entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
WNConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Catherine0505/mar-scf-flow | WNConv2d | false | 17,093 | [
"Apache-2.0"
] | 10 | aa7c3564cb9f2967c5e580a633516dba1b597f98 | https://github.com/Catherine0505/mar-scf-flow/tree/aa7c3564cb9f2967c5e580a633516dba1b597f98 |
NaiveGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | whn09/fastmoe | NaiveGate | false | 4,533 | [
"Apache-2.0"
] | 0 | d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a | https://github.com/whn09/fastmoe/tree/d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a |
SirenLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | idgmatrix/coin | SirenLayer | false | 15,575 | [
"MIT"
] | 84 | 2f2df0614ed4fc866d4b7715ee206081e08b9424 | https://github.com/idgmatrix/coin/tree/2f2df0614ed4fc866d4b7715ee206081e08b9424 |
GlobalMaxPool1d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | aiswaryasankar/Few_shot_exp | GlobalMaxPool1d | false | 1,382 | [
"MIT"
] | 0 | 21c5a5d93dc69715b037a0fd2dca1b6f7d9de773 | https://github.com/aiswaryasankar/Few_shot_exp/tree/21c5a5d93dc69715b037a0fd2dca1b6f7d9de773 |
Anchor3DHead | import torch
import numpy as np
import torch.nn as nn
import torch.utils.dlpack
def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-06):
"""Calculate overlap between two set of bboxes.
If ``is_aligned `` is ``False``, then calculate the overlaps between each
bbox of bboxes1 and bboxe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
import torch.utils.dlpack
assert_size_s... | Jaein94/Open3D-ML | Anchor3DHead | false | 9,359 | [
"MIT"
] | 0 | 815c111229322d562e11ea3148ad6568ccf13d1d | https://github.com/Jaein94/Open3D-ML/tree/815c111229322d562e11ea3148ad6568ccf13d1d |
GroupedChannelNorm | import torch
import torch.utils.data
import torch
import torch.nn as nn
class GroupedChannelNorm(nn.Module):
def __init__(self, num_groups):
super().__init__()
self.num_groups = num_groups
def forward(self, x):
shape = list(x.shape)
new_shape = [shape[0], self.num_groups, sha... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | bronemos/contrastive-unpaired-translation-focal | GroupedChannelNorm | false | 3,246 | [
"BSD-3-Clause"
] | 0 | 50b9008d08a86439ede081a910d02df5da8e32df | https://github.com/bronemos/contrastive-unpaired-translation-focal/tree/50b9008d08a86439ede081a910d02df5da8e32df |
ClipLoss | import torch
from torch import nn
from torch.nn import functional as F
from torch import distributed as dist
import torch.distributed.nn
def gather_features(image_features, text_features, local_loss=False,
gather_with_grad=False, rank=0, world_size=1, use_horovod=False):
if use_horovod:
assert hvd is ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | NYU-DICE-Lab/open_clip | ClipLoss | false | 871 | [
"MIT"
] | 0 | fd71804b503135fb1c7cc8de3a0d6599741c8ed9 | https://github.com/NYU-DICE-Lab/open_clip/tree/fd71804b503135fb1c7cc8de3a0d6599741c8ed9 |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | MED-YAHYAOUI/style-transfer-pytorch | TVLoss | false | 11,662 | [
"MIT"
] | 0 | 867a6a45d964c151d6b94f50153cf535385c9078 | https://github.com/MED-YAHYAOUI/style-transfer-pytorch/tree/867a6a45d964c151d6b94f50153cf535385c9078 |
EncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | salvacarrion/nmt-continual-learning | EncoderLayer | false | 7,618 | [
"MIT"
] | 1 | 302147ac9c270f3341a68a72c803c457f05ff37b | https://github.com/salvacarrion/nmt-continual-learning/tree/302147ac9c270f3341a68a72c803c457f05ff37b |
ConvWS2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AtticusJohnson/mmdetection | ConvWS2d | false | 11,220 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
ReDynamicWeightsCat33 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | lzrobots/dgmn | ReDynamicWeightsCat33 | false | 16,012 | [
"MIT"
] | 54 | 515476b5c6a07dcc3b7a4d2243c541377624bb33 | https://github.com/lzrobots/dgmn/tree/515476b5c6a07dcc3b7a4d2243c541377624bb33 |
SigmoidTransformer | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
from torch.distributions.utils import probs_to_logits
class Bijection(nn.Module):
"""
An invertible transformation.
"""
def __init__(self):
super().__init__()
def forward(self, inputs, context):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.utils.data
import torch.nn.... | probabll/dgm.pt | SigmoidTransformer | false | 7,493 | [
"MIT"
] | 1 | 95b5b1eb798b87c3d621e7416cc1c423c076c865 | https://github.com/probabll/dgm.pt/tree/95b5b1eb798b87c3d621e7416cc1c423c076c865 |
Dropout2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Lakonik/MonoRUn | Dropout2d | false | 13,993 | [
"MIT"
] | 86 | 5bcc5278ea7a6b9cac6b7933c66921fa3011ce9a | https://github.com/Lakonik/MonoRUn/tree/5bcc5278ea7a6b9cac6b7933c66921fa3011ce9a |
Tanh | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | ralphc1212/BNAF | Tanh | false | 4,167 | [
"MIT"
] | 0 | b6e331aa96cdd4496b6eed6c6ce65512a99f4149 | https://github.com/ralphc1212/BNAF/tree/b6e331aa96cdd4496b6eed6c6ce65512a99f4149 |
LinearBlock | import torch
import torch.nn as nn
import torch.nn
import torch.nn.init
import torch.optim
class Model(nn.Module):
""" Class representing sampleable neural network model """
def num_params(self):
""" Get the number of model parameters. """
return sum(p.numel() for p in self.parameters())
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CBIIT/NCI-DOE-Collab-Pilot2-Autoencoder_MD_Simulation_Data | LinearBlock | false | 13,435 | [
"MIT"
] | 51 | 2b1213f944cf5f2c60799099a469989a1f0a6d3a | https://github.com/CBIIT/NCI-DOE-Collab-Pilot2-Autoencoder_MD_Simulation_Data/tree/2b1213f944cf5f2c60799099a469989a1f0a6d3a |
ReturnAsLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | yanxurui/portfolio | ReturnAsLoss | false | 4,598 | [
"MIT"
] | 0 | 032cf47ccac1c5815fd4827bf0d5f3cf43cec990 | https://github.com/yanxurui/portfolio/tree/032cf47ccac1c5815fd4827bf0d5f3cf43cec990 |
USConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.g... | Gaussianer/FasterSeg | USConv2d | false | 17,299 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision.transforms i... | Rick-960123/centermask-mdf-master | SEModule | false | 2,760 | [
"BSD-2-Clause"
] | 0 | 49388b03b9ffb06577cd28b9ddaa68cadb82e926 | https://github.com/Rick-960123/centermask-mdf-master/tree/49388b03b9ffb06577cd28b9ddaa68cadb82e926 |
ActorNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | vlgiitr/Workshop-Spring-2022 | ActorNetwork | false | 16,682 | [
"MIT"
] | 69 | 003ed62c75a876e946eaa481c27224dd38914015 | https://github.com/vlgiitr/Workshop-Spring-2022/tree/003ed62c75a876e946eaa481c27224dd38914015 |
Attention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ZfSangkuan/ASER | Attention | false | 14,715 | [
"MIT"
] | 256 | c34d6f2432b181bae9f4ee4fa70ce270dbc1dee7 | https://github.com/ZfSangkuan/ASER/tree/c34d6f2432b181bae9f4ee4fa70ce270dbc1dee7 |
StandardNLL | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NoteXYX/ACL2017 | StandardNLL | false | 14,131 | [
"Apache-2.0"
] | 119 | 436f59f2aa0044a9d57c95a2a58b2158cb99738d | https://github.com/NoteXYX/ACL2017/tree/436f59f2aa0044a9d57c95a2a58b2158cb99738d |
PreactDoubleLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import copy
import math
impor... | EmoryMLIP/DynamicBlocks | PreactDoubleLayer | false | 17,262 | [
"MIT"
] | 9 | 52acc9fbc1a2640c6ac8922fa18105279ccaea97 | https://github.com/EmoryMLIP/DynamicBlocks/tree/52acc9fbc1a2640c6ac8922fa18105279ccaea97 |
FocalLossBinary | import torch
import torch.nn.functional
import torch.jit
import torch.nn.functional as F
from functools import partial
from torch.nn.modules.loss import _Loss
def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor',
threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mean'):
"""
Compute... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | LeAlex27/nnUNet | FocalLossBinary | false | 2,506 | [
"Apache-2.0"
] | 0 | 9b6912f80904af9eaa4db41cd7e5c7f20058cdde | https://github.com/LeAlex27/nnUNet/tree/9b6912f80904af9eaa4db41cd7e5c7f20058cdde |
RGBDiff | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class RGBDiff(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forw... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import tor... | JinYAnGHe/openvino_training_extensions | RGBDiff | false | 3,036 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
BinaryLogisticRegressionLoss | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SvipRepetitionCounting/TransRAC | BinaryLogisticRegressionLoss | false | 5,867 | [
"Apache-2.0"
] | 1 | eec12553dfa1e2fde6356b0e2703c633d225feb3 | https://github.com/SvipRepetitionCounting/TransRAC/tree/eec12553dfa1e2fde6356b0e2703c633d225feb3 |
RobertaClassificationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | MCplayerFromPRC/CodeT5 | RobertaClassificationHead | false | 1,423 | [
"BSD-3-Clause"
] | 0 | 726ed4a4221be125b6083439667573ef5fef6984 | https://github.com/MCplayerFromPRC/CodeT5/tree/726ed4a4221be125b6083439667573ef5fef6984 |
h_swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | SpectrePrediction/micronet | h_swish | false | 2,841 | [
"MIT"
] | 0 | f56269c7a8744f750e9870f0baa9fb6e68f27b9c | https://github.com/SpectrePrediction/micronet/tree/f56269c7a8744f750e9870f0baa9fb6e68f27b9c |
SinglePITF_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SamHaoYuan/pitf | SinglePITF_Loss | false | 1,007 | [
"MIT"
] | 0 | 5fdebc3b44c6462126876101b052a3980804da79 | https://github.com/SamHaoYuan/pitf/tree/5fdebc3b44c6462126876101b052a3980804da79 |
ShiftedSoftplus | import torch
import torch.nn.functional as F
from torch import nn
class ShiftedSoftplus(nn.Module):
__constants__ = ['beta', 'threshold']
beta: 'int'
threshold: 'int'
def __init__(self, beta: 'int'=1, threshold: 'int'=20) ->None:
super(ShiftedSoftplus, self).__init__()
self.beta = bet... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | cmusatyalab/mega-nerf | ShiftedSoftplus | false | 15,051 | [
"MIT"
] | 107 | 306e06cc316dd4f5c84d0610308bcbc208228fc3 | https://github.com/cmusatyalab/mega-nerf/tree/306e06cc316dd4f5c84d0610308bcbc208228fc3 |
MiniBatchStdDev | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class MiniBatchStdDev(nn.Module):
"""Mini-Batch Standard Deviation"""
def __init__(self, group_size: 'int'=4, eps: 'float'=0.0001) ->None:
super().__init__()
self.group_size = group_size
self.eps = eps
def forwar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = tor... | STomoya/animeface | MiniBatchStdDev | false | 14,368 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
up | import torch
import torch.nn as nn
import torch.nn.functional as F
class up(nn.Module):
def __init__(self, in_ch, out_ch):
super(up, self).__init__()
self.up_scale = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2)
def forward(self, x1, x2):
x2 = self.up_scale(x2)
diffY = x1.si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | aribryan/pytorch_task | up | false | 9,766 | [
"MIT"
] | 0 | c661f201bbf03cfd06a13deb4c1c0c61d017adb1 | https://github.com/aribryan/pytorch_task/tree/c661f201bbf03cfd06a13deb4c1c0c61d017adb1 |
MLPClassifier | import torch
import torch.nn as nn
class MLPClassifier(nn.Module):
def __init__(self, input_dim, target_dim):
super(MLPClassifier, self).__init__()
self.input_dim = input_dim
self.target_dim = target_dim
self.fc1 = nn.Linear(self.input_dim, 128)
self.fc2 = nn.Linear(128, 6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ITU-AI-ML-in-5G-Challenge/-ITU-ML5G-PS-032-KDDI-naist-lsm | MLPClassifier | false | 5,321 | [
"MIT"
] | 1 | f0c54cfde8fb9a5b78e116de7814a1afbd856799 | https://github.com/ITU-AI-ML-in-5G-Challenge/-ITU-ML5G-PS-032-KDDI-naist-lsm/tree/f0c54cfde8fb9a5b78e116de7814a1afbd856799 |
HyperpriorSynthesisDLMM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ahmedfgad/high-fidelity-generative-compression | HyperpriorSynthesisDLMM | false | 6,144 | [
"Apache-2.0"
] | 1 | f3c6aa3472e3c629cbc35eefb0957119c913054a | https://github.com/ahmedfgad/high-fidelity-generative-compression/tree/f3c6aa3472e3c629cbc35eefb0957119c913054a |
DeepMind | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | TianhongDai/Self_Imitation_Learning | DeepMind | false | 14,839 | [
"MIT"
] | 61 | e49003582fa3d875495d84682f2a3332d4922dbc | https://github.com/TianhongDai/Self_Imitation_Learning/tree/e49003582fa3d875495d84682f2a3332d4922dbc |
FCGenerator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | unicredit/ganzo | FCGenerator | false | 16,641 | [
"Apache-2.0"
] | 73 | fb1d270f5091073e8f27da76ab508ab24e5d40e9 | https://github.com/unicredit/ganzo/tree/fb1d270f5091073e8f27da76ab508ab24e5d40e9 |
RMSNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | poliver269/latent-diffusion | RMSNorm | false | 12,901 | [
"MIT"
] | 0 | 08e7c987ad423e3f93125b49980c36302ffe3d82 | https://github.com/poliver269/latent-diffusion/tree/08e7c987ad423e3f93125b49980c36302ffe3d82 |
ScaledL2Norm | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledL2Norm(nn.Module):
def __init__(self, in_channels, initial_scale):
super(ScaledL2Norm, self).__init__()
self.in_channels = in_channels
self.scale = nn.Parameter(torch.Tensor(in_channels))
self.initial_s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | alejodosr/adaptive-inattention | ScaledL2Norm | false | 6,171 | [
"MIT"
] | 1 | ad1c883081e5248704be5ce5c4baa24b2eda1c59 | https://github.com/alejodosr/adaptive-inattention/tree/ad1c883081e5248704be5ce5c4baa24b2eda1c59 |
NTN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | QingkaiZeng/GenTaxo | NTN | false | 8,721 | [
"MIT"
] | 28 | 10257a1714d14c6a4c49cbfa0b507408f718cdf0 | https://github.com/QingkaiZeng/GenTaxo/tree/10257a1714d14c6a4c49cbfa0b507408f718cdf0 |
BatchSpectralShrinkage | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Neronjust2017/TransferBed | BatchSpectralShrinkage | false | 5,680 | [
"MIT"
] | 1 | eaa703a4bc10eaf6216fe1394cd272f6e75489e2 | https://github.com/Neronjust2017/TransferBed/tree/eaa703a4bc10eaf6216fe1394cd272f6e75489e2 |
Variational | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math... | limberc/HyperGAN | Variational | false | 15,907 | [
"MIT"
] | 889 | b074e74abf0ed9b81bd52084706e3707a47e0fe2 | https://github.com/limberc/HyperGAN/tree/b074e74abf0ed9b81bd52084706e3707a47e0fe2 |
Simple_nn | import torch
class Simple_nn(torch.nn.Module):
def __init__(self, dims_in, hidden):
super(Simple_nn, self).__init__()
self.linear1 = torch.nn.Linear(dims_in, hidden)
self.linear2 = torch.nn.Linear(hidden, 2)
self.output = torch.nn.LogSoftmax()
def forward(self, x):
hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | urbanriskmap/timeseries-analysis | Simple_nn | false | 10,897 | [
"MIT"
] | 0 | 6b9a8d1a916ff784cb0de93d6997cd072d1ca6ae | https://github.com/urbanriskmap/timeseries-analysis/tree/6b9a8d1a916ff784cb0de93d6997cd072d1ca6ae |
FusionMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | wx-b/cliport | FusionMax | false | 16,733 | [
"Apache-2.0"
] | 110 | c29b0c4b6b1c4e4da5bda6c7f8c718e36f28a6e8 | https://github.com/wx-b/cliport/tree/c29b0c4b6b1c4e4da5bda6c7f8c718e36f28a6e8 |
ThreeNet | import torch
import torch.nn as nn
class ThreeNet(nn.Module):
"""
A network with three layers. This is used for testing a network with more
than one operation. The network has a convolution layer followed by two
fully connected layers.
"""
def __init__(self, input_dim: 'int', conv_dim: 'int',... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | synthara/M-SFV-SyntharaFVcore | ThreeNet | false | 10,899 | [
"Apache-2.0"
] | 0 | b4d2167a110aaecf3df442f58793ca2cb7b028ba | https://github.com/synthara/M-SFV-SyntharaFVcore/tree/b4d2167a110aaecf3df442f58793ca2cb7b028ba |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | XiangwenNing/pyGAT | GAT | false | 2,980 | [
"MIT"
] | 0 | c4bd8e2be044c6c7481d484875b3c318271cca9c | https://github.com/XiangwenNing/pyGAT/tree/c4bd8e2be044c6c7481d484875b3c318271cca9c |
DownsampleA | import torch
import torch.nn as nn
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat((x, x.mul(0)), 1)
def get... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | code-inter/leak | DownsampleA | false | 9,935 | [
"MIT"
] | 0 | 0e2b12a42f5fbaac4c5fa68627a21aa9a2f3d1d6 | https://github.com/code-inter/leak/tree/0e2b12a42f5fbaac4c5fa68627a21aa9a2f3d1d6 |
BertOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | ali-senguel/fairo-explore | BertOutput | false | 10,391 | [
"MIT"
] | 0 | 893481da270eed1e6d504c71e483d685ca9218d1 | https://github.com/ali-senguel/fairo-explore/tree/893481da270eed1e6d504c71e483d685ca9218d1 |
IrisClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc1 = nn.Linear(4, 10)
self.fc2 = nn.Linear(10, 10)
self.fc3 = nn.Linear(10, 3)
def forward(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | PeterSulcs/mlflow | IrisClassifier | false | 14,158 | [
"Apache-2.0"
] | 10,351 | 14c48e7bb1ca6cd6a3c1b249a486cd98bd5e7051 | https://github.com/PeterSulcs/mlflow/tree/14c48e7bb1ca6cd6a3c1b249a486cd98bd5e7051 |
L2ConstrainedLayer | import torch
from torch import nn
class L2ConstrainedLayer(nn.Module):
def __init__(self, alpha=16):
super().__init__()
self.alpha = alpha
def forward(self, x):
l2 = torch.sqrt((x ** 2).sum())
x = self.alpha * (x / l2)
return x
def get_inputs():
return [torch.ra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | mgoldchild/metric_learning | L2ConstrainedLayer | false | 16,031 | [
"MIT"
] | 58 | 97731bd0922b42df470ec6be34e1138bbcca5fb7 | https://github.com/mgoldchild/metric_learning/tree/97731bd0922b42df470ec6be34e1138bbcca5fb7 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dani3l125/TDNet | ScaledDotProductAttention | false | 15,104 | [
"MIT"
] | 195 | 3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 | https://github.com/dani3l125/TDNet/tree/3f8b5378fcc7f97c26b3760ddaf3d4402cf477d1 |
Mac_Pooling | import torch
import torch.nn as nn
class Mac_Pooling(nn.Module):
def __init__(self):
super(Mac_Pooling, self).__init__()
def forward(self, x):
dim = x.size()
pool = nn.MaxPool2d(dim[-1])
x = pool(x)
return x.view(dim[0], dim[1])
def get_inputs():
return [torch.r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | SIJIEJI/2020-ai-meets-beauty_ntubeauty | Mac_Pooling | false | 5,813 | [
"MIT"
] | 1 | fede564fb3e3029f3fadfe107484c5c7e39c29c5 | https://github.com/SIJIEJI/2020-ai-meets-beauty_ntubeauty/tree/fede564fb3e3029f3fadfe107484c5c7e39c29c5 |
UnaryMaxModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class UnaryMaxModule(torch.nn.Module):
def __init__(self):
super(UnaryMaxModule, self).__init__()
def forward(self, a):
return torch.max(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | UnaryMaxModule | false | 14,682 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class BertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(conf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Clemens123/transformers | BertOutput | false | 13,212 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
BPMLLLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import Tensor
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | PaulStryck/nih-chest-x-ray | BPMLLLoss | false | 931 | [
"MIT"
] | 0 | 92bfef80f49e7e38ad8a0156be43f10879b3f737 | https://github.com/PaulStryck/nih-chest-x-ray/tree/92bfef80f49e7e38ad8a0156be43f10879b3f737 |
DHead | import torch
import torch.nn as nn
from math import *
class DHead(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(256, 1, 4)
def forward(self, x):
output = torch.sigmoid(self.conv(x))
return output
def get_inputs():
return [torch.rand([4, 256, 6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import *
assert_size_stride = torch._C._dynamo.g... | a8252525/NIID-Bench | DHead | false | 3,038 | [
"MIT"
] | 0 | 33df8d3a7b941884eec3c7bd52adb8a9476eb282 | https://github.com/a8252525/NIID-Bench/tree/33df8d3a7b941884eec3c7bd52adb8a9476eb282 |
ExampleBackbone | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | HusterRC/mmsegmentation | ExampleBackbone | false | 5,326 | [
"Apache-2.0"
] | 1 | c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 | https://github.com/HusterRC/mmsegmentation/tree/c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 |
NormLoss | import torch
class NormLoss(torch.nn.Module):
"""
Norm penalty on function
parameters:
p - dimension of norm
"""
def __init__(self, p):
super(NormLoss, self).__init__()
self.p = p
def forward(self, beta):
return torch.norm(beta, p=self.p)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | Filco306/TopologyLayer | NormLoss | false | 13,681 | [
"MIT"
] | 250 | 1d6261017a80cff0ee06bb896ded40777b0989b4 | https://github.com/Filco306/TopologyLayer/tree/1d6261017a80cff0ee06bb896ded40777b0989b4 |
FourierEmbedding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | LS4GAN/uvcgan | FourierEmbedding | false | 8,419 | [
"BSD-2-Clause"
] | 20 | 376439ae2a9be684ff279ddf634fe137aadc5df5 | https://github.com/LS4GAN/uvcgan/tree/376439ae2a9be684ff279ddf634fe137aadc5df5 |
SoftDiceLoss | import torch
import torch.nn as nn
import torch._C
import torch.serialization
def get_tp_fp_fn_tn(net_output, gt, axes=None, mask=None, square=False):
"""
net_output must be (b, c, x, y(, z)))
gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | dkswxd/Swin-Transformer-Semantic-Segmentation | SoftDiceLoss | false | 1,855 | [
"Apache-2.0"
] | 0 | 6af19736e5492a01d8952d4ee86a6d59b21c2ae1 | https://github.com/dkswxd/Swin-Transformer-Semantic-Segmentation/tree/6af19736e5492a01d8952d4ee86a6d59b21c2ae1 |
Concat | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Concat(torch.nn.Module):
""" Concat module for a functional concat"""
def __init__(self, axis: 'int'=0):
super(Concat, self).__init__()
self.axis = axis
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | Rohan-Chaudhury/aimet | Concat | false | 17,995 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
N2 | import torch
import torch.nn as nn
import torch.utils
from typing import Tuple
from abc import ABC
from abc import abstractmethod
import torch.utils.data
class Regularizer(nn.Module, ABC):
@abstractmethod
def forward(self, factors: 'Tuple[torch.Tensor]'):
pass
class N2(Regularizer):
def __init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils
from typing import Tuple
from abc impo... | angusl95/darts-kbc | N2 | false | 1,442 | [
"Apache-2.0"
] | 0 | 85fc6f4bdb7ba73c07d96ce47e96634599b346f9 | https://github.com/angusl95/darts-kbc/tree/85fc6f4bdb7ba73c07d96ce47e96634599b346f9 |
PatchEmbed | import torch
import torch.nn as nn
import torch.nn.parallel
class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(self, img_size=224, stem_conv=False, stem_stride=1,
patch_size=8, in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dy... | Inch-Z/volo | PatchEmbed | false | 11,516 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
KLDivLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing... | masanorihirano/pytorch_extra_mhirano | KLDivLoss | false | 7,168 | [
"MIT"
] | 1 | d19e07445567c069793b7ca1a22a846d7cbce58d | https://github.com/masanorihirano/pytorch_extra_mhirano/tree/d19e07445567c069793b7ca1a22a846d7cbce58d |
HighwayFC | import torch
import torch.nn as nn
class HighwayFC(nn.Module):
def __init__(self, indim, outdim, activation='relu', bias=-1):
super(HighwayFC, self).__init__()
self.indim = indim
self.outdim = outdim
if activation == 'selu':
self.activation = nn.SELU()
elif act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | avinashsai/Highway-Networks | HighwayFC | false | 3,150 | [
"MIT"
] | 0 | fe30629e47b919776f981eaa2bea7d21e648a17f | https://github.com/avinashsai/Highway-Networks/tree/fe30629e47b919776f981eaa2bea7d21e648a17f |
LeastSquaresGenerativeAdversarialLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils... | Liuhong99/CST | LeastSquaresGenerativeAdversarialLoss | false | 8,494 | [
"MIT"
] | 20 | f6653a4ee7968fa3ba875a182670636f648be783 | https://github.com/Liuhong99/CST/tree/f6653a4ee7968fa3ba875a182670636f648be783 |
SurfaceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | kbarkevich/RITnet | SurfaceLoss | false | 7,015 | [
"MIT"
] | 1 | 5df66c656734aecd2987cf27d9359416b136af2e | https://github.com/kbarkevich/RITnet/tree/5df66c656734aecd2987cf27d9359416b136af2e |
NormalIsotropicCovarianceLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | bolajiy/beer | NormalIsotropicCovarianceLayer | false | 14,969 | [
"MIT"
] | 46 | 6fe968c7ca4864437890aa6bd705755c2580696e | https://github.com/bolajiy/beer/tree/6fe968c7ca4864437890aa6bd705755c2580696e |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
"""Attention mechanism written by Gustavo Aguilar https://github.com/gaguilar"""
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.da = hidden_size
self.dh = hidden_size
self.W = nn.Linear(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | DavidInWuhanChina/SemEval-2020-Task10 | Attention | false | 11,347 | [
"MIT"
] | 0 | aadc8030e0c5b49861daacdf7a581e034cbbb026 | https://github.com/DavidInWuhanChina/SemEval-2020-Task10/tree/aadc8030e0c5b49861daacdf7a581e034cbbb026 |
Mul | import torch
import torch as ch
class Mul(ch.nn.Module):
def __init__(self, weight):
super(Mul, self).__init__()
self.weight = weight
def forward(self, x):
return x * self.weight
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'weig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as ch
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | Hadisalman/ffcv | Mul | false | 13,750 | [
"Apache-2.0"
] | 1,969 | 64bd2b9e9c9fc3779ba13ef958ae479ecfac9c7f | https://github.com/Hadisalman/ffcv/tree/64bd2b9e9c9fc3779ba13ef958ae479ecfac9c7f |
CategoricalActor | import torch
from torch.distributions import Categorical
import torch.nn.functional as F
import torch.nn as nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class CategoricalActor(nn.Module):
def __init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | FlickerNiko/ai_lib | CategoricalActor | false | 13,700 | [
"MIT"
] | 99 | 7087d4569c9a827d35dd8735b55a080834d31a82 | https://github.com/FlickerNiko/ai_lib/tree/7087d4569c9a827d35dd8735b55a080834d31a82 |
HiResPose | import torch
import torch.nn as nn
from collections import OrderedDict
from typing import Tuple
import torch.nn.functional as F
class HiResPose(nn.Module):
"""
GNINA HiResPose model architecture.
Parameters
----------
input_dims: tuple
Model input dimensions (channels, depth, height, widt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RMeli/gnina-torch | HiResPose | false | 17,840 | [
"MIT"
] | 5 | eb57e2a62628d39f2a66e7fa1748e80705366761 | https://github.com/RMeli/gnina-torch/tree/eb57e2a62628d39f2a66e7fa1748e80705366761 |
NoiseLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AnimeshKoratana/blurryface | NoiseLayer | false | 49 | [
"Apache-2.0"
] | 0 | c6cb5feec02f6d5af3acb1678336800390715d65 | https://github.com/AnimeshKoratana/blurryface/tree/c6cb5feec02f6d5af3acb1678336800390715d65 |
MS_Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.multiprocessing
assert_size_stride = torch._C... | chiukin/RANet | MS_Block | false | 15,029 | [
"Apache-2.0"
] | 267 | 681a47d9b1f114653290678f02f2d3ecdf4010bc | https://github.com/chiukin/RANet/tree/681a47d9b1f114653290678f02f2d3ecdf4010bc |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | botkop/lark | FocalLoss | false | 1,571 | [
"Apache-2.0"
] | 0 | edb2defdb514213fc121418578b0d9006a55f3a0 | https://github.com/botkop/lark/tree/edb2defdb514213fc121418578b0d9006a55f3a0 |
InputProjectionA | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | anilsathyan7/Portrait-Segmentation | InputProjectionA | false | 14,878 | [
"MIT"
] | 537 | dbf69b043cf70d3362bc500ee620f20807e622d2 | https://github.com/anilsathyan7/Portrait-Segmentation/tree/dbf69b043cf70d3362bc500ee620f20807e622d2 |
EncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | WOMMOW/AIT | EncoderLayer | false | 1,229 | [
"MIT"
] | 0 | 305fe7962bf9c5c24b6854e3ff0b7e2e669bf5a5 | https://github.com/WOMMOW/AIT/tree/305fe7962bf9c5c24b6854e3ff0b7e2e669bf5a5 |
ComplexBatchNormalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | wizofe/urus-mri-recon | ComplexBatchNormalize | false | 4,535 | [
"MIT"
] | 0 | eab8e48dca31d2b936ce69ccc251ec5a4a10facc | https://github.com/wizofe/urus-mri-recon/tree/eab8e48dca31d2b936ce69ccc251ec5a4a10facc |
Fusion | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Fusion(nn.Module):
""" Crazy multi-modal fusion: negative squared difference minus relu'd sum
"""
def __init__(self):
super().__init__()
def forward(self, x, y):
return -(x - y) ** 2 + F.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | TranTony/DFAF-for-VQA.pytorch | Fusion | false | 11,939 | [
"MIT"
] | 0 | eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 | https://github.com/TranTony/DFAF-for-VQA.pytorch/tree/eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 |
DAModule | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Nitin-Mane/External-Attention-pytorch | DAModule | false | 14,128 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
DAModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LiChengChen666/DetectDee | DAModule | false | 9,834 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
GlobalAverage | import torch
import torch.nn as nn
class GlobalAverage(nn.Module):
def __init__(self, rank=2, keepdims=False):
"""
:param rank: dimension of image
:param keepdims: whether to preserve shape after averaging
"""
super().__init__()
self.axes = list(range(2, 2 + rank))... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jokingbear/DM | GlobalAverage | false | 6,976 | [
"MIT"
] | 1 | 9c4dada1756f3d866455a397511d4f3bacfadc60 | https://github.com/jokingbear/DM/tree/9c4dada1756f3d866455a397511d4f3bacfadc60 |
Conv | import torch
import torch.nn as nn
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
:param out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Numb523/FastSpeech2_emotion | Conv | false | 911 | [
"MIT"
] | 0 | a541ce89ddf66625ee57c0a294d0bec1ae701f0c | https://github.com/Numb523/FastSpeech2_emotion/tree/a541ce89ddf66625ee57c0a294d0bec1ae701f0c |
AttentionHead | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import Tensor
def scaled_dot_product_attention(query: 'torch.Tensor', key: 'torch.Tensor',
value: 'torch.Tensor') ->Tensor:
temp = query.bmm(key.transpose(1, 2))
scale = query.size(-1) ** 0.5
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | sabernn/vit-pytorch | AttentionHead | false | 4,227 | [
"MIT"
] | 0 | 21a2671aa92adb941a56ae629f6089f550949fb2 | https://github.com/sabernn/vit-pytorch/tree/21a2671aa92adb941a56ae629f6089f550949fb2 |
Gather1D | import torch
import torch.onnx
import torch.nn as nn
class Gather1D(nn.Module):
def forward(self, x):
return x[[2, 4, 5]]
def get_inputs():
return [torch.rand([6, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | mil-tokyo/webdnn | Gather1D | false | 16,076 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
NormedLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Parskatt/mmdetection | NormedLinear | false | 927 | [
"Apache-2.0"
] | 0 | ee4cfa29e7f479b2454b1f1355f8c05be62d8466 | https://github.com/Parskatt/mmdetection/tree/ee4cfa29e7f479b2454b1f1355f8c05be62d8466 |
DiceBCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | tfmoraes/deep_heart_torch | DiceBCELoss | false | 10,853 | [
"MIT"
] | 0 | 4168ce01d600e69baf82c752a3e57af86861b6ea | https://github.com/tfmoraes/deep_heart_torch/tree/4168ce01d600e69baf82c752a3e57af86861b6ea |
TorchMul | import torch
class TorchMul(torch.nn.Module):
def __init__(self):
super(TorchMul, self).__init__()
def forward(self, x, y):
return torch.mul(x, y)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NVIDIA-AI-IOT-private/torch2trt | TorchMul | false | 10,547 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
Quadratic | import torch
import torch.nn as nn
class Quadratic(nn.Module):
def __init__(self):
super(Quadratic, self).__init__()
def forward(self, x):
return x ** 2
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | craigxchen/Reinforcement-Learning-Function-Approximation | Quadratic | false | 6,485 | [
"MIT"
] | 1 | 09c4df1dd44c6a76a3f574bebc959a19b141f3fe | https://github.com/craigxchen/Reinforcement-Learning-Function-Approximation/tree/09c4df1dd44c6a76a3f574bebc959a19b141f3fe |
WeightedPool | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MicroTensor-ai/episodic-memory | WeightedPool | false | 11,702 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f |
ExampleBackbone | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class ExampleBackbone(nn.Module):
def __init__(self):
super(ExampleBackbone, self).__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
pass
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | HusterRC/mmsegmentation | ExampleBackbone | false | 5,326 | [
"Apache-2.0"
] | 1 | c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 | https://github.com/HusterRC/mmsegmentation/tree/c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 |
ReLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jiwidi/lightning-tutorials | ReLU | false | 15,698 | [
"Apache-2.0"
] | 114 | 70ba437447f345d4d6ba089d5b30fd1da2cbc04b | https://github.com/jiwidi/lightning-tutorials/tree/70ba437447f345d4d6ba089d5b30fd1da2cbc04b |
EqualLinear | from torch.autograd import Function
import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
if input.device.type == 'cpu':
if bias is not None:
rest_dim = [1] * (input.ndim - bias.ndim - 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
from torch import nn
from torch.... | CurtisASmith/stylegan2-pytorch | EqualLinear | false | 11,321 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 139ded3394718b9b8a727949dd46ad77ec2ec746 | https://github.com/CurtisASmith/stylegan2-pytorch/tree/139ded3394718b9b8a727949dd46ad77ec2ec746 |
AUXModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class AUXModule(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x):
x = F.adaptive_max_pool2d(x, output_size=(1, 1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gorogoroyasu/mlcomp | AUXModule | false | 15,440 | [
"Apache-2.0"
] | 166 | fc6572ca5b226b35df97f13badd4420b30468a3b | https://github.com/gorogoroyasu/mlcomp/tree/fc6572ca5b226b35df97f13badd4420b30468a3b |
AdditiveAttention | import torch
from torch import Tensor
from torch.functional import Tensor
import torch.nn as nn
class AdditiveAttention(nn.Module):
"""
Originally from:
https://arxiv.org/pdf/1409.0473v5.pdf
Also referenced to as Content Based Attention:
https://arxiv.org/pdf/1506.03134v1.pdf
Attenti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AxlAlm/SegNLP | AdditiveAttention | false | 4,881 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 |
DecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class XavierLinear(nn.Module):
def __init__(self, d_in, d_out, bias=True):
super().__init__()
self.linear = nn.Linear(d_in, d_out, bias=bias)
nn.init.xavier_normal_(self.linear.weight)
def forward(self, x):
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | muberraozmen/MrMP | DecoderLayer | false | 4,053 | [
"MIT"
] | 0 | da6bcccbad85a682c848ff4aa1121c773d779e57 | https://github.com/muberraozmen/MrMP/tree/da6bcccbad85a682c848ff4aa1121c773d779e57 |
GrayLoss | import torch
from torch import nn
class GrayLoss(nn.Module):
def __init__(self):
super(GrayLoss, self).__init__()
self.l1 = nn.L1Loss()
def forward(self, x):
y = torch.ones_like(x) / 2.0
return 1 / self.l1(x, y)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | GuYuanjie/Deep-Retinex-fusion | GrayLoss | false | 17,349 | [
"MIT"
] | 5 | ffa2a1689fd512c8820fd87cbf665c09bcb142b4 | https://github.com/GuYuanjie/Deep-Retinex-fusion/tree/ffa2a1689fd512c8820fd87cbf665c09bcb142b4 |
ConvBlock | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | aliasghar53/packnet-sfm | ConvBlock | false | 9,773 | [
"MIT"
] | 0 | d07dcbf026194b618a2bd9fc05b599563611f9a3 | https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3 |
Binary | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | yanxurui/portfolio | Binary | false | 4,600 | [
"MIT"
] | 0 | 032cf47ccac1c5815fd4827bf0d5f3cf43cec990 | https://github.com/yanxurui/portfolio/tree/032cf47ccac1c5815fd4827bf0d5f3cf43cec990 |
Net | import torch
import torch.nn as nn
class FcCat(nn.Module):
def __init__(self, nIn, nOut):
super(FcCat, self).__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
class Net(nn.Module):
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Sreehari-S/Tiramisu_DigestPath | Net | false | 1,081 | [
"Apache-2.0"
] | 0 | a884ee911bc60ce997996e0ec2e6036600ffcffa | https://github.com/Sreehari-S/Tiramisu_DigestPath/tree/a884ee911bc60ce997996e0ec2e6036600ffcffa |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bsgiovanini/transformer | MultiHeadAttention | false | 1,589 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
MLPTanH | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | kiathwe97/examples | MLPTanH | false | 10,353 | [
"BSD-3-Clause"
] | 0 | b4a8792023db8c50c7e9fb186bd982edd0dce3ce | https://github.com/kiathwe97/examples/tree/b4a8792023db8c50c7e9fb186bd982edd0dce3ce |
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