test speed w/o bias
MODIFY
test/test_speed_v_torch.py
test/test_speed_v_torch.py
+import os
import unittest
import torch
import time
@@ -5,18 +6,20 @@ import numpy as np
from tinygrad.tensor import Tensor
from tinygrad.nn import Conv2d
+IN_CHANS = [int(x) for x in os.getenv("IN_CHANS", "1,16,64").split(",")]
+
CNT = 5
class TestSpeedVTorch(unittest.TestCase):
def test_conv2d(self):
torch.manual_seed(0)
for bs in [32]:
- for in_chans in [1,16,64]:
+ for in_chans in IN_CHANS:
for out_chans in [64]:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
img_size = 64 if device == 'cuda' else 32
src = torch.rand(bs, in_chans, img_size, img_size)
dat = src.clone().to(device)
- src_conv = torch.nn.Conv2d(in_chans, out_chans, 3)
+ src_conv = torch.nn.Conv2d(in_chans, out_chans, 3, bias=None)
conv = src_conv.to(device)
with torch.no_grad():
val_torch = conv(dat).cpu().numpy().sum()
@@ -30,9 +33,8 @@ class TestSpeedVTorch(unittest.TestCase):
Tensor.no_grad = False
dat = Tensor(src.numpy())
- conv = Conv2d(in_chans, out_chans, 3)
+ conv = Conv2d(in_chans, out_chans, 3, bias=None)
conv.weight = Tensor(src_conv.weight.detach().cpu().numpy())
- conv.bias = Tensor(src_conv.bias.detach().cpu().numpy())
val_tinygrad = conv(dat).numpy().sum()
ets_tinygrad = []
for _ in range(CNT):

test speed w/o bias