# 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 partial
import chex
import jax
from flax import nnx as nn
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 (
MoeCausalLMOutput,
MoeModelOutput,
SequenceClassifierOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
block_wise_ffn,
control_mlp_sharding,
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.layers.norms import RMSNorm
from .arctic_configuration import ArcticConfig
class ArcticAttention(AttentionModule):
"""
ArcticAttention module. This module implements the attention mechanism for the Arctic model,
supporting features like rotary position embeddings and flexible attention implementations.
Attributes:
config (ArcticConfig): Configuration object for the Arctic model.
dtype (jnp.dtype): Data type for computation (e.g., float32). Defaults to float32.
param_dtype (jnp.dtype): Data type for parameters (e.g., float32). Defaults to float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations (e.g., None, 'high', 'highest'). Defaults to None.
rngs (nn.Rngs): Random number generators for the module.
"""
def __init__(
self,
config: ArcticConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
linear = partial(
ParallelLinear,
use_bias=getattr(self.config, "attention_bias", False),
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=nn.initializers.normal(),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.q_proj = linear(
config.hidden_size,
self.num_heads * self.head_dim,
rngs=rngs,
)
self.k_proj = linear(
config.hidden_size,
self.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.v_proj = linear(
config.hidden_size,
self.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.o_proj = linear(
self.num_heads * self.head_dim,
self.num_heads * self.head_dim,
rngs=rngs,
)
self.rotary = self.config.get_basic_rope(
self.dtype,
self.head_dim,
self.head_dim,
True,
)
self.attention_performer = FlexibleAttentionModule(
base_config=config,
softmax_scale=self.head_dim**-0.5,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array | bool],
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,
fcm_mask: tp.Optional[chex.Array] = None,
frequencies: tp.Optional[chex.Array] = None,
):
batch_size, sequence_length = hidden_states.shape[:2]
query_states, key_states, value_states = (
self.q_proj(hidden_states),
self.k_proj(hidden_states),
self.v_proj(hidden_states),
)
query_states = query_states.reshape(
batch_size,
sequence_length,
self.config.num_attention_heads,
self.head_dim,
)
key_states = key_states.reshape(
batch_size,
sequence_length,
self.config.num_key_value_heads,
self.head_dim,
)
value_states = value_states.reshape(
batch_size,
sequence_length,
self.config.num_key_value_heads,
self.head_dim,
)
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,
) = self.concatenate(
query=query_states,
key=key_states,
cache_view=cache_view,
value=value_states,
attention_mask=attention_mask,
causal_mask=causal_mask,
fcm_mask=fcm_mask,
)
attentions = self.attention_performer.forward(
query_states=query_states,
key_states=key_states,
value_states=value_states,
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.o_proj(attn_output)
return attn_output, attentions.attention_weights
class ArcticMLP(nn.Module):
"""
Arctic Multi-Layer Perceptron (MLP) block. This block implements the feed-forward network
used in the Arctic model. It can optionally function as a residual MLP.
Attributes:
config (ArcticConfig): Configuration object for the Arctic model.
dtype (jnp.dtype): Data type for computation. Defaults to jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Defaults to jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Defaults to None.
is_residual_mlp (bool): Whether this MLP block is a residual MLP. Defaults to False.
rngs (nn.Rngs): Random number generators for the module.
"""
def __init__(
self,
config: ArcticConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
is_residual_mlp: bool = False,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.is_residual_mlp = is_residual_mlp
self.hidden_dim = config.hidden_size
self.ffn_dim = (
config.intermediate_size if not self.is_residual_mlp else self.hidden_dim
)
linear_class = partial(
ParallelLinear,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=nn.initializers.normal(),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.w1 = linear_class(self.hidden_dim, self.ffn_dim, rngs=rngs)
self.w3 = linear_class(self.hidden_dim, self.ffn_dim, rngs=rngs)
self.w2 = linear_class(self.ffn_dim, self.hidden_dim, rngs=rngs)
self.act_fn = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_states: chex.Array):
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
w1 = self.act_fn(self.w1(hidden_states))
w3 = self.w3(hidden_states)
return self.w2(w1 * w3)
class ArcticMoeBlock(nn.Module):
"""
Arctic Mixture of Experts (MoE) block. This module implements the MoE layer used in the Arctic model,
routing tokens to different experts based on a gating mechanism.
Attributes:
config (ArcticConfig): Configuration object for the Arctic model.
layer_idx (int): The index of the current layer.
dtype (jnp.dtype): Data type for computation. Defaults to jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Defaults to jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Defaults to None.
rngs (nn.Rngs): Random number generators for the module.
"""
def __init__(
self,
config: ArcticConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
) -> None:
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.dtype = dtype
self.param_dtype = param_dtype
self.rngs = rngs
self.hidden_dim = config.hidden_size
self.num_experts = config.num_local_experts
self.top_k = config.num_experts_per_tok
self.is_moe_layer = (layer_idx + 1) % config.moe_layer_frequency == 0
if self.is_moe_layer:
self.gate = ParallelLinear(
config.hidden_size,
config.num_local_experts,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=nn.initializers.normal(),
rngs=rngs,
)
self.experts = [
ArcticMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for _ in range(config.num_local_experts)
]
else:
self.mlp = ArcticMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
is_residual_mlp=False,
rngs=rngs,
)
def _call_moe(self, hidden_states: chex.Array) -> tp.Tuple[chex.Array, chex.Array]:
"""
Executes the Mixture of Experts (MoE) logic.
Args:
hidden_states (chex.Array): Input hidden states.
Returns:
tp.Tuple[chex.Array, chex.Array]: Tuple containing the final hidden state and the router logits.
"""
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
router_logits = self.gate(hidden_states).astype( # no reshaping is needed
jnp.promote_types(self.dtype, jnp.float32)
)
routing_weights, selected_experts = jax.lax.top_k(
router_logits, k=self.config.num_experts_per_tok
)
routing_weights = jax.nn.softmax(
routing_weights.astype(
jnp.promote_types(self.dtype, jnp.float32),
),
axis=-1,
)
final_hidden_state = jnp.zeros_like(hidden_states)
for index in range(self.config.num_local_experts):
expert_layer_output = (
block_wise_ffn(
self.experts[index],
hidden_states,
self.config.scan_mlp_chunk_size,
)
if self.config.use_scan_mlp
else self.experts[index](hidden_states)
)
expert_layer_output_exp = (
jnp.sum(
jnp.multiply(selected_experts == index, routing_weights),
axis=-1,
)[:, :, None]
* expert_layer_output
)
final_hidden_state += expert_layer_output_exp
return final_hidden_state, router_logits
def __call__(self, hidden_states: chex.Array):
"""
Forward pass for the ArcticMoeBlock.
If the current layer is an MoE layer, it calls the MoE logic (_call_moe).
Otherwise, it passes the input through the standard MLP.
Args:
hidden_states (chex.Array): Input hidden states.
Returns:
tp.Tuple[chex.Array, chex.Array]: Tuple containing the output hidden state and router logits (or 0.0 if not MoE).
"""
if self.is_moe_layer:
return self._call_moe(hidden_states=hidden_states)
return self.mlp(hidden_states), jnp.array(0.0, dtype=hidden_states.dtype)
class ArcticDecoderLayer(nn.Module):
"""
Arctic Decoder Layer. This module combines the ArcticAttention and ArcticMoeBlock (or ArcticMLP)
with layer normalization and residual connections to form a standard Transformer decoder layer.
Attributes:
config (ArcticConfig): Configuration object for the Arctic model.
layer_idx (int): The index of the current layer.
dtype (jnp.dtype): Data type for computation. Defaults to jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Defaults to jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Defaults to None.
rngs (nn.Rngs): Random number generators for the module.
"""
def __init__(
self,
config: ArcticConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
) -> None:
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.dtype = dtype
self.param_dtype = param_dtype
self.rngs = rngs
attn_block = ArcticAttention
mlp_block = ArcticMoeBlock
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
)
self.self_attn = attn_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.block_sparse_moe = mlp_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
layer_idx=layer_idx,
)
self.input_layernorm = RMSNorm(
dim=config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.post_attention_layernorm = RMSNorm(
dim=config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.parallel_attn_mlp_res = (
self.config.parallel_attn_mlp_res and self.block_sparse_moe.is_moe_layer
)
if self.parallel_attn_mlp_res:
self.residual_layernorm = RMSNorm(
dim=config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.residual_mlp = ArcticMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
is_residual_mlp=True,
rngs=rngs,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array | bool],
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,
fcm_mask: tp.Optional[chex.Array] = None,
frequencies: tp.Optional[chex.Array] = None,
) -> tp.Tuple[chex.Array, tp.Optional[chex.Array], chex.Array]:
residual_input = hidden_states
hidden_states = self.input_layernorm(hidden_states)
attn_out = self.self_attn(
hidden_states,
attention_mask,
position_ids,
causal_mask,
cache_view,
cache_metadata,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
hidden_states, self_attn_weights = (
attn_out if output_attentions else (attn_out[0], None)
)
hidden_states = residual_input + hidden_states
residual_attn = hidden_states
if self.parallel_attn_mlp_res:
hidden_states = self.residual_layernorm(hidden_states)
hidden_states = self.residual_mlp(hidden_states)
residual_residual = residual_attn + hidden_states
# parallel mlp moe part
hidden_states = self.post_attention_layernorm(residual_input)
hidden_states, gate_loss = self.block_sparse_moe(hidden_states)
hidden_states = residual_residual + hidden_states
else:
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states, gate_loss = self.block_sparse_moe(hidden_states)
hidden_states = residual_attn + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
outputs += (gate_loss,)
return outputs
[docs]@register_module(
TaskType.BASE_MODULE,
config=ArcticConfig,
model_type="arctic",
)
class ArcticModel(EasyDeLBaseModule):
"""
Core Arctic model architecture. This module implements the main Transformer stack
for the Arctic model, including token embeddings and decoder layers.
Attributes:
config (ArcticConfig): Configuration object for the Arctic model.
dtype (jnp.dtype): Data type for computation. Defaults to jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Defaults to jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Defaults to None.
rngs (nn.Rngs): Random number generators for the module.
"""
def __init__(
self,
config: ArcticConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
) -> None:
"""Initializes the ArcticModel."""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.embed_tokens = nn.Embed(
self.config.vocab_size,
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layers = [
ArcticDecoderLayer(
layer_idx=layer_idx,
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for layer_idx in range(config.num_hidden_layers)
]
self.norm = RMSNorm(
self.config.hidden_size,
eps=self.config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
return_dict: bool = True,
) -> tp.Union[MoeModelOutput, tp.Tuple]:
"""Forward pass through the ArcticModel.
Args:
input_ids (Optional[chex.Array]): Input token IDs.
inputs_embeds (Optional[chex.Array]): Input embeddings (alternative to input_ids).
attention_mask (Optional[chex.Array]): Mask to avoid attending to padding tokens.
position_ids (Optional[chex.Array]): Position IDs for positional embeddings.
segment_ids (Optional[chex.Array]): Segment IDs (if applicable).
output_attentions (Optional[bool]): Whether to return attention weights.
output_hidden_states (Optional[bool]): Whether to return all hidden states.
past_key_values (Optional[TransformerCache | PagedAttentionCache]): Cached key/value states for faster decoding.
cache_metadata (Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention cache.
return_dict (bool): Whether to return a MoeModelOutput object or a tuple.
Returns:
Union[MoeModelOutput, Tuple]: Model outputs, either as a dataclass or a tuple.
"""
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
)
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_losses = ()
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.embed_tokens(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)
hidden_states = inputs_embeds
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.layers))
for idx, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
cache_view=past_key_values.views[idx],
cache_metadata=cache_metadata,
causal_mask=self.causal_mask,
output_attentions=output_attentions,
segment_ids=segment_ids,
frequencies=self.frequencies,
)
hidden_states = outputs[0]
if output_attentions:
all_self_attns += (outputs[1],)
all_router_losses += (outputs[-1],)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
all_hidden_states,
all_self_attns,
all_router_losses,
]
if v is not None
)
return MoeModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
all_router_losses=all_router_losses,
)
[docs]@register_module(
TaskType.CAUSAL_LM,
config=ArcticConfig,
model_type="arctic",
)
class ArcticForCausalLM(EasyDeLBaseModule):
"""
Arctic model specifically adapted for Causal Language Modeling (CLM).
This module wraps the core ArcticModel and adds a language modeling head on top.
Attributes:
config (ArcticConfig): Configuration object for the Arctic model.
dtype (jnp.dtype): Data type for computation. Defaults to jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Defaults to jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Defaults to None.
rngs (nn.Rngs): Random number generators for the module.
"""
def __init__(
self,
config: ArcticConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the ArcticForCausalLM model."""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.model = ArcticModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.lm_head = ParallelLinear(
config.hidden_size,
config.vocab_size,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
use_bias=False,
kernel_init=nn.initializers.normal(config.initializer_range),
rngs=rngs,
**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,
segment_ids: tp.Optional[chex.Array] = None,
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: bool = True,
) -> MoeCausalLMOutput | tp.Tuple:
"""Forward pass through the ArcticForCausalLM model.
Args:
input_ids (Optional[chex.Array]): Input token IDs.
attention_mask (Optional[chex.Array]): Mask to avoid attending to padding tokens.
position_ids (Optional[chex.Array]): Position IDs for positional embeddings.
segment_ids (Optional[chex.Array]): Segment IDs (if applicable).
past_key_values (Optional[TransformerCache | PagedAttentionCache]): Cached key/value states for faster decoding.
cache_metadata (Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention cache.
inputs_embeds (Optional[chex.Array]): Input embeddings (alternative to input_ids).
output_attentions (Optional[bool]): Whether to return attention weights.
output_hidden_states (Optional[bool]): Whether to return all hidden states.
return_dict (bool): Whether to return a MoeCausalLMOutput object or a tuple.
Returns:
Union[MoeCausalLMOutput, Tuple]: Model outputs, including logits, either as a dataclass or a tuple.
"""
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
return_dict=return_dict,
inputs_embeds=inputs_embeds,
segment_ids=segment_ids,
)
hidden_states = outputs.last_hidden_state
if self.config.tie_word_embeddings:
lm_logits = jax.lax.dot_general(
hidden_states,
self.model.embed_tokens.embedding.value.T,
(((hidden_states.ndim - 1), (0,)), ((), ())),
)
else:
lm_logits = self.lm_head(hidden_states)
aux_loss = sum(outputs[-1]) * self.config.router_aux_loss_coef
if not return_dict:
outputs = (lm_logits,) + tuple(
v
for v in [
aux_loss,
outputs.hidden_states,
outputs.attentions,
outputs.all_router_losses,
]
if v is not None
)
return outputs
return MoeCausalLMOutput(
aux_loss=aux_loss,
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
all_router_losses=outputs.all_router_losses,
)
@register_module(
TaskType.SEQUENCE_CLASSIFICATION,
config=ArcticConfig,
model_type="arctic",
)
class ArcticForSequenceClassification(EasyDeLBaseModule):
"""
Arctic model adapted for sequence classification tasks.
This module wraps the core ArcticModel and adds a classification head on top.
Attributes:
config (ArcticConfig): Configuration object for the Arctic model (must include num_labels).
dtype (jnp.dtype): Data type for computation. Defaults to jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Defaults to jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Defaults to None.
rngs (nn.Rngs): Random number generators for the module.
"""
def __init__(
self,
config: ArcticConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the ArcticForSequenceClassification model."""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.model = ArcticModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
assert hasattr(config, "num_labels"), (
"in order to use `SequenceClassification` Models in `EasyDeL` you first need to attach `num_labels` to model `config`"
)
self.score = ParallelLinear(
config.hidden_size,
config.num_labels,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(stddev=config.initializer_range),
precision=precision,
rngs=rngs,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: bool = True,
) -> tp.Union[SequenceClassifierOutput, tp.Tuple]:
"""Forward pass through the ArcticForSequenceClassification model.
Args:
input_ids (Optional[chex.Array]): Input token IDs.
inputs_embeds (Optional[chex.Array]): Input embeddings (alternative to input_ids).
attention_mask (Optional[chex.Array]): Mask to avoid attending to padding tokens.
position_ids (Optional[chex.Array]): Position IDs for positional embeddings.
segment_ids (Optional[chex.Array]): Segment IDs (if applicable).
past_key_values (Optional[TransformerCache | PagedAttentionCache]): Cached key/value states for faster decoding.
cache_metadata (Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention cache.
output_attentions (Optional[bool]): Whether to return attention weights.
output_hidden_states (Optional[bool]): Whether to return all hidden states.
return_dict (bool): Whether to return a SequenceClassifierOutput object or a tuple.
Returns:
Union[SequenceClassifierOutput, Tuple]: Model outputs, including classification logits, either as a dataclass or a tuple.
"""
transformer_outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
inputs_embeds=inputs_embeds,
segment_ids=segment_ids,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (
jnp.argmax(jnp.equal(input_ids, self.config.pad_token_id).astype("i4"), -1)
- 1
)
sequence_lengths = sequence_lengths % input_ids.shape[-1]
else:
sequence_lengths = -1
pooled_logits = logits[jnp.arange(batch_size), sequence_lengths]
aux_loss = sum(transformer_outputs[-1]) * self.config.router_aux_loss_coef
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:] + (aux_loss,)
return output
return SequenceClassifierOutput(
logits=pooled_logits,
past_key_values=past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
aux_loss=aux_loss,
)