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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 softmax over the last dim, computed in fp64 for ground-truth.
The reference deliberately runs in float64 so that fp16 / fp32 accumulation
drift in agent solutions is exposed by the tight tolerance in problem.yaml.
The agent's job is to produce an fp32 softmax whose values match this
double-precision reference within atol=rtol=1e-5 — this requires either
fp32 accumulation or compensated (Kahan) summation when vocab is large.
"""
import torch
import torch.nn as nn
OP_TYPE = "softmax"
SUPPORTED_PRECISIONS = ["fp32"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
class Model(nn.Module):
"""y = softmax(x, dim=-1) computed in fp64 then returned as fp32.
No learned parameters — softmax is parameter-free. We still expose an
empty state_dict so the harness's strict load_state_dict matches.
"""
def __init__(self, batch: int, vocab: int):
super().__init__()
self.batch = batch
self.vocab = vocab
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Promote to fp64 for the ground-truth pathway. Even with double
# precision we still subtract the row-max for stability.
x64 = x.to(torch.float64)
m = x64.amax(dim=-1, keepdim=True)
e = torch.exp(x64 - m)
s = e.sum(dim=-1, keepdim=True)
return (e / s).to(torch.float32)
# Default shape; overridden per-iteration by check.py / benchmark.py.
BATCH = 8
VOCAB = 32768
def get_inputs():
# Mix of moderate-magnitude logits. The shapes module supplies an
# extreme-magnitude variant separately to stress numerical stability.
x = torch.randn(BATCH, VOCAB, dtype=torch.float32) * 4.0
return [x]
def get_init_inputs():
return [BATCH, VOCAB]
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