forked from phoenix-oss/llama-stack-mirror
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
|
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# top-level folder for each specific model found within the models/ directory at
|
|
# the top-level of this source tree.
|
|
|
|
from dataclasses import dataclass
|
|
from enum import Enum
|
|
from typing import Optional
|
|
|
|
|
|
class QuantizationScheme(Enum):
|
|
int4_weight_int8_dynamic_activation = "int4_weight_int8_dynamic_activation"
|
|
|
|
|
|
@dataclass
|
|
class QuantizationArgs:
|
|
scheme: Optional[QuantizationScheme] = None
|
|
group_size: Optional[int] = None
|
|
spinquant: bool = False
|
|
|
|
def __init__(self, **kwargs):
|
|
for k, v in kwargs.items():
|
|
if k == "scheme":
|
|
setattr(self, k, QuantizationScheme(v))
|
|
else:
|
|
if hasattr(self, k):
|
|
setattr(self, k, v)
|
|
|
|
|
|
@dataclass
|
|
class LoRAArgs:
|
|
rank: int
|
|
scale: float
|
|
|
|
|
|
@dataclass
|
|
class ModelArgs:
|
|
dim: int = 4096
|
|
n_layers: int = 32
|
|
n_heads: int = 32
|
|
n_kv_heads: Optional[int] = None
|
|
vocab_size: int = -1
|
|
multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2
|
|
ffn_dim_multiplier: Optional[float] = None
|
|
norm_eps: float = 1e-5
|
|
rope_theta: float = 500000
|
|
use_scaled_rope: bool = False
|
|
|
|
max_batch_size: int = 32
|
|
max_seq_len: int = 2048
|
|
|
|
# vision model params
|
|
vision_chunk_size: int = -1 # image resolution for image models
|
|
vision_max_num_chunks: int = 4
|
|
vision_num_cross_attention_layers: int = -1
|
|
|
|
quantization_args: Optional[QuantizationArgs] = None
|
|
lora_args: Optional[LoRAArgs] = None
|
|
|
|
def __init__(self, **kwargs):
|
|
for k, v in kwargs.items():
|
|
if k == "lora_args":
|
|
setattr(self, k, LoRAArgs(**v))
|
|
elif k == "quantization_args":
|
|
setattr(self, k, QuantizationArgs(**v))
|
|
else:
|
|
if hasattr(self, k):
|
|
setattr(self, k, v)
|
|
|
|
if self.n_kv_heads is None:
|
|
self.n_kv_heads = self.n_heads
|
|
assert self.n_kv_heads <= self.n_heads
|
|
assert self.n_heads % self.n_kv_heads == 0
|
|
assert self.dim % self.n_heads == 0
|