# 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 functools
import typing as tp
import chex
import jax
import jax.numpy as jnp
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 (
BaseModelOutput,
CausalLMOutput,
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 .olmo2_configuration import Olmo2Config
class Olmo2MLP(nn.Module):
"""OLMo-2 MLP module.
This module implements the feed-forward network (MLP) used in the OLMo-2 model.
It consists of gate, up, and down projections with a SiLU activation.
Attributes:
config (Olmo2Config): 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.
gate_proj (ParallelLinear): Linear layer for the gate projection.
down_proj (ParallelLinear): Linear layer for the down projection.
up_proj (ParallelLinear): Linear layer for the up projection.
act_fn (callable): Activation function (SiLU).
"""
def __init__(
self,
config: Olmo2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the Olmo2MLP module.
Args:
config (Olmo2Config): The configuration object for the OLMo-2 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.
"""
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
linear_class = functools.partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.gate_proj = linear_class(
config.hidden_size,
config.intermediate_size,
rngs=rngs,
)
self.down_proj = linear_class(
config.intermediate_size,
config.hidden_size,
rngs=rngs,
)
self.up_proj = linear_class(
config.hidden_size,
config.intermediate_size,
rngs=rngs,
)
self.act_fn = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_states: jnp.ndarray) -> jnp.ndarray:
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
hidden_states = self.down_proj(
self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states)
)
return hidden_states
class Olmo2Attention(AttentionModule):
"""OLMo-2 Attention module.
This module implements the multi-head attention mechanism with rotary position embeddings
and Grouped Query Attention (GQA) used in the OLMo-2 model. It includes RMSNorm applied
to query and key projections before the attention calculation.
Attributes:
config (Olmo2Config): 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.
hidden_size (int): Dimensionality of the hidden states.
num_heads (int): Number of attention heads.
head_dim (int): Dimensionality of each attention head.
num_key_value_groups (int): Number of query head groups for each key/value head.
q_proj (ParallelLinear): Linear layer for query projection.
k_proj (ParallelLinear): Linear layer for key projection.
v_proj (ParallelLinear): Linear layer for value projection.
o_proj (ParallelLinear): Linear layer for the output projection.
q_norm (RMSNorm): RMS Normalization applied to the query projection.
k_norm (RMSNorm): RMS Normalization applied to the key projection.
attention_performer (FlexibleAttentionModule): Module to perform the core attention computation.
rotary (RoPE): Rotary position embedding module.
"""
def __init__(
self,
config: Olmo2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the Olmo2Attention module.
Args:
config (Olmo2Config): The configuration object for the OLMo-2 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.
"""
super().__init__(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 = self.config.num_attention_heads
self.head_dim = self.config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.config.num_key_value_heads
if self.num_key_value_groups == 1:
assert self.num_heads == self.config.num_key_value_heads
linear_class = functools.partial(
ParallelLinear,
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.q_proj = linear_class(
config.hidden_size,
config.num_attention_heads * self.head_dim,
rngs=rngs,
)
self.k_proj = linear_class(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.v_proj = linear_class(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.o_proj = linear_class(
config.num_attention_heads * self.head_dim,
config.hidden_size,
rngs=rngs,
)
self.q_norm = RMSNorm(
self.num_heads * self.head_dim,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.k_norm = RMSNorm(
self.config.num_key_value_heads * self.head_dim,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.attention_performer = FlexibleAttentionModule(
dropout_prob=config.attention_dropout,
base_config=config,
softmax_scale=self.head_dim**-0.5,
)
self.rotary = self.config.get_basic_rope(
self.dtype,
head_size=self.config.hidden_size // self.config.num_attention_heads,
rotary_dim=self.config.hidden_size // self.config.num_attention_heads,
base=self.config.rope_theta,
)
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,
):
"""Forward pass of the Olmo2Attention module.
Args:
hidden_states (chex.Array): Input hidden states. Shape: (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array): Mask to apply on the attention scores. Shape: (batch_size, 1, query_length, key_length).
position_ids (chex.Array): Position indices for the tokens. Shape: (batch_size, sequence_length).
causal_mask (tp.Optional[chex.Array | bool]): Causal mask for ensuring autoregressive behavior.
cache_view (tp.Optional[TransformerCacheView | PagedAttentionCacheView]): Cache view for attention KVs.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
output_attentions (bool): Whether to return attention weights. Default is False.
fcm_mask (tp.Optional[chex.Array]): Flash Chunking Mask (FCM) for attention.
frequencies (tp.Optional[chex.Array]): Precomputed rotary frequency embeddings.
Returns:
tp.Union[tp.Tuple[chex.Array, chex.Array], tp.Tuple[chex.Array]]:
A tuple containing the attention output hidden states. If `output_attentions` is True,
it also includes the attention weights.
"""
batch_size, sequence_length = hidden_states.shape[:2]
query_states, key_states, value_states = (
self.q_norm(self.q_proj(hidden_states)),
self.k_norm(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(
query=query_states,
key=key_states,
positions=position_ids,
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 Olmo2DecoderLayer(nn.Module):
"""OLMo-2 Transformer Decoder Layer.
This module represents a single decoder layer in the OLMo-2 model,
combining self-attention and MLP sub-layers with residual connections
and layer normalization applied before each sub-layer.
Attributes:
config (Olmo2Config): 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.
self_attn (Olmo2Attention): The self-attention module.
mlp (Olmo2MLP): The feed-forward (MLP) module.
input_layernorm (RMSNorm): Layer normalization before the attention layer.
post_attention_layernorm (RMSNorm): Layer normalization before the MLP layer.
"""
def __init__(
self,
config: Olmo2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the Olmo2DecoderLayer.
Args:
config (Olmo2Config): The configuration object for the OLMo-2 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.
"""
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
attn_block = Olmo2Attention
mlp_block = Olmo2MLP
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.mlp = mlp_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.post_attention_layernorm = RMSNorm(
config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.post_feedforward_layernorm = RMSNorm(
config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
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,
):
"""Forward pass of the Olmo2DecoderLayer module.
Args:
hidden_states (chex.Array): Input hidden states. Shape: (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array): Mask to apply on the attention scores. Shape: (batch_size, 1, query_length, key_length).
position_ids (chex.Array): Position indices for the tokens. Shape: (batch_size, sequence_length).
causal_mask (tp.Optional[chex.Array | bool]): Causal mask for ensuring autoregressive behavior.
cache_view (tp.Optional[TransformerCacheView | PagedAttentionCacheView]): Cache view for attention KVs.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
output_attentions (bool): Whether to return attention weights. Default is False.
fcm_mask (tp.Optional[chex.Array]): Flash Chunking Mask (FCM) for attention.
frequencies (tp.Optional[chex.Array]): Precomputed rotary frequency embeddings.
Returns:
tp.Tuple[chex.Array, tp.Optional[chex.Array]]:
A tuple containing the output hidden states and optionally the attention weights.
"""
residual = hidden_states
attention_output = self.self_attn(
hidden_states,
attention_mask,
position_ids,
causal_mask,
cache_view,
cache_metadata,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
hidden_states = attention_output[0]
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = residual + hidden_states
residual = hidden_states
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.post_feedforward_layernorm(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attention_output[1],)
return outputs
[docs]@register_module(
TaskType.BASE_MODULE,
config=Olmo2Config,
model_type="olmo2",
)
class Olmo2Model(EasyDeLBaseModule):
"""The base OLMo-2 model transformer.
This class represents the core transformer architecture of the OLMo-2 model,
consisting of an embedding layer, multiple Olmo2DecoderLayer layers,
and a final RMS normalization layer.
Attributes:
config (Olmo2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
rngs (nn.Rngs): Random number generators.
embed_tokens (nn.Embed): Embedding layer for input tokens.
layers (tp.List[Olmo2DecoderLayer]): List of decoder layers.
norm (RMSNorm): Final layer normalization.
gradient_checkpointing (EasyDeLGradientCheckPointers): Gradient checkpointing configuration.
"""
def __init__(
self,
config: Olmo2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the Olmo2Model.
Args:
config (Olmo2Config): The configuration object for the OLMo-2 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.
"""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.embed_tokens = nn.Embed(
config.vocab_size,
config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layers = [
Olmo2DecoderLayer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for i in range(self.config.num_hidden_layers)
]
self.norm = RMSNorm(
config.hidden_size,
eps=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,
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[BaseModelOutput, tp.Tuple]:
"""Forward pass of the Olmo2Model.
Args:
input_ids (tp.Optional[chex.Array]): Input token IDs. Shape: (batch_size, sequence_length).
inputs_embeds (tp.Optional[chex.Array]): Input embeddings. Shape: (batch_size, sequence_length, hidden_size).
Either `input_ids` or `inputs_embeds` must be provided.
attention_mask (tp.Optional[chex.Array]): Mask to avoid performing attention on padding token indices.
Shape: (batch_size, sequence_length).
position_ids (tp.Optional[chex.Array]): Position indices for the tokens.
Shape: (batch_size, sequence_length).
segment_ids (tp.Optional[chex.Array]): Segment IDs (unused).
past_key_values (tp.Optional[TransformerCache | PagedAttentionCache]): Precomputed key/value states for attention.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention.
output_attentions (tp.Optional[bool]): Whether to return attention weights. Defaults to `config.output_attentions`.
output_hidden_states (tp.Optional[bool]): Whether to return hidden states for all layers.
Defaults to `config.output_hidden_states`.
return_dict (bool): Whether to return a `BaseModelOutput` object or a tuple.
Returns:
tp.Union[BaseModelOutput, tp.Tuple]: The model's output. If `return_dict` is True,
returns a `BaseModelOutput` object containing `last_hidden_state`, `hidden_states` (optional),
and `attentions` (optional). Otherwise, returns a tuple with these elements.
Raises:
ValueError: If neither `input_ids` nor `inputs_embeds` 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"))
batch_size, sequence_length, _ = inputs_embeds.shape
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 ! (Excepted <= {self.config.max_position_embeddings} got {sequence_length})"
)
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, block in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
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 = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions, past_key_values)
else:
outputs = (hidden_states, all_attentions, past_key_values)
if not return_dict:
return tuple(v for v in outputs if v is not None)
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=Olmo2Config,
model_type="olmo2",
)
class Olmo2ForCausalLM(EasyDeLBaseModule):
"""OLMo-2 model with a Causal Language Modeling head.
This model consists of the base OLMo-2 transformer (`Olmo2Model`) followed by a
linear layer (`lm_head`) that projects the transformer's output hidden states
to the vocabulary size, producing logits for next token prediction.
Attributes:
config (Olmo2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
rngs (nn.Rngs): Random number generators.
model (Olmo2Model): The core OLMo-2 transformer model.
lm_head (ParallelLinear): The linear layer for projecting hidden states to vocabulary logits.
"""
def __init__(
self,
config: Olmo2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the Olmo2ForCausalLM model.
Args:
config (Olmo2Config): The configuration object for the OLMo-2 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.
"""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.model = Olmo2Model(
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,
use_bias=False,
kernel_init=jax.nn.initializers.normal(stddev=config.initializer_range),
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
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[CausalLMOutput, tp.Tuple]:
"""Forward pass of the Olmo2ForCausalLM model.
Args:
input_ids (tp.Optional[chex.Array]): Input token IDs. Shape: (batch_size, sequence_length).
inputs_embeds (tp.Optional[chex.Array]): Input embeddings. Shape: (batch_size, sequence_length, hidden_size).
Either `input_ids` or `inputs_embeds` must be provided.
attention_mask (tp.Optional[chex.Array]): Mask to avoid performing attention on padding token indices.
Shape: (batch_size, sequence_length).
position_ids (tp.Optional[chex.Array]): Position indices for the tokens.
Shape: (batch_size, sequence_length).
segment_ids (tp.Optional[chex.Array]): Segment IDs (unused).
past_key_values (tp.Optional[TransformerCache | PagedAttentionCache]): Precomputed key/value states for attention.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention.
output_attentions (tp.Optional[bool]): Whether to return attention weights. Defaults to `config.output_attentions`.
output_hidden_states (tp.Optional[bool]): Whether to return hidden states for all layers.
Defaults to `config.output_hidden_states`.
return_dict (bool): Whether to return a `CausalLMOutput` object or a tuple.
Returns:
tp.Union[CausalLMOutput, tp.Tuple]: The model's output. If `return_dict` is True,
returns a `CausalLMOutput` object containing `logits`, `hidden_states` (optional),
and `attentions` (optional). Otherwise, returns a tuple with these elements.
"""
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,
inputs_embeds=inputs_embeds,
segment_ids=segment_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
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)
if not return_dict:
return (lm_logits,) + outputs[1:]
return CausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)
[docs]@register_module(
TaskType.SEQUENCE_CLASSIFICATION,
config=Olmo2Config,
model_type="olmo2",
)
class Olmo2ForSequenceClassification(EasyDeLBaseModule):
"""OLMo-2 model with a Sequence Classification head.
This model consists of the base OLMo-2 transformer (`Olmo2Model`) followed by a
linear layer (`score`) that projects the transformer's output hidden states
(typically the hidden state of the last token) to the number of classes for classification.
Attributes:
config (Olmo2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations.
rngs (nn.Rngs): Random number generators.
model (Olmo2Model): The core OLMo-2 transformer model.
score (ParallelLinear): The linear layer for classification.
"""
def __init__(
self,
config: Olmo2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the Olmo2ForSequenceClassification model.
Args:
config (Olmo2Config): The configuration object for the OLMo-2 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.
Raises:
AssertionError: If `config.num_labels` is not defined.
"""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.model = Olmo2Model(
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(
self.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=self.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 of the Olmo2ForSequenceClassification model.
Args:
input_ids (tp.Optional[chex.Array]): Input token IDs. Shape: (batch_size, sequence_length).
inputs_embeds (tp.Optional[chex.Array]): Input embeddings. Shape: (batch_size, sequence_length, hidden_size).
Either `input_ids` or `inputs_embeds` must be provided.
attention_mask (tp.Optional[chex.Array]): Mask to avoid performing attention on padding token indices.
Shape: (batch_size, sequence_length).
position_ids (tp.Optional[chex.Array]): Position indices for the tokens.
Shape: (batch_size, sequence_length).
segment_ids (tp.Optional[chex.Array]): Segment IDs (unused).
past_key_values (tp.Optional[TransformerCache | PagedAttentionCache]): Precomputed key/value states for attention.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention.
output_attentions (tp.Optional[bool]): Whether to return attention weights. Defaults to `config.output_attentions`.
output_hidden_states (tp.Optional[bool]): Whether to return hidden states for all layers.
Defaults to `config.output_hidden_states`.
return_dict (bool): Whether to return a `SequenceClassifierOutput` object or a tuple.
Returns:
tp.Union[SequenceClassifierOutput, tp.Tuple]: The model's output. If `return_dict` is True,
returns a `SequenceClassifierOutput` object containing `logits`, `hidden_states` (optional),
and `attentions` (optional). Otherwise, returns a tuple with these elements.
Raises:
ValueError: If `config.pad_token_id` is None and `batch_size > 1`.
"""
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]
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return output
return SequenceClassifierOutput(
logits=pooled_logits,
past_key_values=past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)