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# coding=utf-8 | |
# Copyright 2021 The Fairseq Authors and The Google Flax Team 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 Bart model.""" | |
import math | |
import random | |
from functools import partial | |
from typing import Callable, Optional, Tuple | |
import flax.linen as nn | |
import jax | |
import jax.numpy as jnp | |
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze | |
from flax.linen import combine_masks, make_causal_mask | |
from flax.linen.attention import dot_product_attention_weights | |
from flax.traverse_util import flatten_dict, unflatten_dict | |
from jax import lax | |
from jax.random import PRNGKey | |
from ...modeling_flax_outputs import ( | |
FlaxBaseModelOutput, | |
FlaxBaseModelOutputWithPastAndCrossAttentions, | |
FlaxCausalLMOutputWithCrossAttentions, | |
FlaxSeq2SeqLMOutput, | |
FlaxSeq2SeqModelOutput, | |
FlaxSeq2SeqQuestionAnsweringModelOutput, | |
FlaxSeq2SeqSequenceClassifierOutput, | |
) | |
from ...modeling_flax_utils import ( | |
ACT2FN, | |
FlaxPreTrainedModel, | |
append_call_sample_docstring, | |
append_replace_return_docstrings, | |
overwrite_call_docstring, | |
) | |
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings | |
from .configuration_bart import BartConfig | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "facebook/bart-base" | |
_CONFIG_FOR_DOC = "BartConfig" | |
BART_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 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 ([`BartConfig`]): 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`]. | |
""" | |
BART_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide | |
it. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Indices of decoder input sequence tokens in the vocabulary. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are decoder input IDs?](../glossary#decoder-input-ids) | |
For translation and summarization training, `decoder_input_ids` should be provided. If no | |
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right | |
for denoising pre-training following the paper. | |
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also | |
be used by default. | |
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the | |
paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. | |
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, | |
config.max_position_embeddings - 1]`. | |
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the | |
range `[0, config.max_position_embeddings - 1]`. | |
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. | |
""" | |
BART_ENCODE_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide | |
it. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, | |
config.max_position_embeddings - 1]`. | |
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. | |
""" | |
BART_DECODE_INPUTS_DOCSTRING = r""" | |
Args: | |
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`): | |
Indices of decoder input sequence tokens in the vocabulary. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are decoder input IDs?](../glossary#decoder-input-ids) | |
For translation and summarization training, `decoder_input_ids` should be provided. If no | |
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right | |
for denoising pre-training following the paper. | |
encoder_outputs (`tuple(tuple(jnp.ndarray)`): | |
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) | |
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of | |
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. | |
encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also | |
be used by default. | |
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the | |
paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. | |
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the | |
range `[0, config.max_position_embeddings - 1]`. | |
past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): | |
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast | |
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. | |
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. | |
""" | |
def shift_tokens_right(input_ids: jnp.array, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray: | |
""" | |
Shift input ids one token to the right. | |
""" | |
shifted_input_ids = jnp.zeros_like(input_ids) | |
shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]) | |
shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id) | |
shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) | |
return shifted_input_ids | |
class FlaxBartAttention(nn.Module): | |
config: BartConfig | |
embed_dim: int | |
num_heads: int | |
dropout: float = 0.0 | |
causal: bool = False | |
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}" | |
f" and `num_heads`: {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.init_std), | |
) | |
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() | |
self.out_proj = dense() | |
self.dropout_layer = nn.Dropout(rate=self.dropout) | |
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.num_heads, self.head_dim)) | |
def _merge_heads(self, hidden_states): | |
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) | |
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: jnp.ndarray, | |
key_value_states: Optional[jnp.ndarray] = None, | |
attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
deterministic: bool = True, | |
) -> Tuple[jnp.ndarray]: | |
"""Input shape: Batch x Time x Channel""" | |
# 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.q_proj(hidden_states) | |
# get key, value proj | |
if is_cross_attention: | |
# cross_attentions | |
key_states = self.k_proj(key_value_states) | |
value_states = self.v_proj(key_value_states) | |
else: | |
# self_attention | |
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) | |
# 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.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 FlaxBartEncoderLayer(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self) -> None: | |
self.embed_dim = self.config.d_model | |
self.self_attn = FlaxBartAttention( | |
config=self.config, | |
embed_dim=self.embed_dim, | |
num_heads=self.config.encoder_attention_heads, | |
dropout=self.config.attention_dropout, | |
dtype=self.dtype, | |
) | |
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | |
self.dropout_layer = nn.Dropout(rate=self.config.dropout) | |
self.activation_fn = ACT2FN[self.config.activation_function] | |
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) | |
self.fc1 = nn.Dense( | |
self.config.encoder_ffn_dim, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.init_std), | |
) | |
self.fc2 = nn.Dense( | |
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) | |
) | |
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | |
def __call__( | |
self, | |
hidden_states: jnp.ndarray, | |
attention_mask: jnp.ndarray, | |
output_attentions: bool = True, | |
deterministic: bool = True, | |
) -> Tuple[jnp.ndarray]: | |
residual = hidden_states | |
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask) | |
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) | |
hidden_states = residual + hidden_states | |
hidden_states = self.self_attn_layer_norm(hidden_states) | |
residual = hidden_states | |
hidden_states = self.activation_fn(self.fc1(hidden_states)) | |
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) | |
hidden_states = self.fc2(hidden_states) | |
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) | |
hidden_states = residual + hidden_states | |
hidden_states = self.final_layer_norm(hidden_states) | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (attn_weights,) | |
return outputs | |
class FlaxBartEncoderLayerCollection(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.layers = [ | |
FlaxBartEncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers) | |
] | |
self.layerdrop = self.config.encoder_layerdrop | |
def __call__( | |
self, | |
hidden_states, | |
attention_mask, | |
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 encoder_layer in self.layers: | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) | |
dropout_probability = random.uniform(0, 1) | |
if not deterministic and (dropout_probability < self.layerdrop): # skip the layer | |
layer_outputs = (None, None) | |
else: | |
layer_outputs = encoder_layer( | |
hidden_states, | |
attention_mask, | |
output_attentions, | |
deterministic, | |
) | |
hidden_states = layer_outputs[0] | |
if output_attentions: | |
all_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 FlaxBartDecoderLayer(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self) -> None: | |
self.embed_dim = self.config.d_model | |
self.self_attn = FlaxBartAttention( | |
config=self.config, | |
embed_dim=self.embed_dim, | |
num_heads=self.config.decoder_attention_heads, | |
dropout=self.config.attention_dropout, | |
causal=True, | |
dtype=self.dtype, | |
) | |
self.dropout_layer = nn.Dropout(rate=self.config.dropout) | |
self.activation_fn = ACT2FN[self.config.activation_function] | |
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) | |
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | |
self.encoder_attn = FlaxBartAttention( | |
config=self.config, | |
embed_dim=self.embed_dim, | |
num_heads=self.config.decoder_attention_heads, | |
dropout=self.config.attention_dropout, | |
dtype=self.dtype, | |
) | |
self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | |
self.fc1 = nn.Dense( | |
self.config.decoder_ffn_dim, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.init_std), | |
) | |
self.fc2 = nn.Dense( | |
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) | |
) | |
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | |
def __call__( | |
self, | |
hidden_states: jnp.ndarray, | |
attention_mask: jnp.ndarray, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
output_attentions: bool = True, | |
deterministic: bool = True, | |
) -> Tuple[jnp.ndarray]: | |
residual = hidden_states | |
# Self Attention | |
hidden_states, self_attn_weights = self.self_attn( | |
hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache | |
) | |
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) | |
hidden_states = residual + hidden_states | |
hidden_states = self.self_attn_layer_norm(hidden_states) | |
# Cross-Attention Block | |
cross_attn_weights = None | |
if encoder_hidden_states is not None: | |
residual = hidden_states | |
hidden_states, cross_attn_weights = self.encoder_attn( | |
hidden_states=hidden_states, | |
key_value_states=encoder_hidden_states, | |
attention_mask=encoder_attention_mask, | |
) | |
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) | |
hidden_states = residual + hidden_states | |
hidden_states = self.encoder_attn_layer_norm(hidden_states) | |
# Fully Connected | |
residual = hidden_states | |
hidden_states = self.activation_fn(self.fc1(hidden_states)) | |
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) | |
hidden_states = self.fc2(hidden_states) | |
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) | |
hidden_states = residual + hidden_states | |
hidden_states = self.final_layer_norm(hidden_states) | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (self_attn_weights, cross_attn_weights) | |
return outputs | |
class FlaxBartDecoderLayerCollection(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.layers = [ | |
FlaxBartDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers) | |
] | |
self.layerdrop = self.config.decoder_layerdrop | |
def __call__( | |
self, | |
hidden_states, | |
attention_mask, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
deterministic: bool = True, | |
init_cache: bool = False, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
): | |
# decoder layers | |
all_hidden_states = () if output_hidden_states else None | |
all_self_attns = () if output_attentions else None | |
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None | |
for decoder_layer in self.layers: | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) | |
dropout_probability = random.uniform(0, 1) | |
if not deterministic and (dropout_probability < self.layerdrop): | |
layer_outputs = (None, None, None) | |
else: | |
layer_outputs = decoder_layer( | |
hidden_states, | |
attention_mask=attention_mask, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
init_cache=init_cache, | |
output_attentions=output_attentions, | |
deterministic=deterministic, | |
) | |
hidden_states = layer_outputs[0] | |
if output_attentions: | |
all_self_attns += (layer_outputs[1],) | |
if encoder_hidden_states is not None: | |
all_cross_attentions += (layer_outputs[2],) | |
# add hidden states from the last decoder layer | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
outputs = [hidden_states, all_hidden_states, all_self_attns, 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_self_attns, | |
cross_attentions=all_cross_attentions, | |
) | |
class FlaxBartClassificationHead(nn.Module): | |
"""Head for sentence-level classification tasks.""" | |
config: BartConfig | |
inner_dim: int | |
num_classes: int | |
pooler_dropout: float | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
self.dense = nn.Dense( | |
self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) | |
) | |
self.dropout = nn.Dropout(rate=self.pooler_dropout) | |
self.out_proj = nn.Dense( | |
self.num_classes, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.init_std), | |
) | |
def __call__(self, hidden_states: jnp.ndarray, deterministic: bool): | |
hidden_states = self.dropout(hidden_states, deterministic=deterministic) | |
hidden_states = self.dense(hidden_states) | |
hidden_states = jnp.tanh(hidden_states) | |
hidden_states = self.dropout(hidden_states, deterministic=deterministic) | |
hidden_states = self.out_proj(hidden_states) | |
return hidden_states | |
class FlaxBartEncoder(nn.Module): | |
config: BartConfig | |
embed_tokens: nn.Embed | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.dropout_layer = nn.Dropout(rate=self.config.dropout) | |
embed_dim = self.config.d_model | |
self.padding_idx = self.config.pad_token_id | |
self.max_source_positions = self.config.max_position_embeddings | |
self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0 | |
# Bart is set up so that if padding_idx is specified then offset the embedding ids by 2 | |
# and adjust num_embeddings appropriately. Other models don't have this hack | |
self.offset = 2 | |
self.embed_positions = nn.Embed( | |
self.config.max_position_embeddings + self.offset, | |
embed_dim, | |
embedding_init=jax.nn.initializers.normal(self.config.init_std), | |
dtype=self.dtype, | |
) | |
self.layers = FlaxBartEncoderLayerCollection(self.config, self.dtype) | |
self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
position_ids, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
deterministic: bool = True, | |
): | |
input_shape = input_ids.shape | |
input_ids = input_ids.reshape(-1, input_shape[-1]) | |
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale | |
embed_pos = self.embed_positions(position_ids + self.offset) | |
hidden_states = inputs_embeds + embed_pos | |
hidden_states = self.layernorm_embedding(hidden_states) | |
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) | |
outputs = self.layers( | |
hidden_states, | |
attention_mask, | |
deterministic=deterministic, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
if not return_dict: | |
return outputs | |
return FlaxBaseModelOutput( | |
last_hidden_state=outputs.last_hidden_state, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class FlaxBartDecoder(nn.Module): | |
config: BartConfig | |
embed_tokens: nn.Embed | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.dropout_layer = nn.Dropout(rate=self.config.dropout) | |
embed_dim = self.config.d_model | |
self.padding_idx = self.config.pad_token_id | |
self.max_target_positions = self.config.max_position_embeddings | |
self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 | |
# Bart is set up so that if padding_idx is specified then offset the embedding ids by 2 | |
# and adjust num_embeddings appropriately. Other models don't have this hack | |
self.offset = 2 | |
self.embed_positions = nn.Embed( | |
self.config.max_position_embeddings + self.offset, | |
embed_dim, | |
embedding_init=jax.nn.initializers.normal(self.config.init_std), | |
dtype=self.dtype, | |
) | |
self.layers = FlaxBartDecoderLayerCollection(self.config, self.dtype) | |
self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
position_ids, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
deterministic: bool = True, | |
): | |
input_shape = input_ids.shape | |
input_ids = input_ids.reshape(-1, input_shape[-1]) | |
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale | |
# embed positions | |
positions = self.embed_positions(position_ids + self.offset) | |
hidden_states = inputs_embeds + positions | |
hidden_states = self.layernorm_embedding(hidden_states) | |
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) | |
outputs = self.layers( | |
hidden_states, | |
attention_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
deterministic=deterministic, | |
init_cache=init_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
if not return_dict: | |
return outputs | |
return FlaxBaseModelOutputWithPastAndCrossAttentions( | |
last_hidden_state=outputs.last_hidden_state, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
cross_attentions=outputs.cross_attentions, | |
) | |
class FlaxBartModule(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
def setup(self): | |
self.shared = nn.Embed( | |
self.config.vocab_size, | |
self.config.d_model, | |
embedding_init=jax.nn.initializers.normal(self.config.init_std), | |
dtype=self.dtype, | |
) | |
self.encoder = FlaxBartEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared) | |
self.decoder = FlaxBartDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared) | |
def _get_encoder_module(self): | |
return self.encoder | |
def _get_decoder_module(self): | |
return self.decoder | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
decoder_input_ids, | |
decoder_attention_mask, | |
position_ids, | |
decoder_position_ids, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
deterministic: bool = True, | |
): | |
encoder_outputs = self.encoder( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=deterministic, | |
) | |
decoder_outputs = self.decoder( | |
input_ids=decoder_input_ids, | |
attention_mask=decoder_attention_mask, | |
position_ids=decoder_position_ids, | |
encoder_hidden_states=encoder_outputs[0], | |
encoder_attention_mask=attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=deterministic, | |
) | |
if not return_dict: | |
return decoder_outputs + encoder_outputs | |
return FlaxSeq2SeqModelOutput( | |
last_hidden_state=decoder_outputs.last_hidden_state, | |
decoder_hidden_states=decoder_outputs.hidden_states, | |
decoder_attentions=decoder_outputs.attentions, | |
cross_attentions=decoder_outputs.cross_attentions, | |
encoder_last_hidden_state=encoder_outputs.last_hidden_state, | |
encoder_hidden_states=encoder_outputs.hidden_states, | |
encoder_attentions=encoder_outputs.attentions, | |
) | |
class FlaxBartPreTrainedModel(FlaxPreTrainedModel): | |
config_class = BartConfig | |
base_model_prefix: str = "model" | |
module_class: nn.Module = None | |
def __init__( | |
self, | |
config: BartConfig, | |
input_shape: Tuple[int] = (1, 1), | |
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_ids = jnp.zeros(input_shape, dtype="i4") | |
# make sure initialization pass will work for FlaxBartForSequenceClassificationModule | |
input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id) | |
attention_mask = jnp.ones_like(input_ids) | |
decoder_input_ids = input_ids | |
decoder_attention_mask = jnp.ones_like(input_ids) | |
batch_size, sequence_length = input_ids.shape | |
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) | |
decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) | |
params_rng, dropout_rng = jax.random.split(rng) | |
rngs = {"params": params_rng, "dropout": dropout_rng} | |
random_params = self.module.init( | |
rngs, | |
input_ids, | |
attention_mask, | |
decoder_input_ids, | |
decoder_attention_mask, | |
position_ids, | |
decoder_position_ids, | |
)["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 | |
def init_cache(self, batch_size, max_length, encoder_outputs): | |
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. | |
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): | |
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: | |
`attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) | |
is a sequence of hidden-states at the output of the last layer of the encoder. Used in the | |
cross-attention of the decoder. | |
""" | |
# init input variables to retrieve cache | |
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") | |
decoder_attention_mask = jnp.ones_like(decoder_input_ids) | |
decoder_position_ids = jnp.broadcast_to( | |
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape | |
) | |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): | |
decoder_module = module._get_decoder_module() | |
return decoder_module( | |
decoder_input_ids, | |
decoder_attention_mask, | |
decoder_position_ids, | |
**kwargs, | |
) | |
init_variables = self.module.init( | |
jax.random.PRNGKey(0), | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
decoder_position_ids=decoder_position_ids, | |
encoder_hidden_states=encoder_outputs[0], | |
init_cache=True, | |
method=_decoder_forward, # we only need to call the decoder to init the cache | |
) | |
return unfreeze(init_variables["cache"]) | |
def encode( | |
self, | |
input_ids: jnp.ndarray, | |
attention_mask: Optional[jnp.ndarray] = None, | |
position_ids: Optional[jnp.ndarray] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
train: bool = False, | |
params: dict = None, | |
dropout_rng: PRNGKey = None, | |
): | |
r""" | |
Returns: | |
Example: | |
```python | |
>>> from transformers import AutoTokenizer, FlaxBartForConditionalGeneration | |
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") | |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") | |
>>> text = "My friends are cool but they eat too many carbs." | |
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") | |
>>> encoder_outputs = model.encode(**inputs) | |
```""" | |
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 | |
if attention_mask is None: | |
attention_mask = jnp.ones_like(input_ids) | |
if position_ids is None: | |
batch_size, sequence_length = input_ids.shape | |
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) | |
# Handle any PRNG if needed | |
rngs = {} | |
if dropout_rng is not None: | |
rngs["dropout"] = dropout_rng | |
def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs): | |
encode_module = module._get_encoder_module() | |
return encode_module(input_ids, attention_mask, position_ids, **kwargs) | |
return self.module.apply( | |
{"params": params or self.params}, | |
input_ids=jnp.array(input_ids, dtype="i4"), | |
attention_mask=jnp.array(attention_mask, dtype="i4"), | |
position_ids=jnp.array(position_ids, dtype="i4"), | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=not train, | |
rngs=rngs, | |
method=_encoder_forward, | |
) | |
def decode( | |
self, | |
decoder_input_ids, | |
encoder_outputs, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
decoder_attention_mask: Optional[jnp.ndarray] = None, | |
decoder_position_ids: Optional[jnp.ndarray] = None, | |
past_key_values: dict = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
train: bool = False, | |
params: dict = None, | |
dropout_rng: PRNGKey = None, | |
): | |
r""" | |
Returns: | |
Example: | |
```python | |
>>> import jax.numpy as jnp | |
>>> from transformers import AutoTokenizer, FlaxBartForConditionalGeneration | |
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") | |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") | |
>>> text = "My friends are cool but they eat too many carbs." | |
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") | |
>>> encoder_outputs = model.encode(**inputs) | |
>>> decoder_start_token_id = model.config.decoder_start_token_id | |
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id | |
>>> outputs = model.decode(decoder_input_ids, encoder_outputs) | |
>>> last_decoder_hidden_states = outputs.last_hidden_state | |
```""" | |
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 | |
encoder_hidden_states = encoder_outputs[0] | |
if encoder_attention_mask is None: | |
batch_size, sequence_length = encoder_hidden_states.shape[:2] | |
encoder_attention_mask = jnp.ones((batch_size, sequence_length)) | |
batch_size, sequence_length = decoder_input_ids.shape | |
if decoder_attention_mask is None: | |
decoder_attention_mask = jnp.ones((batch_size, sequence_length)) | |
if decoder_position_ids is None: | |
if past_key_values is not None: | |
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") | |
decoder_position_ids = jnp.broadcast_to( | |
jnp.arange(sequence_length)[None, :], (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} | |
# 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 FlaxBartAttention module | |
if past_key_values: | |
inputs["cache"] = past_key_values | |
mutable = ["cache"] | |
else: | |
mutable = False | |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): | |
decoder_module = module._get_decoder_module() | |
return decoder_module( | |
decoder_input_ids, | |
decoder_attention_mask, | |
decoder_position_ids, | |
**kwargs, | |
) | |
outputs = self.module.apply( | |
inputs, | |
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), | |
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), | |
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=not train, | |
rngs=rngs, | |
mutable=mutable, | |
method=_decoder_forward, | |
) | |
# add updated cache to model output | |
if past_key_values is not None and return_dict: | |
outputs, past = outputs | |
outputs["past_key_values"] = unfreeze(past["cache"]) | |
return outputs | |
elif past_key_values is not None and not return_dict: | |
outputs, past = outputs | |
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] | |
return outputs | |
def __call__( | |
self, | |
input_ids: jnp.ndarray, | |
attention_mask: Optional[jnp.ndarray] = None, | |
decoder_input_ids: Optional[jnp.ndarray] = None, | |
decoder_attention_mask: Optional[jnp.ndarray] = None, | |
position_ids: Optional[jnp.ndarray] = None, | |
decoder_position_ids: Optional[jnp.ndarray] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
train: bool = False, | |
params: dict = None, | |
dropout_rng: PRNGKey = 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 | |
# prepare encoder inputs | |
if attention_mask is None: | |
attention_mask = jnp.ones_like(input_ids) | |
if position_ids is None: | |
batch_size, sequence_length = input_ids.shape | |
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) | |
# prepare decoder inputs | |
if decoder_input_ids is None: | |
decoder_input_ids = shift_tokens_right( | |
input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id | |
) | |
if decoder_attention_mask is None: | |
decoder_attention_mask = jnp.ones_like(decoder_input_ids) | |
if decoder_position_ids is None: | |
batch_size, sequence_length = decoder_input_ids.shape | |
decoder_position_ids = jnp.broadcast_to( | |
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) | |
) | |
# Handle any PRNG if needed | |
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} | |
return self.module.apply( | |
{"params": params or self.params}, | |
input_ids=jnp.array(input_ids, dtype="i4"), | |
attention_mask=jnp.array(attention_mask, dtype="i4"), | |
position_ids=jnp.array(position_ids, dtype="i4"), | |
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), | |
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), | |
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=not train, | |
rngs=rngs, | |
) | |
class FlaxBartModel(FlaxBartPreTrainedModel): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | |
module_class = FlaxBartModule | |
append_call_sample_docstring(FlaxBartModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC) | |
class FlaxBartForConditionalGenerationModule(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 | |
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros | |
def setup(self): | |
self.model = FlaxBartModule(config=self.config, dtype=self.dtype) | |
self.lm_head = nn.Dense( | |
self.model.shared.num_embeddings, | |
use_bias=False, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.init_std), | |
) | |
self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings)) | |
def _get_encoder_module(self): | |
return self.model.encoder | |
def _get_decoder_module(self): | |
return self.model.decoder | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
decoder_input_ids, | |
decoder_attention_mask, | |
position_ids, | |
decoder_position_ids, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
deterministic: bool = True, | |
): | |
outputs = self.model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
position_ids=position_ids, | |
decoder_position_ids=decoder_position_ids, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=deterministic, | |
) | |
hidden_states = outputs[0] | |
if self.config.tie_word_embeddings: | |
shared_embedding = self.model.variables["params"]["shared"]["embedding"] | |
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) | |
else: | |
lm_logits = self.lm_head(hidden_states) | |
lm_logits += jax.lax.stop_gradient(self.final_logits_bias.astype(self.dtype)) | |
if not return_dict: | |
output = (lm_logits,) + outputs[1:] | |
return output | |
return FlaxSeq2SeqLMOutput( | |
logits=lm_logits, | |
decoder_hidden_states=outputs.decoder_hidden_states, | |
decoder_attentions=outputs.decoder_attentions, | |
cross_attentions=outputs.cross_attentions, | |
encoder_last_hidden_state=outputs.encoder_last_hidden_state, | |
encoder_hidden_states=outputs.encoder_hidden_states, | |
encoder_attentions=outputs.encoder_attentions, | |
) | |
class FlaxBartForConditionalGeneration(FlaxBartPreTrainedModel): | |
module_class = FlaxBartForConditionalGenerationModule | |
dtype: jnp.dtype = jnp.float32 | |
def decode( | |
self, | |
decoder_input_ids, | |
encoder_outputs, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
decoder_attention_mask: Optional[jnp.ndarray] = None, | |
decoder_position_ids: Optional[jnp.ndarray] = None, | |
past_key_values: dict = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
train: bool = False, | |
params: dict = None, | |
dropout_rng: PRNGKey = None, | |
): | |
r""" | |
Returns: | |
Example: | |
```python | |
>>> import jax.numpy as jnp | |
>>> from transformers import AutoTokenizer, FlaxBartForConditionalGeneration | |
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") | |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") | |
>>> text = "My friends are cool but they eat too many carbs." | |
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") | |
>>> encoder_outputs = model.encode(**inputs) | |
>>> decoder_start_token_id = model.config.decoder_start_token_id | |
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id | |
>>> outputs = model.decode(decoder_input_ids, encoder_outputs) | |
>>> logits = outputs.logits | |
```""" | |
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 | |
encoder_hidden_states = encoder_outputs[0] | |
if encoder_attention_mask is None: | |
batch_size, sequence_length = encoder_hidden_states.shape[:2] | |
encoder_attention_mask = jnp.ones((batch_size, sequence_length)) | |
batch_size, sequence_length = decoder_input_ids.shape | |
if decoder_attention_mask is None: | |
decoder_attention_mask = jnp.ones((batch_size, sequence_length)) | |
if decoder_position_ids is None: | |
if past_key_values is not None: | |
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") | |
decoder_position_ids = jnp.broadcast_to( | |
jnp.arange(sequence_length)[None, :], (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} | |
# 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 FlaxBartAttention module | |
if past_key_values: | |
inputs["cache"] = past_key_values | |
mutable = ["cache"] | |
else: | |
mutable = False | |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): | |
decoder_module = module._get_decoder_module() | |
outputs = decoder_module( | |
decoder_input_ids, | |
decoder_attention_mask, | |
decoder_position_ids, | |
**kwargs, | |
) | |
hidden_states = outputs[0] | |
if self.config.tie_word_embeddings: | |
shared_embedding = module.model.variables["params"]["shared"]["embedding"] | |
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) | |
else: | |
lm_logits = module.lm_head(hidden_states) | |
lm_logits += module.final_logits_bias.astype(self.dtype) | |
return lm_logits, outputs | |
outputs = self.module.apply( | |
inputs, | |
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), | |
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), | |
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=not train, | |
rngs=rngs, | |
mutable=mutable, | |
method=_decoder_forward, | |
) | |
if past_key_values is None: | |
lm_logits, decoder_outputs = outputs | |
else: | |
(lm_logits, decoder_outputs), past = outputs | |
if return_dict: | |
outputs = FlaxCausalLMOutputWithCrossAttentions( | |
logits=lm_logits, | |
hidden_states=decoder_outputs.hidden_states, | |
attentions=decoder_outputs.attentions, | |
cross_attentions=decoder_outputs.cross_attentions, | |
) | |
else: | |
outputs = (lm_logits,) + decoder_outputs[1:] | |
# add updated cache to model output | |
if past_key_values is not None and return_dict: | |
outputs["past_key_values"] = unfreeze(past["cache"]) | |
return outputs | |
elif past_key_values is not None and not return_dict: | |
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] | |
return outputs | |
def prepare_inputs_for_generation( | |
self, | |
decoder_input_ids, | |
max_length, | |
attention_mask: Optional[jax.Array] = None, | |
decoder_attention_mask: Optional[jax.Array] = None, | |
encoder_outputs=None, | |
**kwargs, | |
): | |
# initializing the cache | |
batch_size, seq_length = decoder_input_ids.shape | |
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) | |
# 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 anyways. | |
# 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 decoder_attention_mask is not None: | |
position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 | |
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_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, | |
"encoder_outputs": encoder_outputs, | |
"encoder_attention_mask": attention_mask, | |
"decoder_attention_mask": extended_attention_mask, | |
"decoder_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["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 | |
return model_kwargs | |
FLAX_BART_CONDITIONAL_GENERATION_DOCSTRING = """ | |
Returns: | |
Summarization example: | |
```python | |
>>> from transformers import AutoTokenizer, FlaxBartForConditionalGeneration | |
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") | |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") | |
>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs." | |
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np") | |
>>> # Generate Summary | |
>>> summary_ids = model.generate(inputs["input_ids"]).sequences | |
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)) | |
``` | |
Mask filling example: | |
```python | |
>>> import jax | |
>>> from transformers import AutoTokenizer, FlaxBartForConditionalGeneration | |
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large") | |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large") | |
>>> TXT = "My friends are <mask> but they eat too many carbs." | |
>>> input_ids = tokenizer([TXT], return_tensors="jax")["input_ids"] | |
>>> logits = model(input_ids).logits | |
>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero()[0].item() | |
>>> probs = jax.nn.softmax(logits[0, masked_index], axis=0) | |
>>> values, predictions = jax.lax.top_k(probs, k=1) | |
>>> tokenizer.decode(predictions).split() | |
``` | |
""" | |
overwrite_call_docstring( | |
FlaxBartForConditionalGeneration, BART_INPUTS_DOCSTRING + FLAX_BART_CONDITIONAL_GENERATION_DOCSTRING | |
) | |
append_replace_return_docstrings( | |
FlaxBartForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC | |
) | |
class FlaxBartForSequenceClassificationModule(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 | |
num_labels: Optional[int] = None | |
def setup(self): | |
self.model = FlaxBartModule(config=self.config, dtype=self.dtype) | |
self.classification_head = FlaxBartClassificationHead( | |
config=self.config, | |
inner_dim=self.config.d_model, | |
num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels, | |
pooler_dropout=self.config.classifier_dropout, | |
) | |
def _get_encoder_module(self): | |
return self.model.encoder | |
def _get_decoder_module(self): | |
return self.model.decoder | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
decoder_input_ids, | |
decoder_attention_mask, | |
position_ids, | |
decoder_position_ids, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
deterministic: bool = True, | |
): | |
outputs = self.model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
position_ids=position_ids, | |
decoder_position_ids=decoder_position_ids, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=deterministic, | |
) | |
hidden_states = outputs[0] # last hidden state | |
eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0) | |
# The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation | |
if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer: | |
if len(jnp.unique(eos_mask.sum(1))) > 1: | |
raise ValueError("All examples must have the same number of <eos> tokens.") | |
if any(eos_mask.sum(1) == 0): | |
raise ValueError("There are missing <eos> tokens in input_ids") | |
# Ensure to keep 1 only for the last <eos> token for each example | |
eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6 | |
eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0) | |
sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1) | |
logits = self.classification_head(sentence_representation, deterministic=deterministic) | |
if not return_dict: | |
output = (logits,) + outputs[1:] | |
return output | |
return FlaxSeq2SeqSequenceClassifierOutput( | |
logits=logits, | |
decoder_hidden_states=outputs.decoder_hidden_states, | |
decoder_attentions=outputs.decoder_attentions, | |
cross_attentions=outputs.cross_attentions, | |
encoder_last_hidden_state=outputs.encoder_last_hidden_state, | |
encoder_hidden_states=outputs.encoder_hidden_states, | |
encoder_attentions=outputs.encoder_attentions, | |
) | |
class FlaxBartForSequenceClassification(FlaxBartPreTrainedModel): | |
module_class = FlaxBartForSequenceClassificationModule | |
dtype = jnp.float32 | |
append_call_sample_docstring( | |
FlaxBartForSequenceClassification, | |
_CHECKPOINT_FOR_DOC, | |
FlaxSeq2SeqSequenceClassifierOutput, | |
_CONFIG_FOR_DOC, | |
) | |
class FlaxBartForQuestionAnsweringModule(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 | |
num_labels = 2 | |
def setup(self): | |
self.model = FlaxBartModule(config=self.config, dtype=self.dtype) | |
self.qa_outputs = nn.Dense( | |
self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) | |
) | |
def _get_encoder_module(self): | |
return self.model.encoder | |
def _get_decoder_module(self): | |
return self.model.decoder | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
decoder_input_ids, | |
decoder_attention_mask, | |
position_ids, | |
decoder_position_ids, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
deterministic: bool = True, | |
): | |
outputs = self.model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
position_ids=position_ids, | |
decoder_position_ids=decoder_position_ids, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=deterministic, | |
) | |
sequence_output = outputs[0] | |
logits = self.qa_outputs(sequence_output) | |
start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1) | |
start_logits = start_logits.squeeze(-1) | |
end_logits = end_logits.squeeze(-1) | |
if not return_dict: | |
output = (start_logits, end_logits) + outputs[1:] | |
return output | |
return FlaxSeq2SeqQuestionAnsweringModelOutput( | |
start_logits=start_logits, | |
end_logits=end_logits, | |
decoder_hidden_states=outputs.decoder_hidden_states, | |
decoder_attentions=outputs.decoder_attentions, | |
cross_attentions=outputs.cross_attentions, | |
encoder_last_hidden_state=outputs.encoder_last_hidden_state, | |
encoder_hidden_states=outputs.encoder_hidden_states, | |
encoder_attentions=outputs.encoder_attentions, | |
) | |
class FlaxBartForQuestionAnswering(FlaxBartPreTrainedModel): | |
module_class = FlaxBartForQuestionAnsweringModule | |
dtype = jnp.float32 | |
append_call_sample_docstring( | |
FlaxBartForQuestionAnswering, | |
_CHECKPOINT_FOR_DOC, | |
FlaxSeq2SeqQuestionAnsweringModelOutput, | |
_CONFIG_FOR_DOC, | |
) | |
class FlaxBartDecoderPreTrainedModel(FlaxPreTrainedModel): | |
config_class = BartConfig | |
base_model_prefix: str = "model" | |
module_class: nn.Module = None | |
def __init__( | |
self, | |
config: BartConfig, | |
input_shape: Tuple[int] = (1, 1), | |
seed: int = 0, | |
dtype: jnp.dtype = jnp.float32, | |
_do_init: bool = True, | |
**kwargs, | |
): | |
config.is_decoder = True | |
config.is_encoder_decoder = False | |
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_ids = jnp.zeros(input_shape, dtype="i4") | |
attention_mask = jnp.ones_like(input_ids) | |
batch_size, sequence_length = input_ids.shape | |
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) | |
params_rng, dropout_rng = jax.random.split(rng) | |
rngs = {"params": params_rng, "dropout": dropout_rng} | |
encoder_hidden_states = jnp.zeros(input_shape + (self.config.d_model,)) | |
encoder_attention_mask = attention_mask | |
module_init_outputs = self.module.init( | |
rngs, | |
input_ids, | |
attention_mask, | |
position_ids, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
return_dict=False, | |
) | |
return module_init_outputs["params"] | |
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: jnp.ndarray, | |
attention_mask: Optional[jnp.ndarray] = None, | |
position_ids: Optional[jnp.ndarray] = None, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
train: bool = False, | |
params: dict = None, | |
past_key_values: dict = None, | |
dropout_rng: PRNGKey = 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 | |
if encoder_hidden_states is not None and encoder_attention_mask is None: | |
batch_size, sequence_length = encoder_hidden_states.shape[:2] | |
encoder_attention_mask = jnp.ones((batch_size, sequence_length)) | |
# prepare decoder inputs | |
if attention_mask is None: | |
attention_mask = jnp.ones_like(input_ids) | |
if position_ids is None: | |
batch_size, sequence_length = input_ids.shape | |
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) | |
# Handle any PRNG if needed | |
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} | |
inputs = {"params": params or self.params} | |
# 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 FlaxBartAttention module | |
if past_key_values: | |
inputs["cache"] = past_key_values | |
mutable = ["cache"] | |
else: | |
mutable = False | |
outputs = self.module.apply( | |
inputs, | |
input_ids=jnp.array(input_ids, dtype="i4"), | |
attention_mask=jnp.array(attention_mask, dtype="i4"), | |
position_ids=jnp.array(position_ids, dtype="i4"), | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
deterministic=not train, | |
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:] | |
return outputs | |
class FlaxBartDecoderWrapper(nn.Module): | |
""" | |
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is | |
used in combination with the [`EncoderDecoderModel`] framework. | |
""" | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
embed_dim = self.config.d_model | |
embed_tokens = nn.Embed( | |
self.config.vocab_size, | |
embed_dim, | |
embedding_init=jax.nn.initializers.normal(self.config.init_std), | |
dtype=self.dtype, | |
) | |
self.decoder = FlaxBartDecoder(config=self.config, embed_tokens=embed_tokens, dtype=self.dtype) | |
def __call__(self, *args, **kwargs): | |
return self.decoder(*args, **kwargs) | |
class FlaxBartForCausalLMModule(nn.Module): | |
config: BartConfig | |
dtype: jnp.dtype = jnp.float32 | |
def setup(self): | |
self.model = FlaxBartDecoderWrapper(config=self.config, dtype=self.dtype) | |
self.lm_head = nn.Dense( | |
self.config.vocab_size, | |
use_bias=False, | |
dtype=self.dtype, | |
kernel_init=jax.nn.initializers.normal(self.config.init_std), | |
) | |
def __call__( | |
self, | |
input_ids, | |
attention_mask, | |
position_ids, | |
encoder_hidden_states: Optional[jnp.ndarray] = None, | |
encoder_attention_mask: Optional[jnp.ndarray] = None, | |
init_cache: bool = False, | |
output_attentions: bool = False, | |
output_hidden_states: bool = False, | |
return_dict: bool = True, | |
deterministic: bool = True, | |
): | |
outputs = self.model( | |
input_ids, | |
attention_mask, | |
position_ids, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
deterministic=deterministic, | |
init_cache=init_cache, | |
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.model.variables["params"]["decoder"]["embed_tokens"]["embedding"] | |
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) | |
else: | |
lm_logits = self.lm_head(hidden_states) | |
if not return_dict: | |
return (lm_logits,) + outputs[1:] | |
return FlaxCausalLMOutputWithCrossAttentions( | |
logits=lm_logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
cross_attentions=outputs.cross_attentions, | |
) | |
class FlaxBartForCausalLM(FlaxBartDecoderPreTrainedModel): | |
module_class = FlaxBartForCausalLMModule | |
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( | |
FlaxBartForCausalLM, | |
_CHECKPOINT_FOR_DOC, | |
FlaxCausalLMOutputWithCrossAttentions, | |
_CONFIG_FOR_DOC, | |
) | |