mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-05-25 19:37:55 +08:00
e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
from typing import List, Optional
|
|
|
|
from transformers import PretrainedConfig
|
|
|
|
|
|
class MLPSpeculatorConfig(PretrainedConfig):
|
|
model_type = "mlp_speculator"
|
|
|
|
attribute_map = {
|
|
"hidden_size": "emb_dim",
|
|
}
|
|
|
|
def __init__(self,
|
|
vocab_size: int = 32000,
|
|
emb_dim: int = 4096,
|
|
inner_dim: int = 0,
|
|
n_predict: int = 3,
|
|
top_k_tokens_per_head: Optional[List[int]] = None,
|
|
n_candidates: int = 5,
|
|
tie_weights: bool = False,
|
|
scale_input: bool = False,
|
|
**kwargs):
|
|
"""
|
|
Initialize an MLPSpeculatorConfig
|
|
|
|
Args:
|
|
vocab_size: int
|
|
the model vocab size
|
|
emb_dim: int
|
|
the model embedding dimension
|
|
inner_dim: int
|
|
the inner dimension of the model. If 0, will be the emb_dim.
|
|
n_predict: int
|
|
the number of lookaheads for the speculator
|
|
top_k_tokens_per_head: List[int]
|
|
Number of tokens to consider from each head when forming the
|
|
candidate tree.
|
|
For each candidate branch in the tree, head n produces topk[n]
|
|
additional sub-branches.
|
|
NOTE: This parameter is currently unused.
|
|
n_candidates: int
|
|
number of child candidates to create per sequence
|
|
tie_weights: bool
|
|
If true, use a single set of weights for every model
|
|
head/stage after the first. The initial projection
|
|
from the base model may have a different size, so that
|
|
stays separate.
|
|
scale_input: bool
|
|
if True, will scale the initial hidden states from
|
|
the base model.
|
|
"""
|
|
if top_k_tokens_per_head is None:
|
|
top_k_tokens_per_head = [5, 4, 3]
|
|
assert len(top_k_tokens_per_head) == n_predict
|
|
self.vocab_size = vocab_size
|
|
self.emb_dim = emb_dim
|
|
self.inner_dim = inner_dim
|
|
self.n_predict = n_predict
|
|
self.top_k_tokens_per_head = top_k_tokens_per_head
|
|
self.n_candidates = n_candidates
|
|
self.num_lookahead_tokens = n_predict
|
|
self.tie_weights = tie_weights
|
|
self.scale_input = scale_input
|
|
|
|
super().__init__(**kwargs)
|