# Copyright 2023 The EASYDEL Author @erfanzar (Erfan Zare Chavoshi).
#
# 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
#
# https://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.
import typing as tp
import chex
import jax
from flax import nnx as nn
from flax.nnx.nn.attention import dot_product_attention_weights
from jax import lax
from jax import numpy as jnp
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
get_dot_general_by_bits,
)
from easydel.layers.attention import AttentionModule, FlexibleAttentionModule
from easydel.layers.caching import (
PagedAttentionCacheView,
PagedAttentionMetadata,
TransformerCache,
TransformerCacheView,
TransformerMetadata,
)
from easydel.layers.linear import ParallelLinear
from .roberta_configuration import RobertaConfig as RobertaConfig
[docs]class RobertaEmbeddings(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32, # the dtype of the computation
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.word_embeddings = nn.Embed(
num_embeddings=self.config.vocab_size,
features=self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.position_embeddings = nn.Embed(
num_embeddings=self.config.max_position_embeddings,
features=self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.token_type_embeddings = nn.Embed(
num_embeddings=self.config.type_vocab_size,
features=self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.LayerNorm = nn.LayerNorm(
self.config.hidden_size,
epsilon=self.config.layer_norm_eps,
param_dtype=param_dtype,
dtype=dtype,
rngs=rngs,
)
self.dropout = nn.Dropout(
rate=self.config.hidden_dropout_prob,
rngs=rngs,
)
def __call__(
self,
input_ids,
token_type_ids,
position_ids,
attention_mask,
):
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"))
hidden_states = inputs_embeds + token_type_embeddings + position_embeds
hidden_states = self.LayerNorm(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
[docs]class RobertaSelfAttention(AttentionModule):
def __init__(
self,
config: RobertaConfig,
causal: bool = False,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
super().__init__(config)
self.causal = causal
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
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.attention_performer = FlexibleAttentionModule(
base_config=config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=0.0,
)
self.query = ParallelLinear(
self.config.hidden_size,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
self.key = ParallelLinear(
self.config.hidden_size,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
self.value = ParallelLinear(
self.config.hidden_size,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
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):
"""
Merges the attention heads into a single hidden state tensor.
Args:
hidden_states (chex.Array): The hidden states with separate head dimensions.
Returns:
chex.Array: The hidden states with merged head dimensions.
"""
return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
causal_mask: tp.Optional[chex.Array] = None,
cache_view: tp.Optional[TransformerCacheView | PagedAttentionCacheView] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
segment_ids: tp.Optional[chex.Array] = None,
key_value_states: tp.Optional[jnp.array] = None,
output_attentions: bool = False,
):
is_cross_attention = key_value_states is not None
query_states = self.query(hidden_states)
if is_cross_attention:
key_states = self.key(key_value_states)
value_states = self.value(key_value_states)
else:
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)
(
key_states,
value_states,
attention_mask,
init_attention_bias,
) = self.concatenate(
query=query_states,
key=key_states,
cache_view=cache_view,
value=value_states,
attention_mask=attention_mask,
causal_mask=causal_mask if self.causal else None,
fcm_mask=None,
sliding_windows=None,
)
if layer_head_mask is None:
out = self.attention_performer.forward(
query_states=query_states,
key_states=key_states,
value_states=value_states,
causal=True,
init_bias=init_attention_bias,
attention_mask=attention_mask,
segment_ids=segment_ids,
)
attn_weights = out.attention_weights
attn_output = out.attention_outputs
else:
attn_weights = dot_product_attention_weights(
query_states,
key_states,
init_bias=init_attention_bias,
dropout_rate=self.config.attention_probs_dropout_prob,
broadcast_dropout=True,
dtype=self.dtype,
precision=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 = self.shard_attention_prod(
attn_output.reshape(attn_output.shape[:2] + (-1,))
)
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
return outputs
[docs]class RobertaSelfOutput(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.dense = ParallelLinear(
self.config.hidden_size,
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
self.LayerNorm = nn.LayerNorm(
self.config.hidden_size,
epsilon=self.config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob, rngs=rngs)
def __call__(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
[docs]class RobertaAttention(nn.Module):
def __init__(
self,
config: RobertaConfig,
causal: bool = False,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.causal = causal
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.self = RobertaSelfAttention(
config=config,
causal=causal,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.output = RobertaSelfOutput(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
causal_mask: tp.Optional[chex.Array] = None,
cache_view: tp.Optional[TransformerCacheView | PagedAttentionCacheView] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
key_value_states=None,
output_attentions: bool = False,
):
attn_outputs = self.self(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_mask=causal_mask if self.causal else None,
layer_head_mask=layer_head_mask,
cache_view=cache_view,
key_value_states=key_value_states,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
hidden_states = self.output(attn_output, hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_outputs[1],)
return outputs
[docs]class RobertaOutput(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.dense = ParallelLinear(
self.config.hidden_size,
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=dtype,
precision=precision,
param_dtype=param_dtype,
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
self.dropout = nn.Dropout(
rate=self.config.hidden_dropout_prob,
rngs=rngs,
)
self.LayerNorm = nn.LayerNorm(
self.config.intermediate_size,
epsilon=self.config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def __call__(self, hidden_states, attention_output):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + attention_output)
return hidden_states
[docs]class RobertaLayer(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.attention = RobertaAttention(
config=config,
causal=config.is_decoder,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.intermediate = RobertaIntermediate(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.output = RobertaOutput(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
if self.config.add_cross_attention:
self.crossattention = RobertaAttention(
config=config,
causal=True,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
causal_mask: tp.Optional[chex.Array] = None,
cache_view: tp.Optional[TransformerCacheView | PagedAttentionCacheView] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
encoder_hidden_states: tp.Optional[chex.Array] = None,
encoder_attention_mask: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
):
# Self Attention
attention_outputs = self.attention(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_mask=causal_mask,
layer_head_mask=layer_head_mask,
cache_view=cache_view,
output_attentions=output_attentions,
)
attention_output = attention_outputs[0]
# Cross-Attention Block
if encoder_hidden_states is not None:
cross_attention_outputs = self.crossattention(
hidden_states=attention_output,
attention_mask=encoder_attention_mask,
layer_head_mask=layer_head_mask,
cache_view=cache_view,
key_value_states=encoder_hidden_states,
output_attentions=output_attentions,
causal_mask=causal_mask,
)
attention_output = cross_attention_outputs[0]
hidden_states = self.intermediate(attention_output)
hidden_states = self.output(hidden_states, attention_output)
outputs = (hidden_states,)
if output_attentions:
outputs += (attention_outputs[1],)
if encoder_hidden_states is not None:
outputs += (cross_attention_outputs[1],)
return outputs
[docs]class RobertaEncoder(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
block = RobertaLayer
block = auto_remat(
block,
policy=config.gradient_checkpointing,
)
self.layer = [
block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for _ in range(config.num_hidden_layers)
]
def __call__(
self,
hidden_states,
attention_mask,
head_mask,
causal_mask: tp.Optional[chex.Array] = None,
encoder_hidden_states: tp.Optional[chex.Array] = None,
encoder_attention_mask: tp.Optional[chex.Array] = None,
past_key_values: tp.Optional[TransformerCacheView] = None,
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.layer)):
raise ValueError(
f"The head_mask should be specified for {len(self.layer)} layer, but it is for "
f" {head_mask.shape[0]}."
)
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.layer))
for i, (layer, cache_view) in enumerate(zip(self.layer, past_key_values.views)):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=head_mask[i] if head_mask is not None else None,
cache_view=cache_view,
causal_mask=causal_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=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 BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
[docs]class RobertaPooler(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.dense = ParallelLinear(
self.config.hidden_size,
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
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)
[docs]class RobertaLMHead(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.dense = ParallelLinear(
self.config.hidden_size,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
self.layer_norm = nn.LayerNorm(
self.config.hidden_size,
epsilon=self.config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.decoder = ParallelLinear(
self.config.vocab_size,
self.config.hidden_size,
dtype=dtype,
use_bias=False,
param_dtype=param_dtype,
precision=precision,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
self.bias = nn.Param(
jax.nn.initializers.zeros(
key=rngs.params(),
shape=(self.config.vocab_size,),
dtype=self.param_dtype,
)
)
def __call__(self, hidden_states, shared_embedding=None):
hidden_states = self.dense(hidden_states)
hidden_states = ACT2FN["gelu"](hidden_states)
hidden_states = self.layer_norm(hidden_states)
if shared_embedding is not None:
self.decoder.kernel.value = shared_embedding.T
self.decoder.bias.value = None
hidden_states = self.decoder(hidden_states)
else:
hidden_states = self.decoder(hidden_states)
bias = self.bias.astype(self.dtype)
hidden_states += bias
return hidden_states
[docs]class RobertaClassificationHead(nn.Module):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.dense = ParallelLinear(
self.config.hidden_size,
self.config.hidden_size,
dtype=dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
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,
rngs=rngs,
)
self.out_proj = ParallelLinear(
self.config.num_labels,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
def __call__(self, hidden_states):
hidden_states = hidden_states[:, 0, :]
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = nn.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
[docs]@register_module(
TaskType.BASE_MODULE,
config=RobertaConfig,
model_type="roberta",
)
class RobertaModel(EasyDeLBaseModule):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32, # the dtype of the computation
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
add_pooling_layer: bool = True,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.embeddings = RobertaEmbeddings(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.encoder = RobertaEncoder(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.pooler = (
RobertaPooler(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
if add_pooling_layer
else None
)
self.add_pooling_layer = add_pooling_layer
def __call__(
self,
input_ids,
attention_mask,
token_type_ids: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
head_mask: tp.Optional[chex.Array] = None,
encoder_hidden_states: tp.Optional[chex.Array] = None,
encoder_attention_mask: tp.Optional[chex.Array] = None,
past_key_values: tp.Optional[tp.Tuple[tp.Tuple[chex.Array, chex.Array]]] = None,
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 attention_mask is None:
attention_mask = jnp.ones_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
)
hidden_states = self.embeddings(
input_ids=input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
)
outputs = self.encoder(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
causal_mask=self.causal_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
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 BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=hidden_states,
pooler_output=pooled,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
[docs]@register_module(
TaskType.SEQUENCE_CLASSIFICATION,
config=RobertaConfig,
model_type="roberta",
)
class RobertaForSequenceClassification(EasyDeLBaseModule):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32, # the dtype of the computation
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.roberta = RobertaModel(
config=config,
dtype=dtype,
add_pooling_layer=False,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.classifier = RobertaClassificationHead(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.roberta(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
if not return_dict:
return (logits,) + outputs[1:]
return SequenceClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]class RobertaForTokenClassification(EasyDeLBaseModule):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32, # the dtype of the computation
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.roberta = RobertaModel(
config=config,
dtype=dtype,
add_pooling_layer=False,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
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,
rngs=rngs,
)
self.classifier = ParallelLinear(
self.config.num_labels,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.roberta(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
if not return_dict:
return (logits,) + outputs[1:]
return TokenClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]class RobertaForQuestionAnswering(EasyDeLBaseModule):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32, # the dtype of the computation
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.roberta = RobertaModel(
config=config,
dtype=dtype,
add_pooling_layer=False,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.qa_outputs = ParallelLinear(
self.config.num_labels,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(bits=config.bits, mode=config.easy_method),
)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.roberta(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
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 QuestionAnsweringModelOutput(
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]@register_module(
TaskType.CAUSAL_LM,
config=RobertaConfig,
model_type="roberta",
)
class RobertaForCausalLM(EasyDeLBaseModule):
def __init__(
self,
config: RobertaConfig,
dtype: jnp.dtype = jnp.float32, # the dtype of the computation
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[lax.Precision] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.roberta = RobertaModel(
config=config,
add_pooling_layer=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.lm_head = RobertaLMHead(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
input_ids: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
token_type_ids: tp.Optional[chex.Array] = None,
head_mask: tp.Optional[chex.Array] = None,
encoder_hidden_states: tp.Optional[chex.Array] = None,
encoder_attention_mask: tp.Optional[chex.Array] = None,
past_key_values: tp.Optional[tp.Tuple[tp.Tuple[chex.Array, chex.Array]]] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.roberta(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
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.roberta.embeddings.word_embeddings.embedding.value
else:
shared_embedding = None
logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)
if not return_dict:
return (logits,) + outputs[1:]
return CausalLMOutputWithCrossAttentions(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)