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https://github.com/tinygrad/tinygrad.git
synced 2026-01-10 07:28:15 -05:00
ebd72ff437
* Split tests Split tests into "Test CPU" and "Test GPU". Add test flag "TEST_DEVICES" which is a comma separated list of devices: CPU,GPU,ANE * Run tests based on provided TEST_DEVICES flag By default will run all "CPU,GPU,ANE" * fix bad quote * Revert changes and use GPU=1 This is done through setting the default Tensor Device to Device.CPU of GPU=1 is set. Run GPU tests: GPU=1 pytest -s -v
98 lines
2.5 KiB
Python
98 lines
2.5 KiB
Python
#!/usr/bin/env python
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import time
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import cProfile
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import pstats
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import unittest
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import torch
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from tinygrad.tensor import Tensor
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def start_profile():
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import time
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pr = cProfile.Profile(timer=lambda: int(time.time()*1e9), timeunit=1e-6)
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pr.enable()
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return pr
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def stop_profile(pr, sort='cumtime'):
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pr.disable()
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ps = pstats.Stats(pr)
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ps.strip_dirs()
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ps.sort_stats(sort)
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ps.print_stats(0.2)
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class TestConvSpeed(unittest.TestCase):
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def test_mnist(self):
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# https://keras.io/examples/vision/mnist_convnet/
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conv = 3
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inter_chan, out_chan = 32, 64
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# ****** torch baseline *******
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torch.backends.mkldnn.enabled = False
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conv = 3
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inter_chan, out_chan = 32, 64
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c1 = torch.randn(inter_chan,1,conv,conv, requires_grad=True)
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c2 = torch.randn(out_chan,inter_chan,conv,conv, requires_grad=True)
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l1 = torch.randn(out_chan*5*5, 10, requires_grad=True)
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c2d = torch.nn.functional.conv2d
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mp = torch.nn.MaxPool2d((2,2))
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lsm = torch.nn.LogSoftmax(dim=1)
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cnt = 5
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fpt, bpt = 0.0, 0.0
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for i in range(cnt):
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et0 = time.time()
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x = torch.randn(128, 1, 28, 28, requires_grad=True)
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x = mp(c2d(x,c1).relu())
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x = mp(c2d(x,c2).relu())
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x = x.reshape(x.shape[0], -1)
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out = lsm(x.matmul(l1))
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out = out.mean()
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et1 = time.time()
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out.backward()
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et2 = time.time()
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fpt += (et1-et0)
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bpt += (et2-et1)
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fpt_baseline = (fpt*1000/cnt)
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bpt_baseline = (bpt*1000/cnt)
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print("torch forward pass: %.3f ms" % fpt_baseline)
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print("torch backward pass: %.3f ms" % bpt_baseline)
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# ****** tinygrad compare *******
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c1 = Tensor(c1.detach().numpy())
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c2 = Tensor(c2.detach().numpy())
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l1 = Tensor(l1.detach().numpy())
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cnt = 5
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fpt, bpt = 0.0, 0.0
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for i in range(1+cnt):
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et0 = time.time()
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x = Tensor.randn(128, 1, 28, 28)
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x = x.conv2d(c1).relu().avg_pool2d()
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x = x.conv2d(c2).relu().max_pool2d()
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x = x.reshape(shape=(x.shape[0], -1))
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out = x.dot(l1).logsoftmax()
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out = out.mean()
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et1 = time.time()
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out.backward()
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et2 = time.time()
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if i == 0:
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pr = start_profile()
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else:
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fpt += (et1-et0)
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bpt += (et2-et1)
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stop_profile(pr, sort='time')
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fpt = (fpt*1000/cnt)
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bpt = (bpt*1000/cnt)
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print("forward pass: %.3f ms, %.2fx off baseline %.3f ms" % (fpt, fpt/fpt_baseline, fpt_baseline))
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print("backward pass: %.3f ms, %.2fx off baseline %.3f ms" % (bpt, bpt/bpt_baseline, bpt_baseline))
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if __name__ == '__main__':
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unittest.main()
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