# Copyright 2025 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.
from functools import cached_property, partial
from typing import ClassVar
import jax
import jax.numpy as jnp
from eformer import common_types
from eformer.escale import apply_logical_sharding
from ejkernel.types import MaskInfo
from flax import nnx as nn
from jax.ad_checkpoint import checkpoint_name
from jaxtyping import Array, Bool, Float, Int
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_unified import UnifiedAttention
from easydel.layers.base_modules import BaseCausalLMModule
from easydel.layers.caching import (
RaggedPagesCache,
RaggedPagesCacheView,
RaggedPagesMetadata,
TransformerCache,
TransformerCacheView,
TransformerMetadata,
)
from easydel.layers.linear import ColumnParallelLinear, RowParallelLinear
from .stablelm_configuration import StableLmConfig
[docs]class StableLmMLP(nn.Module):
"""Multi-Layer Perceptron (MLP) block for the StableLM model.
Attributes:
config (StableLmConfig): Configuration object for the model.
gate_proj (ParallelLinear): Linear layer for the gating mechanism.
down_proj (ParallelLinear): Linear layer for down-projection.
up_proj (ParallelLinear): Linear layer for up-projection.
act_fn (callable): Activation function (specified in config).
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for matrix multiplications.
"""
def __init__(
self,
config: StableLmConfig,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
layer_idx: int,
):
"""Initializes the StableLmMLP module.
Args:
config (StableLmConfig): The configuration object for the model.
dtype (jnp.dtype): Data type for computations (default: jnp.float32).
param_dtype (jnp.dtype): Data type for parameters (default: jnp.float32).
precision (jax.lax.PrecisionLike): Precision setting for JAX operations (default: None).
rngs (nn.Rngs): Random number generators.
"""
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
column_parallel_linear = partial(
ColumnParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
row_parallel_linear = partial(
RowParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.gate_proj = column_parallel_linear(
config.hidden_size,
config.intermediate_size,
rngs=rngs,
)
self.down_proj = row_parallel_linear(
config.intermediate_size,
config.hidden_size,
rngs=rngs,
)
self.up_proj = column_parallel_linear(
config.hidden_size,
config.intermediate_size,
rngs=rngs,
)
self.act_fn = ACT2FN[config.hidden_act]
def __call__(
self, hidden_states: Float[Array, "batch seq_len hidden_dim"]
) -> Float[Array, "batch seq_len hidden_dim"]:
"""Forward pass of the MLP block.
Args:
hidden_states (jnp.ndarray): Input hidden states.
Returns:
jnp.ndarray: Output hidden states after MLP transformation.
"""
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
gate = checkpoint_name(self.act_fn(self.gate_proj(hidden_states)), "mlp_gate")
up = checkpoint_name(self.up_proj(hidden_states), "mlp_up")
hidden_states = checkpoint_name(self.down_proj(gate * up), "mlp_down")
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
return hidden_states
[docs]class StableLmLayerNormPerHead(nn.Module):
"""Applies Layer Normalization independently to each attention head's dimension.
Attributes:
norms (list[nn.LayerNorm]): List of LayerNorm modules, one per head.
"""
def __init__(
self,
head_dim: int,
num_heads: int,
eps: float = 1e-5,
bias: bool = False,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
*,
rngs: nn.Rngs,
):
"""Initializes the StableLmLayerNormPerHead module.
Args:
head_dim (int): The dimension of each attention head.
num_heads (int): The number of attention heads.
eps (float): Epsilon value for LayerNorm (default: 1e-5).
bias (bool): Whether to include bias in LayerNorm (default: False).
dtype (jnp.dtype): Data type for computations (default: jnp.float32).
param_dtype (jnp.dtype): Data type for parameters (default: jnp.float32).
rngs (nn.Rngs): Random number generators.
"""
self.norms = [
nn.LayerNorm(
head_dim,
epsilon=eps,
use_bias=bias,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
for idx in range(num_heads)
]
def __call__(self, hidden_states):
"""Applies LayerNorm per head.
Args:
hidden_states (chex.Array): Input hidden states, expected shape (..., num_heads * head_dim).
Returns:
chex.Array: Hidden states after applying LayerNorm per head, same shape as input.
"""
# hidden_states: [batch, seq_len, num_heads * head_dim]
# Reshape to [batch, seq_len, num_heads, head_dim]
states_per_heads = jnp.split(hidden_states, 1, axis=1)
# Normalize and merge the heads back together
return jnp.concatenate(
[norm(hidden_states) for norm, hidden_states in zip(self.norms, states_per_heads, strict=False)],
axis=1,
)
[docs]class StableLmAttention(UnifiedAttention):
"""StableLM Attention with Q/K normalization.
Inherits Q/K normalization from QKNormAttention.
Features:
- Uses LayerNorm instead of RMSNorm
- Per-head normalization (StableLmLayerNormPerHead)
- Partial RoPE (partial_rotary_factor)
"""
norms_mapping: ClassVar = {
"query_normalization": "q_layernorm",
"key_normalization": "k_layernorm",
}
def __init__(
self,
config: StableLmConfig,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
layer_idx: int,
):
self.qk_layernorm = config.qk_layernorm
self.partial_rotary_factor = config.partial_rotary_factor
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.rotary_emb_dim = int(config.partial_rotary_factor * self.head_dim)
super().__init__(
config,
dtype,
param_dtype,
precision,
rngs=rngs,
layer_idx=layer_idx,
attention_type="standard",
causal=True,
use_qk_norm=config.qk_layernorm,
)
def _create_q_norm(self, config, dtype, param_dtype, rngs):
"""Override to use per-head LayerNorm if qk_layernorm is enabled."""
if not self.qk_layernorm:
return None
return StableLmLayerNormPerHead(
head_dim=self.head_dim,
num_heads=config.num_attention_heads,
eps=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def _create_k_norm(self, config, dtype, param_dtype, rngs):
"""Override to use per-head LayerNorm if qk_layernorm is enabled."""
if not self.qk_layernorm:
return None
return StableLmLayerNormPerHead(
head_dim=self.head_dim,
num_heads=config.num_key_value_heads,
eps=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def _create_rotary(self, config, dtype):
"""Override for partial RoPE."""
return config.get_basic_rope(
dtype,
head_size=int(config.partial_rotary_factor * (config.hidden_size // config.num_attention_heads)),
rotary_dim=self.rotary_emb_dim,
base=config.rope_theta,
)
def __call__(
self,
hidden_states: Float[Array, "batch seq_len hidden_dim"],
mask_info: MaskInfo | None,
position_ids: Int[Array, "batch seq_len"],
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
cache_view: TransformerCacheView | RaggedPagesCacheView | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
output_attentions: bool = False,
frequencies: Float[Array, "seq_len head_dim"] | None = None,
):
"""Forward pass with per-head LayerNorm requiring transpose operations."""
batch_size, sequence_length = hidden_states.shape[:2]
# Project to Q/K/V
query_states, key_states, value_states = (
checkpoint_name(self.query_projection(hidden_states), "attn_query"),
checkpoint_name(self.key_projection(hidden_states), "attn_key"),
checkpoint_name(self.value_projection(hidden_states), "attn_value"),
)
# Reshape to multi-head format
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,
)
if self.qk_layernorm:
query_states = self.query_normalization(query_states.transpose(0, 2, 1, 3)).transpose(0, 2, 1, 3)
key_states = self.key_normalization(key_states.transpose(0, 2, 1, 3)).transpose(0, 2, 1, 3)
query_states, key_states, value_states = self.apply_qkv_shardings(query_states, key_states, value_states)
query_states, key_states = self._apply_rotary(query_states, key_states, position_ids, frequencies)
(
key_states,
value_states,
mask_info,
init_attention_bias,
cache_view,
cache_metadata,
) = self.concatenate(
query=query_states,
key=key_states,
value=value_states,
cache_view=cache_view,
cache_metadata=cache_metadata,
mask_info=mask_info,
)
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,
mask_info=mask_info,
causal=True,
)
attn_output = self.shard_attention_prod(self._merge_heads(attentions.attention_outputs))
attn_output = checkpoint_name(self.output_projection(attn_output), "attn_output")
return AttentionLayerOutput(
attention_output=attn_output,
attention_weight=attentions.attention_weights if output_attentions else None,
cache_view=cache_view,
)
[docs]class StableLmDecoderLayer(nn.Module):
"""A single decoder layer for the StableLM model.
This layer combines self-attention, MLP, and residual connections with layer normalization.
It supports parallel residual connections.
Attributes:
config (StableLmConfig): Configuration object for the model.
self_attn (StableLmAttention): Self-attention module.
mlp (StableLmMLP): MLP module.
input_layernorm (nn.LayerNorm): Layer normalization applied before self-attention.
post_attention_layernorm (nn.LayerNorm): Layer normalization applied after self-attention and before the MLP.
dropout_rng_key (str): Name of the RNG key for dropout.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for matrix multiplications.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: StableLmConfig,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
layer_idx: int,
):
"""Initializes the StableLmDecoderLayer module.
Args:
config (StableLmConfig): The configuration object for the model.
dtype (jnp.dtype): Data type for computations (default: jnp.float32).
param_dtype (jnp.dtype): Data type for parameters (default: jnp.float32).
precision (jax.lax.PrecisionLike): Precision setting for JAX operations (default: None).
rngs (nn.Rngs): Random number generators.
"""
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
attn_block = StableLmAttention
mlp_block = StableLmMLP
self.use_parallel_residual = self.config.use_parallel_residual
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
save_names=config.gradient_checkpointing_targets,
exclude_names=config.gradient_checkpointing_targets,
)
self.self_attn = attn_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
layer_idx=layer_idx,
)
self.mlp = mlp_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
layer_idx=layer_idx,
)
self.input_layernorm = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
if not self.use_parallel_residual:
self.post_attention_layernorm = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.dropout = nn.Dropout(self.config.hidden_dropout, rngs=rngs)
def __call__(
self,
hidden_states: Float[Array, "batch seq_len hidden_dim"],
mask_info: MaskInfo | None,
position_ids: Int[Array, "batch seq_len"],
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
cache_view: TransformerCacheView | RaggedPagesCacheView | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
output_attentions: bool = False,
frequencies: Float[Array, "seq_len head_dim"] | None = None,
):
"""Forward pass of the decoder layer.
Args:
hidden_states (chex.Array): Input hidden states (batch, seq_len, hidden_size).
attention_mask (chex.Array): Attention mask (batch, 1, seq_len, kv_seq_len).
position_ids (chex.Array): Position IDs (batch, seq_len).
causal_mask (tp.Optional[chex.Array | bool]): Causal mask for autoregressive behavior.
cache_view (tp.Optional[TransformerCacheView | RaggedPagesCacheView]):
Cache view for key/value states (optional).
cache_metadata (tp.Optional[TransformerMetadata | RaggedPagesMetadata]):
Metadata for paged attention (optional).
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
output_attentions (bool): Whether to output attention weights (default: False).
fcm_mask (tp.Optional[chex.Array]): Forward causal mask (FCM) mask (optional).
frequencies (tp.Optional[chex.Array]): Precomputed rotary frequencies (optional).
Returns:
tp.Tuple[chex.Array, chex.Array | None]: A tuple containing:
- hidden_states (chex.Array): Output hidden states after the decoder layer.
- attention_outputs (chex.Array | None): Attention weights (if `output_attentions` is True).
"""
assert hidden_states.ndim == 3, f"Input hidden_states should be 3 dimensions, got {hidden_states.ndim}"
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
attn_outputs = self.self_attn(
hidden_states,
mask_info,
position_ids,
mode,
cache_view,
cache_metadata,
output_attentions,
frequencies,
)
if self.use_parallel_residual:
if self.config.use_scan_mlp:
hidden_states = block_wise_ffn(self.mlp, hidden_states, self.config.scan_mlp_chunk_size)
else:
hidden_states = self.mlp(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + residual + attn_outputs.attention_output
else:
residual = residual + attn_outputs.attention_output
if self.config.use_scan_mlp:
hidden_states = block_wise_ffn(
self.mlp,
self.post_attention_layernorm(residual),
self.config.scan_mlp_chunk_size,
)
else:
hidden_states = self.mlp(self.post_attention_layernorm(residual))
hidden_states = self.dropout(hidden_states)
hidden_states = 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=StableLmConfig, model_type="stablelm")
class StableLmModel(EasyDeLBaseModule):
"""The base StableLM transformer model.
This class implements the core transformer architecture, including embedding layers,
decoder layers, and final normalization.
Attributes:
config (StableLmConfig): Configuration object for the model.
embed_tokens (nn.Embed): Embedding layer for input tokens.
layers (nn.List[StableLmDecoderLayer]): List of decoder layers.
norm (nn.LayerNorm): Final layer normalization.
gradient_checkpointing (str): Gradient checkpointing strategy.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for matrix multiplications.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: StableLmConfig,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the StableLmModel module.
Args:
config (StableLmConfig): The configuration object for the model.
dtype (jnp.dtype): Data type for computations (default: jnp.float32).
param_dtype (jnp.dtype): Data type for parameters (default: jnp.float32).
precision (jax.lax.PrecisionLike): Precision setting for JAX operations (default: None).
rngs (nn.Rngs): Random number generators.
"""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
embed_block = auto_remat(
nn.Embed,
policy=config.gradient_checkpointing,
save_names=config.gradient_checkpointing_targets,
exclude_names=config.gradient_checkpointing_targets,
)
self.embed_tokens = embed_block(
config.vocab_size,
config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layers = [
StableLmDecoderLayer(
config=config,
layer_idx=idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for idx in range(config.num_hidden_layers)
]
self.norm = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
@cached_property
def frequencies(self):
"""Cached property for precomputed rotary frequencies."""
rotary_emb_dim = int(
self.config.partial_rotary_factor * (self.config.hidden_size // self.config.num_attention_heads)
)
self._frequencies = self.config.get_basic_frequencies(
head_size=rotary_emb_dim,
rotary_dim=rotary_emb_dim,
)
def __call__(
self,
input_ids: Int[Array, "batch seq_len"] | None = None,
inputs_embeds: Float[Array, "batch seq_len hidden_dim"] | None = None,
attention_mask: Bool[Array, "batch seq_len"] | None = None,
mask_info: MaskInfo | None = None,
position_ids: Int[Array, "batch seq_len"] | None = None,
mode: common_types.RUNTIME_MODE_TYPES | None = None, # type:ignore
past_key_values: TransformerCache | RaggedPagesCache | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
) -> BaseModelOutput:
"""Forward pass of the StableLM model.
Args:
input_ids (tp.Optional[chex.Array]): Input token IDs (batch, seq_len).
Mutually exclusive with `inputs_embeds`.
inputs_embeds (tp.Optional[chex.Array]): Input embeddings (batch, seq_len, hidden_size).
Mutually exclusive with `input_ids`.
attention_mask (tp.Optional[chex.Array]): Attention mask (batch, seq_len). Usually used for padding tokens.
position_ids (tp.Optional[chex.Array]): Position IDs (batch, seq_len). If None, automatically generated.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
output_attentions (tp.Optional[bool]): Whether to output attention weights (default defined by config).
output_hidden_states (tp.Optional[bool]): Whether to output hidden states for all layers
(default defined by config).
past_key_values (tp.Optional[TransformerCache | RaggedPagesCache]):
Precomputed key/value states for caching.
cache_metadata (tp.Optional[TransformerMetadata | RaggedPagesMetadata]):
Metadata for paged attention (optional).
Returns:
BaseModelOutput: The model output, either as a `BaseModelOutput` object or a tuple.
Raises:
ValueError: If both `input_ids` and `inputs_embeds` are provided or neither is provided.
"""
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"))
sequence_length = inputs_embeds.shape[1]
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
assert sequence_length <= self.config.max_position_embeddings, (
f"Maximum Position Embedding Reached ! "
f"(Excepted <= {self.config.max_position_embeddings} got {sequence_length})"
)
mask_info = MaskInfo.dynamic_init(
mask_info=mask_info,
input_ids=input_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
)
if position_ids is None:
position_ids = mask_info.q_position_ids
hidden_states = 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.layers))
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
for idx, block in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states=hidden_states,
mask_info=mask_info,
position_ids=position_ids,
mode=mode,
cache_view=past_key_values.views[idx],
cache_metadata=cache_metadata,
output_attentions=output_attentions,
frequencies=self.frequencies,
)
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.norm(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] def get_encoder(self):
"""
Returns the encoder part of the model's graph definition.
Decoder-Only models don't have an encoder.
"""
raise NotImplementedError("This is a decoder-only model and does not have an encoder.")
[docs] def get_decoder(self):
"""
Returns the decoder part of the model's graph definition.
"""
return self
[docs] def get_lm_head(self):
"""
Returns the language model head of the module.
Base Models don't have a Language Model Head.
"""
raise NotImplementedError("The base model does not have a language model head.")
[docs] def get_embedding(self):
"""
Returns the embedding layer of the module.
"""
return self.embed_tokens
[docs]@register_module(TaskType.CAUSAL_LM, config=StableLmConfig, model_type="stablelm")
class StableLmForCausalLM(BaseCausalLMModule[StableLmModel, StableLmConfig]):
"""StableLM model with a Causal Language Modeling (CLM) head."""
_task_type = TaskType.CAUSAL_LM
_model_type = "stablelm"
_config_class = StableLmConfig
def __init__(
self,
config: StableLmConfig,
dtype: jnp.dtype = jnp.bfloat16,
param_dtype: jnp.dtype = jnp.bfloat16,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
base_model_class=StableLmModel,
base_model_name="model",
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
lm_head_bias=False,
)
def __call__(
self,
input_ids: Int[Array, "batch seq_len"] | None = None,
inputs_embeds: Float[Array, "batch seq_len hidden_dim"] | None = None,
attention_mask: Bool[Array, "batch seq_len"] | None = None,
mask_info: MaskInfo | None = None,
position_ids: Int[Array, "batch seq_len"] | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
mode: common_types.RUNTIME_MODE_TYPES | None = None, # type:ignore
past_key_values: TransformerCache | RaggedPagesCache | None = None,
cache_metadata: TransformerMetadata | RaggedPagesMetadata | None = None,
apply_lm_head: bool = True,
) -> CausalLMOutput:
"""Forward pass of the StableLM model for Causal Language Modeling.
Args:
input_ids (tp.Optional[chex.Array]): Input token IDs (batch, seq_len).
Mutually exclusive with `inputs_embeds`.
inputs_embeds (tp.Optional[chex.Array]): Input embeddings (batch, seq_len, hidden_size).
Mutually exclusive with `input_ids`.
attention_mask (tp.Optional[chex.Array]): Attention mask (batch, seq_len). Usually used for padding tokens.
position_ids (tp.Optional[chex.Array]): Position IDs (batch, seq_len). If None, automatically generated.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
output_attentions (tp.Optional[bool]): Whether to output attention weights (default defined by config).
output_hidden_states (tp.Optional[bool]): Whether to output hidden states for all
layers (default defined by config).
past_key_values (tp.Optional[TransformerCache | RaggedPagesCache]):
Precomputed key/value states for caching.
cache_metadata (tp.Optional[TransformerMetadata | RaggedPagesMetadata]):
Metadata for paged attention (optional).
Returns:
CausalLMOutput: The model output, including logits, hidden states, and attentions.
"""
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
mask_info=mask_info,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
inputs_embeds=inputs_embeds,
)
hidden_states = outputs.last_hidden_state
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
lm_logits = None
if apply_lm_head:
lm_logits = self.apply_lm_head(hidden_states)
return CausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
last_hidden_state=outputs.last_hidden_state,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)
[docs] def get_encoder(self):
"""
Returns the encoder part of the model's graph definition.
Decoder-Only models don't have an encoder.
"""
raise NotImplementedError("This is a decoder-only model and does not have an encoder.")
[docs] def get_decoder(self):
"""
Returns the decoder part of the model's graph definition.
"""
return self.model.get_decoder()
[docs] def get_lm_head(self):
"""
Returns the language model head of the module.
"""
return self.lm_head
[docs] def get_embedding(self):
"""
Returns the embedding layer of the module.
"""
return self.model.get_embedding()