torch.compile (inductor)
PyTorch · python · MIT
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8_Matmul_with_irregular_shapes_.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-8-matmul-with-irregular-shapes-torch-compile-inductor?include=source"interfacepython · torch_compile_inductor
symbolModel.forward
Compatibility
measured onNVIDIA H100
declared hardwaredeclared only
architectures—
dtypes
Benchmark evidence
2 measurements across 1 GPU, fastest first.
Operation / workload
Hardware
Latency
Rank
Observed
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:f29fc68abb4bdaf67c464969dc69a56f6a947e28f069bb892a5daa23fe8cc504
license declaredMIT
license concludedMIT
imported2026-08-26
Kernel source
8_Matmul_with_irregular_shapes_.py34 lines
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a single matrix multiplication (C = A * B) with irregular shapes
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
"""
Performs matrix multiplication of A and B.
Args:
A: Input tensor with shape (M, K).
B: Input tensor with shape (K, N).
Returns:
C: Output tensor with shape (M, N).
"""
return torch.matmul(A, B)
M = 8205
K = 2949
N = 5921
def get_inputs():
A = torch.rand(M, K)
B = torch.rand(K, N)
return [A, B]
def get_init_inputs():
return [] # No special initialization inputs neededscrolls · 34 lines total
Source code from KernelBench, © 2023 Anne Ouyang, Simon Guo, Azalia Mirhoseini (Scaling Intelligence Lab, Stanford University), MIT License · MIT
Best evidence level for this revision: reported
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