gpt-5.5-priority · original · l12_default_gpt
error 8_Matmul_with_irregular_shapes_.py
model
gpt-5.5-priority
turns / max
2 / 10
tool calls
1
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
original
runtime
—
ref runtime
0.00 μs
speedup
—
started
2026-05-02T21:41:37Z
reference PyTorch source (the prompt the agent sees)
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 needed
turn 04.0s turn · 4.0s LLM · 0.0s tools
reasoning
**Exploring matmul performance** I’m thinking about producing a ModelNew matrix multiplication function for fixed sizes with fp32 precision. It needs to be faster than PyTorch's implementation. The calculations involve an irregular size of 8205x2949 multiplied by 2949x5921, which is about 143 GFLOP. While PyTorch's cublas is quick, doing a custom implementation might be challenging. However, I wonder if PyTorch’s matmul introduces overhead, so I need to look into implementing a good SGEMM and understanding GPU details.
call → get_gpu_specs
callget_gpu_specs {}
result ← get_gpu_specs (ok)
okget_gpu_specs GPU specs for NVIDIA H100 80GB HBM3: total memory (runtime): 79.1 GB spec entry: H100 GPU Architecture: Hopper GPU Memory: 80GB Memory Bandwidth: 3.35 TB/s FP64 TFLOPS: 34 FP64 Tensor Core TFLOPS: 67 FP32 TFLOPS: 67 TF32 Tensor Core TFLOPS: 989 with sparsity BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity FP16 Tensor Core TFLOPS: 1979 with sparsity FP8 Tensor Core TFLOPS: 3958 with sparsity INT8 Tensor Core TOPS: 3958 with sparsity Register File Size: 64K 32-bit registers per SM Maximum number of registers per thread: 255 Maximum number of thread blocks per SM: 32 Shared memory capacity per SM: 228 KB Maximum shared memory per thread block: 227 KB
turn 17196.0s turn · 7196.0s LLM · 0.0s tools
feedbackLLM call failed: _WorkItemTimeout: work item exceeded soft timeout budget