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 | pytorch_code stringlengths 200 4.05k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
RpowInt | # 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 RpowInt(torch.nn.Module):
def __init__(self):
super(RpowInt, self).__init__()
def forward(self, x):
return 2 ** 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | bunderhi/torch2trt | RpowInt | false | 1,650 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 2 ** x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
Perplexity | # 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 as nn
from torch.nn.modules.loss import CrossEntropyLoss
class Perplexity(CrossEntropyLoss):
__constants__ = ['weight', 'ignore_index', 'reduction']
def __init__(self, weight=None, size_average=None, ignore_index=-100,
reduce=None):
super(Perplexity, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn a... | cesarali/Tyche | Perplexity | false | 1,651 | [
"MIT"
] | 0 | d892df9e0b982f538ae38221ff5848f6d726a4fb | https://github.com/cesarali/Tyche/tree/d892df9e0b982f538ae38221ff5848f6d726a4fb | import torch
from torch import nn as nn
from torch.nn.modules.loss import CrossEntropyLoss
class Model(CrossEntropyLoss):
__constants__ = ['weight', 'ignore_index', 'reduction']
def __init__(self, weight=None, size_average=None, ignore_index=-100,
reduce=None):
super().__init__(weight, size_a... |
NoiseInjection | # 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 NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand(... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | celdeldel/style_conditionnal_gan | NoiseInjection | false | 1,652 | [
"MIT"
] | 0 | 3a4623560af1e12d46e2f9ffa9726c29df9d5680 | https://github.com/celdeldel/style_conditionnal_gan/tree/3a4623560af1e12d46e2f9ffa9726c29df9d5680 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand([4, 4, 4,... |
Stub | # 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.data
class Stub(nn.Module):
def __init__(self, shape):
super(Stub, self).__init__()
self.shape = shape
return
def forward(self, x):
return x.new_ones(self.shape)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | cestcedric/TSSR-GAN | Stub | false | 1,653 | [
"BSD-2-Clause",
"MIT"
] | 0 | d6e1b50409e0f0591660552993e6d5b70d41e766 | https://github.com/cestcedric/TSSR-GAN/tree/d6e1b50409e0f0591660552993e6d5b70d41e766 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, shape):
super().__init__()
self.shape = shape
return
def forward(self, x):
return x.new_ones(self.shape)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_i... |
FusedDownsample | # 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
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bi... | 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
from math import sqrt
assert_size_stride = torch._C._dynamo... | celdeldel/style_conditionnal_gan | FusedDownsample | false | 1,654 | [
"MIT"
] | 0 | 3a4623560af1e12d46e2f9ffa9726c29df9d5680 | https://github.com/celdeldel/style_conditionnal_gan/tree/3a4623560af1e12d46e2f9ffa9726c29df9d5680 | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class Model(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bias = torch... |
CustomReLU | # 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 CustomReLU(nn.Module):
def __init__(self, max_z=6.0):
super(CustomReLU, self).__init__()
self.max_z = max_z
def forward(self, x):
return torch.clamp(x, min=0, max=self.max_z)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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... | charlesmackin/tiny | CustomReLU | false | 1,655 | [
"Apache-2.0"
] | 0 | bf8afc5cfc15e12efdd3bca0d559adfdfc435981 | https://github.com/charlesmackin/tiny/tree/bf8afc5cfc15e12efdd3bca0d559adfdfc435981 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, max_z=6.0):
super().__init__()
self.max_z = max_z
def forward(self, x):
return torch.clamp(x, min=0, max=self.max_z)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
re... |
FusedUpsample | # 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
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
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 import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | celdeldel/style_conditionnal_gan | FusedUpsample | false | 1,656 | [
"MIT"
] | 0 | 3a4623560af1e12d46e2f9ffa9726c29df9d5680 | https://github.com/celdeldel/style_conditionnal_gan/tree/3a4623560af1e12d46e2f9ffa9726c29df9d5680 | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class Model(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias = torch... |
_UpsampleLinear | # 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
import torch.utils.data
class _UpsampleLinear(nn.Module):
def __init__(self, scale):
super(_UpsampleLinear, self).__init__()
self._mode = 'linear', 'bilinear', 'trilinear'
self.scale = scale
def forward(self, x, scale... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | cestcedric/TSSR-GAN | _UpsampleLinear | false | 1,657 | [
"BSD-2-Clause",
"MIT"
] | 0 | d6e1b50409e0f0591660552993e6d5b70d41e766 | https://github.com/cestcedric/TSSR-GAN/tree/d6e1b50409e0f0591660552993e6d5b70d41e766 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self, scale):
super().__init__()
self._mode = 'linear', 'bilinear', 'trilinear'
self.scale = scale
def forward(self, x, scale=None):
scale = scale o... |
FlawDetectorCriterion | # 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 FlawDetectorCriterion(nn.Module):
""" Criterion of the flaw detector.
"""
def __init__(self):
super(FlawDetectorCriterion, self).__init__()
def forward(self, pred, gt, is_ssl=False, reduction=True):
loss = F.mse... | 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... | charlesCXK/PixelSSL | FlawDetectorCriterion | false | 1,658 | [
"Apache-2.0"
] | 0 | 2e85e12c1db5b24206bfbbf2d7f6348ae82b2105 | https://github.com/charlesCXK/PixelSSL/tree/2e85e12c1db5b24206bfbbf2d7f6348ae82b2105 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" Criterion of the flaw detector.
"""
def __init__(self):
super().__init__()
def forward(self, pred, gt, is_ssl=False, reduction=True):
loss = F.mse_loss(pred, gt, reduction='none')
i... |
ActivationNoise | # 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 ActivationNoise(nn.Module):
"""Gaussian noise regularizer.
Args:
sigma (float, optional): relative standard deviation used to generate the
noise. Relative means that it will be multiplied by the magnitude of
the value your are adding th... | 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... | charlesmackin/tiny | ActivationNoise | false | 1,659 | [
"Apache-2.0"
] | 0 | bf8afc5cfc15e12efdd3bca0d559adfdfc435981 | https://github.com/charlesmackin/tiny/tree/bf8afc5cfc15e12efdd3bca0d559adfdfc435981 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Gaussian noise regularizer.
Args:
sigma (float, optional): relative standard deviation used to generate the
noise. Relative means that it will be multiplied by the magnitude of
the value your are adding the noise to... |
MeanVoxelFeatureExtractor | # 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 VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class MeanVoxelFeatureExtractor(VoxelF... | 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... | charlesyz/PCDet | MeanVoxelFeatureExtractor | false | 1,660 | [
"Apache-2.0"
] | 0 | 1eb6b1dc5a3d563d7532b1c8ee3be007cbeafc80 | https://github.com/charlesyz/PCDet/tree/1eb6b1dc5a3d563d7532b1c8ee3be007cbeafc80 | import torch
import torch.nn as nn
class VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class Model(VoxelFeatureExtractor):
... |
make_dilation_dense | # 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 make_dilation_dense(nn.Module):
def __init__(self, nChannels, growthRate, kernel_size=3):
super(make_dilation_dense, self).__init__()
self.conv = nn.Conv2d(nChannels, growthRate, kernel_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 as nn
import ... | cestcedric/TSSR-GAN | make_dilation_dense | false | 1,661 | [
"BSD-2-Clause",
"MIT"
] | 0 | d6e1b50409e0f0591660552993e6d5b70d41e766 | https://github.com/cestcedric/TSSR-GAN/tree/d6e1b50409e0f0591660552993e6d5b70d41e766 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self, nChannels, growthRate, kernel_size=3):
super().__init__()
self.conv = nn.Conv2d(nChannels, growthRate, kernel_size=
kernel_size, padding=(kernel_size -... |
SelfAttentionRE | # 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 import nn
class SelfAttentionRE(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.query_mlp = nn.Linear(in_features=emb_dim, out_features=1)
self.value_mlp = nn.Linear(in_features=emb_dim, out_features=emb_dim)
de... | 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.... | chaitanya2334/lsm | SelfAttentionRE | false | 1,662 | [
"MIT"
] | 0 | 504c732238b419cd77e7e0a97af040778ee9c7dd | https://github.com/chaitanya2334/lsm/tree/504c732238b419cd77e7e0a97af040778ee9c7dd | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.query_mlp = nn.Linear(in_features=emb_dim, out_features=1)
self.value_mlp = nn.Linear(in_features=emb_dim, out_features=emb_dim)
def forward(... |
FFN | # 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 collections import OrderedDict
class FFN(nn.Module):
def __init__(self, layer_arch, input_size, output_size, bias=True):
super(FFN, self).__init__()
self.layer_arch = layer_arch
self.input_size = input_size
self.output_size = output_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 as nn
from co... | charlesxin97/ToolFinder_binder | FFN | false | 1,663 | [
"Apache-2.0"
] | 0 | 49467d5519adcd6d881e57d460c97c37b6a45add | https://github.com/charlesxin97/ToolFinder_binder/tree/49467d5519adcd6d881e57d460c97c37b6a45add | import torch
import torch.nn as nn
from collections import OrderedDict
class Model(nn.Module):
def __init__(self, layer_arch, input_size, output_size, bias=True):
super().__init__()
self.layer_arch = layer_arch
self.input_size = input_size
self.output_size = output_size
se... |
FCDiscriminatorCriterion | # 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 FCDiscriminatorCriterion(nn.Module):
def __init__(self):
super(FCDiscriminatorCriterion, self).__init__()
def forward(self, pred, gt):
loss = F.binary_cross_entropy_with_logits(pred, gt, reduction='none')
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
import torc... | charlesCXK/PixelSSL | FCDiscriminatorCriterion | false | 1,664 | [
"Apache-2.0"
] | 0 | 2e85e12c1db5b24206bfbbf2d7f6348ae82b2105 | https://github.com/charlesCXK/PixelSSL/tree/2e85e12c1db5b24206bfbbf2d7f6348ae82b2105 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, gt):
loss = F.binary_cross_entropy_with_logits(pred, gt, reduction='none')
return torch.mean(loss, dim=(1, 2, 3))
def get_inputs... |
PolicyNetwork | # 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
from torch.distributions import Normal
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, action_range=
1.0, init_w=0.003, log_std_min=-20, log_std_max=2):
super(PolicyNetwo... | 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.... | chagri/SOTA-RL-Algorithms | PolicyNetwork | false | 1,665 | [
"Apache-2.0"
] | 0 | 58b416e7c706d8426dc402482e72ca7283568e71 | https://github.com/chagri/SOTA-RL-Algorithms/tree/58b416e7c706d8426dc402482e72ca7283568e71 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, action_range=
1.0, init_w=0.003, log_std_min=-20, log_std_max=2):
super().__init__()
... |
TemporalAggregation_Mean | # 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 math import sqrt as sqrt
from itertools import product as product
class TemporalAggregation_Mean(nn.Module):
def __init__(self, cfg):
super(TemporalAggregation_Mean, self).__init__()
self.K = cfg.K
def forw... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
assert_size_stride = torch._C._dynamo.guards.as... | burhanmudassar/pytorch-action-detection | TemporalAggregation_Mean | false | 1,666 | [
"MIT"
] | 0 | 16afb9312248d73c0e2be56ac733e0a33040307e | https://github.com/burhanmudassar/pytorch-action-detection/tree/16afb9312248d73c0e2be56ac733e0a33040307e | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class Model(nn.Module):
def __init__(self, cfg):
super().__init__()
self.K = cfg.K
def forward(self, s):
s = s.view(s.size(0) // sel... |
GlobalMaxPool1D | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as functional
class GlobalMaxPool1D(torch.nn.Module):
def __init__(self):
super(GlobalMaxPool1D, self).__init__()
def forward(self, x):
"""
x shape: (batch_size, channel, seq_len)
return shape: (batch_size, channel, 1)
"""
... | 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... | charliemorning/mlws | GlobalMaxPool1D | false | 1,667 | [
"MIT"
] | 0 | 8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | https://github.com/charliemorning/mlws/tree/8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | import torch
import torch.nn.functional as functional
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
"""
x shape: (batch_size, channel, seq_len)
return shape: (batch_size, channel, 1)
"""
return functional.max_pool1d... |
GlobalAvgPool1D | # 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 functional
class GlobalAvgPool1D(torch.nn.Module):
def __init__(self):
super(GlobalAvgPool1D, self).__init__()
def forward(self, x):
"""
x shape: (batch_size, channel, seq_len)
return shape: (batch_size, channel, 1)
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | charliemorning/mlws | GlobalAvgPool1D | false | 1,668 | [
"MIT"
] | 0 | 8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | https://github.com/charliemorning/mlws/tree/8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | import torch
import torch.nn.functional as functional
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
"""
x shape: (batch_size, channel, seq_len)
return shape: (batch_size, channel, 1)
"""
return functional.avg_pool1d... |
Mask | # 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 Mask(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
return seq.where(torch.eq(seq_mask, 1), torch.zeros_like(seq))
def get_inputs():
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | charliemorning/mlws | Mask | false | 1,669 | [
"MIT"
] | 0 | 8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | https://github.com/charliemorning/mlws/tree/8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
return seq.where(torch.eq(seq_mask, 1), torch.zeros_like(seq))
def get_inputs():
return... |
Network | # 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 Network(nn.Module):
def __init__(self, input_size, nb_action):
super(Network, self).__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 32)
self.fc2 = n... | 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_... | cheapmouse94/Machine_Learning-Gates-python | Network | false | 1,670 | [
"MIT"
] | 0 | 1e159ccf8f9a5db9104fa3926b85750787676e15 | https://github.com/cheapmouse94/Machine_Learning-Gates-python/tree/1e159ccf8f9a5db9104fa3926b85750787676e15 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_size, nb_action):
super().__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 32)
self.fc2 = nn.Linear(32, 64... |
FeedForwardNN | # 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
class FeedForwardNN(nn.Module):
"""
A standard in_dim-64-64-out_dim Feed Forward Neural Network.
"""
def __init__(self, in_dim, out_dim):
"""
Initialize the network and set up the layers.
Parameters:
i... | 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_... | chenjun-110/WZCQ | FeedForwardNN | false | 1,671 | [
"Apache-2.0"
] | 0 | e2de7743ad671e8632cfa084638555d7f1deb42f | https://github.com/chenjun-110/WZCQ/tree/e2de7743ad671e8632cfa084638555d7f1deb42f | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
A standard in_dim-64-64-out_dim Feed Forward Neural Network.
"""
def __init__(self, in_dim, out_dim):
"""
Initialize the network and set up the layers.
Parameters:
in_dim - ... |
MLPEncoder | # 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 MLPEncoder(nn.Module):
def __init__(self, d_in, d_out):
super(MLPEncoder, self).__init__()
H1 = 100
H2 = 10
self.d_in = d_in
self.l1 = nn.Linear(d_in, H1)
self.l11 = nn.Linear(H1, H2)
... | 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.... | cheng-xie/motionEncode | MLPEncoder | false | 1,672 | [
"MIT"
] | 0 | fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 | https://github.com/cheng-xie/motionEncode/tree/fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, d_in, d_out):
super().__init__()
H1 = 100
H2 = 10
self.d_in = d_in
self.l1 = nn.Linear(d_in, H1)
self.l11 = nn.Linear(H1, H2)
self.l2 = nn.Linear(H... |
posFFN1d | # 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 posFFN1d(nn.Module):
def __init__(self, d_hid, d_inner_hid, window=1, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d_hid, d_inner_hid, kernel_size=window)
self.relu = nn.ReLU()
self.w_2 = nn.Conv1d(d_inner_hid, d_hid, kernel_size=wi... | 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.... | chaitanya2334/lsm | posFFN1d | false | 1,673 | [
"MIT"
] | 0 | 504c732238b419cd77e7e0a97af040778ee9c7dd | https://github.com/chaitanya2334/lsm/tree/504c732238b419cd77e7e0a97af040778ee9c7dd | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, d_hid, d_inner_hid, window=1, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d_hid, d_inner_hid, kernel_size=window)
self.relu = nn.ReLU()
self.w_2 = nn.Conv1d(d_inner_hid, d_hid, kernel_size=windo... |
BaseModel | # 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 torchvision.transforms import *
import torch.nn as nn
import torch.nn.functional as F
class BaseModel(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision.transforms i... | bsm8734/BC_stage1_Image_Classification | BaseModel | false | 1,674 | [
"MIT"
] | 0 | f915a6fb6748bd9041b1dc2e917d732e202e9cc3 | https://github.com/bsm8734/BC_stage1_Image_Classification/tree/f915a6fb6748bd9041b1dc2e917d732e202e9cc3 | import torch
from torchvision.transforms import *
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1... |
LinearCombine | # 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 LinearCombine(nn.Module):
def __init__(self, layers_num, trainable=True, input_aware=False,
word_level=False):
super(LinearCombine, self).__init__()
self.input_aware = input_aware
self.word_level = word_level... | 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... | charliemorning/mlws | LinearCombine | false | 1,675 | [
"MIT"
] | 0 | 8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | https://github.com/charliemorning/mlws/tree/8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, layers_num, trainable=True, input_aware=False,
word_level=False):
super().__init__()
self.input_aware = input_aware
self.word_level = word_level
if input_aware:
... |
autoencoder | # 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
class autoencoder(torch.nn.Module):
def __init__(self, inputDim):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
h_size = 128
c_size = 8
use_bias = True
... | 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
assert_size_stride = torch._C... | charlesmackin/tiny | autoencoder | false | 1,676 | [
"Apache-2.0"
] | 0 | bf8afc5cfc15e12efdd3bca0d559adfdfc435981 | https://github.com/charlesmackin/tiny/tree/bf8afc5cfc15e12efdd3bca0d559adfdfc435981 | import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self, inputDim):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
h_size = 128
c_size = 8
use_bias = True
sup... |
MLPDecoder | # 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 MLPDecoder(nn.Module):
def __init__(self, d_in, d_out):
super(MLPDecoder, self).__init__()
H1 = 10
H2 = 100
self._d_in = d_in
self._d_out = d_out
self.l1 = nn.Linear(d_in, H1)
self.l11... | 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_... | cheng-xie/motionEncode | MLPDecoder | false | 1,677 | [
"MIT"
] | 0 | fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 | https://github.com/cheng-xie/motionEncode/tree/fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, d_in, d_out):
super().__init__()
H1 = 10
H2 = 100
self._d_in = d_in
self._d_out = d_out
self.l1 = nn.Linear(d_in, H1)
self.l11 = nn.Linear(H1, H2)
... |
AE | # 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
import torch.nn as nn
import torch.utils.data
class AE(nn.Module):
""" Class for the AE using Fully Connected
"""
def __init__(self, opt):
super().__init__()
assert opt.isize % 4 == 0, 'input size has to be a multiple of 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 import triton_helpers
from torch._inductor.runtime.... | cerbero94/GAN_CP | AE | false | 1,678 | [
"MIT"
] | 0 | e255f5f5b3733c55d47997c1ffc4161529701f8a | https://github.com/cerbero94/GAN_CP/tree/e255f5f5b3733c55d47997c1ffc4161529701f8a | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
""" Class for the AE using Fully Connected
"""
def __init__(self, opt):
super().__init__()
assert opt.isize % 4 == 0, 'input size has to be a multiple of 4'
... |
my_BinaryCross | # 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
class my_BinaryCross(nn.Module):
def __init__(self, args):
super(my_BinaryCross, self).__init__()
self.args = args
def forward(self, output, target, beat):
modif_beat = 1.0 / torch.exp(beat) * 10
... | 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
... | carsault/chord_sequence_prediction | my_BinaryCross | false | 1,679 | [
"MIT"
] | 0 | 6eb539a963ca6350bcf0c88b8d8756775ad7c488 | https://github.com/carsault/chord_sequence_prediction/tree/6eb539a963ca6350bcf0c88b8d8756775ad7c488 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
def forward(self, output, target, beat):
modif_beat = 1.0 / torch.exp(beat) * 10
modif_beat[modif_beat < 7] =... |
GATLayer | # 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 torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
class GATLayer(Module):
def __init__(self, input_channel, output_channel, use_bias=True):
super(GATLayer, self).__init__()
self.use_bias = u... | 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.... | chengsilin/Graph_model | GATLayer | false | 1,680 | [
"MIT"
] | 0 | 0d9714a8b02196fabf5b0ecd0680b7269a22c53b | https://github.com/chengsilin/Graph_model/tree/0d9714a8b02196fabf5b0ecd0680b7269a22c53b | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
class Model(Module):
def __init__(self, input_channel, output_channel, use_bias=True):
super().__init__()
self.use_bias = use_bias
s... |
FeedForward | # 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 numpy as np
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class 全连接层(nn.Module):
def __init__(self, 输入_接口, 输出_接口):
super().__init__()
np.random.seed(1)
self.weig... | 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 math
import ... | chenjun-110/WZCQ | FeedForward | false | 1,681 | [
"Apache-2.0"
] | 0 | e2de7743ad671e8632cfa084638555d7f1deb42f | https://github.com/chenjun-110/WZCQ/tree/e2de7743ad671e8632cfa084638555d7f1deb42f | import math
import torch
import numpy as np
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class 全连接层(nn.Module):
def __init__(self, 输入_接口, 输出_接口):
super().__init__()
np.random.seed(1)
self.weig... |
L1Part | # 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 itertools import chain as chain
import torch.utils.data
from collections import OrderedDict
import torch.hub
import torch.nn.parallel
import torch.optim
class concatLayer(nn.Module):
def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
super(concat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import chain as chain
import torch.utils.da... | byeongjokim/LateTemporalModeling3DCNN_for_sign | L1Part | false | 1,682 | [
"MIT"
] | 0 | e3a802fcf91dc3930aea782464ee34d9b747d3ab | https://github.com/byeongjokim/LateTemporalModeling3DCNN_for_sign/tree/e3a802fcf91dc3930aea782464ee34d9b747d3ab | import torch
import torch.nn as nn
from itertools import chain as chain
import torch.utils.data
from collections import OrderedDict
import torch.hub
import torch.nn.parallel
import torch.optim
class concatLayer(nn.Module):
def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
super().__in... |
判断状态 | # 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 numpy as np
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class 全连接层(nn.Module):
def __init__(self, 输入_接口, 输出_接口):
super().__init__()
np.random.seed(1)
self.weig... | 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 math
import ... | chenjun-110/WZCQ | 判断状态 | false | 1,683 | [
"Apache-2.0"
] | 0 | e2de7743ad671e8632cfa084638555d7f1deb42f | https://github.com/chenjun-110/WZCQ/tree/e2de7743ad671e8632cfa084638555d7f1deb42f | import math
import torch
import numpy as np
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class 全连接层(nn.Module):
def __init__(self, 输入_接口, 输出_接口):
super().__init__()
np.random.seed(1)
self.weig... |
SoftDiceLoss | # 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.utils.data.sampler import *
import torch.nn as nn
import torch.nn.functional as F
class SoftDiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(SoftDiceLoss, self).__init__()
def forward(self, logits, targets):
num = targets.size(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.utils.data.sampler import *
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | chicm/carvana | SoftDiceLoss | false | 1,684 | [
"Apache-2.0"
] | 0 | 493a19fbb2fdab1cc1b4b95d97742684e4144229 | https://github.com/chicm/carvana/tree/493a19fbb2fdab1cc1b4b95d97742684e4144229 | import torch
from torch.utils.data.sampler import *
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, weight=None, size_average=True):
super().__init__()
def forward(self, logits, targets):
num = targets.size(0)
probs = F.sigmoid(logits)... |
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
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.d... | 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.... | chenxiaoyu523/FEAT3D | ScaledDotProductAttention | false | 1,685 | [
"MIT"
] | 0 | ba45ba7c26628a7cc0070b010f4f33893cdac926 | https://github.com/chenxiaoyu523/FEAT3D/tree/ba45ba7c26628a7cc0070b010f4f33893cdac926 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
class Model(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(... |
BCELoss2d | # 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.utils.data.sampler import *
import torch.nn as nn
import torch.nn.functional as F
class BCELoss2d(nn.Module):
def __init__(self, weight=None, size_average=True):
super(BCELoss2d, self).__init__()
self.bce_loss = nn.BCELoss(weight, size_average)
def forward(self, logit... | 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.... | chicm/carvana | BCELoss2d | false | 1,686 | [
"Apache-2.0"
] | 0 | 493a19fbb2fdab1cc1b4b95d97742684e4144229 | https://github.com/chicm/carvana/tree/493a19fbb2fdab1cc1b4b95d97742684e4144229 | import torch
from torch.utils.data.sampler import *
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, weight=None, size_average=True):
super().__init__()
self.bce_loss = nn.BCELoss(weight, size_average)
def forward(self, logits, targets):
... |
L2Part | # 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 itertools import chain as chain
import torch.utils.data
from collections import OrderedDict
import torch.hub
import torch.nn.parallel
import torch.optim
class concatLayer(nn.Module):
def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
super(concat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import chain as chain
import torch.utils.da... | byeongjokim/LateTemporalModeling3DCNN_for_sign | L2Part | false | 1,687 | [
"MIT"
] | 0 | e3a802fcf91dc3930aea782464ee34d9b747d3ab | https://github.com/byeongjokim/LateTemporalModeling3DCNN_for_sign/tree/e3a802fcf91dc3930aea782464ee34d9b747d3ab | import torch
import torch.nn as nn
from itertools import chain as chain
import torch.utils.data
from collections import OrderedDict
import torch.hub
import torch.nn.parallel
import torch.optim
class concatLayer(nn.Module):
def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
super().__in... |
CMVN | # 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 CMVN(nn.Module):
__constants__ = ['mode', 'dim', 'eps']
def __init__(self, mode='global', dim=2, eps=1e-10):
super(CMVN, self).__init__()
if mode != 'global':
raise NotImplementedError(
'Only support global mean variance nor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | chiluen/s3prl | CMVN | false | 1,688 | [
"Apache-2.0"
] | 0 | c81838f6414d3c4767de355144449e40f86c7066 | https://github.com/chiluen/s3prl/tree/c81838f6414d3c4767de355144449e40f86c7066 | import torch
import torch.nn as nn
class Model(nn.Module):
__constants__ = ['mode', 'dim', 'eps']
def __init__(self, mode='global', dim=2, eps=1e-10):
super().__init__()
if mode != 'global':
raise NotImplementedError(
'Only support global mean variance normalizatio... |
FFB | # 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 math import sqrt as sqrt
from itertools import product as product
class FFB(nn.Module):
def __init__(self, c1, c2, size):
super(FFB, self).__init__()
self.conv_y1 = nn.Conv2d(c1, c1, kernel_size=1)
self.act1 = nn.ReLU(inplace=True)
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produc... | chickenshawama/CFEnet2 | FFB | false | 1,689 | [
"MIT"
] | 0 | f20dda7ad2923bed4af5d63e705b6849a48e73df | https://github.com/chickenshawama/CFEnet2/tree/f20dda7ad2923bed4af5d63e705b6849a48e73df | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class Model(nn.Module):
def __init__(self, c1, c2, size):
super().__init__()
self.conv_y1 = nn.Conv2d(c1, c1, kernel_size=1)
self.act1 = nn.ReLU(inplace=True)
self.conv_y2 = n... |
PrecomputedNorm | # 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 PrecomputedNorm(nn.Module):
"""Normalization using Pre-computed Mean/Std.
Args:
stats: Precomputed (mean, std).
axis: Axis setting used to calculate mean/variance.
"""
def __init__(self, stats, axis=[1, 2]):
super().__init__()
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | chiluen/s3prl | PrecomputedNorm | false | 1,690 | [
"Apache-2.0"
] | 0 | c81838f6414d3c4767de355144449e40f86c7066 | https://github.com/chiluen/s3prl/tree/c81838f6414d3c4767de355144449e40f86c7066 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Normalization using Pre-computed Mean/Std.
Args:
stats: Precomputed (mean, std).
axis: Axis setting used to calculate mean/variance.
"""
def __init__(self, stats, axis=[1, 2]):
super().__init__()
self.axis =... |
AlReLU | # 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
class AlReLU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return self.alrelu(input)
def alrelu(self, x):
alpha = 0.01
return torch.maximum(torch.abs(alpha * x), x)
def get_inputs():
return [torch.ran... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | chiranthans23/jigsaw-severity-comments | AlReLU | false | 1,691 | [
"MIT"
] | 0 | b92345ff5bf0c2d2fb243b81edd98adc66a2c4ee | https://github.com/chiranthans23/jigsaw-severity-comments/tree/b92345ff5bf0c2d2fb243b81edd98adc66a2c4ee | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return self.alrelu(input)
def alrelu(self, x):
alpha = 0.01
return torch.maximum(torch.abs(alpha * x), x)
def get_inputs():
return [torch.rand... |
ScaledSiLU | # 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 ScaledSiLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | chris-price19/ocp | ScaledSiLU | false | 1,692 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
de... |
SpatialAttentionGate | # 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 SpatialAttentionGate(nn.Module):
def __init__(self, channel, reduction=16):
super(SpatialAttentionGate, self).__init__()
self.fc1 = nn.Conv2d(channel, reduction, kernel_size=1, padding=0)
self.fc2 = nn.Conv2d(reducti... | 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_... | chicm/clouds | SpatialAttentionGate | false | 1,693 | [
"MIT"
] | 0 | 66baff6527a55767ba39a531edec6f230d5e58e8 | https://github.com/chicm/clouds/tree/66baff6527a55767ba39a531edec6f230d5e58e8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, channel, reduction=16):
super().__init__()
self.fc1 = nn.Conv2d(channel, reduction, kernel_size=1, padding=0)
self.fc2 = nn.Conv2d(reduction, 1, kernel_size=1, padding=0)
def... |
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, num_classes=8):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=(3, 3))
self.conv2 = nn.Conv2d(10, 20, kernel_size=(3, 3))
nn.init.xavier_uniform_(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 import triton_helpers
from torch._inductor.runtime.... | chapnitsky/DL | Net | false | 1,694 | [
"MIT"
] | 0 | a91e6abd7abc81261ba6797de9a2c6f95b4dcb71 | https://github.com/chapnitsky/DL/tree/a91e6abd7abc81261ba6797de9a2c6f95b4dcb71 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes=8):
super().__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=(3, 3))
self.conv2 = nn.Conv2d(10, 20, kernel_size=(3, 3))
nn.init.xavier_uniform_(self.conv1.... |
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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask ... | 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.... | chenjun-110/WZCQ | MultiHeadAttention | false | 1,695 | [
"Apache-2.0"
] | 0 | e2de7743ad671e8632cfa084638555d7f1deb42f | https://github.com/chenjun-110/WZCQ/tree/e2de7743ad671e8632cfa084638555d7f1deb42f | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask ... |
ExponentialEnvelope | # 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 ExponentialEnvelope(torch.nn.Module):
"""
Exponential envelope function that ensures a smooth cutoff,
as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom
and Nonlocal Effects
"""
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | chris-price19/ocp | ExponentialEnvelope | false | 1,696 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | import torch
class Model(torch.nn.Module):
"""
Exponential envelope function that ensures a smooth cutoff,
as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom
and Nonlocal Effects
"""
def __init__(self)... |
PolynomialEnvelope | # 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 PolynomialEnvelope(torch.nn.Module):
"""
Polynomial envelope function that ensures a smooth cutoff.
Parameters
----------
exponent: int
Exponent of the envelope function.
"""
def __init__(self, exponent):
super().__init__()
assert expone... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | chris-price19/ocp | PolynomialEnvelope | false | 1,697 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | import torch
class Model(torch.nn.Module):
"""
Polynomial envelope function that ensures a smooth cutoff.
Parameters
----------
exponent: int
Exponent of the envelope function.
"""
def __init__(self, exponent):
super().__init__()
assert exponent > 0
... |
SelfAttentionPooling | # 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 SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentio... | 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.... | chiluen/s3prl | SelfAttentionPooling | false | 1,698 | [
"Apache-2.0"
] | 0 | c81838f6414d3c4767de355144449e40f86c7066 | https://github.com/chiluen/s3prl/tree/c81838f6414d3c4767de355144449e40f86c7066 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super().__init__()
self.W... |
Sine | # 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 Sine(nn.Module):
def __init__(self, w0: 'float'=30.0):
super(Sine, self).__init__()
self.w0 = w0
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return torch.sin(self.w0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | chris-price19/ocp | Sine | false | 1,699 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, w0: 'float'=30.0):
super().__init__()
self.w0 = w0
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return torch.sin(self.w0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init... |
SiQU | # 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 SiQU(torch.nn.Module):
def __init__(self):
super().__init__()
self._activation = torch.nn.SiLU()
def forward(self, x):
return x * self._activation(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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | chris-price19/ocp | SiQU | false | 1,700 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self._activation = torch.nn.SiLU()
def forward(self, x):
return x * self._activation(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
SphericalBesselBasis | # 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 numpy as np
class SphericalBesselBasis(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int'... | 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 math
import numpy as np
assert_size_stride = torch._C._dynamo.guar... | chris-price19/ocp | SphericalBesselBasis | false | 1,701 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | import math
import torch
import numpy as np
class Model(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int', cutoff: 'floa... |
LeNet300 | # 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 *
import torch.nn as nn
import torch.nn.functional as F
class LeNet300(nn.Module):
def __init__(self, num_classes=10):
super(LeNet300, self).__init__()
self.fc1 = nn.Linear(784, 300)
self.fc2 = nn.Linear(300, 100)
self.fc3 = nn.Linear(100, 10)
... | 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 typing import *
import t... | chomd90/snip | LeNet300 | false | 1,702 | [
"MIT"
] | 0 | 04aa8ca76364c61c3f6013832827fa292402652b | https://github.com/chomd90/snip/tree/04aa8ca76364c61c3f6013832827fa292402652b | import torch
from typing import *
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes=10):
super().__init__()
self.fc1 = nn.Linear(784, 300)
self.fc2 = nn.Linear(300, 100)
self.fc3 = nn.Linear(100, 10)
def forward(self,... |
GaussianSmearing | # 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 GaussianSmearing(nn.Module):
def __init__(self, in_features, start=0, end=1, num_freqs=50):
super(GaussianSmearing, self).__init__()
self.num_freqs = num_freqs
offset = torch.linspace(start, end, num_freqs)
self.coeff = -0.5 / (offset[1] - ... | 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... | chris-price19/ocp | GaussianSmearing | false | 1,703 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_features, start=0, end=1, num_freqs=50):
super().__init__()
self.num_freqs = num_freqs
offset = torch.linspace(start, end, num_freqs)
self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2
se... |
PixelWiseModel | # 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 as nn
class PixelWiseModel(nn.Module):
"""
Baseline class for pixelwise models
Args:
"""
def __init__(self, const, **kwargs):
super(PixelWiseModel, self).__init__()
self.const = const
def forward(self, x):
ret = torch.zeros_like(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 import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | ciubecca/3dunet-cavity | PixelWiseModel | false | 1,704 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | import torch
from torch import nn as nn
class Model(nn.Module):
"""
Baseline class for pixelwise models
Args:
"""
def __init__(self, const, **kwargs):
super().__init__()
self.const = const
def forward(self, x):
ret = torch.zeros_like(x)
ret[:] = self.const
... |
SubPixelConvolutionalBlock | # 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 SubPixelConvolutionalBlock(nn.Module):
"""
A subpixel convolutional block, comprising convolutional, pixel-shuffle, and PReLU activation layers.
"""
def __init__(self, kernel_size=3, n_channels=64, scaling_factor=3):
"""
:param kernel_size: kern... | 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... | chilung/NCTU_Adv_DNN_HW4 | SubPixelConvolutionalBlock | false | 1,705 | [
"MIT"
] | 0 | 967f228c6e80cbc703a89ee611b90ef5e037da40 | https://github.com/chilung/NCTU_Adv_DNN_HW4/tree/967f228c6e80cbc703a89ee611b90ef5e037da40 | import torch
from torch import nn
class Model(nn.Module):
"""
A subpixel convolutional block, comprising convolutional, pixel-shuffle, and PReLU activation layers.
"""
def __init__(self, kernel_size=3, n_channels=64, scaling_factor=3):
"""
:param kernel_size: kernel size of the convol... |
LeNet_300_100 | # 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 *
import torch.nn as nn
import torch.nn.functional as F
class LeNet_300_100(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 300)
self.fc2 = nn.Linear(300, 100)
self.fc3 = nn.Linear(100, 10)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | chomd90/snip | LeNet_300_100 | false | 1,706 | [
"MIT"
] | 0 | 04aa8ca76364c61c3f6013832827fa292402652b | https://github.com/chomd90/snip/tree/04aa8ca76364c61c3f6013832827fa292402652b | import torch
from typing import *
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 300)
self.fc2 = nn.Linear(300, 100)
self.fc3 = nn.Linear(100, 10)
def forward(self, x):
x =... |
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 as nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
strid... | 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 ... | chris-rgr/image-background-remove-tool | Conv2d | false | 1,707 | [
"Apache-2.0"
] | 0 | b57b44099e0e35c90833bed010b071aa39efdc80 | https://github.com/chris-rgr/image-background-remove-tool/tree/b57b44099e0e35c90833bed010b071aa39efdc80 | import torch
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels, kernel_size,
stride, padding, d... |
AvgPoolPadding | # 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 AvgPoolPadding(nn.Module):
def __init__(self, num_filters, channels_in, stride):
super(AvgPoolPadding, self).__init__()
self.identity = nn.AvgPool2d(stride, stride=stride)
self.num_zeros = num_filters - channels_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | citelab/fastsync | AvgPoolPadding | false | 1,708 | [
"Apache-2.0"
] | 0 | 8e2166f87fc53479b57fef536a971c3a2e6e4309 | https://github.com/citelab/fastsync/tree/8e2166f87fc53479b57fef536a971c3a2e6e4309 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_filters, channels_in, stride):
super().__init__()
self.identity = nn.AvgPool2d(stride, stride=stride)
self.num_zeros = num_filters - channels_in
def forward(self, x):
... |
QNetwork | # 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 QNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=0.003):
super(QNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size)
self.linear2 = nn.Linear(h... | 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_... | chagri/SOTA-RL-Algorithms | QNetwork | false | 1,709 | [
"Apache-2.0"
] | 0 | 58b416e7c706d8426dc402482e72ca7283568e71 | https://github.com/chagri/SOTA-RL-Algorithms/tree/58b416e7c706d8426dc402482e72ca7283568e71 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=0.003):
super().__init__()
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidde... |
IdentityPadding | # 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 IdentityPadding(nn.Module):
def __init__(self, num_filters, channels_in, stride):
super(IdentityPadding, self).__init__()
self.identity = nn.MaxPool2d(1, stride=stride)
self.num_zeros = num_filters - channels_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | citelab/fastsync | IdentityPadding | false | 1,710 | [
"Apache-2.0"
] | 0 | 8e2166f87fc53479b57fef536a971c3a2e6e4309 | https://github.com/citelab/fastsync/tree/8e2166f87fc53479b57fef536a971c3a2e6e4309 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_filters, channels_in, stride):
super().__init__()
self.identity = nn.MaxPool2d(1, stride=stride)
self.num_zeros = num_filters - channels_in
def forward(self, x):
... |
WeightedSmoothL1Loss | # 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 as nn
class WeightedSmoothL1Loss(nn.SmoothL1Loss):
def __init__(self, threshold, initial_weight, apply_below_threshold=True):
super().__init__(reduction='none')
self.threshold = threshold
self.apply_below_threshold = apply_below_threshold
self.wei... | 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 import nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | ciubecca/3dunet-cavity | WeightedSmoothL1Loss | false | 1,711 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | import torch
from torch import nn as nn
class Model(nn.SmoothL1Loss):
def __init__(self, threshold, initial_weight, apply_below_threshold=True):
super().__init__(reduction='none')
self.threshold = threshold
self.apply_below_threshold = apply_below_threshold
self.weight = initial_w... |
CNN | # 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 CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = n... | 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.... | chihina/Classification_dogs_and_cats | CNN | false | 1,712 | [
"MIT"
] | 0 | 8797ff688592c148a1c13881394716e117db95a3 | https://github.com/chihina/Classification_dogs_and_cats/tree/8797ff688592c148a1c13881394716e117db95a3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linea... |
GCNModelVAE | # 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 torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... | 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.... | chundiliu/random_rewrite | GCNModelVAE | false | 1,713 | [
"MIT"
] | 0 | fd106642da82b0ad42b8b0fa405147b321d67cbb | https://github.com/chundiliu/random_rewrite/tree/fd106642da82b0ad42b8b0fa405147b321d67cbb | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... |
MaxMin | # 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.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class MaxMin(nn.Module):
def __init__(self):
super(MaxMin, self).__init__()
def forward(self, x):
y = torch.reshape(x, (x.sh... | 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.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.... | ckodser/a_robust_subnetwork_hiddes | MaxMin | false | 1,714 | [
"Apache-2.0"
] | 0 | 171b43dc7b4446ba722a4c51c52bf561f77e5c55 | https://github.com/ckodser/a_robust_subnetwork_hiddes/tree/171b43dc7b4446ba722a4c51c52bf561f77e5c55 | import torch
import torch.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
y = torch.reshape(x, (x.shape[0], x.sha... |
GCNModelAE | # 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 torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... | 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.... | chundiliu/random_rewrite | GCNModelAE | false | 1,715 | [
"MIT"
] | 0 | fd106642da82b0ad42b8b0fa405147b321d67cbb | https://github.com/chundiliu/random_rewrite/tree/fd106642da82b0ad42b8b0fa405147b321d67cbb | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... |
ExtResNetBlock | # 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 as nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding):
"""
Create 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
from torch import n... | ciubecca/3dunet-cavity | ExtResNetBlock | false | 1,716 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | import torch
from torch import nn as nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding):
"""
Create a l... |
BCEDiceLoss | # 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 as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed... | 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 ... | ciubecca/3dunet-cavity | BCEDiceLoss | false | 1,717 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | import torch
from torch import nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed... |
LogisticRegression | # 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 as nn
class LogisticRegression(torch.nn.Module):
def __init__(self, **kwargs):
super(LogisticRegression, self).__init__()
self.linear = nn.Linear(1, 1)
def forward(self, x):
xin = x.flatten()[:, None]
output = torch.sigmoid(self.linear(xin))
... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | ciubecca/3dunet-cavity | LogisticRegression | false | 1,718 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | import torch
from torch import nn as nn
class Model(torch.nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.linear = nn.Linear(1, 1)
def forward(self, x):
xin = x.flatten()[:, None]
output = torch.sigmoid(self.linear(xin))
return output.reshape(x.shape... |
SkipLastTargetChannelWrapper | # 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 as nn
from torch.nn import MSELoss
class SkipLastTargetChannelWrapper(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super(SkipLastTargetChannelWrapper, self).__init__()
self.l... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | ciubecca/3dunet-cavity | SkipLastTargetChannelWrapper | false | 1,719 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | import torch
from torch import nn as nn
from torch.nn import MSELoss
class Model(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super().__init__()
self.loss = loss
self.squeeze_channel = squeeze_channel... |
GroupWiseLinear | # 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
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class GroupWiseLinear(nn.Module):
def __init__(self, num_class, hidden_dim, bias=True):
super().__init__()
self.num_class = num_class
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 math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
as... | ckvic3/query2labels | GroupWiseLinear | false | 1,720 | [
"MIT"
] | 0 | e9c30e1b445be773be397a093fa66aef71d54556 | https://github.com/ckvic3/query2labels/tree/e9c30e1b445be773be397a093fa66aef71d54556 | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self, num_class, hidden_dim, bias=True):
super().__init__()
self.num_class = num_class
self.hidden_di... |
ConvNet | # 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 ConvNet(nn.Module):
"""Small ConvNet for CONIC."""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 =... | 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.... | citelab/fastsync | ConvNet | false | 1,721 | [
"Apache-2.0"
] | 0 | 8e2166f87fc53479b57fef536a971c3a2e6e4309 | https://github.com/citelab/fastsync/tree/8e2166f87fc53479b57fef536a971c3a2e6e4309 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Small ConvNet for CONIC."""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = n... |
BCEDiceProbLoss | # 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 as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed... | 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 ... | ciubecca/3dunet-cavity | BCEDiceProbLoss | false | 1,722 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | import torch
from torch import nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed... |
AsymmetricLoss | # 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.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class AsymmetricLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
super(AsymmetricLoss... | 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... | ckvic3/query2labels | AsymmetricLoss | false | 1,723 | [
"MIT"
] | 0 | e9c30e1b445be773be397a093fa66aef71d54556 | https://github.com/ckvic3/query2labels/tree/e9c30e1b445be773be397a093fa66aef71d54556 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
super().__init__()
se... |
RMSPool | # 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
import torch.nn.parallel
import torch.utils.data
class RMSPool(nn.Module):
def __init__(self, kernel_size, stride):
super(RMSPool, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
def forward(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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_s... | cleverhans-lab/model-extraction-iclr | RMSPool | false | 1,724 | [
"MIT"
] | 0 | 805205287876423621baca9d5e990edfe68ea803 | https://github.com/cleverhans-lab/model-extraction-iclr/tree/805205287876423621baca9d5e990edfe68ea803 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, kernel_size, stride):
super().__init__()
self.kernel_size = kernel_size
self.stride = stride
def forward(self, x):
x ... |
eca_channel | # 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
class eca_channel(nn.Module):
def __init__(self, channel, k_size=3):
super(eca_channel, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1
) // 2, bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.g... | closest-git/QuantumFold | eca_channel | false | 1,725 | [
"Apache-2.0"
] | 0 | 2dc6afa96cdee91b58f66543b440a2d7a8d32a8a | https://github.com/closest-git/QuantumFold/tree/2dc6afa96cdee91b58f66543b440a2d7a8d32a8a | import torch
import torch.nn as nn
import torch.utils
class Model(nn.Module):
def __init__(self, channel, k_size=3):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1
) // 2, bias=False)
self.... |
LinearActivation | # 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 torch.nn import Module
import torch
import torch.nn as nn
class LinearActivation(Module):
def __init__(self, in_features, out_features, act='gelu', bias=True):
super(LinearActivation, self).__init__()
self.Linear = nn.Linear(in_features, out_features, bias=bias)
if act == 'relu':
... | 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.nn impor... | codecaution/Hetu | LinearActivation | false | 1,726 | [
"Apache-2.0"
] | 0 | e278732c2fe3554c8d576585f5bcbf79ade31b68 | https://github.com/codecaution/Hetu/tree/e278732c2fe3554c8d576585f5bcbf79ade31b68 | from torch.nn import Module
import torch
import torch.nn as nn
class Model(Module):
def __init__(self, in_features, out_features, act='gelu', bias=True):
super().__init__()
self.Linear = nn.Linear(in_features, out_features, bias=bias)
if act == 'relu':
self.act_fn = nn.ReLU()
... |
BertLayerNorm | # 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 torch.nn import Module
import torch
import torch.nn as nn
class BertLayerNorm(Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.shape = torch.Size((hidden_size,))
self.eps = eps
self.weight = nn.Parameter(torch.ones(hidden_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 libdevice
from torch.nn import Module
import torch.nn as nn
assert_size_stride = torch._C... | codecaution/Hetu | BertLayerNorm | false | 1,727 | [
"Apache-2.0"
] | 0 | e278732c2fe3554c8d576585f5bcbf79ade31b68 | https://github.com/codecaution/Hetu/tree/e278732c2fe3554c8d576585f5bcbf79ade31b68 | from torch.nn import Module
import torch
import torch.nn as nn
class Model(Module):
def __init__(self, hidden_size, eps=1e-12):
super().__init__()
self.shape = torch.Size((hidden_size,))
self.eps = eps
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Para... |
AsymmetricLossOptimized | # 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.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(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 libdevice, math as tl_math
import torc... | ckvic3/query2labels | AsymmetricLossOptimized | false | 1,728 | [
"MIT"
] | 0 | e9c30e1b445be773be397a093fa66aef71d54556 | https://github.com/ckvic3/query2labels/tree/e9c30e1b445be773be397a093fa66aef71d54556 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(self, gamma_neg=4, ga... |
BertSelfOutput | # 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
import torch.nn as nn
class BertSelfOutput(nn.Module):
def __init__(self, config):
super(BertSelfOutput, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_... | 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 ... | cjinchao/mner | BertSelfOutput | false | 1,729 | [
"MIT"
] | 0 | 12776280da314eb7ef22511aa18ca9af0764fb32 | https://github.com/cjinchao/mner/tree/12776280da314eb7ef22511aa18ca9af0764fb32 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, config.
lay... |
Qnet | # 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 random
import torch
import torch.nn as nn
import torch.nn.functional as F
class Qnet(nn.Module):
def __init__(self):
super(Qnet, self).__init__()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 2)
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
from torch._inductor.runtime import triton_helpers
import random
import torch.nn... | co24428/minimalRL | Qnet | false | 1,730 | [
"MIT"
] | 0 | c758e733438fce1d8421551e75f2117ec3f82028 | https://github.com/co24428/minimalRL/tree/c758e733438fce1d8421551e75f2117ec3f82028 | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 2)
def forward(self, x):
x = F.relu(se... |
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
from torch.nn import Module
import torch
import torch.nn as nn
class BertLayerNorm(Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.shape = torch.Size((hidden_size,))
self.eps = eps
self.w... | 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.nn impor... | codecaution/Hetu | BertOutput | false | 1,731 | [
"Apache-2.0"
] | 0 | e278732c2fe3554c8d576585f5bcbf79ade31b68 | https://github.com/codecaution/Hetu/tree/e278732c2fe3554c8d576585f5bcbf79ade31b68 | from _paritybench_helpers import _mock_config
from torch.nn import Module
import torch
import torch.nn as nn
class BertLayerNorm(Module):
def __init__(self, hidden_size, eps=1e-12):
super().__init__()
self.shape = torch.Size((hidden_size,))
self.eps = eps
self.weight = nn.Paramete... |
Triangle | # 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 Triangle(nn.Module):
def forward(self, x):
return x.abs()
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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | continual-ml/mpcl | Triangle | false | 1,732 | [
"Apache-2.0"
] | 0 | 3b7112882ee832212494072e2f5ea7779e3b8aa6 | https://github.com/continual-ml/mpcl/tree/3b7112882ee832212494072e2f5ea7779e3b8aa6 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x):
return x.abs()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
RightSVDLayer | # 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
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class RightSVDLayer(nn.Module):
def __init__(self, iw, ow, dropout=None, bias=True):
super().__init__()
self.weight = Parameter(torch.Tensor(iw, ow))
self.dropout = drop... | 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 math
import torch.nn as nn
from torch.nn.parameter import Parameter
asser... | collodi/ml_svd | RightSVDLayer | false | 1,733 | [
"MIT"
] | 0 | 67a608ca10d3d37bf861e4e7490e62d298fa83b9 | https://github.com/collodi/ml_svd/tree/67a608ca10d3d37bf861e4e7490e62d298fa83b9 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, iw, ow, dropout=None, bias=True):
super().__init__()
self.weight = Parameter(torch.Tensor(iw, ow))
self.dropout = dropout
... |
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 torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | clairecw/TD3 | Critic | false | 1,734 | [
"MIT"
] | 0 | 22840c0ee1620086c2495d859be2c3b7a4c70f3f | https://github.com/clairecw/TD3/tree/22840c0ee1620086c2495d859be2c3b7a4c70f3f | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 1)
self.l4 = nn.... |
NormalizeLinear | # 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
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
class NormalizeLinear(nn.Module):
def __init__(self, act_dim, k_value):
super().__init__()
self.lin = nn.Linear(act_dim, k_value)
def normalize(self):
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | coupriec/enc_dec | NormalizeLinear | false | 1,735 | [
"MIT"
] | 0 | 5e31a1cd384f24ac48e932b623780bd0c70103d9 | https://github.com/coupriec/enc_dec/tree/5e31a1cd384f24ac48e932b623780bd0c70103d9 | import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self, act_dim, k_value):
super().__init__()
self.lin = nn.Linear(act_dim, k_value)
def normalize(self):
self.lin.wei... |
_ConvReLU_ | # 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 _ConvReLU_(nn.Sequential):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, relu=True):
super(_ConvReLU_, self).__init__()
self.add_module('conv', nn.Conv2d(in_channels=in_channels,
out_channels=out_c... | 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
assert_s... | cplusx/SIGN | _ConvReLU_ | false | 1,736 | [
"Apache-2.0"
] | 0 | 9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | https://github.com/cplusx/SIGN/tree/9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | import torch
from torch import nn
class Model(nn.Sequential):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, relu=True):
super().__init__()
self.add_module('conv', nn.Conv2d(in_channels=in_channels,
out_channels=out_channels, kernel_size=... |
LeftSVDLayer | # 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
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class LeftSVDLayer(nn.Module):
def __init__(self, ih, oh, dropout=None, bias=True):
super().__init__()
self.weight = Parameter(torch.Tensor(oh, ih))
self.dropout = dropo... | 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 math
import torch.nn as nn
from torch.nn.parameter import Parameter
asser... | collodi/ml_svd | LeftSVDLayer | false | 1,737 | [
"MIT"
] | 0 | 67a608ca10d3d37bf861e4e7490e62d298fa83b9 | https://github.com/collodi/ml_svd/tree/67a608ca10d3d37bf861e4e7490e62d298fa83b9 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, ih, oh, dropout=None, bias=True):
super().__init__()
self.weight = Parameter(torch.Tensor(oh, ih))
self.dropout = dropout
... |
BertAttention | # 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
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | 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.... | cjinchao/mner | BertAttention | false | 1,738 | [
"MIT"
] | 0 | 12776280da314eb7ef22511aa18ca9af0764fb32 | https://github.com/cjinchao/mner/tree/12776280da314eb7ef22511aa18ca9af0764fb32 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidden size (... |
_ASPPModule | # 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 _ASPPModule(nn.Module):
"""Atrous Spatial Pyramid Pooling"""
def __init__(self, in_channels, out_channels, pyramids):
super(_ASPPModule, self).__init__()
self.stages = nn.Module()
for i, (dilation, padding) in enumerate(zip(pyramids, pyramids)):... | 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... | cplusx/SIGN | _ASPPModule | false | 1,739 | [
"Apache-2.0"
] | 0 | 9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | https://github.com/cplusx/SIGN/tree/9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | import torch
from torch import nn
class Model(nn.Module):
"""Atrous Spatial Pyramid Pooling"""
def __init__(self, in_channels, out_channels, pyramids):
super().__init__()
self.stages = nn.Module()
for i, (dilation, padding) in enumerate(zip(pyramids, pyramids)):
self.stage... |
Conv2dBlock | # 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 import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | 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.functional as... | cplusx/SIGN | Conv2dBlock | false | 1,740 | [
"Apache-2.0"
] | 0 | 9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | https://github.com/cplusx/SIGN/tree/9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super().__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.weight = N... |
BertSelfAttention | # 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
import torch.nn.functional as F
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
... | 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.... | codecaution/Hetu | BertSelfAttention | false | 1,741 | [
"Apache-2.0"
] | 0 | e278732c2fe3554c8d576585f5bcbf79ade31b68 | https://github.com/codecaution/Hetu/tree/e278732c2fe3554c8d576585f5bcbf79ade31b68 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... |
Q | # 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 Q(nn.Module):
def __init__(self, state_dim, action_dim, hidden):
super(Q, self).__init__()
self.fc1 = nn.Linear(state_dim + action_dim, hidden)
self.fc2 = nn.Linear(hidden, hidden)
self.fc3 = nn.Linear(hidden... | 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_... | crislmfroes/Parallel-Manipulation-DRL | Q | false | 1,742 | [
"MIT"
] | 0 | b63bd17b933feb5d2844f1db596cd4126380244b | https://github.com/crislmfroes/Parallel-Manipulation-DRL/tree/b63bd17b933feb5d2844f1db596cd4126380244b | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim, hidden):
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, hidden)
self.fc2 = nn.Linear(hidden, hidden)
self.fc3 = nn.Linear(hidden, 1... |
_SelfAttention_ | # 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 _SelfAttention_(nn.Module):
def __init__(self, in_planes):
super(_SelfAttention_, self).__init__()
self.f = nn.Conv2d(in_planes, in_planes, (1, 1))
self.g = nn.Conv2d(in_planes, in_planes, (1, 1))
self.h = nn.Conv2d(in_planes, in_planes, (1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | cplusx/SIGN | _SelfAttention_ | false | 1,743 | [
"Apache-2.0"
] | 0 | 9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | https://github.com/cplusx/SIGN/tree/9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_planes):
super().__init__()
self.f = nn.Conv2d(in_planes, in_planes, (1, 1))
self.g = nn.Conv2d(in_planes, in_planes, (1, 1))
self.h = nn.Conv2d(in_planes, in_planes, (1, 1))
self.sm = nn.Softm... |
TransformerEncoderLayer | # 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 Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
class TransformerEncoderLayer(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.... | cimeister/neural-transducer | TransformerEncoderLayer | false | 1,744 | [
"MIT"
] | 0 | e4dfc718bbcf93254ce23750e5428c5131ddfb98 | https://github.com/cimeister/neural-transducer/tree/e4dfc718bbcf93254ce23750e5428c5131ddfb98 | import torch
import torch.nn as nn
import torch.nn.functional as F
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
class Model(nn.Module):
def __init__(sel... |
BertAttention | # 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 BertLayerNorm(Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.shape = torch.Size((hidden_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.... | codecaution/Hetu | BertAttention | false | 1,745 | [
"Apache-2.0"
] | 0 | e278732c2fe3554c8d576585f5bcbf79ade31b68 | https://github.com/codecaution/Hetu/tree/e278732c2fe3554c8d576585f5bcbf79ade31b68 | 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 BertLayerNorm(Module):
def __init__(self, hidden_size, eps=1e-12):
super().__init__()
self.shape = torch.Size((hidden_size,))
self... |
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 torch
import torch.nn as nn
import torch.nn.functional as F
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
class TransformerDecoderLayer(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.... | cimeister/neural-transducer | TransformerDecoderLayer | false | 1,746 | [
"MIT"
] | 0 | e4dfc718bbcf93254ce23750e5428c5131ddfb98 | https://github.com/cimeister/neural-transducer/tree/e4dfc718bbcf93254ce23750e5428c5131ddfb98 | import torch
import torch.nn as nn
import torch.nn.functional as F
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
class Model(nn.Module):
def __init__(sel... |
Mish | # 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
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class Mish(nn.Module):
def forward(self, x):
return x.mul_(F.softplus(x).tanh())
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.optim.lr_scheduler
import t... | csharpshooter/DeepLearning | Mish | false | 1,747 | [
"MIT"
] | 0 | c1d20660c32076468970f7376931e1fcd0d2644e | https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class Model(nn.Module):
def forward(self, x):
return x.mul_(F.softplus(x).tanh())
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def... |
SoftBinaryCrossEntropyLoss | # 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.cpp_extension
class SoftBinaryCrossEntropyLoss(torch.nn.Module):
def __init__(self, tau=1.0):
super().__init__()
self.tau = tau
self.bce_logit = torch.nn.BCEWithLogitsLoss()
def forward(self, pred, true):
logits = pred / self.tau
l = se... | 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... | crobbins327/semanticGAN_WSI | SoftBinaryCrossEntropyLoss | false | 1,748 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 | import torch
import torch.utils.cpp_extension
class Model(torch.nn.Module):
def __init__(self, tau=1.0):
super().__init__()
self.tau = tau
self.bce_logit = torch.nn.BCEWithLogitsLoss()
def forward(self, pred, true):
logits = pred / self.tau
l = self.bce_logit(logits, ... |
LogCoshLoss | # 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.cpp_extension
class LogCoshLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, true, pred):
loss = true - pred
return torch.mean(torch.log(torch.cosh(loss + 1e-12)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), ... | 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... | crobbins327/semanticGAN_WSI | LogCoshLoss | false | 1,749 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 | import torch
import torch.utils.cpp_extension
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, true, pred):
loss = true - pred
return torch.mean(torch.log(torch.cosh(loss + 1e-12)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.... |
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