# 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
import jax.numpy as jnp
from eformer import common_types
from eformer.escale import apply_logical_sharding
from flax import nnx as nn
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.modeling_outputs import (
AttentionLayerOutput,
BaseModelOutput,
CausalLMOutput,
DecoderLayerOutput,
SequenceClassifierOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
block_wise_ffn,
get_dot_general_by_bits,
)
from easydel.layers.attention import AttentionModule, FlexibleAttentionModule
from easydel.layers.caching import (
PagedAttentionCache,
PagedAttentionCacheView,
PagedAttentionMetadata,
TransformerCache,
TransformerCacheView,
TransformerMetadata,
)
from easydel.layers.linear import ParallelLinear
from easydel.layers.norms import RMSNorm
from .llama_configuration import LlamaConfig
[docs]class LlamaMLP(nn.Module):
def __init__(
self,
config: LlamaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
linear_class = partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=self.config.mlp_bias,
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)
self.down_proj = linear_class(config.intermediate_size, config.hidden_size)
self.up_proj = linear_class(config.hidden_size, config.intermediate_size)
self.dropout = nn.Dropout(rate=self.config.resid_pdrop, rngs=rngs)
self.act_fn = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_states: jnp.ndarray) -> jnp.ndarray:
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
gate = self.act_fn(self.gate_proj(hidden_states))
up = self.up_proj(hidden_states)
hidden_states = self.down_proj(gate * up)
hidden_states = self.dropout(hidden_states)
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
return hidden_states
[docs]class LlamaAttention(AttentionModule):
def __init__(
self,
config: LlamaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.hidden_size = config.hidden_size
head_dim = config.hidden_size // config.num_attention_heads
self.head_dim = getattr(config, "head_dim", head_dim)
self.num_key_value_groups = (
self.config.num_attention_heads // self.config.num_key_value_heads
)
if self.num_key_value_groups == 1:
assert self.config.num_attention_heads == self.config.num_key_value_heads
linear_class = partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=config.attention_bias,
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.rotary = self.config.get_basic_rope(
self.dtype,
self.head_dim,
self.head_dim,
True,
)
self.attention_performer = FlexibleAttentionModule(
base_config=self.config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=self.config.attention_dropout,
)
self.resid_dropout = nn.Dropout(rate=config.resid_pdrop, rngs=rngs)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array | bool],
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
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, chex.Array]:
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),
)
qshape = (
batch_size,
sequence_length,
self.config.num_attention_heads,
self.head_dim,
)
kv_shape = (
batch_size,
sequence_length,
self.config.num_key_value_heads,
self.head_dim,
)
query_states = query_states.reshape(qshape)
key_states = key_states.reshape(kv_shape)
value_states = value_states.reshape(kv_shape)
(
query_states,
key_states,
value_states,
) = self.apply_qkv_shardings(query_states, key_states, value_states)
query_states, key_states = self.rotary(
positions=position_ids,
query=query_states,
key=key_states,
frequencies=frequencies,
)
(
key_states,
value_states,
attention_mask,
init_attention_bias,
cache_view,
) = self.concatenate(
query=query_states,
key=key_states,
cache_view=cache_view,
cache_metadata=cache_metadata,
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,
mode=mode,
bias=None,
cache_metadata=cache_metadata,
cache_view=cache_view,
init_bias=init_attention_bias,
attention_mask=attention_mask,
segment_ids=segment_ids,
causal=True,
dropout_rng=self.rngs.params(),
)
attn_output = self.resid_dropout(
self.o_proj(
self.shard_attention_prod(
attn_output=self._merge_heads(attentions.attention_outputs)
)
),
)
return AttentionLayerOutput(
attention_output=attn_output,
attention_weight=attentions.attention_weights if output_attentions else None,
cache_view=cache_view,
)
[docs]class LlamaDecoderLayer(nn.Module):
def __init__(
self,
config: LlamaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
attn_block = LlamaAttention
mlp_block = LlamaMLP
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.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,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array | bool],
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
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,
):
attn_outputs = self.self_attn(
self.input_layernorm(hidden_states),
attention_mask,
position_ids,
causal_mask,
mode,
cache_view,
cache_metadata,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
hidden_states = hidden_states + attn_outputs.attention_output
feed_forward_input = self.post_attention_layernorm(hidden_states)
if self.config.use_scan_mlp:
feed_forward_hidden_states = block_wise_ffn(
self.mlp,
feed_forward_input,
self.config.scan_mlp_chunk_size,
)
else:
feed_forward_hidden_states = self.mlp(feed_forward_input)
hidden_states = hidden_states + feed_forward_hidden_states
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=LlamaConfig,
model_type="llama",
)
class LlamaModel(EasyDeLBaseModule):
"""Llama model implementation.
This implements the Llama language model architecture, utilizing transformer blocks
with RMSNorm, rotary position embeddings, and a specific attention mechanism.
Attributes:
config (LlamaConfig): Configuration for the model.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision: Precision setting for JAX operations.
"""
def __init__(
self,
config: LlamaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.embed_tokens = nn.Embed(
num_embeddings=self.config.vocab_size,
features=self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
rngs=rngs,
)
self.dropout = nn.Dropout(rate=self.config.embd_pdrop, rngs=rngs)
self.layers = [
LlamaDecoderLayer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for _ in range(self.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,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> BaseModelOutput:
"""Forward pass through the Llama model.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key/value states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[BaseModelOutput, Tuple]: Model outputs (last hidden state, optional hidden states, optional attentions)
"""
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 = self.dropout(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,
attention_mask=attention_mask,
position_ids=position_ids,
mode=mode,
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.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]@register_module(
TaskType.CAUSAL_LM,
config=LlamaConfig,
model_type="llama",
)
class LlamaForCausalLM(EasyDeLBaseModule):
"""Llama model with a language modeling head for causal language modeling tasks.
This model is a transformer-based language model with causal attention masks
applied to perform autoregressive language generation.
Attributes:
config (LlamaConfig): Configuration for the model.
dtype (jnp.dtype): Data type for computations (default is jnp.float32).
param_dtype (jnp.dtype): Data type for parameters (default is jnp.float32).
precision (tp.Optional[tp.Union[str, jax.lax.Precision]]): Precision setting for JAX operations.
"""
def __init__(
self,
config: LlamaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.model = LlamaModel(
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=self.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,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> CausalLMOutput:
"""Forward pass through the Llama model for causal language modeling.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key/value states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[CausalLMOutput, Tuple]: Model outputs (logits, optional hidden states, optional attentions)
"""
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
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,
segment_ids=segment_ids,
)
hidden_states = outputs.last_hidden_state
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
if self.config.tie_word_embeddings:
lm_logits = jax.lax.dot_general(
hidden_states,
self.model.embed_tokens.embedding.value.T,
(((hidden_states.ndim - 1), (0,)), ((), ())),
)
else:
lm_logits = self.lm_head(hidden_states)
return CausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)
[docs]@register_module(
TaskType.SEQUENCE_CLASSIFICATION,
config=LlamaConfig,
model_type="llama",
)
class LlamaForSequenceClassification(EasyDeLBaseModule):
"""Llama model for sequence classification tasks.
This class extends the base Llama model by adding a linear classification head
to perform sequence classification tasks such as sentiment analysis or text classification.
Attributes:
config (LlamaConfig): Configuration for the model.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision: Precision setting for JAX operations.
"""
def __init__(
self,
config: LlamaConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.model = LlamaModel(
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,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> SequenceClassifierOutput:
"""Forward pass through the Llama model for sequence classification.
This method processes input sequences through the Llama model and applies
a classification head to the output.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key/value states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[SequenceClassifierOutput, Tuple]: Classification outputs including logits and optional model outputs
"""
transformer_outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
inputs_embeds=inputs_embeds,
segment_ids=segment_ids,
)
hidden_states = transformer_outputs.last_hidden_state
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]
return SequenceClassifierOutput(
logits=pooled_logits,
past_key_values=past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)