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8a26a95 | 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 | import torch
from kernels.benchmark import Benchmark
def rmsnorm_reference(x: torch.Tensor, eps: float) -> torch.Tensor:
rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps)
return x / rms
class TinygradRmsBenchmark(Benchmark):
seed: int = 42
def setup(self):
batch_size = 32
seq_len = 512
hidden_size = 1024
self.eps = 1e-6
self.x = torch.randn(
batch_size, seq_len, hidden_size, device=self.device, dtype=torch.float32
)
self.out = torch.empty_like(self.x)
def benchmark_base(self):
self.out = self.kernel.tinygrad_rms_norm_simple(self.x, self.eps)
def verify_base(self) -> torch.Tensor:
return rmsnorm_reference(self.x, self.eps)
def setup_large(self):
# Note: hidden_size must be 1024 (kernel constraint)
batch_size = 64
seq_len = 1024
hidden_size = 1024
self.eps = 1e-6
self.x = torch.randn(
batch_size, seq_len, hidden_size, device=self.device, dtype=torch.float32
)
self.out = torch.empty_like(self.x)
def benchmark_large(self):
self.out = self.kernel.tinygrad_rms_norm_simple(self.x, self.eps)
def verify_large(self) -> torch.Tensor:
return rmsnorm_reference(self.x, self.eps)
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