mirror of https://github.com/commaai/tinygrad.git
193 lines
7.2 KiB
Python
193 lines
7.2 KiB
Python
import unittest
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from tinygrad.shape.symbolic import Variable
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from tinygrad.helpers import getenv
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from tinygrad.tensor import Tensor
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from examples.gpt2 import Attention
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import numpy as np
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class TestSymbolicOps(unittest.TestCase):
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def test_plus1(self):
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def f(a): return (a+1).realize()
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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a = Tensor.rand(3, i)
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symbolic = f(a.reshape(3, vi)).reshape(3, i).numpy()
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expected = f(a).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_add(self):
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def f(a, b): return (a+b).realize()
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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a = Tensor.rand(3, i)
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b = Tensor.rand(3, i)
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symbolic = f(a.reshape(3, vi), b.reshape(3, vi)).reshape(3, i).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_matmul(self):
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def f(a, b): return (a@b).realize()
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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a = Tensor.rand(3, i)
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b = Tensor.rand(i, 5)
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symbolic = f(a.reshape(3, vi), b.reshape(vi, 5)).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_attention(self, dropout_p=0.0):
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def f(q, k, v): return Tensor.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), dropout_p=dropout_p).realize()
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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q = Tensor.rand(2, 1, 4, 8)
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k = Tensor.rand(2, i, 4, 8)
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v = Tensor.rand(2, i, 4, 8)
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symbolic = f(q, k.reshape(2, vi, 4, 8), v.reshape(2, vi, 4, 8)).reshape(2, 4, 1, 8).numpy()
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expected = f(q, k, v).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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@unittest.skipIf(getenv("MOCKHIP"), "MOCKHIP only compiles and does not run")
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def test_attention_training(self):
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with Tensor.train():
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self.test_attention(dropout_p=0.0)
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with self.assertRaises(AssertionError):
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# symbolic shape dropout is not supported
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self.test_attention(dropout_p=0.5)
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def test_attention_pos_0_sz_0(self):
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Attention(128, 8)(Tensor.ones(1, 0, 128), Variable("start_pos", 0, 128).bind(0), None)
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def test_attention_pos_0_sz_1(self):
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Attention(128, 8)(Tensor.ones(1, 1, 128), Variable("start_pos", 0, 128).bind(0), None)
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def test_attention_pos_0_sz_2(self):
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Attention(128, 8)(Tensor.ones(1, 2, 128), Variable("start_pos", 0, 128).bind(0), None)
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def test_cat_dim0(self):
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def f(a, b): return a.cat(b, dim=0).realize()
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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a = Tensor.rand(i, 3)
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b = Tensor.rand(2, 3)
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symbolic = f(a.reshape(vi, 3), b).reshape(i+2, 3).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_cat_dim1(self):
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def f(a, b): return a.cat(b, dim=1).realize()
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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a = Tensor.rand(3, i)
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b = Tensor.rand(3, 2)
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symbolic = f(a.reshape(3, vi), b).reshape(3, i+2).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_cat_dim0_two_vars(self):
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def f(a, b): return a.cat(b, dim=0).realize()
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for i in range(1, 5):
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for j in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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vj = Variable("j", 1, 10).bind(j)
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a = Tensor.rand(i, 3)
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b = Tensor.rand(j, 3)
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symbolic = f(a.reshape(vi, 3), b.reshape(vj, 3)).reshape(i+j, 3).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_cat_dim1_two_vars(self):
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def f(a, b): return a.cat(b, dim=1).realize()
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for i in range(1, 5):
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for j in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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vj = Variable("j", 1, 10).bind(j)
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a = Tensor.rand(3, i)
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b = Tensor.rand(3, j)
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symbolic = f(a.reshape(3, vi), b.reshape(3, vj)).reshape(3, i+j).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_two_vars_plus1_ij(self):
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def f(a, b): return (a@b+1).realize()
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for i in range(1, 5):
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for j in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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vj = Variable("j", 1, 10).bind(j)
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a = Tensor.rand(i, 3)
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b = Tensor.rand(3, j)
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symbolic = f(a.reshape(vi, 3), b.reshape(3, vj)).reshape(i, j).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_two_vars_plus1_ji(self):
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# reverse the order of variables
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def f(a, b): return (a@b+1).realize()
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for i in range(1, 5):
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for j in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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vj = Variable("j", 1, 10).bind(j)
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a = Tensor.rand(j, 3)
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b = Tensor.rand(3, i)
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symbolic = f(a.reshape(vj, 3), b.reshape(3, vi)).reshape(j, i).numpy()
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expected = f(a, b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_shrink(self):
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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a = Tensor.rand(7, 11)
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symbolic = a.shrink(((3,5),(vi,vi+2)))
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symbolic = symbolic.numpy()
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expected = a.shrink(((3,5),(i,i+2))).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_ones_sum(self):
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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t = Tensor.ones(i)
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symbolic = t.reshape(vi).sum().item()
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expected = t.sum().item()
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np.testing.assert_equal(symbolic, expected)
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def test_mean(self):
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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for axis in [None, 0, 1]:
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a = Tensor.rand(i, 3)
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expected = a.mean(axis).numpy()
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symbolic = a.reshape(vi, 3).mean(axis).reshape(expected.shape).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_mean_2d(self):
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for i in range(1, 5):
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for j in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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vj = Variable("j", 1, 10).bind(j)
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for axis in [None, 0, 1]:
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a = Tensor.rand(i, j)
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expected = a.mean(axis).numpy()
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symbolic = a.reshape(vi, vj).mean(axis).reshape(expected.shape).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_var(self):
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for i in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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for axis in [None, 0, 1]:
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a = Tensor.rand(i, 3)
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expected = a.var(axis).numpy()
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symbolic = a.reshape(vi, 3).var(axis).reshape(expected.shape).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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def test_var_2d(self):
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for i in range(1, 5):
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for j in range(1, 5):
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vi = Variable("i", 1, 10).bind(i)
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vj = Variable("j", 1, 10).bind(j)
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for axis in [None, 0, 1]:
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a = Tensor.rand(i, j)
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expected = a.var(axis).numpy()
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symbolic = a.reshape(vi, vj).var(axis).reshape(expected.shape).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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if __name__ == '__main__':
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unittest.main() |