
)
if self.config.do_stable_layer_norm:
self.encoder = FlaxWav2Vec2StableLayerNormEncoder(self.config, dtype=self.dtype)
else:
raise NotImplementedError("``config.do_stable_layer_norm is False`` is currently not supported.")
self.adapter = FlaxWav2Vec2Adapter(self.config, dtype=self.dtype) if self.config.add_adapter else None
def __call__(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
deterministic=True,
output_attentions=None,
output_hidden_states=None,
freeze_feature_encoder=False,
return_dict=None,
):
extract_features = self.feature_extractor(input_values, freeze_feature_encoder=freeze_feature_encoder)
# make sure that no loss is computed on padded inputs
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(
extract_features.shape[1], attention_mask, add_adapter=False
)
hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic)
if mask_time_indices is not None: # apply SpecAugment along time axis with given indices
hidden_states = jnp.where(
jnp.broadcast_to(mask_time_indices[:, :, None], hidden_states.shape),
jnp.broadcast_to(self.masked_spec_embed[None, None, :], hidden_states.shape),
hidden_states,
)
encoder_outputs = self.encoder(
hidden_states,
attention_mask=attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
if self.adapter is not None:
hidden_states = self.adapter(hidden_states)
if not return_dict:
return (hidden_states, extract_features) + encoder_outputs[1:]