# 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 jax import lax
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import register_module
from easydel.infra.modeling_outputs import (
FlaxBaseModelOutput,
FlaxCausalLMOutput,
)
from easydel.infra.utils import (
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.modules.xerxes.xerxes_configuration import XerxesConfig as XerxesConfig
from easydel.utils.helpers import get_logger
logger = get_logger(__name__)
[docs]class RMSNorm(nn.Module):
def __init__(
self,
dim: int,
eps: float = 1e-6,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
):
self.dim = dim
self.eps = eps
self.dtype = dtype
self.param_dtype = param_dtype
self.kernel = nn.Param(jnp.ones(self.dim, dtype=param_dtype))
def _norm(self, x: jnp.ndarray) -> jnp.ndarray:
return x / lax.sqrt(
jnp.square(x.astype(jnp.float32)).mean(-1, keepdims=True) + self.eps
)
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
if self.dtype in [
jnp.float8_e4m3b11fnuz,
jnp.float8_e4m3fn,
jnp.float8_e4m3fnuz,
jnp.float8_e5m2,
jnp.float8_e5m2fnuz,
]:
x = x.astype(jnp.float32)
else:
x = x.astype(jnp.promote_types(self.dtype, jnp.float32))
output = self._norm(x).astype(self.dtype)
return (self.kernel.value.astype(self.dtype)) * output
[docs]class XerxesAttention(FlaxAttentionModule):
def __init__(
self,
config: XerxesConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
causal: bool = True,
is_cross_attention: bool = False,
*,
rngs: nn.Rngs,
):
super().__init__(config)
self.config = config
self.layer_idx = layer_idx
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.causal = causal
self.is_cross_attention = is_cross_attention
self.rngs = rngs
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.head_dim
self.attention_softmax_in_fp32 = self.dtype is not jnp.float32
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
kernel = jax.nn.initializers.normal(config.initializer_range)
linear_class = functools.partial(
nn.Linear,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
use_bias=False,
kernel_init=kernel,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.q_proj = linear_class(
self.embed_dim,
self.num_heads * self.head_dim,
rngs=rngs,
)
self.k_proj = linear_class(
self.embed_dim,
self.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.v_proj = linear_class(
self.embed_dim,
self.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.o_proj = linear_class(
self.num_heads * self.head_dim,
self.embed_dim,
rngs=rngs,
)
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(
self.dtype,
self.head_dim,
self.head_dim,
True,
)
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_heads * self.head_dim,)
)
def _split_heads(self, hidden_states, num_heads):
return hidden_states.reshape(hidden_states.shape[:2] + (num_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]
(query_states, key_states, value_states) = (
self.q_proj(hidden_states),
self.k_proj(hidden_states),
self.v_proj(hidden_states),
)
query_states = query_states.reshape(
batch_size,
sequence_length,
self.num_heads,
self.head_dim,
)
key_states = key_states.reshape(
batch_size,
sequence_length,
self.num_key_value_heads,
self.head_dim,
)
value_states = value_states.reshape(
batch_size,
sequence_length,
self.num_key_value_heads,
self.head_dim,
)
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,
) = 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,
sliding_windows=4096 if bool((self.layer_idx % 2) == 0) else None,
)
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.shard_attention_prod(
self._merge_heads(attentions.attention_outputs)
)
attn_output = self.o_proj(attn_output)
return (
(attn_output, attentions.attention_weights)
if output_attentions
else (attn_output, None)
)
[docs]class XerxesMLP(nn.Module):
def __init__(
self,
config: XerxesConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
kernel_init = jax.nn.initializers.normal(config.initializer_range)
self.act = functools.partial(nn.gelu, approximate=True)
linear_class = functools.partial(
nn.Linear,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=kernel_init,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.gate_proj = linear_class(
self.config.hidden_size, self.config.intermediate_size, rngs=rngs
)
self.up_proj = linear_class(
self.config.hidden_size, self.config.intermediate_size, rngs=rngs
)
self.down_proj = linear_class(
self.config.intermediate_size, self.config.hidden_size, rngs=rngs
)
def __call__(self, hidden_states):
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
return self.down_proj(
self.act(self.gate_proj(hidden_states)) * self.up_proj(hidden_states)
)
[docs]class XerxesSparseMoeBlock(nn.Module):
def __init__(
self,
config: XerxesConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[None, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.gate = nn.Linear(
self.config.hidden_size,
self.config.num_local_experts,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=nn.initializers.normal(config.initializer_range),
rngs=rngs,
)
self.experts = [
XerxesMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for _ in range(self.config.num_local_experts)
]
def __call__(self, hidden_states: chex.Array) -> tp.Tuple[chex.Array, chex.Array]:
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
router_logits = self.gate(hidden_states).astype(
jnp.promote_types(self.dtype, jnp.float32)
)
routing_weights, selected_experts = jax.lax.top_k(
router_logits, k=self.config.num_experts_per_tok
)
routing_weights = jax.nn.softmax(
routing_weights.astype(jnp.promote_types(self.dtype, jnp.float32)), axis=-1
)
final_hidden_state = jnp.zeros_like(hidden_states)
for index in range(self.config.num_local_experts):
expert_layer_output = (
block_wise_ffn(
self.layers[index],
hidden_states,
self.config.scan_mlp_chunk_size,
)
if self.config.use_scan_mlp
else self.layers[index](hidden_states)
)
expert_layer_output_exp = (
jnp.sum(jnp.multiply(selected_experts == index, routing_weights), axis=-1)[
:, :, None
]
* expert_layer_output
)
final_hidden_state += expert_layer_output_exp
return (
final_hidden_state,
router_logits,
)
[docs]class XerxesDecoderLayer(nn.Module):
def __init__(
self,
config: XerxesConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.layer_idx = layer_idx
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
mlp_block = XerxesSparseMoeBlock if self.config.xe_moe else XerxesMLP
attn_block = XerxesAttention
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
)
self.self_attn = attn_block(
self.config,
layer_idx=self.layer_idx,
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,
)
rms = functools.partial(
RMSNorm,
dim=self.config.hidden_size,
eps=self.config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
)
self.input_layernorm = rms()
self.post_attention_layernorm = rms()
self.pre_feedforward_layernorm = rms()
self.post_feedforward_layernorm = rms()
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.input_layernorm(hidden_states)
hidden_states, attn_weight = self.self_attn(
hidden_states,
attention_mask,
position_ids,
causal_mask,
cache_view,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.pre_feedforward_layernorm(hidden_states)
if self.config.use_scan_mlp and not self.config.xe_moe:
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
return hidden_states, attn_weight
[docs]@register_module(
"base-module",
config=XerxesConfig,
model_type="xerxes",
embedding_layer_names=["embed_tokens"],
)
class XerxesModel(EasyDeLBaseModule):
def __init__(
self,
config: XerxesConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.hidden_size = self.config.hidden_size
self.embed_tokens = nn.Embed(
self.config.vocab_size,
self.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layers = [
XerxesDecoderLayer(
self.config,
layer_idx=i,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for i in range(self.config.num_hidden_layers)
]
self.norm = RMSNorm(
dim=self.config.hidden_size,
eps=self.config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
)
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]:
"""
Forward pass through the Xerxes module.
Args:
input_ids (chex.Array): Input tensor containing token IDs.
attention_mask (chex.Array): Mask for attention.
position_ids (chex.Array): Positional indices.
segment_ids (tp.Optional[chex.Array]): Segment IDs for different input parts.
inputs_embeds (tp.Optional[chex.Array]): Embedded input tensor.
output_attentions (tp.Optional[bool]): If True, output attention weights.
output_hidden_states (tp.Optional[bool]): If True, output hidden states.
init_cache (bool): If True, initialize cache for decoding.
deterministic (bool): If True, disable dropout.
return_dict (bool): If True, return a dictionary of outputs.
Returns:
FlaxBaseModelOutput | tp.Tuple: Model output, either as a named tuple or a standard tuple.
"""
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
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
inputs_embeds = inputs_embeds * (self.config.hidden_size**0.5)
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),
)
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, block in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = block(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
cache_view=past_key_values.views[idx],
causal_mask=self.causal_mask,
output_attentions=output_attentions,
segment_ids=segment_ids,
frequencies=self.frequencies,
)
hidden_states = outputs[0]
if output_attentions:
all_attentions += (outputs[1],)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states = outputs[1] + (hidden_states,)
outputs = (hidden_states, all_hidden_states) + outputs[2:]
else:
outputs = (hidden_states,) + outputs[1:]
if not return_dict:
return tuple(v for v in outputs if v 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(
"causal-language-model",
config=XerxesConfig,
model_type="xerxes",
embedding_layer_names=["embed_tokens"],
)
class XerxesForCausalLM(EasyDeLBaseModule):
def __init__(
self,
config: XerxesConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.model = XerxesModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.lm_head = nn.Linear(
self.config.hidden_size,
self.config.vocab_size,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
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,
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]:
"""
Forward pass through the Xerxes module.
Args:
input_ids (tp.Optional[chex.Array]): Input tensor containing token IDs.
attention_mask (tp.Optional[chex.Array]): Mask for attention.
position_ids (tp.Optional[chex.Array]): Positional indices.
segment_ids (tp.Optional[chex.Array]): Segment IDs for different input parts.
inputs_embeds (tp.Optional[chex.Array]): Embedded input tensor.
output_attentions (tp.Optional[bool]): If True, output attention weights.
output_hidden_states (tp.Optional[bool]): If True, output hidden states.
init_cache (bool): If True, initialize cache for decoding.
deterministic (bool): If True, disable dropout.
return_dict (bool): If True, return a dictionary of outputs.
Returns:
FlaxCausalLMOutput | tp.Tuple: Model output, either as a named tuple or a standard tuple.
"""
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,
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.embed_tokens.embedding.value.T,
(((hidden_states.ndim - 1), (0,)), ((), ())),
)
else:
lm_logits = self.lm_head(hidden_states)
lm_logits = jax.nn.tanh(lm_logits / 30.0) * 30.0
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,
)