# 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 math
from jax.sharding import PartitionSpec
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
[docs]@register_config("mamba2")
class Mamba2Config(EasyDeLBaseConfig):
"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read
the documentation from [`EasyDeLBaseConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50280):
Vocabulary size of the Mamba model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
state_size (`int`, *optional*, defaults to 16):
State size of the Mamba model.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
The index of the padding token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 0):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*, defaults to 0):
The id of the *end-of-sequence* token.
expand (`int`, *optional*, defaults to 2):
Expansion factor for the intermediate size.
conv_kernel (`int`, *optional*, defaults to 4):
Kernel size of the convolution layer.
use_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the linear layers.
use_conv_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the convolution layer.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) to use in the encoder and pooler. If string,
`"gelu"`, `"relu"`, `"swish"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.1):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
residual_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether to compute the residual connection in float32.
time_step_rank (`str` or `int`, *optional*, defaults to `"auto"`):
The rank of the time step embedding. If set to `"auto"`, the rank is calculated as
`math.ceil(self.hidden_size / 16)`.
time_step_scale (`float`, *optional*, defaults to 1.0):
The scale factor for the time step embedding.
time_step_min (`float`, *optional*, defaults to 0.001):
The minimum value for the time step embedding.
time_step_max (`float`, *optional*, defaults to 0.1):
The maximum value for the time step embedding.
time_step_floor (`float`, *optional*, defaults to 1e-4):
The floor value for the time step embedding.
rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
Whether to rescale the pre-norm residual.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
"""
model_type: str = "mamba2"
def __init__(
self,
num_heads=128,
head_dim=64,
vocab_size=32768,
hidden_size=4096,
state_size=128,
num_hidden_layers=64,
layer_norm_epsilon=1e-5,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
expand=2,
conv_kernel=4,
n_groups=8,
use_bias=False,
use_conv_bias=True,
hidden_act="silu",
initializer_range=0.1,
residual_in_fp32=True,
time_step_rank="auto",
time_step_min=0.001,
time_step_max=0.1,
time_step_floor=1e-4,
time_step_limit=(0.0, float("inf")),
rescale_prenorm_residual=False,
use_cache=True,
norm_before_gate=True,
rms_norm=True,
chunk_size=256,
tie_word_embeddings=False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.state_size = state_size
self.num_hidden_layers = num_hidden_layers
self.layer_norm_epsilon = layer_norm_epsilon
self.conv_kernel = conv_kernel
self.expand = expand
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.use_bias = use_bias
self.use_conv_bias = use_conv_bias
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.time_step_rank = (
math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank
)
self.time_step_min = time_step_min
self.time_step_max = time_step_max
self.time_step_floor = time_step_floor
self.rescale_prenorm_residual = rescale_prenorm_residual
self.residual_in_fp32 = residual_in_fp32
self.use_cache = use_cache
self.n_groups = n_groups
self.num_heads = num_heads
self.head_dim = head_dim
self.norm_before_gate = norm_before_gate
self.rms_norm = rms_norm
self.state_size = state_size
self.chunk_size = chunk_size
self.time_step_limit = time_step_limit
self.tie_word_embeddings = tie_word_embeddings
self.gradient_checkpointing = gradient_checkpointing
self.intermediate_size = int(expand * hidden_size)
super().__init__(**kwargs)
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the model.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
return (
# Embeddings
("backbone/embeddings/embedding", PartitionSpec("tp", ("fsdp", "sp"))),
# Language model head
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("lm_head/bias", PartitionSpec("tp")),
# Mixer layers
("mixer/in_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mixer/in_proj/bias", PartitionSpec("tp")),
("mixer/out_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mixer/out_proj/bias", PartitionSpec(("fsdp", "sp"))),
# Conv1d in mixer (3D kernel)
("mixer/conv1d/kernel", PartitionSpec(None, None, "tp")),
("mixer/conv1d/bias", PartitionSpec("tp")),
# State space parameters
("mixer/A_log", PartitionSpec(None)),
("mixer/D", PartitionSpec(None)),
("mixer/dt_bias", PartitionSpec(None)),
# Normalization layers
("mixer/norm/kernel", PartitionSpec(None)),
("backbone/layers/.*/norm/kernel", PartitionSpec(None)),
("backbone/norm_f/kernel", PartitionSpec(None)),
# Catch-all
(".*", PartitionSpec(None)),
)
[docs] def add_jax_args(
self,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
):
self.gradient_checkpointing = gradient_checkpointing