# 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 cached_property
import chex
import jax
from flax import nnx as nn
from jax import numpy as jnp
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.modeling_outputs import (
FlaxBaseModelOutput,
FlaxCausalLMOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
block_wise_ffn,
control_mlp_sharding,
get_dot_general_by_bits,
)
from easydel.layers.attention import FlaxAttentionModule, FlexibleAttentionModule
from easydel.layers.caching import TransformerCache, TransformerCacheView
from easydel.layers.norms import RMSNorm
from easydel.modules.openelm.openelm_configuration import (
OpenELMConfig as OpenELMConfig,
)
from easydel.modules.openelm.openelm_configuration import (
make_divisible,
)
class OpenELMMultiHeadCausalAttention(FlaxAttentionModule):
def __init__(
self,
config: OpenELMConfig,
layer_idx: int,
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.layer_idx = layer_idx
head_dim = config.head_dim
q_heads = config.num_query_heads[layer_idx]
k_heads = config.num_kv_heads[layer_idx]
v_heads = config.num_kv_heads[layer_idx]
self.qkv_proj = nn.Linear(
config.model_dim,
(q_heads + k_heads + v_heads) * head_dim,
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),
)
if config.normalize_qk_projections:
self.q_norm = RMSNorm(
dim=config.head_dim,
dtype=self.dtype,
param_dtype=self.param_dtype,
eps=1e-6,
rngs=rngs,
)
self.k_norm = RMSNorm(
dim=config.head_dim,
dtype=self.dtype,
param_dtype=self.param_dtype,
eps=1e-6,
rngs=rngs,
)
else:
self.q_norm = None
self.k_norm = None
self.out_proj = nn.Linear(
q_heads * head_dim,
config.model_dim,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.head_dim = head_dim
self.attention_performer = FlexibleAttentionModule(
base_config=config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=0.0,
)
self.head_dim = config.head_dim
self.num_q_heads = q_heads
self.num_k_heads = k_heads
self.num_v_heads = v_heads
self.transformer_dim = config.model_dim
self.num_groups = self.num_q_heads // self.num_k_heads
self.rotary = self.config.get_basic_rope(
self.dtype,
head_size=self.config.head_dim,
rotary_dim=self.config.head_dim,
base=self.config.rope_freq_constant,
)
def _merge_heads(self, hidden_states):
"""
Merges the attention heads into a single hidden state tensor.
Args:
hidden_states (chex.Array): The hidden states with separate head dimensions.
Returns:
chex.Array: The hidden states with merged head dimensions.
"""
return hidden_states.reshape(
hidden_states.shape[:2] + (self.num_q_heads * self.head_dim,)
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: chex.Array,
cache_view: tp.Optional[TransformerCacheView] = 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.
"""
batch_size, sequence_length = hidden_states.shape[:2]
output_attentions = False
# [B, S, d] --> [B, S, (q_h + k_h + v_h) * h]
qkv = self.qkv_proj(hidden_states)
# [B, S, (q_h + k_h + v_h) * h] --> [B, S, (q_h + k_h + v_h), h]
qkv = qkv.reshape(
batch_size,
sequence_length,
self.num_q_heads + self.num_k_heads + self.num_v_heads,
self.head_dim,
)
# [B, S, (q_h + k_h + v_h), h] --> [B, (q_h + k_h + v_h), S, h]
qkv = qkv.transpose(0, 2, 1, 3)
# [B, (q_h + k_h + v_h), S, h] --> [B, q_h, S h], [B, k_h, S, h], [B, v_h, S, h]
query_states = qkv[
:,
: self.num_q_heads,
:,
:,
]
key_states = qkv[
:,
self.num_q_heads : self.num_k_heads + self.num_q_heads,
:,
:,
]
value_states = qkv[
:,
self.num_k_heads + self.num_q_heads :,
:,
:,
]
if self.q_norm is not None:
query_states = self.q_norm(query_states)
if self.k_norm is not None:
key_states = self.k_norm(key_states)
query_states, key_states, value_states = map(
lambda x: x.transpose(0, 2, 1, 3),
[query_states, key_states, value_states],
)
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,
init_bias=init_attention_bias,
attention_mask=attention_mask,
segment_ids=segment_ids,
causal=True,
dropout_rng=self.rngs.params(),
)
attn_output = self.out_proj(
self.shard_attention_prod(self._merge_heads(attentions.attention_outputs))
)
outputs = (
(attn_output, attentions.attention_weights)
if output_attentions
else (attn_output, None)
)
return outputs
class OpenELMFeedForwardNetwork(nn.Module):
def __init__(
self,
config: OpenELMConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.layer_idx = layer_idx
ffn_multiplier = config.ffn_multipliers[layer_idx]
intermediate_dim = int(
make_divisible(
ffn_multiplier * config.model_dim, # type:ignore
divisor=config.ffn_dim_divisor,
)
)
if config.ffn_with_glu:
# FFN with Gated linear unit, as described in https://arxiv.org/abs/2002.05202v1.
self.proj_1 = nn.Linear(
config.model_dim,
2 * intermediate_dim,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.proj_2 = nn.Linear(
intermediate_dim,
config.model_dim,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.ffn_with_glu = True
else:
self.proj_1 = nn.Linear(
config.model_dim,
intermediate_dim,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.proj_2 = nn.Linear(
intermediate_dim,
config.model_dim,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.ffn_with_glu = False
self.act = ACT2FN[config.activation_fn_name]
def __call__(self, hidden_states: chex.Array) -> chex.Array:
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
if self.ffn_with_glu:
y_12 = self.proj_1(hidden_states)
y_1, y_2 = jnp.split(y_12, 2, axis=-1)
return self.proj_2(self.act(y_1) * y_2)
else:
return self.proj_2(self.act(self.proj_1(hidden_states)))
class OpenELMDecoderLayer(nn.Module):
def __init__(
self,
config: OpenELMConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.layer_idx = layer_idx
attn_block = OpenELMMultiHeadCausalAttention
mlp_block = OpenELMFeedForwardNetwork
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
)
self.attn = attn_block(
config=config,
layer_idx=layer_idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.ffn = mlp_block(
config=config,
layer_idx=layer_idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.ffn_norm = RMSNorm(
self.config.model_dim,
dtype=dtype,
param_dtype=param_dtype,
eps=1e-6,
rngs=rngs,
)
self.attn_norm = RMSNorm(
self.config.model_dim,
dtype=dtype,
param_dtype=param_dtype,
eps=1e-6,
rngs=rngs,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: chex.Array,
cache_view: tp.Optional[TransformerCacheView] = 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 module block.
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.
"""
residual = hidden_states
hidden_states = self.attn_norm(hidden_states)
hidden_states, self_attn_weights = self.attn(
hidden_states,
attention_mask,
position_ids,
causal_mask,
cache_view,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.ffn_norm(hidden_states)
if self.config.use_scan_mlp:
feed_forward_hidden_states = block_wise_ffn(
self.ffn,
hidden_states,
self.config.scan_mlp_chunk_size,
)
else:
feed_forward_hidden_states = self.ffn(hidden_states)
hidden_states = residual + feed_forward_hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs # type:ignore
[docs]@register_module(
TaskType.BASE_MODULE,
config=OpenELMConfig,
model_type="openelm",
)
class OpenELMModel(EasyDeLBaseModule):
def __init__(
self,
config: OpenELMConfig,
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.token_embeddings = nn.Embed(
config.vocab_size,
config.model_dim,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layers = [
OpenELMDecoderLayer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
layer_idx=i,
rngs=rngs,
)
for i in range(self.config.num_transformer_layers)
]
self.norm = RMSNorm(
config.model_dim,
dtype=self.dtype,
param_dtype=self.param_dtype,
eps=1e-6,
rngs=rngs,
)
if config.share_input_output_layers:
self.classifier = None
else:
self.classifier = nn.Linear(
config.model_dim,
config.vocab_size,
use_bias=False,
rngs=rngs,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
)
self.num_transformer_layers = config.num_transformer_layers
@cached_property
def frequencies(self):
return self.config.get_basic_frequencies(
head_size=self.config.head_dim,
rotary_dim=self.config.head_dim,
base=self.config.rope_freq_constant,
)
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] = None,
return_dict: bool = True,
) -> tp.Union[FlaxBaseModelOutput, tp.Tuple]:
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if inputs_embeds is None and input_ids is not None:
inputs_embeds = self.token_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
assert sequence_length <= self.config.max_context_length, (
f"Maximum Position Embedding Reached ! (Excepted <= {self.config.max_context_length} 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)
if attention_mask.ndim == 2:
attention_mask = jnp.expand_dims(attention_mask, (1, 2))
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.layers))
hidden_states = inputs_embeds
for idx, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
output = layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
cache_view=past_key_values.views[idx],
output_attentions=output_attentions,
segment_ids=segment_ids,
position_ids=position_ids,
causal_mask=self.causal_mask,
frequencies=self.frequencies,
)
hidden_states = output[0]
if output_attentions:
output_attentions += (output[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(value for value in outputs if value is not None)
return FlaxBaseModelOutput(
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=OpenELMConfig,
model_type="openelm",
)
class OpenELMForCausalLM(EasyDeLBaseModule):
def __init__(
self,
config: OpenELMConfig,
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.transformer = OpenELMModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.lm_head = nn.Linear(
config.model_dim,
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] = None,
return_dict: bool = True,
) -> tp.Union[FlaxCausalLMOutput, tp.Tuple]:
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
segment_ids=segment_ids,
)
hidden_states = outputs[0]
if self.config.share_input_output_layers:
lm_logits = jax.lax.dot_general(
hidden_states,
self.transformer.token_embeddings.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 FlaxCausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)