entry_point stringlengths 1 65 | original_triton_code stringlengths 4.5k 619k | python_code stringlengths 208 60.9k | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
LxmertSelfAttentionLayer | # 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... | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.checkpoint
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise Value... | 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.... | Clemens123/transformers | LxmertSelfAttentionLayer | false | 11,815 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
MegatronGelu | # 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 torch.nn
import torch.onnx
class MegatronGelu(torch.nn.Module):
def forward(self, x):
return x * 0.5 * (torch.erf(x / 1.41421) + 1.0)
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.... | RyanUnderhill/onnxruntime | MegatronGelu | false | 11,816 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
Alignment | # 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... | from _paritybench_helpers import _mock_config
from torch.nn import Module
import math
import torch
import torch.nn.functional as f
import torch.nn as nn
class Module(nn.Module):
def __init__(self):
super().__init__()
self.summary = {}
def add_summary(self, name, val):
if self.trainin... | 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.... | IamHimon/re2 | Alignment | false | 11,817 | [
"Apache-2.0"
] | 0 | d16b0ffc385f7b118a6160d035250da8d6320534 | https://github.com/IamHimon/re2/tree/d16b0ffc385f7b118a6160d035250da8d6320534 |
DeepNN_v2 | # 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 torch.nn as nn
class DeepNN_v2(nn.Module):
def __init__(self, X_dim, i_dropout_rate, h_dropout_rate):
super().__init__()
self.v2_layer1 = nn.Linear(X_dim, 256, bias=True)
self.v2_layer2 = nn.Linear(256, 256, bias=True)
self.v2_layer3 = nn.Linear(256, 1, bias=Tr... | 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 ... | SBIlab/NetBio | DeepNN_v2 | false | 11,818 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
TransformerDecoderLayer | # 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 math
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
"""
A model layer which implements a simplified version of masked attention, as
introduced by "Attention Is All You Need" (https://arxiv.org/abs/1706.03762).
Usage:
attn = MultiHeadAttention(embed_dim, num_heads... | 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.... | Michellemingxuan/stanford_cs231n | TransformerDecoderLayer | false | 11,819 | [
"MIT"
] | 0 | b1d0a5a4a3b2fe5d685e34a4ebd810cbc56ec143 | https://github.com/Michellemingxuan/stanford_cs231n/tree/b1d0a5a4a3b2fe5d685e34a4ebd810cbc56ec143 |
Downsample | # 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 torch.nn as nn
import torch.hub
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels,
kernel_size=3, st... | 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.hub
assert_size_stride = torch._C._dynamo.gua... | Rushi314/taming-transformers | Downsample | false | 11,820 | [
"MIT"
] | 0 | 4c0309823f57be3ca2266c1244e3efce13aaee98 | https://github.com/Rushi314/taming-transformers/tree/4c0309823f57be3ca2266c1244e3efce13aaee98 |
Net | # 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 torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(32 * 32 * 3, 512)
self.fc2 = nn.Linear(512, 128)
self.fc3 = nn.Linear(128, 10)
def forward(self, x):
x = x.vie... | 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_... | Rui-Chun/CNN-with-Numpy | Net | false | 11,821 | [
"MIT"
] | 0 | 0bc73040c7ada2581d2db3d6e4b2396fa98a4bde | https://github.com/Rui-Chun/CNN-with-Numpy/tree/0bc73040c7ada2581d2db3d6e4b2396fa98a4bde |
MetaLayerNorm | # 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 re
import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
---... | 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 re
import warnings
import torch.nn as nn
from collections import Ordered... | SDivakarBhat/pytorch-meta | MetaLayerNorm | false | 11,822 | [
"MIT"
] | 0 | 74cbc8ae625d85c6b954aad159ccb26b523b2240 | https://github.com/SDivakarBhat/pytorch-meta/tree/74cbc8ae625d85c6b954aad159ccb26b523b2240 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | # 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 torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency(torch
.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency
, 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.... | RyanUnderhill/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 11,823 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
MegatronFastGelu | # 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 torch.nn
import torch.onnx
class MegatronFastGelu(torch.nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 +
0.044715 * x * x)))
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.... | RyanUnderhill/onnxruntime | MegatronFastGelu | false | 11,824 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
MaskedMSELoss | # 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 torch.nn as nn
class MaskedMSELoss(nn.Module):
def __init__(self):
super(MaskedMSELoss, self).__init__()
self.loss = nn.MSELoss(reduction='sum')
def forward(self, pred, target, mask):
"""
pred -> batch*seq_len
target -> batch*seq_len
mask -... | 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... | RaleLee/conv-emotion | MaskedMSELoss | false | 11,825 | [
"MIT"
] | 0 | 1b07223cbdfd52eb31e913e982d18ff1ed3daf08 | https://github.com/RaleLee/conv-emotion/tree/1b07223cbdfd52eb31e913e982d18ff1ed3daf08 |
NeuralNetNonDifferentiableOutput | # 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 torch.nn
import torch.onnx
class NeuralNetNonDifferentiableOutput(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetNonDifferentiableOutput, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu = torch.... | 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
import torch.... | RyanUnderhill/onnxruntime | NeuralNetNonDifferentiableOutput | false | 11,826 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
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 math
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
"""
A model layer which implements a simplified version of masked attention, as
introduced by "Attention Is All You Need" (https://arxiv.org/abs/1706.03762).
Usage:
attn = MultiHeadAttention(embed_dim, num_heads... | 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.... | Michellemingxuan/stanford_cs231n | MultiHeadAttention | false | 11,827 | [
"MIT"
] | 0 | b1d0a5a4a3b2fe5d685e34a4ebd810cbc56ec143 | https://github.com/Michellemingxuan/stanford_cs231n/tree/b1d0a5a4a3b2fe5d685e34a4ebd810cbc56ec143 |
NeuralNetMultiplePositionalArgumentsVarKeyword | # 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 torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsVarKeyword(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsVarKeyword, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size... | 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
import torch.... | RyanUnderhill/onnxruntime | NeuralNetMultiplePositionalArgumentsVarKeyword | false | 11,828 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | # 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 torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency(torch.
nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency,
self).__init__()
s... | 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.... | RyanUnderhill/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | false | 11,829 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
MyCustomFunctionReluModel | # 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 torch.nn
import torch.onnx
class MyCustomFunctionReluModel(torch.nn.Module):
def __init__(self):
super().__init__()
class MyReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
... | 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
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_siz... | RyanUnderhill/onnxruntime | MyCustomFunctionReluModel | false | 11,830 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
DeepNN_v3 | # 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 torch.nn as nn
class DeepNN_v3(nn.Module):
def __init__(self, X_dim, i_dropout_rate, h_dropout_rate):
super().__init__()
self.v3_layer1 = nn.Linear(X_dim, 128, bias=True)
self.v3_layer2 = nn.Linear(128, 128, bias=True)
self.v3_layer3 = nn.Linear(128, 128, bias=... | 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 ... | SBIlab/NetBio | DeepNN_v3 | false | 11,831 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
DDPGConvBody | # 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 torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class DDPGConvBody(nn.Module):
def __init__(self, in_channels=4):
... | 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 ... | Rajawat23/DeepRL | DDPGConvBody | false | 11,832 | [
"MIT"
] | 0 | 9f77dfbc593f9c9055254c781f97983b9630dad2 | https://github.com/Rajawat23/DeepRL/tree/9f77dfbc593f9c9055254c781f97983b9630dad2 |
NeuralNetPartialNoGradModel | # 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 torch.nn
import torch.onnx
class NeuralNetPartialNoGradModel(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetPartialNoGradModel, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size).requires_grad_(
False)
... | 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
import torch.... | RyanUnderhill/onnxruntime | NeuralNetPartialNoGradModel | false | 11,833 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
DeepNN_v1 | # 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 torch.nn as nn
class DeepNN_v1(nn.Module):
def __init__(self, X_dim, i_dropout_rate, h_dropout_rate):
super().__init__()
self.v1_layer1 = nn.Linear(X_dim, 512, bias=True)
self.v1_layer2 = nn.Linear(512, 1, bias=True)
self.i_dropout = nn.Dropout(i_dropout_rate)
... | 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 ... | SBIlab/NetBio | DeepNN_v1 | false | 11,834 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
TwoLayerFCBodyWithAction | # 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 torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class TwoLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | 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 ... | RaviTej310/mrpvf | TwoLayerFCBodyWithAction | false | 11,835 | [
"MIT"
] | 0 | f026b4704f26b85161de26ada5d6390ab549fbbd | https://github.com/RaviTej310/mrpvf/tree/f026b4704f26b85161de26ada5d6390ab549fbbd |
down | # 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 torch.nn.functional as F
import torch.nn as nn
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class to create a UNet like NN architecture.
... | 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... | Remosy/v2e | down | false | 11,836 | [
"MIT"
] | 0 | efc81cbcc113ca55d1631603323150be5ef8eb30 | https://github.com/Remosy/v2e/tree/efc81cbcc113ca55d1631603323150be5ef8eb30 |
_Transition | # 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... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torchvision.transforms import *
class _Transition(nn.Module):
def __init__(self, in_channels, args):
super(_Transition, self).__init__()
self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x... | 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
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | RobbieEarle/robustness | _Transition | false | 11,837 | [
"Apache-2.0"
] | 0 | 2f4381900015bf7fcd9975d43b8104d2d14f8568 | https://github.com/RobbieEarle/robustness/tree/2f4381900015bf7fcd9975d43b8104d2d14f8568 |
DeepNN_v4 | # 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 torch.nn as nn
class DeepNN_v4(nn.Module):
def __init__(self, X_dim, i_dropout_rate, h_dropout_rate):
super().__init__()
self.v4_layer1 = nn.Linear(X_dim, 128, bias=True)
self.v4_layer2 = nn.Linear(128, 128, bias=True)
self.v4_layer3 = nn.Linear(128, 64, bias=T... | 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.... | SBIlab/NetBio | DeepNN_v4 | false | 11,838 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
ProposalNet | # 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 torch.nn as nn
import torch.utils.data
class ProposalNet(nn.Module):
def __init__(self, in_features=2048):
super(ProposalNet, self).__init__()
self.down1 = nn.Conv2d(2048, 128, 3, 1, 1)
self.down2 = nn.Conv2d(128, 128, 3, 2, 1)
self.down3 = nn.Conv2d(128, 128, ... | 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 ... | Liuhongzhi2018/ClassNet | ProposalNet | false | 11,839 | [
"MIT"
] | 0 | 7d427dc9b8c38abf0a4eedfdeb75c09c59aa7185 | https://github.com/Liuhongzhi2018/ClassNet/tree/7d427dc9b8c38abf0a4eedfdeb75c09c59aa7185 |
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 typing import Optional
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
"""
Overview:
Implementation of dot product attentionn with scaling.
"""
def __init__(self, d_k: 'int', dropout: 'float'=0.0) ->None:
super(Scaled... | 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.... | PaParaZz1/DI-engine | ScaledDotProductAttention | false | 11,840 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
LSTM | # 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 torch.nn as nn
import torch.nn.functional as F
class LSTM(nn.Module):
def __init__(self, input_size, cell_size, hidden_size):
"""
cell_size is the size of cell_state.
hidden_size is the size of hidden_state, or say the output_state of each step
"""
supe... | 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 ... | SakastLord/STGAT | LSTM | false | 11,841 | [
"MIT"
] | 0 | 664843b3a55ac55383de1d5400d731376476ea03 | https://github.com/SakastLord/STGAT/tree/664843b3a55ac55383de1d5400d731376476ea03 |
Encoder | # 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 torch.nn as nn
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=kernel_size // 2, bias=bias... | 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... | PaParaZz1/DI-engine | Encoder | false | 11,842 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
TimeBlock | # 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 torch.nn as nn
import torch.nn.functional as F
class TimeBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
:param in_channels: Number of input features at each node in each time
step.
:param out_channels: Desired number of outp... | 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_... | SakastLord/STGAT | TimeBlock | false | 11,843 | [
"MIT"
] | 0 | 664843b3a55ac55383de1d5400d731376476ea03 | https://github.com/SakastLord/STGAT/tree/664843b3a55ac55383de1d5400d731376476ea03 |
PixBlock | # 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 torch.nn as nn
class PixBlock(nn.Module):
def __init__(self, in_size, out_size=3, scale=2, norm=None):
super(PixBlock, self).__init__()
self.conv1 = nn.Conv2d(in_size, out_size * 2 ** scale, 1, 1)
self.up = nn.PixelShuffle(scale)
def forward(self, x):
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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | MohamadHMousavi/demo_wsi_superres | PixBlock | false | 11,844 | [
"MIT"
] | 0 | 7e846470aa228affa62ea77c38c138dde087a0de | https://github.com/MohamadHMousavi/demo_wsi_superres/tree/7e846470aa228affa62ea77c38c138dde087a0de |
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
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
def __init__(self, embed_dim, num_heads, attn_dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
"""
Multi-headed attention. This module can... | 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.... | SCUT-IEL/CMAA | MultiheadAttention | false | 11,845 | [
"MIT"
] | 0 | 1af9e7a7a75e754a7208e361d8128ef58b716941 | https://github.com/SCUT-IEL/CMAA/tree/1af9e7a7a75e754a7208e361d8128ef58b716941 |
TokenEmbedding | # 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 math
import torch
from torch import Tensor
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TokenEmbedding(nn.Module):
def __init__(self, vocab_size: 'int', emb_... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | LeeSHa00/PyTorch-tutorials-kr | TokenEmbedding | false | 11,846 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
GLU | # 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 torch.nn as nn
class GLU(nn.Module):
"""
Overview:
Gating Linear Unit.
This class does a thing like this:
.. code::python
# Inputs: input, context, output_size
# The gate value is a learnt function of the input.
gate = sigmoid(l... | 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... | PaParaZz1/DI-engine | GLU | false | 11,847 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
LabelSmoothCELoss | # 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 torch.nn.functional as F
import torch.nn as nn
def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
) ->torch.FloatTensor:
"""
Overview:
Convert a ``torch.LongTensor`` to one hot encoding.
This implementation can be slightly faster than ``torch.nn.fu... | 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
... | PaParaZz1/DI-engine | LabelSmoothCELoss | false | 11,848 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
TracedModule | # 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 torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TracedModule(torch.nn.Module):
def forward(self, x):
x = x.type(torch.float32)
return torch.floor(torch.sqrt(x) ... | 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.quantization
import torch.onnx
import torch.nn.parallel
import tor... | LeeSHa00/PyTorch-tutorials-kr | TracedModule | false | 11,849 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
SENet | # 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 torch.nn as nn
class SENet(nn.Module):
"""support estimation network"""
def __init__(self, input_size: 'int', hidden_size: 'int', output_dims:
'int') ->None:
super(SENet, self).__init__()
self.l_1 = nn.Linear(input_size, hidden_size)
self.l_2 = nn.Linear(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.triton_helpers import libdevice
import torch.nn as ... | PaParaZz1/DI-engine | SENet | false | 11,850 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
Critic | # 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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
""" outputs the limits for the values in the hidden layer for initialisation"""
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
... | 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 numpy as np
import tor... | SHIVOH/DeepReinforcementLearning-DDPG-for-RoboticsControl | Critic | false | 11,851 | [
"MIT"
] | 0 | f3e811a3ae3eb603173c2475bbfe1de91074ecdc | https://github.com/SHIVOH/DeepReinforcementLearning-DDPG-for-RoboticsControl/tree/f3e811a3ae3eb603173c2475bbfe1de91074ecdc |
ResidualBlock | # 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 torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, activation='relu'):
super().__init__()
self.in_channels, self.out_channels, self.activation = (in_channels,
out_channels, activation)
self.blocks = nn.Identity()
... | 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... | PaParaZz1/DI-engine | ResidualBlock | false | 11,852 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
Conv1d | # 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 torch.nn as nn
import torch.nn.functional as F
class Conv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding='same'):
"""
inputs: [N, T, C_in]
outputs: [N, T, C_out]
"""
super().__init__()
if paddi... | 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... | Sala7efelninja/GST-Tacotron | Conv1d | false | 11,853 | [
"MIT"
] | 0 | e69a5663832a2c3639d4afbb85092a35be621380 | https://github.com/Sala7efelninja/GST-Tacotron/tree/e69a5663832a2c3639d4afbb85092a35be621380 |
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
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
input:
query --- [N, T_q, query_dim]
key --- [N, T_k, key_dim]
output:
out --- [N, T_q, num_units]
"""
def __init__(self, query_dim, key_dim, num_units, num_heads):
... | 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.... | Sala7efelninja/GST-Tacotron | MultiHeadAttention | false | 11,854 | [
"MIT"
] | 0 | e69a5663832a2c3639d4afbb85092a35be621380 | https://github.com/Sala7efelninja/GST-Tacotron/tree/e69a5663832a2c3639d4afbb85092a35be621380 |
ATOCAttentionUnit | # 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 typing import Union
from typing import Dict
import torch.nn as nn
class ATOCAttentionUnit(nn.Module):
"""
Overview:
the attention unit of the atoc network. We now implement it as two-layer MLP, same as the original paper
Interface:
__init__, forward
.. note::
... | 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_... | PaParaZz1/DI-engine | ATOCAttentionUnit | false | 11,855 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
Head | # 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 torch.nn as nn
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=kernel_size // 2, bias=bias... | 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... | PaParaZz1/DI-engine | Head | false | 11,856 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
FeatureEmbedder | # 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 numpy as np
import torch.nn as nn
from torch.utils import tensorboard as tensorboard
class FeatureEmbedder(nn.Module):
def __init__(self, d_feat, d_model):
super(FeatureEmbedder, self).__init__()
self.d_model = d_model
self.embedder = nn.Linear(d_feat, d_model)
... | 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
from to... | Harbar-Inbound/BMT | FeatureEmbedder | false | 11,857 | [
"MIT"
] | 0 | ec8826f0633db754c7ea8d206672aa0b6b6048fd | https://github.com/Harbar-Inbound/BMT/tree/ec8826f0633db754c7ea8d206672aa0b6b6048fd |
BinaryCrossEntropyLoss | # 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 torch.nn as nn
class BinaryCrossEntropyLoss(nn.Module):
"""Cross entropy loss with label smoothing regularizer.
Reference:
Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
With label smoothing, the label :math:`y` for a class is computed by... | 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... | RndmVariableQ/deep-person-reid | BinaryCrossEntropyLoss | false | 11,858 | [
"MIT"
] | 0 | 9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 | https://github.com/RndmVariableQ/deep-person-reid/tree/9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 |
Skew | # 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 torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Skew(nn.Module):
def forward(self, X):
A = X.triu(1)
return A - A.transpose(-1, -2)
d... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | LeeSHa00/PyTorch-tutorials-kr | Skew | false | 11,859 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
MetaBilinear | # 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 re
import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
---... | 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 re
import warnings
import torch.nn as nn
from collections import OrderedDict
assert_size_stride = torch._C._dynamo.guards.assert_size... | SDivakarBhat/pytorch-meta | MetaBilinear | false | 11,860 | [
"MIT"
] | 0 | 74cbc8ae625d85c6b954aad159ccb26b523b2240 | https://github.com/SDivakarBhat/pytorch-meta/tree/74cbc8ae625d85c6b954aad159ccb26b523b2240 |
BridgeConnection | # 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 torch.nn as nn
from torch.utils import tensorboard as tensorboard
class BridgeConnection(nn.Module):
def __init__(self, in_dim, out_dim, dout_p):
super(BridgeConnection, self).__init__()
self.norm = nn.LayerNorm(in_dim)
self.linear = nn.Linear(in_dim, out_dim)
... | 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.... | Harbar-Inbound/BMT | BridgeConnection | false | 11,861 | [
"MIT"
] | 0 | ec8826f0633db754c7ea8d206672aa0b6b6048fd | https://github.com/Harbar-Inbound/BMT/tree/ec8826f0633db754c7ea8d206672aa0b6b6048fd |
OutputTransition | # 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 nn
class OutputTransition(nn.Module):
def __init__(self, inChans, n_labels):
super(OutputTransition, self).__init__()
self.final_conv = nn.Conv3d(inChans, n_labels, kernel_size=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.sigm... | 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | SeanDeloddere/ModelsGenesis | OutputTransition | false | 11,862 | [
"MIT"
] | 0 | 1c4d1439626b42906311a38aa5f8d4fbd7a2517a | https://github.com/SeanDeloddere/ModelsGenesis/tree/1c4d1439626b42906311a38aa5f8d4fbd7a2517a |
MultiheadedAttention | # 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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
from torch.utils import tensorboard as tensorboard
def attention(Q, K, V, mask, dropout=None):
d_k = Q.size(-1)
QKt = Q.matmul(K.transpose(-1, -2))
sm_input = QKt / np.sqrt(d_k)
if mask is not None:
sm_input ... | 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.... | Harbar-Inbound/BMT | MultiheadedAttention | false | 11,863 | [
"MIT"
] | 0 | ec8826f0633db754c7ea8d206672aa0b6b6048fd | https://github.com/Harbar-Inbound/BMT/tree/ec8826f0633db754c7ea8d206672aa0b6b6048fd |
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
import torch.nn.functional as F
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, in_dim, out_dim, out_heads, relation_dim=0, residual
=False, projection=True, layer_norm=True):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim... | 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.... | PaParaZz1/DI-engine | MultiHeadAttention | false | 11,864 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
RewardModelNetwork | # 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 torch.nn as nn
class RewardModelNetwork(nn.Module):
def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
'int') ->None:
super(RewardModelNetwork, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, ou... | 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 ... | PaParaZz1/DI-engine | RewardModelNetwork | false | 11,865 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
AvgPoolPad | # 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 torch.nn as nn
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=False)
def forwa... | 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... | RndmVariableQ/deep-person-reid | AvgPoolPad | false | 11,866 | [
"MIT"
] | 0 | 9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 | https://github.com/RndmVariableQ/deep-person-reid/tree/9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 |
Log_Cosh_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
class Log_Cosh_Loss(torch.nn.Module):
def forward(self, logits, labels):
return torch.mean(torch.log(torch.cosh(labels - logits)))
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | ShengboWang1/wave-u-net-DEMAND28 | Log_Cosh_Loss | false | 11,867 | [
"MIT"
] | 0 | fe8b57220d885d5fdad33b303c0565f2286ba549 | https://github.com/ShengboWang1/wave-u-net-DEMAND28/tree/fe8b57220d885d5fdad33b303c0565f2286ba549 |
Simplified_Pose_Model | # 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 collections import OrderedDict
import torch.nn as nn
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if 'pool' in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
layers.append((layer_name, l... | 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 collections import Order... | Schwartz-Zha/My_Pose_Estimation | Simplified_Pose_Model | false | 11,868 | [
"MIT"
] | 0 | 0ccaccf58498b2200842c155b735e1103c28c5ba | https://github.com/Schwartz-Zha/My_Pose_Estimation/tree/0ccaccf58498b2200842c155b735e1103c28c5ba |
HardAttn | # 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.nn import functional as F
import torch.nn as nn
class HardAttn(nn.Module):
"""Hard Attention (Sec. 3.1.II)"""
def __init__(self, in_channels):
super(HardAttn, self).__init__()
self.fc = nn.Linear(in_channels, 4 * 2)
self.init_params()
def init_params(self)... | 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 ... | RndmVariableQ/deep-person-reid | HardAttn | false | 11,869 | [
"MIT"
] | 0 | 9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 | https://github.com/RndmVariableQ/deep-person-reid/tree/9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 |
FocalLossSigmoid | # 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 torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class FocalLossSigmoid(nn.Module):
"""
sigmoid version focal loss
"""
def __init__(self, alpha=0.25, gamma=2, size_average=False):
super(FocalLossSigmoid, self).__init__()
self.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
... | Shi-Yuyao/SSD_Pytorch | FocalLossSigmoid | false | 11,870 | [
"MIT"
] | 0 | 870732682935a8523b5232fac3bdb080c5a59cf9 | https://github.com/Shi-Yuyao/SSD_Pytorch/tree/870732682935a8523b5232fac3bdb080c5a59cf9 |
MaxPoolPad | # 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 torch.nn as nn
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
def forward(self, x):
x = self.pad(x)
x = self.pool(x)
... | 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... | RndmVariableQ/deep-person-reid | MaxPoolPad | false | 11,871 | [
"MIT"
] | 0 | 9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 | https://github.com/RndmVariableQ/deep-person-reid/tree/9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 |
VAE | # 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 nn
import torch.nn.functional as F
class VAE(nn.Module):
def __init__(self, n_features):
super(VAE, self).__init__()
self.fc1 = nn.Linear(n_features, 1000)
self.fc2 = nn.Linear(1000, n_features)
def encode(self, x):
h1 = F.relu(self.fc1(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
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | ShengquanChen/stPlus | VAE | false | 11,872 | [
"MIT"
] | 0 | b2af43a4fe78230ddf95cab75c114e25527800e1 | https://github.com/ShengquanChen/stPlus/tree/b2af43a4fe78230ddf95cab75c114e25527800e1 |
ContinousRotReprDecoder | # 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 torch.nn as nn
import torch.nn.functional as F
class ContinousRotReprDecoder(nn.Module):
def __init__(self):
super(ContinousRotReprDecoder, self).__init__()
def forward(self, module_input):
reshaped_input = module_input.view(-1, 3, 2)
b1 = F.normalize(reshaped_inp... | 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... | ShivamDuggal4/human_body_prior | ContinousRotReprDecoder | false | 11,873 | [
"Xnet",
"X11"
] | 0 | e5544560e98ff3bb6d2492b2b32660dd3defed92 | https://github.com/ShivamDuggal4/human_body_prior/tree/e5544560e98ff3bb6d2492b2b32660dd3defed92 |
ScaleNorm | # 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 torch.nn as nn
class ScaleNorm(nn.Module):
"""ScaleNorm"""
def __init__(self, scale, eps=1e-05):
super(ScaleNorm, self).__init__()
self.scale = scale
self.eps = eps
def forward(self, x):
norm = self.scale / torch.norm(x, dim=1, keepdim=True).clamp(min=... | 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... | Siujohnjai/MS-G3D | ScaleNorm | false | 11,874 | [
"MIT"
] | 0 | 615b1002ba1780f6d1fc4f7b93c9525c07aeed6a | https://github.com/Siujohnjai/MS-G3D/tree/615b1002ba1780f6d1fc4f7b93c9525c07aeed6a |
Charbonnier | # 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 torch.nn as nn
import torch.utils.model_zoo
class Charbonnier(nn.Module):
def __init__(self):
super(Charbonnier, self).__init__()
self.eps = 1e-06
def forward(self, X, Y):
diff = torch.add(X, -Y)
error = torch.sqrt(diff * diff + self.eps)
loss = to... | 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
import... | SimoneDutto/EDSR | Charbonnier | false | 11,875 | [
"MIT"
] | 0 | a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 | https://github.com/SimoneDutto/EDSR/tree/a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 |
UnfoldTemporalWindows | # 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 torch.nn as nn
class UnfoldTemporalWindows(nn.Module):
def __init__(self, window_size, window_stride, window_dilation=1):
super().__init__()
self.window_size = window_size
self.window_stride = window_stride
self.window_dilation = window_dilation
self.pa... | 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... | Siujohnjai/MS-G3D | UnfoldTemporalWindows | false | 11,876 | [
"MIT"
] | 0 | 615b1002ba1780f6d1fc4f7b93c9525c07aeed6a | https://github.com/Siujohnjai/MS-G3D/tree/615b1002ba1780f6d1fc4f7b93c9525c07aeed6a |
Policy | # 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 torch.nn.functional as F
import torch.nn as nn
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.conv1 = nn.Conv2d(2, 4, kernel_size=6, stride=2, bias=False)
self.conv2 = nn.Conv2d(4, 16, kernel_size=6, stride=4)
self.size = 9 * 9... | 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_... | ShirelJosef/deep-reinforcement-learning | Policy | false | 11,877 | [
"MIT"
] | 0 | 63979b975c71e730c9d4c66e39efac210260dd18 | https://github.com/ShirelJosef/deep-reinforcement-learning/tree/63979b975c71e730c9d4c66e39efac210260dd18 |
FeatureResizer | # 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 torch.utils.data
import torch
import torch.nn
import torch.optim
import torch.utils
from torch import nn
import torch.distributed
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a l... | 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.utils.... | ShoufaChen/mdetr-1 | FeatureResizer | false | 11,878 | [
"Apache-2.0"
] | 0 | 3d9e40891ffdd39d6a5bf56730d468ace142752f | https://github.com/ShoufaChen/mdetr-1/tree/3d9e40891ffdd39d6a5bf56730d468ace142752f |
Cauchy | # 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 torch.nn as nn
import torch.utils.model_zoo
class Cauchy(nn.Module):
def __init__(self):
super(Cauchy, self).__init__()
self.c = 1.0
def forward(self, X, Y):
r = torch.add(X, -Y)
ra = torch.abs(r)
error = 0.5 * self.c ** 2 * torch.log(1 + (ra / sel... | 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
... | SimoneDutto/EDSR | Cauchy | false | 11,879 | [
"MIT"
] | 0 | a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 | https://github.com/SimoneDutto/EDSR/tree/a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 |
StableBCELoss | # 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
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def get_in... | 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
assert_size_stride = t... | Song-Jingyu/Cylinder3D | StableBCELoss | false | 11,880 | [
"Apache-2.0"
] | 0 | 36b59db5b45850b9657a9606e39c084dd650d750 | https://github.com/Song-Jingyu/Cylinder3D/tree/36b59db5b45850b9657a9606e39c084dd650d750 |
BCEAfterSigmoidLoss | # 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 import nn
from torch.nn import functional
import torch.autograd
class Loss(nn.Module):
"""A loss function."""
class PointwiseLoss(Loss):
"""Pointwise loss functions compute an independent loss term for each triple-label pair."""
class BCEAfterSigmoidLoss(PointwiseLoss):
"""A lo... | 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 torch ... | Sina-Baharlou/pykeen | BCEAfterSigmoidLoss | false | 11,881 | [
"MIT"
] | 0 | 89984e0f7a490f3c0f0d936564b7744097130d15 | https://github.com/Sina-Baharlou/pykeen/tree/89984e0f7a490f3c0f0d936564b7744097130d15 |
Fair | # 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 torch.nn as nn
import torch.utils.model_zoo
class Fair(nn.Module):
def __init__(self):
super(Fair, self).__init__()
self.c = 1.0
def forward(self, X, Y):
r = torch.add(X, -Y)
ra = torch.abs(r)
error = self.c ** 2 * (ra / self.c - torch.log(1 + 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 math as tl_math
import torch.nn as nn
... | SimoneDutto/EDSR | Fair | false | 11,882 | [
"MIT"
] | 0 | a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 | https://github.com/SimoneDutto/EDSR/tree/a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 |
OneLayerFCBodyWithAction | # 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 torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class OneLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | 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 ... | RaviTej310/mrpvf | OneLayerFCBodyWithAction | false | 11,883 | [
"MIT"
] | 0 | f026b4704f26b85161de26ada5d6390ab549fbbd | https://github.com/RaviTej310/mrpvf/tree/f026b4704f26b85161de26ada5d6390ab549fbbd |
Actor | # 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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | 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.... | ShirelJosef/deep-reinforcement-learning | Actor | false | 11,884 | [
"MIT"
] | 0 | 63979b975c71e730c9d4c66e39efac210260dd18 | https://github.com/ShirelJosef/deep-reinforcement-learning/tree/63979b975c71e730c9d4c66e39efac210260dd18 |
Encoder | # 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 nn
from torch.nn import functional as F
class Encoder(nn.Module):
def __init__(self, channel=512, out_class=1, is_dis=True):
super(Encoder, self).__init__()
self.is_dis = is_dis
self.channel = channel
n_class = out_class
self.conv1 = nn.Conv3... | 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.... | ShadowTwin41/alpha-WGAN-SigmaRat | Encoder | false | 11,885 | [
"MIT"
] | 0 | 051bb8c5d7b8248e9c724d3de87c0fd771d7070f | https://github.com/ShadowTwin41/alpha-WGAN-SigmaRat/tree/051bb8c5d7b8248e9c724d3de87c0fd771d7070f |
Critic | # 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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | 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
assert_size_stride = torch._C._dynamo.g... | ShirelJosef/deep-reinforcement-learning | Critic | false | 11,886 | [
"MIT"
] | 0 | 63979b975c71e730c9d4c66e39efac210260dd18 | https://github.com/ShirelJosef/deep-reinforcement-learning/tree/63979b975c71e730c9d4c66e39efac210260dd18 |
RegressorNet | # 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 numpy as np
from torch import nn
from torch import optim
from torch import relu
def weighted_mse_loss(inputs, target, sample_weight):
if sample_weight is not None:
return (sample_weight * (inputs - target) ** 2).mean()
else:
return ((inputs - target) ** 2).mean()
class 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
import numpy as np
from torch... | SirPopiel/IWDA | RegressorNet | false | 11,887 | [
"MIT"
] | 0 | 5693b0704f1abf9f69f92fba243599c5f4056a3c | https://github.com/SirPopiel/IWDA/tree/5693b0704f1abf9f69f92fba243599c5f4056a3c |
MultiAttributeLoss | # 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 torch.nn.functional as F
class MultiAttributeLoss(torch.nn.Module):
def __init__(self):
super(MultiAttributeLoss, self).__init__()
def forward(self, input, target):
product = 1
count = len(input)
for i in range(count):
attribute_loss = F.cross_... | 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
assert_size_stride = t... | Spandan-Madan/generalization_biased_category_pose | MultiAttributeLoss | false | 11,888 | [
"MIT"
] | 0 | c7c289c9a75544782d5240af2286cfdd03c4b35e | https://github.com/Spandan-Madan/generalization_biased_category_pose/tree/c7c289c9a75544782d5240af2286cfdd03c4b35e |
TorchJaccardLoss | # 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
class TorchJaccardLoss(torch.nn.modules.Module):
def __init__(self):
super(TorchJaccardLoss, self).__init__()
def forward(self, outputs, targets):
eps = 1e-15
jaccard_target = (targets == 1).float()
jaccard_output = torch.sigmoid(outputs)
intersection = (... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Spiruel/solaris | TorchJaccardLoss | false | 11,889 | [
"Apache-2.0"
] | 0 | eb2ce05265a462d69b01ee2b621a85a3e9082402 | https://github.com/Spiruel/solaris/tree/eb2ce05265a462d69b01ee2b621a85a3e9082402 |
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
from torch.nn import functional as F
import torch.nn as nn
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, self.inplace) / 6.0
return out * x
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | SpikeKing/MobileNetV3-Classification-PyTorch | h_swish | false | 11,890 | [
"MIT"
] | 0 | ab8d64c27ace7c70bfd1611bd8452947218d9b21 | https://github.com/SpikeKing/MobileNetV3-Classification-PyTorch/tree/ab8d64c27ace7c70bfd1611bd8452947218d9b21 |
TFSamepaddingLayer | # 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 torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class TFSamepaddingLayer(nn.Module):
"""To align with tf `same` padding.
Putting this before any conv layer that need padding
Assuming kernel has Height == Width for simplicity
"""
def __init__(self, ... | 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Srijay-lab/hover_net | TFSamepaddingLayer | false | 11,891 | [
"MIT"
] | 0 | 3f28f97bc1ed892bbe00b75a06be4334743d47d5 | https://github.com/Srijay-lab/hover_net/tree/3f28f97bc1ed892bbe00b75a06be4334743d47d5 |
FreqEncoder | # 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 torch.nn as nn
class FreqEncoder(nn.Module):
def __init__(self, input_dim, max_freq_log2, N_freqs, log_sampling=True,
include_input=True, periodic_fns=(torch.sin, torch.cos)):
super().__init__()
self.input_dim = input_dim
self.include_input = include_input
... | 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... | StanfordMSL/torch-ngp | FreqEncoder | false | 11,892 | [
"MIT"
] | 0 | fc5c70bd5739ce39f7f9765e2ac73ecab86bc64a | https://github.com/StanfordMSL/torch-ngp/tree/fc5c70bd5739ce39f7f9765e2ac73ecab86bc64a |
DepthL1Loss | # 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 torch.nn as nn
class DepthL1Loss(nn.Module):
def __init__(self, eps=1e-05):
super(DepthL1Loss, self).__init__()
self.eps = eps
def forward(self, pred, gt):
bs = pred.size()[0]
img1 = torch.zeros_like(pred)
img2 = torch.zeros_like(gt)
img1 =... | 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
... | StannisZhou/FFB6D | DepthL1Loss | false | 11,893 | [
"MIT"
] | 0 | 5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe | https://github.com/StannisZhou/FFB6D/tree/5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe |
OFLoss | # 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.nn.modules.loss import _Loss
def of_l1_loss(pred_ofsts, kp_targ_ofst, labels, sigma=1.0, normalize=True,
reduce=False):
"""
:param pred_ofsts: [bs, n_kpts, n_pts, c]
:param kp_targ_ofst: [bs, n_pts, n_kpts, c]
:param labels: [bs, n_pts, 1]
"""
w = (... | 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
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | StannisZhou/FFB6D | OFLoss | false | 11,894 | [
"MIT"
] | 0 | 5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe | https://github.com/StannisZhou/FFB6D/tree/5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe |
TorchFocalLoss | # 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 torch.nn.functional as F
from torch import nn
class TorchFocalLoss(nn.Module):
"""Implementation of Focal Loss[1]_ modified from Catalyst [2]_ .
Arguments
---------
gamma : :class:`int` or :class:`float`
Focusing parameter. See [1]_ .
alpha : :class:`int` or :class:`fl... | 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 torch ... | Spiruel/solaris | TorchFocalLoss | false | 11,895 | [
"Apache-2.0"
] | 0 | eb2ce05265a462d69b01ee2b621a85a3e9082402 | https://github.com/Spiruel/solaris/tree/eb2ce05265a462d69b01ee2b621a85a3e9082402 |
CosLoss | # 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.nn.modules.loss import _Loss
class CosLoss(_Loss):
def __init__(self, eps=1e-05):
super(CosLoss, self).__init__(True)
self.eps = eps
def forward(self, pred_ofsts, kp_targ_ofst, labels, normalize=True):
"""
:param pred_ofsts: [bs, n_kpts, n_pts, c]... | 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
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.g... | StannisZhou/FFB6D | CosLoss | false | 11,896 | [
"MIT"
] | 0 | 5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe | https://github.com/StannisZhou/FFB6D/tree/5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe |
Critic | # 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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, full_state_size, full_action_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
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | SriramPingali/P3_collaborate_complete | Critic | false | 11,897 | [
"MIT"
] | 0 | 66df22c9eb7577b15adcaa7bbc1796dbd333af2e | https://github.com/SriramPingali/P3_collaborate_complete/tree/66df22c9eb7577b15adcaa7bbc1796dbd333af2e |
OfstMapL1Loss | # 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 torch.nn as nn
class OfstMapL1Loss(nn.Module):
def __init__(self, eps=1e-05):
super().__init__()
self.eps = eps
def forward(self, rgb_labels, pred, gt, normalize=True, reduce=True):
wgt = (rgb_labels > 1e-08).float()
bs, n_kpts, c, h, w = pred.size()
... | 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... | StannisZhou/FFB6D | OfstMapL1Loss | false | 11,898 | [
"MIT"
] | 0 | 5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe | https://github.com/StannisZhou/FFB6D/tree/5e7534805cd2e397427886d9a2a8ecfbb4f6cdfe |
Envelope | # 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 torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | THinnerichs/pytorch_geometric | Envelope | false | 11,899 | [
"MIT"
] | 0 | 90c2126895b21313a23657f4e845acc782d11bf5 | https://github.com/THinnerichs/pytorch_geometric/tree/90c2126895b21313a23657f4e845acc782d11bf5 |
Actor | # 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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, fc1_uni... | 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 numpy as np
... | SriramPingali/P3_collaborate_complete | Actor | false | 11,900 | [
"MIT"
] | 0 | 66df22c9eb7577b15adcaa7bbc1796dbd333af2e | https://github.com/SriramPingali/P3_collaborate_complete/tree/66df22c9eb7577b15adcaa7bbc1796dbd333af2e |
Actor | # 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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
""" outputs the limits for the values in the hidden layer for initialisation"""
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
... | 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.... | SHIVOH/DeepReinforcementLearning-DDPG-for-RoboticsControl | Actor | false | 11,901 | [
"MIT"
] | 0 | f3e811a3ae3eb603173c2475bbfe1de91074ecdc | https://github.com/SHIVOH/DeepReinforcementLearning-DDPG-for-RoboticsControl/tree/f3e811a3ae3eb603173c2475bbfe1de91074ecdc |
Conv2d | # 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 torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
def keep_variance_fn(x):
return x + 0.001
class Conv2d(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias... | 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.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pa... | THAKAORI/SalsaNext | Conv2d | false | 11,902 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
Symmetric | # 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 torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Symmetric(nn.Module):
def forward(self, X):
return X.triu() + X.triu(1).transpose(-1, -2)
def ge... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | LeeSHa00/PyTorch-tutorials-kr | Symmetric | false | 11,903 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
Softmax | # 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 torch.nn as nn
def keep_variance_fn(x):
return x + 0.001
class Softmax(nn.Module):
def __init__(self, dim=1, keep_variance_fn=None):
super(Softmax, self).__init__()
self.dim = dim
self._keep_variance_fn = keep_variance_fn
def forward(self, features_mean, fea... | 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... | THAKAORI/SalsaNext | Softmax | false | 11,904 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
APL | # 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 nn
from torch.nn.parameter import Parameter
class APL(nn.Module):
"""
Implementation of APL (ADAPTIVE PIECEWISE LINEAR UNITS) unit:
.. math::
APL(x_i) = max(0,x) + \\sum_{s=1}^{S}{a_i^s * max(0, -x + b_i^s)}
with trainable parameters a and b, parameter... | 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
from torch.nn.parameter import Parameter
assert_size_stride = torch.... | THEFASHIONGEEK/Echo | APL | false | 11,905 | [
"MIT"
] | 0 | 8dcf279ca528f2bfd255f79de07c1a221512c6a0 | https://github.com/THEFASHIONGEEK/Echo/tree/8dcf279ca528f2bfd255f79de07c1a221512c6a0 |
SimpleNet | # 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 torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class SimpleNet(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
"""Defines layers of a neural network.
:param input_dim: Number of input features
:param hidden_dim: Size of ... | 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 ... | Stas-Medvedev/ML-Case-Studies | SimpleNet | false | 11,906 | [
"MIT"
] | 0 | 88aa33334245cd028cf3adfba4ba3eecaef32708 | https://github.com/Stas-Medvedev/ML-Case-Studies/tree/88aa33334245cd028cf3adfba4ba3eecaef32708 |
BeitAttention | # 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... | from _paritybench_helpers import _mock_config
import math
import torch
from typing import List
from typing import Tuple
from torch import nn
from typing import Set
import torch.utils.checkpoint
def find_pruneable_heads_and_indices(heads: 'List[int]', n_heads: 'int',
head_size: 'int', already_pruned_heads: 'Set[in... | 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.... | Clemens123/transformers | BeitAttention | false | 11,907 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
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... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.data
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
"""Construct a layernorm module in the TF style (epsilon inside the square root)."""
super(BertLayerNorm, self).__ini... | 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... | Stephen0808/WebQA | BertOutput | false | 11,908 | [
"Apache-2.0"
] | 0 | b9758932a9d0d75167ec837bb6ee8bc571c64681 | https://github.com/Stephen0808/WebQA/tree/b9758932a9d0d75167ec837bb6ee8bc571c64681 |
MaxPool2d | # 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 numpy as np
import torch.nn as nn
from numbers import Number
def keep_variance_fn(x):
return x + 0.001
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))... | 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 numpy as np
import torch.nn as nn
from numbers import N... | THAKAORI/SalsaNext | MaxPool2d | false | 11,909 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
ShiftedSoftplus | # 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 torch.nn.functional as F
import torch.utils.data
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - self.shift
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
assert_size_stride = torch._C._dynamo.... | THinnerichs/pytorch_geometric | ShiftedSoftplus | false | 11,910 | [
"MIT"
] | 0 | 90c2126895b21313a23657f4e845acc782d11bf5 | https://github.com/THinnerichs/pytorch_geometric/tree/90c2126895b21313a23657f4e845acc782d11bf5 |
SReLU | # 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 nn
from torch.nn.parameter import Parameter
class SReLU(nn.Module):
"""
SReLU (S-shaped Rectified Linear Activation Unit): a combination of three linear functions, which perform mapping R → R with the following formulation:
.. math::
h(x_i) = \\left\\{\\begin{matri... | 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 torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | THEFASHIONGEEK/Echo | SReLU | false | 11,911 | [
"MIT"
] | 0 | 8dcf279ca528f2bfd255f79de07c1a221512c6a0 | https://github.com/THEFASHIONGEEK/Echo/tree/8dcf279ca528f2bfd255f79de07c1a221512c6a0 |
RPN_Up | # 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 torch.nn as nn
import torch.nn.functional as F
class RPN_Up(nn.Module):
"""
For SiamRPN
"""
def __init__(self, anchor_nums=5, inchannels=256, outchannels=256,
cls_type='thicker'):
super(RPN_Up, self).__init__()
self.anchor_nums = anchor_nums
self.in... | 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.functional as F
assert_size_stride = torch... | Re3write/siamdw | RPN_Up | false | 11,912 | [
"MIT"
] | 0 | f5d7d4bda36cb8c14e93b460fbc77bb225aa8572 | https://github.com/Re3write/siamdw/tree/f5d7d4bda36cb8c14e93b460fbc77bb225aa8572 |
IdentityMessage | # 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 torch.utils.data
class IdentityMessage(torch.nn.Module):
def __init__(self, raw_msg_dim: 'int', memory_dim: 'int', time_dim: 'int'):
super(IdentityMessage, self).__init__()
self.out_channels = raw_msg_dim + 2 * memory_dim + time_dim
def forward(self, z_src, z_dst, raw_msg... | 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | THinnerichs/pytorch_geometric | IdentityMessage | false | 11,913 | [
"MIT"
] | 0 | 90c2126895b21313a23657f4e845acc782d11bf5 | https://github.com/THinnerichs/pytorch_geometric/tree/90c2126895b21313a23657f4e845acc782d11bf5 |
InnerProductDecoder | # 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 torch.utils.data
class InnerProductDecoder(torch.nn.Module):
"""The inner product decoder from the `"Variational Graph Auto-Encoders"
<https://arxiv.org/abs/1611.07308>`_ paper
.. math::
\\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})
where :math:`\\mathbf{Z} \\in \\mathbb{R}^{N ... | 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | THinnerichs/pytorch_geometric | InnerProductDecoder | false | 11,914 | [
"MIT"
] | 0 | 90c2126895b21313a23657f4e845acc782d11bf5 | https://github.com/THinnerichs/pytorch_geometric/tree/90c2126895b21313a23657f4e845acc782d11bf5 |
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