2 to 3x slower than torch
@@ -15,6 +15,7 @@ import cProfile
import pstats
import unittest
import numpy as np
+import torch
from tinygrad.tensor import Tensor
def profile_conv(bs, chans, conv, cnt=10):
@@ -75,7 +76,8 @@ class TestConvSpeed(unittest.TestCase):
x = x.conv2d(c1).relu().maxpool2x2()
x = x.conv2d(c2).relu().maxpool2x2()
x = x.reshape(Tensor(np.array((x.shape[0], -1))))
- out = x.dot(l1).logsoftmax().mean()
+ out = x.dot(l1).logsoftmax()
+ out = out.mean()
et1 = time.time()
out.backward()
et2 = time.time()
@@ -86,9 +88,48 @@ class TestConvSpeed(unittest.TestCase):
bpt += (et2-et1)
stop_profile(pr, sort='time')
+ fpt = (fpt*1000/cnt)
+ bpt = (bpt*1000/cnt)
+ print("forward pass: %.3f ms, %.2fx off baseline %.3f ms" % (fpt, fpt/self.fpt_baseline, self.fpt_baseline))
+ print("backward pass: %.3f ms, %.2fx off baseline %.3f ms" % (bpt, bpt/self.bpt_baseline, self.bpt_baseline))
+
+ # get torch baseline
+ @classmethod
+ def setUpClass(self):
+ conv = 3
+ inter_chan, out_chan = 32, 64
+ c1 = torch.randn(inter_chan,1,conv,conv, requires_grad=True)
+ c2 = torch.randn(out_chan,inter_chan,conv,conv, requires_grad=True)
+ l1 = torch.randn(out_chan*5*5, 10, requires_grad=True)
+
+ c2d = torch.nn.functional.conv2d
+ mp = torch.nn.MaxPool2d((2,2))
+ lsm = torch.nn.LogSoftmax(dim=1)
- print("forward pass: %.3f ms" % (fpt*1000/cnt))
- print("backward pass: %.3f ms" % (bpt*1000/cnt))
+ cnt = 5
+ fpt, bpt = 0.0, 0.0
+ for i in range(1+cnt):
+ et0 = time.time()
+ x = torch.randn(128, 1, 28, 28, requires_grad=True)
+ x = mp(c2d(x,c1).relu())
+ x = mp(c2d(x,c2).relu())
+ x = x.reshape(x.shape[0], -1)
+ out = lsm(x.matmul(l1))
+ out = out.mean()
+ et1 = time.time()
+ out.backward()
+ et2 = time.time()
+ if i == 0:
+ pr = start_profile()
+ else:
+ fpt += (et1-et0)
+ bpt += (et2-et1)
+
+ stop_profile(pr, sort='time')
+ self.fpt_baseline = (fpt*1000/cnt)
+ self.bpt_baseline = (bpt*1000/cnt)
+ print("forward pass: %.3f ms" % self.fpt_baseline)
+ print("backward pass: %.3f ms" % self.bpt_baseline)
if __name__ == '__main__':
