# 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,
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import typing as tp
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("llama")
class LlamaConfig(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 32000):
Vocabulary size of the Llama 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 4096):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
number_rep_kv (`int`, *optional*, defaults to 1):
Number of repetitions for the key and value vectors.
num_key_value_heads (`int`, *optional*):
Number of key and value heads for each attention layer in the Transformer encoder. Will default to
`number_rep_kv * num_attention_heads` if not set.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 2048 or 4096).
head_dim (`int`, *optional*):
head_dim for attention qkv.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the rms normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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`.
bos_token_id (`int`, *optional*, defaults to 0):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*, defaults to 1):
The id of the *end-of-sequence* token.
resid_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
rope_theta (`float`, *optional*, defaults to 10000.0):
The theta value to use for rotary position embeddings.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use attention bias.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie the weights of the input embeddings and the output embeddings.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
fcm_min_ratio (`float`, *optional*, defaults to -1):
The minimum ratio for Flash Attention.
fcm_max_ratio (`float`, *optional*, defaults to -1):
The maximum ratio for Flash Attention.
rope_scaling (`tp.Dict[str, tp.Union[str, float]]`, *optional*):
The configuration for rope scaling.
scan_mlp_chunk_size (`int`, *optional*, defaults to 1024):
The chunk size to use when scanning the MLP.
bits (`int`, *optional*):
The number of bits to quantize the model to.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The hidden activation function to use.
pretraining_tp (`int`, *optional*, defaults to 1):
The tensor parallelism degree used during pretraining.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the MLP.
scan_layers (`bool`, *optional*, defaults to `False`):
Whether to use the scan implementation for the layers.
"""
model_type: str = "llama"
def __init__(
self,
vocab_size: int = 32000,
hidden_size: int = 4096,
intermediate_size: int = 11008,
num_hidden_layers: int = 32,
num_attention_heads: int = 32,
number_rep_kv: int = 1,
head_dim: tp.Optional[int] = None,
num_key_value_heads: tp.Optional[int] = None,
max_position_embeddings: int = 2048,
rms_norm_eps: float = 1e-6,
initializer_range: float = 0.02,
use_cache: bool = True,
bos_token_id: int = 0,
eos_token_id: int = 1,
resid_pdrop: float = 0.0,
embd_pdrop: float = 0.0,
attention_dropout: float = 0.0,
rope_theta: float = 10000.0,
attention_bias: bool = False,
tie_word_embeddings: bool = False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
fcm_min_ratio: float = -1,
fcm_max_ratio: float = -1,
rope_scaling: tp.Dict[str, tp.Union[str, float]] = None,
scan_mlp_chunk_size: int = 1024,
bits: tp.Optional[int] = None,
hidden_act: str = "silu",
pretraining_tp: int = 1,
mlp_bias: bool = False,
scan_layers: bool = False,
**kwargs,
):
num_key_value_heads = num_key_value_heads or number_rep_kv * num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.vocab_size = vocab_size
self.number_rep_kv = number_rep_kv
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.pretraining_tp = pretraining_tp
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attention_dropout = attention_dropout
self.gradient_checkpointing = gradient_checkpointing
self.mlp_bias = mlp_bias
self.fcm_min_ratio = fcm_min_ratio
self.hidden_act = hidden_act
self.fcm_max_ratio = fcm_max_ratio
self.rope_scaling = rope_scaling
self.bits = bits
self.scan_layers = scan_layers
self.head_dim = (
head_dim if head_dim is not None else hidden_size // num_attention_heads
)
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
scan_mlp_chunk_size=scan_mlp_chunk_size,
bits=bits,
**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 (
("embed_tokens/embedding", PartitionSpec(("fsdp", "tp"), "sp")),
("self_attn/q_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/k_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/v_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/o_proj/kernel", PartitionSpec(("sp", "fsdp"), "tp")),
("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] def attach_custom_arguments(
self,
resid_pdrop: float = 0.0,
embd_pdrop: float = 0.0,
attention_dropout: float = 0.0,
tie_word_embeddings: bool = False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
fcm_min_ratio: float = 0.0,
fcm_max_ratio: float = 0.0,
number_rep_kv: int = 1,
bits: tp.Optional[int] = None,
rope_theta: float = 10000.0,
attention_bias: bool = False,
hidden_act: str = "silu",
scan_layers: bool = True,
**kwargs,
):
"""The attach_custom_arguments function adds the following arguments to the Transformer class:
Args:
self: Refer to the current object
resid_pdrop: float: Set the dropout rate for residual
connections
embd_pdrop: float: Set the probability of dropping an
embedding
attention_dropout: float: Set the probability of dropping
out the attention layer
tie_word_embeddings: bool: Tie the word embeddings to the
decoder
gradient_checkpointing: str: Control the amount of memory
used by jax
fcm_min_ratio: float: Control the minimum ratio of the
number of chunks to be used in flash-based computation
fcm_max_ratio: float: Set the maximum ratio of the number of
input tokens to output tokens
number_rep_kv: int: Determine how many times the key and
value vectors are repeated
bits: tp.Optional[int]: Determine the number of bits used in
the quantization
rope_theta: float : rope_theta for compute rope
attention_bias: bool : whenever to use attention bias or no
hidden_act: str : hidden_act for mlp
scan_layers: bool: Determine whether to use scan layers or
not
"""
self.scan_layers = scan_layers
self.embd_pdrop = embd_pdrop
self.number_rep_kv = number_rep_kv
self.resid_pdrop = resid_pdrop
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.hidden_act = hidden_act
self.tie_word_embeddings = tie_word_embeddings
self.gradient_checkpointing = gradient_checkpointing
self.fcm_min_ratio = fcm_min_ratio
self.fcm_max_ratio = fcm_max_ratio
self.bits = bits
[docs] @staticmethod
def get_weight_decay_exclusions():
return tuple()
[docs] @staticmethod
def rng_keys():
return "params", "dropout", "fcm"
@property
def granted_freq_max_position_embedding(self) -> int:
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)
class VisionLlamaConfig(LlamaConfig):
def __init__(
self,
vision_vocab_size=8448,
tie_vision_embeddings=False,
sample_mode="all",
**kwargs,
):
super().__init__(**kwargs)
self.vision_vocab_size = vision_vocab_size
self.tie_vision_embeddings = tie_vision_embeddings
self.sample_mode = sample_mode
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 (
("embed_tokens/embedding", PartitionSpec(("fsdp", "sp"), "tp")),
("embed_vision/embedding", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/q_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/k_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/v_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/o_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("vision_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
@property
def granted_freq_max_position_embedding(self) -> int:
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)