mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-08 09:14:21 +08:00
83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
from typing import Any
|
|
|
|
from transformers.configuration_utils import PretrainedConfig
|
|
|
|
|
|
class FlexOlmoConfig(PretrainedConfig):
|
|
model_type = "flex_olmo"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=100352,
|
|
hidden_size=4096,
|
|
intermediate_size=11008,
|
|
num_hidden_layers=32,
|
|
num_attention_heads=32,
|
|
num_key_value_heads=None,
|
|
hidden_act="silu",
|
|
max_position_embeddings=4096,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-06,
|
|
use_cache=True,
|
|
pad_token_id=100277,
|
|
bos_token_id=None,
|
|
eos_token_id=100257,
|
|
tie_word_embeddings=False,
|
|
rope_parameters: dict[str, Any] | None = None,
|
|
attention_bias=False,
|
|
attention_dropout=0.0,
|
|
num_experts_per_tok=5,
|
|
num_experts=7,
|
|
output_router_logits=False,
|
|
router_aux_loss_coef=0.01,
|
|
norm_topk_prob=False,
|
|
**kwargs,
|
|
):
|
|
if "architectures" not in kwargs:
|
|
kwargs["architectures"] = ["FlexOlmoForCausalLM"]
|
|
|
|
super().__init__(
|
|
pad_token_id=pad_token_id,
|
|
bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
tie_word_embeddings=tie_word_embeddings,
|
|
**kwargs,
|
|
)
|
|
self.vocab_size = vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
# for backward compatibility
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.hidden_act = hidden_act
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.use_cache = use_cache
|
|
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
|
|
rope_scaling = kwargs.pop("rope_scaling", None)
|
|
rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
|
|
rope_theta = kwargs.pop("rope_theta", 500000.0)
|
|
if "rope_theta" not in rope_parameters:
|
|
rope_parameters["rope_theta"] = rope_theta
|
|
self.rope_parameters = rope_parameters
|
|
self.attention_bias = attention_bias
|
|
self.attention_dropout = attention_dropout
|
|
self.num_experts_per_tok = num_experts_per_tok
|
|
self.num_experts = num_experts
|
|
self.output_router_logits = output_router_logits
|
|
self.router_aux_loss_coef = router_aux_loss_coef
|
|
self.norm_topk_prob = norm_topk_prob
|
|
# Validate the correctness of rotary position embeddings parameters
|
|
# BC: if there is a 'type' field, move it to 'rope_type'.
|
|
if self.rope_parameters is not None and "type" in self.rope_parameters:
|
|
self.rope_parameters["rope_type"] = self.rope_parameters["type"]
|