forked from tinygrad/tinygrad
801564f31b
* fixed division by zero for fast operations * made et closer to 0 * replace POW llop with SQRT * updated mlops to swap SQRT and POW llops * updated hlops to swap POW and SQRT * added sqrt llop to cpu runtime * added sqrt llop to cstyle codegen * added POW llop to llvm ir codegen * added SQRT llop to torch runtime * moved pow from mlops to hlops * found a better way to do reverse pow * fixed indentation * added SQRT llop to triton * update docs to match new llops * removed POW operator from assembly codegen * added sqrt and rsqrt to pow hlop * rewrote pow function in tensor.py * Adjust tolerance * Adjust for adamw * Reduce for Adam too * removed accidental leftover code * removed all of accidental code * added rsqrt test * removed pow from mlops again it was added back when resolving merge conflicts --------- Co-authored-by: Jacky Lee <[email protected]>
98 lines
4.4 KiB
Python
98 lines
4.4 KiB
Python
import numpy as np
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from tinygrad.helpers import dtypes
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from tinygrad.nn import Linear
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import torch
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import unittest
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from tinygrad.tensor import Tensor
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from tinygrad.nn.optim import Adam, SGD, AdamW
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np.random.seed(1337)
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x_init = np.random.randn(1,4).astype(np.float32)
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W_init = np.random.randn(4,4).astype(np.float32)
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m_init = np.random.randn(1,4).astype(np.float32)
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class TinyNet():
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def __init__(self, tensor):
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self.x = tensor(x_init.copy(), requires_grad=True)
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self.W = tensor(W_init.copy(), requires_grad=True)
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self.m = tensor(m_init.copy())
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def forward(self):
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out = self.x.matmul(self.W).relu()
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# print(out.detach().numpy())
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out = out.log_softmax(1)
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out = out.mul(self.m).add(self.m).sum()
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return out
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def step(tensor, optim, steps=1, kwargs={}):
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net = TinyNet(tensor)
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optim = optim([net.x, net.W], **kwargs)
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for _ in range(steps):
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out = net.forward()
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optim.zero_grad()
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out.backward()
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optim.step()
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return net.x.detach().numpy(), net.W.detach().numpy()
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class TestOptim(unittest.TestCase):
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def _test_optim(self, tinygrad_optim, torch_optim, steps, opts, atol, rtol):
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for x,y in zip(step(Tensor, tinygrad_optim, steps, kwargs=opts),
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step(torch.tensor, torch_optim, steps, kwargs=opts)):
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np.testing.assert_allclose(x, y, atol=atol, rtol=rtol)
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def _test_sgd(self, steps, opts, atol, rtol): self._test_optim(SGD, torch.optim.SGD, steps, opts, atol, rtol)
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def _test_adam(self, steps, opts, atol, rtol): self._test_optim(Adam, torch.optim.Adam, steps, opts, atol, rtol)
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def _test_adamw(self, steps, opts, atol, rtol): self._test_optim(AdamW, torch.optim.AdamW, steps, opts, atol, rtol)
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def test_sgd(self): self._test_sgd(1, {'lr': 0.001}, 1e-6, 0)
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def test_sgd_high_lr(self): self._test_sgd(1, {'lr': 10}, 1e-6, 1e-5)
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def test_sgd_wd(self): self._test_sgd(1, {'lr': 0.001, 'weight_decay': 0.1}, 1e-6, 0)
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def test_sgd_high_lr_wd(self): self._test_sgd(1, {'lr': 10, 'weight_decay': 0.1}, 1e-6, 1e-5)
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def test_multistep_sgd(self): self._test_sgd(10, {'lr': 0.001}, 1e-6, 0)
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def test_multistep_sgd_high_lr(self): self._test_sgd(10, {'lr': 10}, 1e-6, 3e-4)
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def test_multistep_sgd_wd(self): self._test_sgd(10, {'lr': 0.001, 'weight_decay': 0.1}, 1e-6, 0)
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def test_multistep_sgd_high_lr_wd(self): self._test_sgd(10, {'lr': 9, 'weight_decay': 0.1}, 1e-6, 3e-4)
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def test_multistep_sgd_momentum(self): self._test_sgd(10, {'lr': 0.001, 'momentum': 0.9}, 1e-6, 0)
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def test_multistep_sgd_high_lr_momentum(self): self._test_sgd(10, {'lr': 10, 'momentum': 0.9}, 1e-5, 3e-4)
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def test_multistep_sgd_momentum_wd(self): self._test_sgd(10, {'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0.1}, 1e-6, 0)
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def test_multistep_sgd_high_lr_momentum_wd(self): self._test_sgd(10, {'lr': 10, 'momentum': 0.9, 'weight_decay': 0.1}, 1e-5, 3e-4)
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def test_multistep_sgd_nesterov_momentum(self): self._test_sgd(10, {'lr': 0.001, 'momentum': 0.9, 'nesterov': True}, 1e-5, 0)
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def test_multistep_sgd_high_lr_nesterov_momentum(self): self._test_sgd(10, {'lr': 10, 'momentum': 0.9, 'nesterov': True}, 1e-5, 3e-4)
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def test_multistep_sgd_nesterov_momentum_wd(self): self._test_sgd(10, {'lr': 0.001, 'momentum': 0.9, 'nesterov': True, 'weight_decay': 0.1}, 1e-5, 0)
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def test_multistep_sgd_high_lr_nesterov_momentum_wd(self): self._test_sgd(10, {'lr': 9, 'momentum': 0.9, 'nesterov': True, 'weight_decay': 0.1}, 1e-5, 3e-4)
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def test_adam(self): self._test_adam(1, {'lr': 0.001}, 1e-5, 0)
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def test_adam_high_lr(self): self._test_adam(1, {'lr': 10}, 1e-5, 1e-5)
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def test_adamw(self): self._test_adamw(1, {'lr': 0.001}, 1e-5, 0)
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def test_adamw_high_lr(self): self._test_adamw(1, {'lr': 10}, 1e-5, 1e-5)
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def test_multistep_adam(self): self._test_adam(10, {'lr': 0.001}, 1e-5, 0)
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def test_multistep_adam_high_lr(self): self._test_adam(10, {'lr': 10}, 2e-4, 5e-4)
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def test_multistep_adamw(self): self._test_adamw(10, {'lr': 0.001}, 1e-5, 0)
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def test_multistep_adamw_high_lr(self): self._test_adamw(10, {'lr': 10}, 5e-4, 2e-3)
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def test_duped_weights(self):
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for Opt in [Adam, AdamW, SGD]:
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losses = []
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for i in range(2):
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w = Tensor(x_init.copy())
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opt = Opt([w], lr=0.1) if i == 0 else Opt([w, w], lr=0.1)
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loss = None
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for _ in range(3):
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loss = w.sum()
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opt.zero_grad()
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loss.backward()
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opt.step()
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losses.append(loss.numpy())
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np.testing.assert_allclose(losses[0], losses[1], atol=1e-4, rtol=0)
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if __name__ == '__main__':
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unittest.main()
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