mradermacher/gpt-oss-20b-triton-kernel-i1-GGUF
21B • Updated • 67
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 | messages stringlengths 8.63k 1.48M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
SumAggregator | # 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 SumAggregator(nn.Module):
def __init__(self):
super(SumAggregator, self).__init__()
def forward(self, neighbor):
return torch.sum(neighbor, dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AlexMinhao/NAS_GNN | SumAggregator | false | 0 | [
"Apache-2.0"
] | 0 | 89183988a96e1d6baed910ab3843c13282f8b077 | https://github.com/AlexMinhao/NAS_GNN/tree/89183988a96e1d6baed910ab3843c13282f8b077 | [
{
"content": "entry_point: SumAggregator\n\n# AOT ID: ['0_inference']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimport random\nimport os\nimport tempfile\nfrom math import inf, nan\nfrom torch._inductor.hooks import run_intermediate_hooks\nfrom torch._inductor.utils import maybe_pr... |
LinearEmbedding | # 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.utils.data
import torch.nn as nn
class LinearEmbedding(nn.Module):
def __init__(self, inp_size, d_model):
super(LinearEmbedding, self).__init__()
self.lut = nn.Linear(inp_size, d_model)
self.d_model = d_model
def forward(self, x):
return ... | 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | Akhil-Raj/Trajectory-Transformer | LinearEmbedding | false | 1 | [
"MIT"
] | 0 | dd09fda99443f6afb59d962026573162219ea6a9 | https://github.com/Akhil-Raj/Trajectory-Transformer/tree/dd09fda99443f6afb59d962026573162219ea6a9 | [
{
"content": "entry_point: LinearEmbedding\n\n# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimport random\nimport os\nimport tempfile\nfrom math import inf, nan\nfrom torch._inductor.hooks import run_intermediate_hooks\nfrom torch._inductor.utils import maybe_pr... |
CustomizeLayer | # 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 CustomizeLayer(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.in_dim = in_dim
self.scale = nn.Parameter(torch.Tensor(self.in_dim))
self.bias = nn.Parameter(torch.Tensor(self.in_dim))
def forward(self, x):
norm ... | 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_... | Abhishekvats1997/Torch-Pruning | CustomizeLayer | false | 2 | [
"MIT"
] | 0 | b322a42d1c9032cc9644332d33a9662ca6ed44ac | https://github.com/Abhishekvats1997/Torch-Pruning/tree/b322a42d1c9032cc9644332d33a9662ca6ed44ac | [
{
"content": "entry_point: CustomizeLayer\n\n# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimport random\nimport os\nimport tempfile\nfrom math import inf, nan\nfrom torch._inductor.hooks import run_intermediate_hooks\nfrom torch._inductor.utils import maybe_pro... |
LayerNorm | # 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 LayerNorm(nn.Module):
def __init__(self, weights, eps=1e-05):
super().__init__()
self.gamma = nn.Parameter(torch.ones(weights))
self.beta = nn.Parameter(torch.zeros(weights))
self.eps = eps
def forward(self, x):
mean = x.mean(-... | 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_... | AWilcke/Dissertation | LayerNorm | false | 3 | [
"MIT"
] | 0 | b85ad38a7f336ee290d5883f5e942f54e140d0d0 | https://github.com/AWilcke/Dissertation/tree/b85ad38a7f336ee290d5883f5e942f54e140d0d0 | [
{
"content": "entry_point: LayerNorm\n\n# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimport random\nimport os\nimport tempfile\nfrom math import inf, nan\nfrom torch._inductor.hooks import run_intermediate_hooks\nfrom torch._inductor.utils import maybe_profile\... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Construct a layernorm module (See citation for details).
"""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | Akhil-Raj/Trajectory-Transformer | LayerNorm | false | 4 | [
"MIT"
] | 0 | dd09fda99443f6afb59d962026573162219ea6a9 | https://github.com/Akhil-Raj/Trajectory-Transformer/tree/dd09fda99443f6afb59d962026573162219ea6a9 | [
{
"content": "entry_point: LayerNorm\n\n# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimport random\nimport os\nimport tempfile\nfrom math import inf, nan\nfrom torch._inductor.hooks import run_intermediate_hooks\nfrom torch._inductor.utils import maybe_profile\... |
Norm | # 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 Norm(nn.Module):
def __init__(self, n_state, axis=-1, epsilon=1e-05):
super().__init__()
self.n_state = n_state
self.g = nn.Parameter(torch.ones([self.n_state]))
self.b = nn.Parameter(torch.zeros([self.n_state]))
self.axis = axis
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Aalanli/MusicGeneration | Norm | false | 5 | [
"MIT"
] | 0 | 7d268322d692013d8ac6e70be31741cea519fa28 | https://github.com/Aalanli/MusicGeneration/tree/7d268322d692013d8ac6e70be31741cea519fa28 | [
{
"content": "entry_point: Norm\n\n# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimport random\nimport os\nimport tempfile\nfrom math import inf, nan\nfrom torch._inductor.hooks import run_intermediate_hooks\nfrom torch._inductor.utils import maybe_profile\nfrom... |
BehlerAngular | # 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 BehlerAngular(nn.Module):
"""
Compute Behler type angular contribution of the angle spanned by three atoms:
:math:`2^{(1-\\zeta)} (1 + \\lambda \\cos( {\\theta}_{ijk} ) )^\\zeta`
Sets of zetas with lambdas of -1 and +1 are generated automatically.
A... | 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... | AlexanderDKazakov/schnetpack | BehlerAngular | false | 6 | [
"MIT"
] | 0 | 97b82469d977981b500e439a6c93696d8dac8a3f | https://github.com/AlexanderDKazakov/schnetpack/tree/97b82469d977981b500e439a6c93696d8dac8a3f | [
{
"content": "entry_point: BehlerAngular\n\n# AOT ID: ['0_inference']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimport random\nimport os\nimport tempfile\nfrom math import inf, nan\nfrom torch._inductor.hooks import run_intermediate_hooks\nfrom torch._inductor.utils import maybe_pr... |
BottleneckBlock | "# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimp(...TRUNCATED) | "import math\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nimport torch.nn.parallel(...TRUNCATED) | "import torch\nfrom torch._inductor.select_algorithm import extern_kernels\nimport triton\nimport tr(...TRUNCATED) | Aamer98/FeatureNorm | BottleneckBlock | false | 7 | [
"MIT"
] | 0 | fbf3d3b4cef81b3351347d272eb51b6cdd9f0cc5 | https://github.com/Aamer98/FeatureNorm/tree/fbf3d3b4cef81b3351347d272eb51b6cdd9f0cc5 | "[\n {\n \"content\": \"entry_point: BottleneckBlock\\n\\n# AOT ID: ['0_forward']\\nfrom ctypes (...TRUNCATED) |
Mlp | "# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimp(...TRUNCATED) | "import torch\nimport torch.nn as nn\n\n\nclass Conv1d(nn.Module):\n\n def __init__(self, nf, nx,(...TRUNCATED) | "import torch\nfrom torch._inductor.select_algorithm import extern_kernels\nimport triton\nimport tr(...TRUNCATED) | Aalanli/MusicGeneration | Mlp | false | 9 | [
"MIT"
] | 0 | 7d268322d692013d8ac6e70be31741cea519fa28 | https://github.com/Aalanli/MusicGeneration/tree/7d268322d692013d8ac6e70be31741cea519fa28 | "[\n {\n \"content\": \"entry_point: Mlp\\n\\n# AOT ID: ['0_forward']\\nfrom ctypes import c_voi(...TRUNCATED) |
GCN | "# AOT ID: ['0_forward']\nfrom ctypes import c_void_p, c_long, c_int\nimport torch\nimport math\nimp(...TRUNCATED) | "from torch.nn import Module\nimport math\nimport torch\nimport torch.nn.functional as F\nimport tor(...TRUNCATED) | "import torch\nfrom torch._inductor.select_algorithm import extern_kernels\nimport triton\nimport tr(...TRUNCATED) | AlexMinhao/NAS_GNN | GCN | false | 10 | [
"Apache-2.0"
] | 0 | 89183988a96e1d6baed910ab3843c13282f8b077 | https://github.com/AlexMinhao/NAS_GNN/tree/89183988a96e1d6baed910ab3843c13282f8b077 | "[\n {\n \"content\": \"entry_point: GCN\\n\\n# AOT ID: ['0_forward']\\nfrom ctypes import c_voi(...TRUNCATED) |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 17