333 lines
12 KiB
Python
333 lines
12 KiB
Python
import unittest
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from test.helpers import assert_jit_cache_len
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from tinygrad import Variable, Tensor, TinyJit
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from tinygrad.helpers import RANGEIFY
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import numpy as np
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class TestSymbolicJit(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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jf = TinyJit(f)
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a = Tensor.rand(3, 10)
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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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symbolic = jf(a[:, :vi])[:3, :i].numpy()
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expected = f(a[:, :i]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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def test_plus1_pad(self):
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# TODO: without contiguous, the pad is not captured in jit
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def f(a): return (a+1).pad((None, (0, 10-a.shape[1]))).contiguous().realize()
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jf = TinyJit(f)
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a = Tensor.rand(3, 10)
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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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symbolic = jf(a[:, :vi]).numpy()
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expected = f(a[:, :i]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1 if RANGEIFY else 2) # one add and one pad, can be one kernel?
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def test_add(self):
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def f(a, b): return (a+b).realize()
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jf = TinyJit(f)
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a = Tensor.rand(3, 10)
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b = Tensor.rand(3, 10)
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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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symbolic = jf(a[:, :vi], b[:, :vi])
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symbolic = symbolic[:3, :i].numpy()
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expected = f(a[:, :i], b[:, :i]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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def test_matmul(self):
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def f(a, b): return (a@b).realize()
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jf = TinyJit(f)
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a = Tensor.rand(3, 10)
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b = Tensor.rand(10, 5)
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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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symbolic = jf(a[:, :vi], b[:vi, :]).numpy()
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expected = f(a[:, :i], b[:i, :]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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def test_mixed_with_no_symbol_kernel(self):
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def f(a, b):
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s = (a@b).realize()
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s = (s+s).realize() # this one does not have symbols in input
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return s
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jf = TinyJit(f)
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a = Tensor.rand(3, 10)
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b = Tensor.rand(10, 5)
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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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symbolic = jf(a[:, :vi], b[:vi, :]).numpy()
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expected = f(a[:, :i], b[:i, :]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 2)
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def test_attention(self):
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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)).realize()
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jf = TinyJit(f)
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q = Tensor.rand(2, 1, 4, 8)
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k = Tensor.rand(2, 10, 4, 8)
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v = Tensor.rand(2, 10, 4, 8)
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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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symbolic = jf(q, k[:, :vi], v[:, :vi])[:2, :4, :1, :8].numpy()
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expected = f(q, k[:, :i], v[:, :i]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 4 if RANGEIFY else 5)
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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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jf = TinyJit(f)
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a = Tensor.rand(10, 3)
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b = Tensor.rand(2, 3)
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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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symbolic = jf(a[:vi], b)[:i+2, :3].numpy()
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expected = f(a[:i], b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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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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jf = TinyJit(f)
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a = Tensor.rand(3, 10)
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b = Tensor.rand(3, 2)
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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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symbolic = jf(a[:, :vi], b)[:3, :i+2].numpy()
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expected = f(a[:, :i], b).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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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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jf = TinyJit(f)
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a = Tensor.rand(10, 3)
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b = Tensor.rand(10, 3)
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for i in range(2, 5):
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for j in range(2, 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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symbolic = jf(a[:vi], b[:vj])[:i+j, :3].numpy()
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expected = f(a[:i], b[:j]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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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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jf = TinyJit(f)
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a = Tensor.rand(3, 10)
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b = Tensor.rand(3, 10)
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for i in range(2, 5):
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for j in range(2, 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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symbolic = jf(a[:, :vi], b[:, :vj])[:3, :i+j].numpy()
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expected = f(a[:, :i], b[:, :j]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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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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jf = TinyJit(f)
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a = Tensor.rand(10, 3)
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b = Tensor.rand(3, 10)
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for i in range(2, 5):
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for j in range(2, 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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symbolic = jf(a[:vi, :], b[:, :vj])[:i, :j].numpy()
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expected = f(a[:i, :], b[:, :j]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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def test_two_vars_plus1_ji(self):
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def f(a, b): return (a@b+1).realize()
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jf = TinyJit(f)
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a = Tensor.rand(10, 3)
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b = Tensor.rand(3, 10)
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for i in range(2, 5):
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for j in range(2, 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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symbolic = jf(a[:vj, :], b[:, :vi])[:j, :i].numpy()
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expected = f(a[:j, :], b[:, :i]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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def test_jit_symbolic_shape_mismatch(self):
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@TinyJit
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def add(a, b): return (a+b).realize()
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a = Tensor.rand(3, 10)
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b = Tensor.rand(3, 10)
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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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add(a[:, :vi], b[:, :vi])
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vi2 = Variable("i", 1, 10).bind(7)
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a = Tensor.rand(3, 7)[:, :vi2]
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bad = Tensor.rand(4, 7)[:, :vi2]
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with self.assertRaises(AssertionError):
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add(a, bad)
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def test_shrink(self):
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# shrink is a movement, so we pair it with a simple function to test the JIT interaction
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def f(a): return (a+1).realize()
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jf = TinyJit(f)
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a = Tensor.rand(7, 11)
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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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symbolic = a.shrink(((3,5),(vi,vi+2)))
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symbolic = jf(symbolic).numpy()
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expected = f(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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assert_jit_cache_len(jf, 1)
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def test_slice(self):
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# slice is a movement, so we pair it with a simple function to test the JIT interaction
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def f(a): return (a+1).realize()
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jf = TinyJit(f)
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a = Tensor.rand(7, 11)
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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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symbolic = a[3:5, vi:vi+2]
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symbolic = jf(symbolic).numpy()
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expected = f(a[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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assert_jit_cache_len(jf, 1)
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def test_slice_var_shape(self):
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def f(a): return (a+1).realize()
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jf = TinyJit(f)
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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.ones(vi, 11).contiguous()
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symbolic = a[:, 1:2]
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symbolic = jf(symbolic)[:i, :1].numpy()
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expected = f(a[:i, :][:, 1:2]).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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assert_jit_cache_len(jf, 1)
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def test_ones_sum(self):
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def f(a): return a.sum().realize()
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jf = TinyJit(f)
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t = Tensor.ones(10)
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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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symbolic = jf(t[:vi]).item()
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expected = f(t[:i]).item()
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np.testing.assert_equal(symbolic, expected)
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def test_mean(self):
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def f(a): return a.mean().realize()
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def f0(a): return a.mean(0).realize()
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def f1(a): return a.mean(1).realize()
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jf = TinyJit(f)
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jf0 = TinyJit(f0)
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jf1 = TinyJit(f1)
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a = Tensor.rand(10, 3)
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b = Tensor.rand(10, 3)
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c = Tensor.rand(10, 3)
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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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# axis = None
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symbolic = jf(a[:vi]).numpy()
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expected = a[:i].mean().numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 0
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symbolic = jf0(b[:vi]).numpy()
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expected = b[:i].mean(0).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 1
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symbolic = jf1(c[:vi])[:i].numpy()
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expected = c[:i].mean(1).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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def f(a): return a.mean().realize()
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def f0(a): return a.mean(0).realize()
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def f1(a): return a.mean(1).realize()
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jf = TinyJit(f)
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jf0 = TinyJit(f0)
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jf1 = TinyJit(f1)
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a = Tensor.rand(10, 10)
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b = Tensor.rand(10, 10)
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c = Tensor.rand(10, 10)
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for i in range(2, 5):
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for j in range(2, 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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# axis = None
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symbolic = jf(a[:vi, :vj]).numpy()
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expected = a[:i, :j].mean().numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 0
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symbolic = jf0(b[:vi, :vj])[:j].numpy()
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expected = b[:i, :j].mean(0).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 1
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symbolic = jf1(c[:vi, :vj])[:i].numpy()
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expected = c[:i, :j].mean(1).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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def f(a): return a.var().realize()
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def f0(a): return a.var(0).realize()
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def f1(a): return a.var(1).realize()
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jf = TinyJit(f)
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jf0 = TinyJit(f0)
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jf1 = TinyJit(f1)
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a = Tensor.rand(10, 3)
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b = Tensor.rand(10, 3)
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c = Tensor.rand(10, 3)
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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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# axis = None
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symbolic = jf(a[:vi]).numpy()
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expected = a[:i].var().numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 0
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symbolic = jf0(b[:vi]).numpy()
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expected = b[:i].var(0).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 1
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symbolic = jf1(c[:vi])[:i].numpy()
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expected = c[:i].var(1).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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def f(a): return a.var().realize()
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def f0(a): return a.var(0).realize()
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def f1(a): return a.var(1).realize()
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jf = TinyJit(f)
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jf0 = TinyJit(f0)
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jf1 = TinyJit(f1)
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a = Tensor.rand(10, 10)
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b = Tensor.rand(10, 10)
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c = Tensor.rand(10, 10)
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for i in range(2, 5):
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for j in range(2, 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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# axis = None
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symbolic = jf(a[:vi, :vj]).numpy()
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expected = a[:i, :j].var().numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 0
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symbolic = jf0(b[:vi, :vj])[:j].numpy()
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expected = b[:i, :j].var(0).numpy()
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np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
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# axis = 1
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symbolic = jf1(c[:vi, :vj])[:i].numpy()
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expected = c[:i, :j].var(1).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()
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