# 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 einops import rearrange
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 .internlm2_configuration import InternLM2Config
class InternLM2Attention(AttentionModule):
"""InternLM2 Attention module.
Attributes:
config (InternLM2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation. Default is jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Default is jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Default is None.
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_heads (int): Number of key/value heads (for GQA).
num_key_value_groups (int): Number of query head groups for each key/value head.
max_position_embeddings (int): Maximum sequence length supported.
wqkv (ParallelLinear): Linear layer for query, key, and value projections.
wo (ParallelLinear): Linear layer for the output projection.
attention_performer (FlexibleAttentionModule): Module to perform the core attention computation.
rotary (RoPE): Rotary position embedding module.
"""
def __init__(
self,
config: InternLM2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the InternLM2Attention module.
Args:
config (InternLM2Config): The configuration object for the InternLM2 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 = 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
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.wqkv = ParallelLinear(
config.hidden_size,
(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
dtype=dtype,
param_dtype=param_dtype,
use_bias=config.bias,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.wo = ParallelLinear(
self.num_heads * self.head_dim,
config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
use_bias=config.bias,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.attention_performer = FlexibleAttentionModule(
base_config=config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=0.0,
)
self.rotary = self.config.get_basic_rope(
dtype=self.dtype,
head_size=self.head_dim,
rotary_dim=self.head_dim,
base=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 attention 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): Causal mask for ensuring autoregressive behavior.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
deterministic (bool): If True, disables dropout for deterministic behavior.
init_cache (bool): If True, initializes cache for caching keys and values.
output_attentions (bool): If True, outputs attention weights alongside the hidden states.
fcm_mask (tp.Optional[chex.Array]): fcm mask to be combined with attn mask and causal mask.
Returns:
tp.Tuple[chex.Array, chex.Array]: A tuple containing the attention output and the attention weights.
"""
qkv_states = rearrange(
self.wqkv(hidden_states),
"b q (h gs d) -> b q h gs d",
gs=2 + self.num_key_value_groups,
d=self.head_dim,
)
query_states = qkv_states[..., : self.num_key_value_groups, :]
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
key_states = qkv_states[..., -2, :]
value_states = qkv_states[..., -1, :]
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.wo(
self.shard_attention_prod(self._merge_heads(attentions.attention_outputs))
)
outputs = (
(attn_output, attentions.attention_weights)
if output_attentions
else (attn_output,)
)
return outputs
class InternLM2MLP(nn.Module):
"""InternLM2 MLP module.
Attributes:
config (InternLM2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation. Default is jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Default is jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Default is None.
w1 (ParallelLinear): First linear transformation (gate projection).
w3 (ParallelLinear): Second linear transformation (up projection).
w2 (ParallelLinear): Third linear transformation (down projection).
act_fn (callable): Activation function (e.g., SiLU).
"""
def __init__(
self,
config: InternLM2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the InternLM2MLP module.
Args:
config (InternLM2Config): The configuration object for the InternLM2 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 = functools.partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=self.precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.w1 = linear(config.hidden_size, config.intermediate_size, rngs=rngs)
self.w3 = linear(config.hidden_size, config.intermediate_size, rngs=rngs)
self.w2 = linear(config.intermediate_size, config.hidden_size, rngs=rngs)
self.act_fn = ACT2FN[config.hidden_act]
def __call__(self, hidden_states: jnp.ndarray) -> jnp.ndarray:
"""Forward pass of the MLP module.
Args:
hidden_states (jnp.ndarray): Input hidden states.
Returns:
jnp.ndarray: Output hidden states after MLP transformation.
"""
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
return self.w2(self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states))
class InternLM2Block(nn.Module):
"""InternLM2 Transformer Block.
This module combines the self-attention layer and the MLP layer with residual connections
and layer normalization.
Attributes:
config (InternLM2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation. Default is jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Default is jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Default is None.
attention (InternLM2Attention): The self-attention module.
feed_forward (InternLM2MLP): The feed-forward (MLP) module.
attention_norm (RMSNorm): Layer normalization before the attention layer.
ffn_norm (RMSNorm): Layer normalization before the MLP layer.
"""
def __init__(
self,
config: InternLM2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the InternLM2Block module.
Args:
config (InternLM2Config): The configuration object for the InternLM2 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 = InternLM2Attention
mlp_block = InternLM2MLP
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
)
self.attention = attn_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.feed_forward = mlp_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.attention_norm = RMSNorm(
dim=config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.ffn_norm = RMSNorm(
dim=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 InternLM2Block.
Applies self-attention, followed by a residual connection and layer normalization,
and then applies the MLP layer, followed by another residual connection and layer normalization.
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 (tp.Optional[chex.Array | bool]): Causal mask for 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 (unused in standard InternLM2).
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 output hidden states. If `output_attentions` is True,
it also includes the attention weights.
"""
attn_outputs = self.attention(
self.attention_norm(hidden_states),
attention_mask,
position_ids,
causal_mask,
cache_view,
cache_metadata,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
attn_output = attn_outputs[0]
hidden_states = hidden_states + attn_output
feed_forward_input = self.ffn_norm(hidden_states)
if self.config.use_scan_mlp:
feed_forward_hidden_states = block_wise_ffn(
self.feed_forward, feed_forward_input, self.config.scan_mlp_chunk_size
)
else:
feed_forward_hidden_states = self.feed_forward(feed_forward_input)
hidden_states = hidden_states + feed_forward_hidden_states
return (hidden_states,) + attn_outputs[1:]
[docs]@register_module(
TaskType.BASE_MODULE,
config=InternLM2Config,
model_type="internlm2",
)
class InternLM2Model(EasyDeLBaseModule):
"""The base InternLM2 model transformer.
This class represents the core transformer architecture of the InternLM2 model,
consisting of embedding layers, multiple transformer blocks, and a final
layer normalization.
Attributes:
config (InternLM2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation. Default is jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Default is jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Default is None.
embed_tokens (nn.Embed): Embedding layer for input tokens.
layers (tp.Sequence[InternLM2Block]): Sequence of transformer blocks.
norm (RMSNorm): Final layer normalization.
gradient_checkpointing (EasyDeLGradientCheckPointers): Gradient checkpointing configuration.
scan_layers (bool): Whether to use JAX scan for layer processing.
blocks_class (InternLM2Block): The class used for the transformer blocks.
"""
def __init__(
self,
config: InternLM2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the InternLM2Model.
Args:
config (InternLM2Config): The configuration object for the InternLM2 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.tok_embeddings = 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 = [
InternLM2Block(
config=config,
rngs=rngs,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
)
for i in range(config.num_hidden_layers)
]
self.norm = RMSNorm(
dim=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,
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[BaseModelOutput, tp.Tuple]:
"""Forward pass of the InternLM2Model.
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).
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`.
past_key_values (tp.Optional[TransformerCache | PagedAttentionCache]): Precomputed key/value states for attention.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention.
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 inputs_embeds is None and input_ids is not None:
inputs_embeds = self.tok_embeddings(input_ids.astype("i4"))
else:
raise ValueError("you should specify inputs_embeds or input_ids one of them")
batch_size, sequence_length = inputs_embeds.shape[:2]
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),
)
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.ndim == 2:
attention_mask = jnp.expand_dims(attention_mask, (1, 2))
hidden_states = inputs_embeds
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.layers))
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
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,
causal_mask=self.causal_mask,
cache_view=past_key_values.views[idx],
cache_metadata=cache_metadata,
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)
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=InternLM2Config,
model_type="internlm2",
)
class InternLM2ForCausalLM(EasyDeLBaseModule):
"""InternLM2 model with a Causal Language Modeling head.
This model consists of the base InternLM2 transformer (`InternLM2Model`) 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 (InternLM2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation. Default is jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Default is jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Default is None.
rngs (nn.Rngs): Random number generators.
module (InternLM2Model): The core InternLM2 transformer model.
lm_head (ParallelLinear): The linear layer for projecting hidden states to vocabulary logits.
"""
def __init__(
self,
config: InternLM2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the InternLM2ForCausalLM model.
Args:
config (InternLM2Config): The configuration object for the InternLM2 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 = InternLM2Model(
config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.output = ParallelLinear(
config.hidden_size,
config.vocab_size,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(stddev=config.initializer_range),
precision=precision,
**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,
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[CausalLMOutput, tp.Tuple]:
"""Forward pass of the InternLM2ForCausalLM 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).
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`.
past_key_values (tp.Optional[TransformerCache | PagedAttentionCache]): Precomputed key/value states for attention.
cache_metadata (tp.Optional[TransformerMetadata | PagedAttentionMetadata]): Metadata for paged attention.
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,
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[0]
if self.config.tie_word_embeddings:
lm_logits = jax.lax.dot_general(
hidden_states,
self.model.tok_embeddings.embedding.value.T,
(((hidden_states.ndim - 1), (0,)), ((), ())),
)
else:
lm_logits = self.output(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=InternLM2Config,
model_type="internlm2",
)
class InternLM2ForSequenceClassification(EasyDeLBaseModule):
"""InternLM2 model with a Sequence Classification head.
This model consists of the base InternLM2 transformer (`InternLM2Model`) followed by a
linear layer (`score`) that projects the transformer's output hidden states
(typically the hidden state of the first token) to the number of classes for classification.
Attributes:
config (InternLM2Config): Configuration object for the model.
dtype (jnp.dtype): Data type for computation. Default is jnp.float32.
param_dtype (jnp.dtype): Data type for parameters. Default is jnp.float32.
precision (jax.lax.PrecisionLike): Precision setting for JAX operations. Default is None.
rngs (nn.Rngs): Random number generators.
module (InternLM2Model): The core InternLM2 transformer model.
score (ParallelLinear): The linear layer for classification.
"""
def __init__(
self,
config: InternLM2Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the InternLM2ForSequenceClassification model.
Args:
config (InternLM2Config): The configuration object for the InternLM2 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 = InternLM2Model(
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 InternLM2ForSequenceClassification 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.
"""
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,
)