# 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, partial
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 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.modules.stablelm.stablelm_configuration import (
StableLmConfig as StableLmConfig,
)
[docs]class StableLmMLP(nn.Module):
def __init__(
self,
config: StableLmConfig,
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(
nn.Linear,
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.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[config.hidden_act]
def __call__(self, hidden_states: jnp.ndarray) -> jnp.ndarray:
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
return self.down_proj(
self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states)
)
[docs]class StableLmLayerNormPerHead(nn.Module):
def __init__(
self,
head_dim: int,
num_heads: int,
eps: float = 1e-5,
bias: bool = False,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
*,
rngs: nn.Rngs,
):
self.norms = [
nn.LayerNorm(
head_dim,
epsilon=eps,
use_bias=bias,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
for idx in range(num_heads)
]
def __call__(self, hidden_states):
states_per_heads = jnp.split(hidden_states, 1, axis=1)
# Normalize and merge the heads back together
return jnp.concatenate(
[
norm(hidden_states) for norm, hidden_states in zip(self.norms, states_per_heads)
],
axis=1,
)
[docs]class StableLmAttention(FlaxAttentionModule):
def __init__(
self,
config: StableLmConfig,
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
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
self.rope_theta = config.rope_theta
self.partial_rotary_factor = config.partial_rotary_factor
if self.num_key_value_groups == 1:
assert self.config.num_attention_heads == self.config.num_key_value_heads
linear_class = partial(
nn.Linear,
dtype=dtype,
param_dtype=param_dtype,
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,
use_bias=self.config.use_qkv_bias,
rngs=rngs,
)
self.k_proj = linear_class(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
use_bias=self.config.use_qkv_bias,
rngs=rngs,
)
self.v_proj = linear_class(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
use_bias=self.config.use_qkv_bias,
rngs=rngs,
)
self.o_proj = linear_class(
config.num_attention_heads * self.head_dim,
config.hidden_size,
use_bias=False,
rngs=rngs,
)
self.rotary_emb_dim = int(self.config.partial_rotary_factor * self.head_dim)
self.attention_performer = FlexibleAttentionModule(
base_config=config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=config.attention_dropout,
)
self.qk_layernorm = config.qk_layernorm
if self.qk_layernorm:
self.q_layernorm = StableLmLayerNormPerHead(
head_dim=self.head_dim,
num_heads=config.num_attention_heads,
eps=config.layer_norm_eps,
dtype=self.dtype,
param_dtype=self.param_dtype,
rngs=rngs,
)
self.k_layernorm = StableLmLayerNormPerHead(
head_dim=self.head_dim,
num_heads=config.num_key_value_heads,
eps=config.layer_norm_eps,
dtype=self.dtype,
param_dtype=self.param_dtype,
rngs=rngs,
)
self.rotary = self.config.get_basic_rope(
self.dtype,
head_size=int(
config.partial_rotary_factor
* (config.hidden_size // config.num_attention_heads)
),
rotary_dim=self.rotary_emb_dim,
base=config.rope_theta,
)
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,
):
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.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,
)
if self.qk_layernorm:
query_states = self.q_layernorm(query_states.transpose(0, 2, 1, 3)).transpose(
0, 2, 1, 3
)
key_states = self.k_layernorm(key_states.transpose(0, 2, 1, 3)).transpose(
0, 2, 1, 3
)
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,
)
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)
outputs = (
(attn_output, attentions.attention_weights)
if output_attentions
else (attn_output,)
)
return outputs
[docs]class StableLmDecoderLayer(nn.Module):
def __init__(
self,
config: StableLmConfig,
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 = StableLmAttention
mlp_block = StableLmMLP
self.use_parallel_residual = self.config.use_parallel_residual
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 = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
if not self.use_parallel_residual:
self.post_attention_layernorm = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.dropout = nn.Dropout(self.config.hidden_dropout, 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,
):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
attn_out = self.self_attn(
hidden_states,
attention_mask,
position_ids,
causal_mask,
cache_view,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
attn_out, self_attn_weights = (
(attn_out[0], attn_out[1]) if len(attn_out) == 2 else (attn_out[0], None)
)
if self.use_parallel_residual:
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.dropout(hidden_states)
hidden_states = hidden_states + residual + attn_out
else:
residual = residual + attn_out
if self.config.use_scan_mlp:
hidden_states = block_wise_ffn(
self.mlp,
self.post_attention_layernorm(residual),
self.config.scan_mlp_chunk_size,
)
else:
hidden_states = self.mlp(self.post_attention_layernorm(residual))
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + residual
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
[docs]@register_module(
"base-module",
config=StableLmConfig,
model_type="stablelm",
embedding_layer_names=["embed_tokens"],
layernorm_names=[
"input_layernorm",
"post_attention_layernorm",
"norm",
"norms",
],
)
class StableLmModel(EasyDeLBaseModule):
def __init__(
self,
config: StableLmConfig,
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.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embed(
config.vocab_size,
config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layers = [
StableLmDecoderLayer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for idx in range(config.num_hidden_layers)
]
self.norm = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
@cached_property
def frequencies(self):
rotary_emb_dim = int(
self.config.partial_rotary_factor
* (self.config.hidden_size // self.config.num_attention_heads)
)
self._frequencies = self.config.get_basic_frequencies(
head_size=rotary_emb_dim,
rotary_dim=rotary_emb_dim,
)
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 StableLm 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.
"""
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)
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))
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],
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)
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(
"causal-language-model",
config=StableLmConfig,
model_type="stablelm",
embedding_layer_names=["embed_tokens"],
layernorm_names=[
"input_layernorm",
"post_attention_layernorm",
"norm",
"norms",
],
)
class StableLmForCausalLM(EasyDeLBaseModule):
def __init__(
self,
config: StableLmConfig,
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 = StableLmModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vocab_size = self.config.vocab_size
self.lm_head = nn.Linear(
config.hidden_size,
config.vocab_size,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
precision=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,
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.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)
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,
)