github
/
InternLM
mirror of https://github.com/InternLM/InternLM
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

importerror

pull/532/head
lijiaxing 2023-12-11 13:53:48 +08:00
parent 472671688f
commit e57ca246d9
1 changed files with 6 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
tools/transformers/internlm_model/modeling_internlm.py
View File

@ -28,7 +28,6 @@ import torch.utils.checkpoint
from torch import nn from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN from transformers.activations import ACT2FN
from transformers.generation.streamers import BaseStreamer
from transformers.modeling_outputs import ( from transformers.modeling_outputs import (
BaseModelOutputWithPast, BaseModelOutputWithPast,
CausalLMOutputWithPast, CausalLMOutputWithPast,
@ -42,6 +41,11 @@ from transformers.utils import (
replace_return_docstrings, replace_return_docstrings,
) )
try:
from transformers.generation.streamers import BaseStreamer
except: # noqa # pylint: disable=bare-except
BaseStreamer = None
from .configuration_internlm import InternLMConfig from .configuration_internlm import InternLMConfig
logger = logging.get_logger(__name__) logger = logging.get_logger(__name__)
@ -113,6 +117,7 @@ class InternLMRotaryEmbedding(torch.nn.Module):
base (int, optional): The rotation position encodes the rotation Angle base number. Defaults to 10000. base (int, optional): The rotation position encodes the rotation Angle base number. Defaults to 10000.
device (Any, optional): Running device. Defaults to None. device (Any, optional): Running device. Defaults to None.
""" """
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__() super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))