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80692f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | """Naive top-k reference: torch.topk over the last dim.
This is the correctness oracle. The agent's solution must produce the same
top-k values (and equivalent indices modulo ties) within the tolerance
declared in problem.yaml. Note that solution.py is FORBIDDEN from calling
torch.topk / torch.sort / torch.kthvalue (see problem.yaml).
"""
import torch
import torch.nn as nn
OP_TYPE = "topk"
SUPPORTED_PRECISIONS = ["fp32"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
class Model(nn.Module):
"""Top-k over the last dim of a 2D tensor.
Input:
x: (batch, n) fp32
Output:
values: (batch, k) fp32, sorted descending
indices: (batch, k) int64, into the last dim of x
"""
def __init__(self, batch: int, n: int, k: int):
super().__init__()
self.batch, self.n, self.k = batch, n, k
# No learned parameters, but declare a dummy buffer so state_dict
# is non-empty and load_state_dict(strict=True) is meaningful.
self.register_buffer("_dummy", torch.zeros(1))
def forward(self, x: torch.Tensor):
values, indices = torch.topk(x, k=self.k, dim=-1, largest=True, sorted=True)
return values, indices
# Module-level shims rebuilt by check.py / benchmark.py per shape.
batch = 64
n = 8192
k = 8
def get_inputs():
# fp32 input drawn from a roughly Gaussian distribution; ties unlikely
# but possible. Seed is set by the caller.
x = torch.randn(batch, n, dtype=torch.float32)
return [x]
def get_init_inputs():
return [batch, n, k]
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