# 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
from functools import cached_property, partial
import chex
import jax
import jax.numpy as jnp
from eformer import common_types
from eformer.escale import apply_logical_sharding
from flax import nnx as nn
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.modeling_outputs import (
AttentionLayerOutput,
BaseModelOutput,
CausalLMOutput,
DecoderLayerOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
block_wise_ffn,
get_dot_general_by_bits,
)
from easydel.layers.attention import AttentionModule, FlexibleAttentionModule
from easydel.layers.caching import (
PagedAttentionCache,
PagedAttentionCacheView,
PagedAttentionMetadata,
TransformerCache,
TransformerCacheView,
TransformerMetadata,
)
from easydel.layers.linear import ParallelLinear
from easydel.utils.helpers import get_logger
from .gpt_j_configuration import GPTJConfig as GPTJConfig
logger = get_logger(__name__)
[docs]class GPTJAttention(AttentionModule):
"""GPT-J Attention module.
This module implements the attention mechanism used in the GPT-J model,
including rotary position embeddings.
Attributes:
config (GPTJConfig): Configuration object for the model.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
causal (bool): Whether the attention is causal.
is_cross_attention (bool): Whether the attention is cross-attention.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
causal: bool = True,
is_cross_attention: bool = False,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.precision = precision
self.dtype = dtype
self.rngs = rngs
self.is_cross_attention = is_cross_attention
self.causal = causal
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.rotary_dim = config.rotary_dim
linear = partial(
ParallelLinear,
self.embed_dim,
self.embed_dim,
use_bias=False,
dtype=dtype,
kernel_init=nn.initializers.normal(config.initializer_range),
param_dtype=param_dtype,
precision=precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.q_proj, self.k_proj, self.v_proj = (
linear(rngs=rngs),
linear(rngs=rngs),
linear(rngs=rngs),
)
self.out_proj = linear(rngs=rngs)
self.resid_dropout = nn.Dropout(rate=config.resid_pdrop, rngs=rngs)
self.rotary = self.config.get_basic_rope(
self.dtype,
head_size=self.embed_dim,
rotary_dim=self.rotary_dim,
base=10000,
is_neox_style=False,
)
self.attention_performer = FlexibleAttentionModule(
dropout_prob=config.attn_pdrop,
base_config=config,
softmax_scale=self.head_dim**-0.5,
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(
hidden_states.shape[:2] + (self.num_heads, self.head_dim)
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
cache_view: tp.Optional[TransformerCacheView | PagedAttentionCacheView] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
causal_mask: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
frequencies: tp.Optional[chex.Array] = None,
):
"""Forward pass of the GPTJAttention module.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (chex.Array): Mask to apply on the attention scores.
position_ids (chex.Array): Position indices for the tokens.
causal_mask (chex.Array, optional): Causal mask for ensuring autoregressive behavior.
segment_ids (tp.Optional[chex.Array], optional): Segment IDs for segment-based attention.
cache_view (tp.Optional[TransformerCacheView | PagedAttentionCacheView], optional): Cache view for key_states/value_states states.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata], optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
frequencies (tp.Optional[chex.Array], optional): Precomputed rotary frequencies.
Returns:
tp.Tuple[chex.Array, tp.Optional[chex.Array]]: A tuple containing the attention output and optionally the attention weights.
"""
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
(
query_states,
key_states,
value_states,
) = self.apply_qkv_shardings(query_states, key_states, value_states)
query_states, key_states = self.rotary(
positions=position_ids,
query=query_states,
key=key_states,
frequencies=frequencies,
)
(
key_states,
value_states,
attention_mask,
init_attention_bias,
cache_view,
) = self.concatenate(
query=query_states,
key=key_states,
value=value_states,
cache_view=cache_view,
attention_mask=attention_mask,
causal_mask=causal_mask,
fcm_mask=None,
)
attentions = self.attention_performer.forward(
query_states=query_states,
key_states=key_states,
value_states=value_states,
mode=mode,
bias=None,
cache_metadata=cache_metadata,
cache_view=cache_view,
init_bias=init_attention_bias,
attention_mask=attention_mask,
segment_ids=segment_ids,
causal=True,
dropout_rng=self.rngs.params(),
)
attn_output = self.shard_attention_prod(
self._merge_heads(attentions.attention_outputs)
)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
return AttentionLayerOutput(
attention_output=attn_output,
attention_weight=attentions.attention_weights if output_attentions else None,
cache_view=cache_view,
)
[docs]class GPTJMLP(nn.Module):
"""GPT-J MLP module.
This module implements the feed-forward network used in the GPT-J model.
Attributes:
config (GPTJConfig): Configuration object for the model.
intermediate_size (int): Dimensionality of the intermediate layer.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: GPTJConfig,
intermediate_size: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
self.config: GPTJConfig = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.intermediate_size = intermediate_size
embed_dim = config.hidden_size
kernel_init = nn.initializers.normal(config.initializer_range)
self.fc_in = ParallelLinear(
embed_dim,
intermediate_size,
dtype=dtype,
param_dtype=dtype,
precision=precision,
kernel_init=kernel_init,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.fc_out = ParallelLinear(
intermediate_size,
embed_dim,
dtype=dtype,
param_dtype=dtype,
precision=precision,
kernel_init=kernel_init,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(rate=config.resid_pdrop)
def __call__(self, hidden_states):
"""Forward pass of the GPTJMLP module.
Args:
hidden_states (chex.Array): Input hidden states.
Returns:
chex.Array: Output hidden states after processing through the MLP.
"""
hidden_states = self.dropout(self.fc_out(self.act(self.fc_in(hidden_states))))
return hidden_states
[docs]class GPTJBlock(nn.Module):
"""GPT-J Transformer block.
This module represents a single transformer block in the GPT-J model,
containing self-attention and MLP sub-layers with residual connections
and layer normalization.
Attributes:
config (GPTJConfig): Configuration object for the model.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
self.config: GPTJConfig = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
hidden_size = self.config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
attn_block = GPTJAttention
mlp_block = GPTJMLP
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
)
self.ln_1 = nn.LayerNorm(
self.config.hidden_size,
epsilon=config.layer_norm_epsilon,
dtype=dtype,
param_dtype=dtype,
rngs=rngs,
)
self.attn = attn_block(
config,
dtype=dtype,
param_dtype=dtype,
precision=precision,
rngs=rngs,
)
self.mlp = mlp_block(
config,
inner_dim,
dtype=dtype,
param_dtype=dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
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,
output_attentions: bool = False,
frequencies: tp.Optional[chex.Array] = None,
):
"""Forward pass of the GPTJBlock module.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (chex.Array): Mask to apply on the attention scores.
position_ids (chex.Array): Position indices for the tokens.
causal_mask (chex.Array, optional): Causal mask for ensuring autoregressive behavior.
segment_ids (tp.Optional[chex.Array], optional): Segment IDs for segment-based attention.
cache_view (tp.Optional[TransformerCacheView | PagedAttentionCacheView], optional): Cache view for key_states/value_states states.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata], optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
frequencies (tp.Optional[chex.Array], optional): Precomputed rotary frequencies.
Returns:
tp.Tuple[chex.Array, tp.Optional[chex.Array]]: A tuple containing the output hidden states and optionally the attention weights.
"""
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
attention_mask,
position_ids,
mode,
cache_view,
cache_metadata,
causal_mask,
segment_ids,
output_attentions,
frequencies,
)
attn_output = attn_outputs.attention_output
if self.config.use_scan_mlp:
feed_forward_hidden_states = block_wise_ffn(
self.mlp,
hidden_states,
self.config.scan_mlp_chunk_size,
)
else:
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = attn_output + feed_forward_hidden_states + residual
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
return DecoderLayerOutput(
hidden_states=hidden_states,
attention_weight=attn_outputs.attention_weight,
cache_view=attn_outputs.cache_view,
)
[docs]@register_module(
TaskType.BASE_MODULE,
config=GPTJConfig,
model_type="gptj",
)
class GPTJModel(EasyDeLBaseModule):
"""GPT-J model implementation.
This class implements the main GPT-J transformer model architecture, consisting of
an embedding layer, multiple GPTJBlock layers, and a final layer normalization.
Attributes:
config (GPTJConfig): Configuration object for the model.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.embed_dim = config.hidden_size
self.wte = nn.Embed(
self.config.vocab_size,
self.embed_dim,
embedding_init=nn.initializers.normal(stddev=config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.dropout = nn.Dropout(
rate=self.config.embd_pdrop,
rngs=rngs,
)
self.h = [
GPTJBlock(
config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for i in range(self.config.num_hidden_layers)
]
self.ln_f = nn.LayerNorm(
self.config.hidden_size,
epsilon=self.config.layer_norm_epsilon,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
@cached_property
def frequencies(self):
embed_dim = self.config.hidden_size
num_heads = self.config.num_attention_heads
head_dim = embed_dim // num_heads
rotary_dim = self.config.rotary_dim
return self.config.get_basic_frequencies(
rotary_dim=rotary_dim,
head_size=head_dim,
base=10000,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
extra_embedding: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
):
"""Forward pass through the GPTJModel.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key_states/value_states states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
extra_embedding (chex.Array, optional): Additional embedding to add to input embeddings.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[BaseModelOutput, Tuple]: Model outputs (last hidden state, optional hidden states, optional attentions)
"""
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids.astype("i4"))
batch_size, sequence_length, _ = inputs_embeds.shape
if attention_mask is None:
attention_mask = jnp.ones((batch_size, sequence_length), "b1")
else:
if attention_mask.dtype != jnp.bool:
attention_mask = jnp.astype(attention_mask == 1, "b1")
if position_ids is None:
position_ids = jnp.broadcast_to(
jnp.clip(jnp.cumsum(attention_mask, axis=-1) - 1, a_min=0),
(batch_size, sequence_length),
).astype(jnp.int32)
assert sequence_length <= self.config.max_position_embeddings, (
f"Maximum Position Embedding Reached ! (Excepted <= {self.config.max_position_embeddings} got {sequence_length})"
)
hidden_states = (
inputs_embeds + extra_embedding if extra_embedding is not None else inputs_embeds
)
if mode is None:
mode = (
common_types.MODE_DECODE
if sequence_length == 1 and past_key_values is not None
else common_types.MODE_TRAIN
)
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.h))
hidden_states = self.dropout(inputs_embeds)
for idx, block in enumerate(self.h):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states=hidden_states,
attention_mask=attention_mask,
mode=mode,
cache_view=past_key_values.views[idx],
cache_metadata=cache_metadata,
position_ids=position_ids,
output_attentions=output_attentions,
segment_ids=segment_ids,
frequencies=self.frequencies,
causal_mask=self.causal_mask,
)
hidden_states = layer_outputs.hidden_states
if output_attentions:
all_attentions += (layer_outputs.attention_weight,)
past_key_values[idx] = layer_outputs.cache_view
hidden_states = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
past_key_values=past_key_values,
)
[docs]@register_module(
TaskType.CAUSAL_LM,
config=GPTJConfig,
model_type="gptj",
)
class GPTJForCausalLM(EasyDeLBaseModule):
"""GPT-J model with a language modeling head.
This model extends the base GPTJModel by adding a linear layer on top to
predict the next token in a sequence, making it suitable for causal language
modeling tasks.
Attributes:
config (GPTJConfig): Configuration object for the model.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.transformer = GPTJModel(
self.config,
dtype=self.dtype,
param_dtype=self.dtype,
precision=self.precision,
rngs=rngs,
)
self.lm_head = ParallelLinear(
config.hidden_size,
config.vocab_size,
rngs=rngs,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(stddev=config.initializer_range),
param_dtype=self.dtype,
precision=self.precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
extra_embedding: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
):
"""Forward pass through the GPTJForCausalLM model.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key_states/value_states states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
extra_embedding (chex.Array, optional): Additional embedding to add to input embeddings.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[CausalLMOutput, Tuple]: Model outputs (logits, optional hidden states, optional attentions)
"""
outputs = self.transformer(
input_ids=input_ids,
extra_embedding=extra_embedding,
segment_ids=segment_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = outputs.last_hidden_state
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
if self.config.tie_word_embeddings:
lm_logits = jax.lax.dot_general(
hidden_states,
self.transformer.wte.embedding.value_states.T,
(((hidden_states.ndim - 1), (0,)), ((), ())),
)
else:
lm_logits = self.lm_head(hidden_states)
return CausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)