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# coding=utf-8 | |
# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
from typing import Callable, Optional, Tuple | |
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 import combine_masks, make_causal_mask | |
from flax.linen import partitioning as nn_partitioning | |
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 ( | |
FlaxBaseModelOutputWithPastAndCrossAttentions, | |
FlaxBaseModelOutputWithPooling, | |
FlaxBaseModelOutputWithPoolingAndCrossAttentions, | |
FlaxCausalLMOutputWithCrossAttentions, | |
FlaxMaskedLMOutput, | |
FlaxMultipleChoiceModelOutput, | |
FlaxNextSentencePredictorOutput, | |
FlaxQuestionAnsweringModelOutput, | |
FlaxSequenceClassifierOutput, | |
FlaxTokenClassifierOutput, | |
) | |
from ...modeling_flax_utils import ( | |
ACT2FN, | |
FlaxPreTrainedModel, | |
append_call_sample_docstring, | |
append_replace_return_docstrings, | |
overwrite_call_docstring, | |
) | |
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging | |
from .configuration_bert import BertConfig | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "bert-base-uncased" | |
_CONFIG_FOR_DOC = "BertConfig" | |
remat = nn_partitioning.remat | |
class FlaxBertForPreTrainingOutput(ModelOutput): | |
""" | |
Output type of [`BertForPreTraining`]. | |
Args: | |
prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): | |
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). | |
seq_relationship_logits (`jnp.ndarray` of shape `(batch_size, 2)`): | |
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation | |
before SoftMax). | |
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. | |
""" | |
prediction_logits: jnp.ndarray = None | |
seq_relationship_logits: jnp.ndarray = None | |
hidden_states: Optional[Tuple[jnp.ndarray]] = None | |
attentions: Optional[Tuple[jnp.ndarray]] = None | |
BERT_START_DOCSTRING = r""" | |
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the | |
library implements for all its model (such as downloading, saving and converting weights from PyTorch models) | |
This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) | |
subclass. Use it as a regular Flax linen 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 ([`BertConfig`]): 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`]. | |
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`]. | |
""" | |
BERT_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`numpy.ndarray` of shape `({0})`): | |
Indices of input sequence tokens in the vocabulary. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`numpy.ndarray` of shape `({0})`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*): | |
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, | |
1]`: | |
- 0 corresponds to a *sentence A* token, | |
- 1 corresponds to a *sentence B* token. | |
[What are token type IDs?](../glossary#token-type-ids) | |
position_ids (`numpy.ndarray` of shape `({0})`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, | |
config.max_position_embeddings - 1]`. | |
head_mask (`numpy.ndarray` of shape `({0})`, `optional): | |
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: | |
- 1 indicates the head is **not masked**, | |
- 0 indicates the head is **masked**. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
""" | |
class FlaxBertEmbeddings(nn.Module): | |
"""Construct the embeddings from word, position and token_type embeddings.""" | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.word_embeddings = nn.Embed( | |
self.config.vocab_size, | |
self.config.hidden_size, | |
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), | |
dtype=self.dtype, | |
) | |
self.position_embeddings = nn.Embed( | |
self.config.max_position_embeddings, | |
self.config.hidden_size, | |
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), | |
dtype=self.dtype, | |
) | |
self.token_type_embeddings = nn.Embed( | |
self.config.type_vocab_size, | |
self.config.hidden_size, | |
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), | |
dtype=self.dtype, | |
) | |
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) | |
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) | |
def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True): | |
# Embed | |
inputs_embeds = self.word_embeddings(input_ids.astype("i4")) | |
position_embeds = self.position_embeddings(position_ids.astype("i4")) | |
token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4")) | |
# Sum all embeddings | |
hidden_states = inputs_embeds + token_type_embeddings + position_embeds | |
# Layer Norm | |
hidden_states = self.LayerNorm(hidden_states) | |
hidden_states = self.dropout(hidden_states, deterministic=deterministic) | |
return hidden_states | |
class FlaxBertSelfAttention(nn.Module): | |
config: BertConfig | |
causal: bool = False | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.head_dim = self.config.hidden_size // self.config.num_attention_heads | |
if self.config.hidden_size % self.config.num_attention_heads != 0: | |
raise ValueError( | |
"`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` " | |
" : {self.config.num_attention_heads}" | |
) | |
self.query = nn.Dense( | |
self.config.hidden_size, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.initializer_range), | |
) | |
self.key = nn.Dense( | |
self.config.hidden_size, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.initializer_range), | |
) | |
self.value = nn.Dense( | |
self.config.hidden_size, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.initializer_range), | |
) | |
if self.causal: | |
self.causal_mask = make_causal_mask( | |
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" | |
) | |
def _split_heads(self, hidden_states): | |
return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim)) | |
def _merge_heads(self, hidden_states): | |
return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,)) | |
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache | |
def _concatenate_to_cache(self, key, value, query, attention_mask): | |
""" | |
This function takes projected key, value states from a single input token and concatenates the states to cached | |
states from previous steps. This function is slighly adapted from the official Flax repository: | |
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 | |
""" | |
# detect if we're initializing by absence of existing cache data. | |
is_initialized = self.has_variable("cache", "cached_key") | |
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) | |
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) | |
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) | |
if is_initialized: | |
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape | |
# update key, value caches with our new 1d spatial slices | |
cur_index = cache_index.value | |
indices = (0,) * len(batch_dims) + (cur_index, 0, 0) | |
key = lax.dynamic_update_slice(cached_key.value, key, indices) | |
value = lax.dynamic_update_slice(cached_value.value, value, indices) | |
cached_key.value = key | |
cached_value.value = value | |
num_updated_cache_vectors = query.shape[1] | |
cache_index.value = cache_index.value + num_updated_cache_vectors | |
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. | |
pad_mask = jnp.broadcast_to( | |
jnp.arange(max_length) < cur_index + num_updated_cache_vectors, | |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), | |
) | |
attention_mask = combine_masks(pad_mask, attention_mask) | |
return key, value, attention_mask | |
def __call__( | |
self, | |
hidden_states, | |
attention_mask, | |
layer_head_mask, | |
key_value_states: Optional[jnp.array] = None, | |
init_cache: bool = False, | |
deterministic=True, | |
output_attentions: bool = False, | |
): | |
# if key_value_states are provided this layer is used as a cross-attention layer | |
# for the decoder | |
is_cross_attention = key_value_states is not None | |
batch_size = hidden_states.shape[0] | |
# get query proj | |
query_states = self.query(hidden_states) | |
# get key, value proj | |
if is_cross_attention: | |
# cross_attentions | |
key_states = self.key(key_value_states) | |
value_states = self.value(key_value_states) | |
else: | |
# self_attention | |
key_states = self.key(hidden_states) | |
value_states = self.value(hidden_states) | |
query_states = self._split_heads(query_states) | |
key_states = self._split_heads(key_states) | |
value_states = self._split_heads(value_states) | |
# handle cache prepare causal attention mask | |
if self.causal: | |
query_length, key_length = query_states.shape[1], key_states.shape[1] | |
if self.has_variable("cache", "cached_key"): | |
mask_shift = self.variables["cache"]["cache_index"] | |
max_decoder_length = self.variables["cache"]["cached_key"].shape[1] | |
causal_mask = lax.dynamic_slice( | |
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) | |
) | |
else: | |
causal_mask = self.causal_mask[:, :, :query_length, :key_length] | |
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) | |
# combine masks if needed | |
if attention_mask is not None and self.causal: | |
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) | |
attention_mask = combine_masks(attention_mask, causal_mask) | |
elif self.causal: | |
attention_mask = causal_mask | |
elif attention_mask is not None: | |
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) | |
# During fast autoregressive decoding, we feed one position at a time, | |
# and cache the keys and values step by step. | |
if self.causal and (self.has_variable("cache", "cached_key") or init_cache): | |
key_states, value_states, attention_mask = self._concatenate_to_cache( | |
key_states, value_states, query_states, attention_mask | |
) | |
# 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.config.attention_probs_dropout_prob > 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.config.attention_probs_dropout_prob, | |
broadcast_dropout=True, | |
deterministic=deterministic, | |
dtype=self.dtype, | |
precision=None, | |
) | |
# Mask heads if we want to | |
if layer_head_mask is not None: | |
attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask) | |
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) | |
attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) | |
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) | |
return outputs | |
class FlaxBertSelfOutput(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.dense = nn.Dense( | |
self.config.hidden_size, | |
kernel_init=jax.nn.initializers.normal(self.config.initializer_range), | |
dtype=self.dtype, | |
) | |
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) | |
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) | |
def __call__(self, hidden_states, input_tensor, deterministic: bool = True): | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.dropout(hidden_states, deterministic=deterministic) | |
hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
return hidden_states | |
class FlaxBertAttention(nn.Module): | |
config: BertConfig | |
causal: bool = False | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
self.self = FlaxBertSelfAttention(self.config, causal=self.causal, dtype=self.dtype) | |
self.output = FlaxBertSelfOutput(self.config, dtype=self.dtype) | |
def __call__( | |
self, | |
hidden_states, | |
attention_mask, | |
layer_head_mask, | |
key_value_states=None, | |
init_cache=False, | |
deterministic=True, | |
output_attentions: bool = False, | |
): | |
# Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length) | |
# FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable | |
# with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length) | |
attn_outputs = self.self( | |
hidden_states, | |
attention_mask, | |
layer_head_mask=layer_head_mask, | |
key_value_states=key_value_states, | |
init_cache=init_cache, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
) | |
attn_output = attn_outputs[0] | |
hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (attn_outputs[1],) | |
return outputs | |
class FlaxBertIntermediate(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.dense = nn.Dense( | |
self.config.intermediate_size, | |
kernel_init=jax.nn.initializers.normal(self.config.initializer_range), | |
dtype=self.dtype, | |
) | |
self.activation = ACT2FN[self.config.hidden_act] | |
def __call__(self, hidden_states): | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.activation(hidden_states) | |
return hidden_states | |
class FlaxBertOutput(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.dense = 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.hidden_dropout_prob) | |
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) | |
def __call__(self, hidden_states, attention_output, deterministic: bool = True): | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.dropout(hidden_states, deterministic=deterministic) | |
hidden_states = self.LayerNorm(hidden_states + attention_output) | |
return hidden_states | |
class FlaxBertLayer(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.attention = FlaxBertAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype) | |
self.intermediate = FlaxBertIntermediate(self.config, dtype=self.dtype) | |
self.output = FlaxBertOutput(self.config, dtype=self.dtype) | |
if self.config.add_cross_attention: | |
self.crossattention = FlaxBertAttention(self.config, causal=False, dtype=self.dtype) | |
def __call__( | |
self, | |
hidden_states, | |
attention_mask, | |
layer_head_mask, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
): | |
# Self Attention | |
attention_outputs = self.attention( | |
hidden_states, | |
attention_mask, | |
layer_head_mask=layer_head_mask, | |
init_cache=init_cache, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
) | |
attention_output = attention_outputs[0] | |
# Cross-Attention Block | |
if encoder_hidden_states is not None: | |
cross_attention_outputs = self.crossattention( | |
attention_output, | |
attention_mask=encoder_attention_mask, | |
layer_head_mask=layer_head_mask, | |
key_value_states=encoder_hidden_states, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
) | |
attention_output = cross_attention_outputs[0] | |
hidden_states = self.intermediate(attention_output) | |
hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (attention_outputs[1],) | |
if encoder_hidden_states is not None: | |
outputs += (cross_attention_outputs[1],) | |
return outputs | |
class FlaxBertLayerCollection(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
gradient_checkpointing: bool = False | |
def setup(self): | |
if self.gradient_checkpointing: | |
FlaxBertCheckpointLayer = remat(FlaxBertLayer, static_argnums=(5, 6, 7)) | |
self.layers = [ | |
FlaxBertCheckpointLayer(self.config, name=str(i), dtype=self.dtype) | |
for i in range(self.config.num_hidden_layers) | |
] | |
else: | |
self.layers = [ | |
FlaxBertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) | |
] | |
def __call__( | |
self, | |
hidden_states, | |
attention_mask, | |
head_mask, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
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 | |
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None | |
# Check if head_mask has a correct number of layers specified if desired | |
if head_mask is not None: | |
if head_mask.shape[0] != (len(self.layers)): | |
raise ValueError( | |
f"The head_mask should be specified for {len(self.layers)} layers, but it is for " | |
f" {head_mask.shape[0]}." | |
) | |
for i, layer in enumerate(self.layers): | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
layer_outputs = layer( | |
hidden_states, | |
attention_mask, | |
head_mask[i] if head_mask is not None else None, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
init_cache, | |
deterministic, | |
output_attentions, | |
) | |
hidden_states = layer_outputs[0] | |
if output_attentions: | |
all_attentions += (layer_outputs[1],) | |
if encoder_hidden_states is not None: | |
all_cross_attentions += (layer_outputs[2],) | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions) | |
if not return_dict: | |
return tuple(v for v in outputs if v is not None) | |
return FlaxBaseModelOutputWithPastAndCrossAttentions( | |
last_hidden_state=hidden_states, | |
hidden_states=all_hidden_states, | |
attentions=all_attentions, | |
cross_attentions=all_cross_attentions, | |
) | |
class FlaxBertEncoder(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.layer = FlaxBertLayerCollection( | |
self.config, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
def __call__( | |
self, | |
hidden_states, | |
attention_mask, | |
head_mask, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
return self.layer( | |
hidden_states, | |
attention_mask, | |
head_mask=head_mask, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
init_cache=init_cache, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
class FlaxBertPooler(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.dense = nn.Dense( | |
self.config.hidden_size, | |
kernel_init=jax.nn.initializers.normal(self.config.initializer_range), | |
dtype=self.dtype, | |
) | |
def __call__(self, hidden_states): | |
cls_hidden_state = hidden_states[:, 0] | |
cls_hidden_state = self.dense(cls_hidden_state) | |
return nn.tanh(cls_hidden_state) | |
class FlaxBertPredictionHeadTransform(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype) | |
self.activation = ACT2FN[self.config.hidden_act] | |
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) | |
def __call__(self, hidden_states): | |
hidden_states = self.dense(hidden_states) | |
hidden_states = self.activation(hidden_states) | |
return self.LayerNorm(hidden_states) | |
class FlaxBertLMPredictionHead(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros | |
def setup(self): | |
self.transform = FlaxBertPredictionHeadTransform(self.config, dtype=self.dtype) | |
self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False) | |
self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,)) | |
def __call__(self, hidden_states, shared_embedding=None): | |
hidden_states = self.transform(hidden_states) | |
if shared_embedding is not None: | |
hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) | |
else: | |
hidden_states = self.decoder(hidden_states) | |
bias = jnp.asarray(self.bias, self.dtype) | |
hidden_states += bias | |
return hidden_states | |
class FlaxBertOnlyMLMHead(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype) | |
def __call__(self, hidden_states, shared_embedding=None): | |
hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding) | |
return hidden_states | |
class FlaxBertOnlyNSPHead(nn.Module): | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
self.seq_relationship = nn.Dense(2, dtype=self.dtype) | |
def __call__(self, pooled_output): | |
return self.seq_relationship(pooled_output) | |
class FlaxBertPreTrainingHeads(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype) | |
self.seq_relationship = nn.Dense(2, dtype=self.dtype) | |
def __call__(self, hidden_states, pooled_output, shared_embedding=None): | |
prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding) | |
seq_relationship_score = self.seq_relationship(pooled_output) | |
return prediction_scores, seq_relationship_score | |
class FlaxBertPreTrainedModel(FlaxPreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = BertConfig | |
base_model_prefix = "bert" | |
module_class: nn.Module = None | |
def __init__( | |
self, | |
config: BertConfig, | |
input_shape: Tuple = (1, 1), | |
seed: int = 0, | |
dtype: jnp.dtype = jnp.float32, | |
_do_init: bool = True, | |
gradient_checkpointing: bool = False, | |
**kwargs, | |
): | |
module = self.module_class( | |
config=config, | |
dtype=dtype, | |
gradient_checkpointing=gradient_checkpointing, | |
**kwargs, | |
) | |
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) | |
def enable_gradient_checkpointing(self): | |
self._module = self.module_class( | |
config=self.config, | |
dtype=self.dtype, | |
gradient_checkpointing=True, | |
) | |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: | |
# init input tensors | |
input_ids = jnp.zeros(input_shape, dtype="i4") | |
token_type_ids = jnp.zeros_like(input_ids) | |
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) | |
attention_mask = jnp.ones_like(input_ids) | |
head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) | |
params_rng, dropout_rng = jax.random.split(rng) | |
rngs = {"params": params_rng, "dropout": dropout_rng} | |
if self.config.add_cross_attention: | |
encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,)) | |
encoder_attention_mask = attention_mask | |
module_init_outputs = self.module.init( | |
rngs, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
return_dict=False, | |
) | |
else: | |
module_init_outputs = self.module.init( | |
rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False | |
) | |
random_params = module_init_outputs["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 | |
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache | |
def init_cache(self, batch_size, max_length): | |
r""" | |
Args: | |
batch_size (`int`): | |
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. | |
max_length (`int`): | |
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized | |
cache. | |
""" | |
# init input variables to retrieve cache | |
input_ids = jnp.ones((batch_size, max_length), dtype="i4") | |
attention_mask = jnp.ones_like(input_ids, dtype="i4") | |
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) | |
init_variables = self.module.init( | |
jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True | |
) | |
return unfreeze(init_variables["cache"]) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask=None, | |
token_type_ids=None, | |
position_ids=None, | |
head_mask=None, | |
encoder_hidden_states=None, | |
encoder_attention_mask=None, | |
params: dict = None, | |
dropout_rng: jax.random.PRNGKey = None, | |
train: bool = False, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
past_key_values: dict = 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 | |
# init input tensors if not passed | |
if token_type_ids is None: | |
token_type_ids = jnp.zeros_like(input_ids) | |
if position_ids is None: | |
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) | |
if attention_mask is None: | |
attention_mask = jnp.ones_like(input_ids) | |
if head_mask is None: | |
head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) | |
# Handle any PRNG if needed | |
rngs = {} | |
if dropout_rng is not None: | |
rngs["dropout"] = dropout_rng | |
inputs = {"params": params or self.params} | |
if self.config.add_cross_attention: | |
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed | |
# down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be | |
# changed by FlaxBertAttention module | |
if past_key_values: | |
inputs["cache"] = past_key_values | |
mutable = ["cache"] | |
else: | |
mutable = False | |
outputs = self.module.apply( | |
inputs, | |
jnp.array(input_ids, dtype="i4"), | |
jnp.array(attention_mask, dtype="i4"), | |
token_type_ids=jnp.array(token_type_ids, dtype="i4"), | |
position_ids=jnp.array(position_ids, dtype="i4"), | |
head_mask=jnp.array(head_mask, dtype="i4"), | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
deterministic=not train, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
rngs=rngs, | |
mutable=mutable, | |
) | |
# add updated cache to model output | |
if past_key_values is not None and return_dict: | |
outputs, past_key_values = outputs | |
outputs["past_key_values"] = unfreeze(past_key_values["cache"]) | |
return outputs | |
elif past_key_values is not None and not return_dict: | |
outputs, past_key_values = outputs | |
outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] | |
else: | |
outputs = self.module.apply( | |
inputs, | |
jnp.array(input_ids, dtype="i4"), | |
jnp.array(attention_mask, dtype="i4"), | |
token_type_ids=jnp.array(token_type_ids, dtype="i4"), | |
position_ids=jnp.array(position_ids, dtype="i4"), | |
head_mask=jnp.array(head_mask, dtype="i4"), | |
deterministic=not train, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
rngs=rngs, | |
) | |
return outputs | |
class FlaxBertModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
add_pooling_layer: bool = True | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.embeddings = FlaxBertEmbeddings(self.config, dtype=self.dtype) | |
self.encoder = FlaxBertEncoder( | |
self.config, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
self.pooler = FlaxBertPooler(self.config, dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids: Optional[jnp.ndarray] = None, | |
position_ids: Optional[jnp.ndarray] = None, | |
head_mask: Optional[jnp.ndarray] = None, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# make sure `token_type_ids` is correctly initialized when not passed | |
if token_type_ids is None: | |
token_type_ids = jnp.zeros_like(input_ids) | |
# make sure `position_ids` is correctly initialized when not passed | |
if position_ids is None: | |
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) | |
hidden_states = self.embeddings( | |
input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic | |
) | |
outputs = self.encoder( | |
hidden_states, | |
attention_mask, | |
head_mask=head_mask, | |
deterministic=deterministic, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
init_cache=init_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = outputs[0] | |
pooled = self.pooler(hidden_states) if self.add_pooling_layer else None | |
if not return_dict: | |
# if pooled is None, don't return it | |
if pooled is None: | |
return (hidden_states,) + outputs[1:] | |
return (hidden_states, pooled) + outputs[1:] | |
return FlaxBaseModelOutputWithPoolingAndCrossAttentions( | |
last_hidden_state=hidden_states, | |
pooler_output=pooled, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
cross_attentions=outputs.cross_attentions, | |
) | |
class FlaxBertModel(FlaxBertPreTrainedModel): | |
module_class = FlaxBertModule | |
append_call_sample_docstring(FlaxBertModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC) | |
class FlaxBertForPreTrainingModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
self.cls = FlaxBertPreTrainingHeads(config=self.config, dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
if self.config.tie_word_embeddings: | |
shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"] | |
else: | |
shared_embedding = None | |
hidden_states = outputs[0] | |
pooled_output = outputs[1] | |
prediction_scores, seq_relationship_score = self.cls( | |
hidden_states, pooled_output, shared_embedding=shared_embedding | |
) | |
if not return_dict: | |
return (prediction_scores, seq_relationship_score) + outputs[2:] | |
return FlaxBertForPreTrainingOutput( | |
prediction_logits=prediction_scores, | |
seq_relationship_logits=seq_relationship_score, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBertForPreTraining(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForPreTrainingModule | |
FLAX_BERT_FOR_PRETRAINING_DOCSTRING = """ | |
Returns: | |
Example: | |
```python | |
>>> from transformers import AutoTokenizer, FlaxBertForPreTraining | |
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
>>> model = FlaxBertForPreTraining.from_pretrained("bert-base-uncased") | |
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np") | |
>>> outputs = model(**inputs) | |
>>> prediction_logits = outputs.prediction_logits | |
>>> seq_relationship_logits = outputs.seq_relationship_logits | |
``` | |
""" | |
overwrite_call_docstring( | |
FlaxBertForPreTraining, | |
BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BERT_FOR_PRETRAINING_DOCSTRING, | |
) | |
append_replace_return_docstrings( | |
FlaxBertForPreTraining, output_type=FlaxBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC | |
) | |
class FlaxBertForMaskedLMModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
add_pooling_layer=False, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
self.cls = FlaxBertOnlyMLMHead(config=self.config, dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = outputs[0] | |
if self.config.tie_word_embeddings: | |
shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"] | |
else: | |
shared_embedding = None | |
# Compute the prediction scores | |
logits = self.cls(hidden_states, shared_embedding=shared_embedding) | |
if not return_dict: | |
return (logits,) + outputs[1:] | |
return FlaxMaskedLMOutput( | |
logits=logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBertForMaskedLM(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForMaskedLMModule | |
append_call_sample_docstring(FlaxBertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC) | |
class FlaxBertForNextSentencePredictionModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
self.cls = FlaxBertOnlyNSPHead(dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
return_dict = return_dict if return_dict is not None else self.config.return_dict | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
pooled_output = outputs[1] | |
seq_relationship_scores = self.cls(pooled_output) | |
if not return_dict: | |
return (seq_relationship_scores,) + outputs[2:] | |
return FlaxNextSentencePredictorOutput( | |
logits=seq_relationship_scores, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBertForNextSentencePrediction(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForNextSentencePredictionModule | |
FLAX_BERT_FOR_NEXT_SENT_PRED_DOCSTRING = """ | |
Returns: | |
Example: | |
```python | |
>>> from transformers import AutoTokenizer, FlaxBertForNextSentencePrediction | |
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
>>> model = FlaxBertForNextSentencePrediction.from_pretrained("bert-base-uncased") | |
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." | |
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." | |
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="jax") | |
>>> outputs = model(**encoding) | |
>>> logits = outputs.logits | |
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random | |
``` | |
""" | |
overwrite_call_docstring( | |
FlaxBertForNextSentencePrediction, | |
BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BERT_FOR_NEXT_SENT_PRED_DOCSTRING, | |
) | |
append_replace_return_docstrings( | |
FlaxBertForNextSentencePrediction, output_type=FlaxNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC | |
) | |
class FlaxBertForSequenceClassificationModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
classifier_dropout = ( | |
self.config.classifier_dropout | |
if self.config.classifier_dropout is not None | |
else self.config.hidden_dropout_prob | |
) | |
self.dropout = nn.Dropout(rate=classifier_dropout) | |
self.classifier = nn.Dense( | |
self.config.num_labels, | |
dtype=self.dtype, | |
) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
pooled_output = outputs[1] | |
pooled_output = self.dropout(pooled_output, deterministic=deterministic) | |
logits = self.classifier(pooled_output) | |
if not return_dict: | |
return (logits,) + outputs[2:] | |
return FlaxSequenceClassifierOutput( | |
logits=logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBertForSequenceClassification(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForSequenceClassificationModule | |
append_call_sample_docstring( | |
FlaxBertForSequenceClassification, | |
_CHECKPOINT_FOR_DOC, | |
FlaxSequenceClassifierOutput, | |
_CONFIG_FOR_DOC, | |
) | |
class FlaxBertForMultipleChoiceModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) | |
self.classifier = nn.Dense(1, dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
num_choices = input_ids.shape[1] | |
input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None | |
attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None | |
token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None | |
position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
pooled_output = outputs[1] | |
pooled_output = self.dropout(pooled_output, deterministic=deterministic) | |
logits = self.classifier(pooled_output) | |
reshaped_logits = logits.reshape(-1, num_choices) | |
if not return_dict: | |
return (reshaped_logits,) + outputs[2:] | |
return FlaxMultipleChoiceModelOutput( | |
logits=reshaped_logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBertForMultipleChoice(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForMultipleChoiceModule | |
overwrite_call_docstring( | |
FlaxBertForMultipleChoice, BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") | |
) | |
append_call_sample_docstring( | |
FlaxBertForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC | |
) | |
class FlaxBertForTokenClassificationModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
dtype=self.dtype, | |
add_pooling_layer=False, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
classifier_dropout = ( | |
self.config.classifier_dropout | |
if self.config.classifier_dropout is not None | |
else self.config.hidden_dropout_prob | |
) | |
self.dropout = nn.Dropout(rate=classifier_dropout) | |
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = outputs[0] | |
hidden_states = self.dropout(hidden_states, deterministic=deterministic) | |
logits = self.classifier(hidden_states) | |
if not return_dict: | |
return (logits,) + outputs[1:] | |
return FlaxTokenClassifierOutput( | |
logits=logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBertForTokenClassification(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForTokenClassificationModule | |
append_call_sample_docstring( | |
FlaxBertForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC | |
) | |
class FlaxBertForQuestionAnsweringModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
dtype=self.dtype, | |
add_pooling_layer=False, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = outputs[0] | |
logits = self.qa_outputs(hidden_states) | |
start_logits, end_logits = logits.split(self.config.num_labels, axis=-1) | |
start_logits = start_logits.squeeze(-1) | |
end_logits = end_logits.squeeze(-1) | |
if not return_dict: | |
return (start_logits, end_logits) + outputs[1:] | |
return FlaxQuestionAnsweringModelOutput( | |
start_logits=start_logits, | |
end_logits=end_logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBertForQuestionAnswering(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForQuestionAnsweringModule | |
append_call_sample_docstring( | |
FlaxBertForQuestionAnswering, | |
_CHECKPOINT_FOR_DOC, | |
FlaxQuestionAnsweringModelOutput, | |
_CONFIG_FOR_DOC, | |
) | |
class FlaxBertForCausalLMModule(nn.Module): | |
config: BertConfig | |
dtype: jnp.dtype = jnp.float32 | |
gradient_checkpointing: bool = False | |
def setup(self): | |
self.bert = FlaxBertModule( | |
config=self.config, | |
add_pooling_layer=False, | |
dtype=self.dtype, | |
gradient_checkpointing=self.gradient_checkpointing, | |
) | |
self.cls = FlaxBertOnlyMLMHead(config=self.config, dtype=self.dtype) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
position_ids, | |
token_type_ids: Optional[jnp.ndarray] = None, | |
head_mask: Optional[jnp.ndarray] = None, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
deterministic: bool = True, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# Model | |
outputs = self.bert( | |
input_ids, | |
attention_mask, | |
token_type_ids, | |
position_ids, | |
head_mask, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
init_cache=init_cache, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = outputs[0] | |
if self.config.tie_word_embeddings: | |
shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"] | |
else: | |
shared_embedding = None | |
# Compute the prediction scores | |
logits = self.cls(hidden_states, shared_embedding=shared_embedding) | |
if not return_dict: | |
return (logits,) + outputs[1:] | |
return FlaxCausalLMOutputWithCrossAttentions( | |
logits=logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
cross_attentions=outputs.cross_attentions, | |
) | |
class FlaxBertForCausalLM(FlaxBertPreTrainedModel): | |
module_class = FlaxBertForCausalLMModule | |
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None): | |
# initializing the cache | |
batch_size, seq_length = input_ids.shape | |
past_key_values = self.init_cache(batch_size, max_length) | |
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. | |
# But since the decoder uses a causal mask, those positions are masked anyway. | |
# Thus, we can create a single static attention_mask here, which is more efficient for compilation | |
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") | |
if attention_mask is not None: | |
position_ids = attention_mask.cumsum(axis=-1) - 1 | |
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) | |
else: | |
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) | |
return { | |
"past_key_values": past_key_values, | |
"attention_mask": extended_attention_mask, | |
"position_ids": position_ids, | |
} | |
def update_inputs_for_generation(self, model_outputs, model_kwargs): | |
model_kwargs["past_key_values"] = model_outputs.past_key_values | |
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 | |
return model_kwargs | |
append_call_sample_docstring( | |
FlaxBertForCausalLM, | |
_CHECKPOINT_FOR_DOC, | |
FlaxCausalLMOutputWithCrossAttentions, | |
_CONFIG_FOR_DOC, | |
) | |