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673 lines
22 KiB
673 lines
22 KiB
#!/usr/bin/env python
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import unittest, functools
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import numpy as np
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from hypothesis import given, settings, strategies as strat
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from test.helpers import assert_jit_cache_len, not_support_multi_device, REAL_DEV
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from tinygrad.tensor import Tensor
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from tinygrad.engine.jit import TinyJit
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from tinygrad.device import Device
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from tinygrad.helpers import Context, JIT, GlobalCounters
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from tinygrad.dtype import dtypes
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from extra.models.unet import ResBlock
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def _simple_test(add, extract=lambda x: x, N=10):
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for _ in range(5):
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a = Tensor.randn(N, N)
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b = Tensor.randn(N, N)
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c = add(a, b)
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np.testing.assert_allclose(extract(c).numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(add, 1)
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class TestJit(unittest.TestCase):
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@settings(deadline=2e4)
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@unittest.skipUnless(REAL_DEV in ["LLVM", "CPU"], f"no support on {REAL_DEV}")
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@given(strat.sampled_from([Tensor.exp2, Tensor.log2, Tensor.sin]))
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def test_approx_jit_timeout(self, op):
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with Context(TRANSCENDENTAL=2):
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model = [ResBlock(16, 24, 16) for _ in range(4)]
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@TinyJit
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def fw_approx(t, t2):
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for l in model: t = l(t, t2)
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return op(t).realize()
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fw_approx(Tensor.empty(4, 16, 8, 8), Tensor.empty(1, 24))
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def test_simple_jit(self):
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@TinyJit
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def add(a, b): return (a+b).realize()
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_simple_test(add)
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def test_simple_jit_reset(self):
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@TinyJit
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def add(a, b): return (a+b).realize()
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_simple_test(add)
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add.reset()
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_simple_test(add, N=20)
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def test_simple_jit_norealize(self):
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@TinyJit
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def add(a, b): return (a+b)
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_simple_test(add)
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def test_simple_jit_norealize_list(self):
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@TinyJit
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def add(a, b): return [a+b]
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_simple_test(add, extract=lambda x: x[0])
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def test_simple_jit_norealize_dict(self):
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@TinyJit
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def add(a, b): return {"billy": a+b}
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_simple_test(add, extract=lambda x: x["billy"])
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def test_jit_multiple_outputs(self):
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@TinyJit
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def f(a, b): return (a+b).realize(), (a-b).realize(), (a*b).realize()
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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c, d, e = f(a, b)
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np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(d.numpy(), a.numpy()-b.numpy(), atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(e.numpy(), a.numpy()*b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(f, 3)
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def test_nothing_jitted(self):
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@TinyJit
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def add(a, b): return None
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with self.assertRaises(AssertionError):
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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add(a, b)
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def test_jit_zero_does_not_jit(self):
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@TinyJit
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def add(a, b): return (a+b).realize()
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with Context(JIT=0):
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for i in range(5):
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a = Tensor([i])
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b = Tensor([i])
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c = add(a, b)
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np.testing.assert_allclose(c.numpy(), 2*i)
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assert_jit_cache_len(add, 0)
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def test_jit_not_capturing(self):
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@TinyJit
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def add(a, b):
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Tensor.zeros(4, 4).contiguous().realize() # no-op kernel is captured
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return (a+b).realize()
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for i in range(5):
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a = Tensor([i])
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b = Tensor([i])
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c = add(a, b)
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np.testing.assert_allclose(c.numpy(), 2*i)
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assert_jit_cache_len(add, 2)
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@TinyJit
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def add2(a, b):
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with Context(CAPTURING=0): # not captured
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Tensor.zeros(4, 4).contiguous().realize()
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return (a+b).realize()
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for i in range(5):
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a = Tensor([i])
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b = Tensor([i])
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c = add2(a, b)
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np.testing.assert_allclose(c.numpy(), 2*i)
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assert_jit_cache_len(add2, 1)
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def test_jit_shape_mismatch(self):
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@TinyJit
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def add(a, b): return (a+b).realize()
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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add(a, b)
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bad = Tensor.randn(20, 20)
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with self.assertRaises(AssertionError):
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add(a, bad)
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def test_jit_shape_views_mismatch(self):
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@TinyJit
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def add(a): return (a+1).realize()
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with self.assertRaises(AssertionError):
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for i in range(1,5):
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# a has an offset that the kernel doesn't know about
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a = Tensor.randn(10, 10).realize()[:, i:i+2]
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add(a)
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def test_jit_duplicate_fail(self):
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# the jit doesn't support duplicate arguments
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@TinyJit
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def add(a, b): return (a+b).realize()
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a = Tensor.randn(10, 10)
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with self.assertRaises(AssertionError):
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add(a, a)
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def test_jit_assign(self, dtype=dtypes.float32):
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@TinyJit
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def add(a):
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a += 1
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a.realize()
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a = Tensor.zeros(1, dtype=dtype).contiguous().realize()
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for _ in range(5): add(a)
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self.assertEqual(a.item(), 5)
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def test_jit_assign_int8(self): self.test_jit_assign(dtypes.int8)
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def test_kwargs_jit(self):
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@TinyJit
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def add_kwargs(first, second): return (first+second).realize()
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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c = add_kwargs(first=a, second=b)
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np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(add_kwargs, 1)
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def test_reorder_kwargs_jit(self):
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@TinyJit
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def add_kwargs(first, second): return (first/second).realize()
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for _ in range(2):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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c = add_kwargs(second=b, first=a)
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np.testing.assert_allclose(c.numpy(), a.numpy()/b.numpy(), atol=1e-4, rtol=1e-5)
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for _ in range(2):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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c = add_kwargs(first=a, second=b)
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np.testing.assert_allclose(c.numpy(), a.numpy()/b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(add_kwargs, 1)
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def test_array_jit(self):
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@TinyJit
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def add_array(a, arr): return (a+arr[0]).realize()
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for i in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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a.realize(), b.realize()
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c = add_array(a, [b])
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if i >= 2:
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# should fail once jitted since jit can't handle arrays
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np.testing.assert_allclose(np.any(np.not_equal(c.numpy(),a.numpy()+b.numpy())), True, atol=1e-4, rtol=1e-5)
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else:
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np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(add_array, 1)
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def test_jit_copyin(self):
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@TinyJit
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def f(a):
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return a + Tensor([1,2,3])
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for _ in range(5):
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b = Tensor.randn(3)
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c = f(b)
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np.testing.assert_allclose(c.numpy(), b.numpy()+[1,2,3], atol=1e-4, rtol=1e-5)
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def test_method_jit(self):
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class Fun:
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def __init__(self):
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self.a = Tensor.randn(10, 10)
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@TinyJit
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def __call__(self, b:Tensor) -> Tensor:
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return (self.a+b).realize()
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fun = Fun()
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for _ in range(5):
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b = Tensor.randn(10, 10)
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c = fun(b)
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np.testing.assert_allclose(c.numpy(), fun.a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(fun.__call__.func.__self__, 1)
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def test_jit_size1_input(self):
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@TinyJit
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def f(a, b): return (a+b).realize()
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a = Tensor([1, 2, 3])
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for i in range(5):
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np.testing.assert_allclose(f(a, Tensor([i])).numpy(), (a+i).numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(f, 1)
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def test_jit_output_non_tensor_fail(self):
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@TinyJit
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def f(a, b, i): return (a+b).realize(), i
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output1, output2 = [], []
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expect1, expect2 = [], []
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for i in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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o1, o2 = f(a, b, i)
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output1.append(o1.numpy().copy())
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output2.append(o2)
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expect1.append(a.numpy().copy()+b.numpy().copy())
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expect2.append(i)
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np.testing.assert_allclose(output1, expect1, atol=1e-4, rtol=1e-5)
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# the jit only works with Tensor outputs
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assert output2 != expect2
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assert_jit_cache_len(f, 1)
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def test_jit_random_regen(self):
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def f(a, b):
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rn = Tensor.randn(*a.shape)
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return ((a+b)*rn).realize()
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a = Tensor.randn(10, 10).realize() # realize these before resetting the random seed
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b = Tensor.randn(10, 10).realize()
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Tensor.manual_seed(1234)
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jf = TinyJit(f)
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res = set()
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for _ in range(5):
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o1 = jf(a, b)
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res.add(o1.numpy()[0][0])
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assert len(res) == 5, "All values should be different, rand works in jit."
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Tensor.manual_seed(1234)
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jf2 = TinyJit(f)
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res2 = set()
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for _ in range(5):
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o1 = jf2(a, b)
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res2.add(o1.numpy()[0][0])
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assert len(res2) == 5, "All values should be different, rand works in jit."
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assert res == res2, "Jit rand is not reproducible with the same seed"
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Tensor.manual_seed(3421)
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jf3 = TinyJit(f)
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res3 = set()
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for _ in range(5):
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o1 = jf3(a, b)
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res3.add(o1.numpy()[0][0])
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assert len(res3) == 5, "All values should be different, rand works in jit."
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assert res3 != res2, "Jit rand is diff with diff seeds"
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@unittest.expectedFailure # TODO: fix
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def test_jit_v_nojit_random_regen(self):
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def f(a, b):
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rn = Tensor.randn(*a.shape)
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rn = rn * a
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rn2 = Tensor.randn(*a.shape)
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rn2 = rn2 * b
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rn = rn + rn2
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rn2 = rn2 + Tensor.randn(*a.shape)
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return ((a+b)*rn).realize(), ((a+b)*rn2).realize()
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Tensor.manual_seed(0)
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a = Tensor.randn(10, 10).realize() # realize these before resetting the random seed
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b = Tensor.randn(10, 10).realize()
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Tensor.manual_seed(1234)
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without_jit = set()
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for _ in range(5):
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o1, o2 = f(a, b)
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without_jit.add(o1.numpy()[0][0])
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without_jit.add(o2.numpy()[0][0])
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assert len(without_jit) == 10, "All values should be different."
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Tensor.manual_seed(1234)
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jf = TinyJit(f)
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with_jit = set()
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for _ in range(5):
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o1, o2 = jf(a, b)
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with_jit.add(o1.numpy()[0][0])
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with_jit.add(o2.numpy()[0][0])
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assert len(with_jit) == 10, "All values should be different."
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assert with_jit == without_jit, "Jit rand produced different values from no jit."
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def test_jit_multiple_random_regen(self):
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def f(a, b):
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rn = Tensor.randn(*a.shape)
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rn = rn * a
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rn2 = Tensor.randn(*a.shape)
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rn2 = rn2 * b
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rn = rn + rn2
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rn2 = rn2 + Tensor.randn(*a.shape)
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return ((a+b)*rn).realize(), ((a+b)*rn2).realize()
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a = Tensor.randn(10, 10).realize() # realize these before resetting the random seed
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b = Tensor.randn(10, 10).realize()
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Tensor.manual_seed(1234)
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jf = TinyJit(f)
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res = set()
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for _ in range(5):
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o1, o2 = jf(a, b)
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res.add(o1.numpy()[0][0])
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res.add(o2.numpy()[0][0])
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assert len(res) == 10, "All values should be different, rand works in jit."
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Tensor.manual_seed(1234)
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jf2 = TinyJit(f)
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res2 = set()
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for _ in range(5):
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o1, o2 = jf2(a, b)
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res2.add(o1.numpy()[0][0])
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res2.add(o2.numpy()[0][0])
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assert len(res2) == 10, "All values should be different, rand works in jit."
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assert res == res2, "Jit rand is not reproducible with the same seed"
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Tensor.manual_seed(3421)
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jf3 = TinyJit(f)
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res3 = set()
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for _ in range(5):
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o1, o2 = jf3(a, b)
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res3.add(o1.numpy()[0][0])
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res3.add(o2.numpy()[0][0])
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assert len(res3) == 10, "All values should be different, rand works in jit."
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assert res3 != res2, "Jit rand is diff with diff seeds"
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#@unittest.expectedFailure # requires contiguous folding
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def test_jit_random_after_unrealized_random(self):
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@TinyJit
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def f(): return Tensor.rand()
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Tensor.manual_seed(1234)
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Tensor.rand()
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res = [f().numpy() for _ in range(3)]
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assert res[1] != res[2]
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def test_jit_realization_and_sampling(self):
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w = Tensor.eye(5)
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@TinyJit
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def foo (x): return w.dot(x).realize()
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arg = [
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Tensor([1,2,3,4,5]),
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Tensor([1,3,3,4,6]),
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Tensor([1,2,5,4,7]),
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Tensor([0,2,3,1,0]),
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]
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Y = [foo(e).numpy() for e in arg]
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foo(Tensor([7,7,7,7,7]))
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want = [[1., 2., 3., 4., 5.],
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[1., 3., 3., 4., 6.],
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[1., 2., 5., 4., 7.],
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[0., 2., 3., 1., 0.]]
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np.testing.assert_allclose(want, Y)
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def test_jit_buffer_behavior(self):
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@TinyJit
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def foo(x) -> Tensor: return x.sum().realize()
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result_1 = foo(Tensor([1] * 2))
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result_2 = foo(Tensor([2] * 2))
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result_3 = foo(Tensor([3] * 2))
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# expect the buffer to share underlying buffer
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np.testing.assert_allclose(result_1.numpy(), [2], atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(result_2.numpy(), [6], atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(result_3.numpy(), [6], atol=1e-4, rtol=1e-5)
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def test_jit_batch_split(self):
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if Device[Device.DEFAULT].graph is None or JIT >= 2: raise unittest.SkipTest("only test graphs")
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# Create long jit with 83 kernels.
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def f(a, b, c, d, e):
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for _ in range(80):
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a = (a+b).realize()
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y = (a*c).realize()
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z = (y*d).realize()
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w = (z*e)
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return w.realize()
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a = Tensor.randn(10, 10).realize()
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b = Tensor.randn(10, 10).realize()
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c = Tensor.randn(10, 10).realize()
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d = Tensor.randn(10, 10).realize()
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e = Tensor.randn(10, 10).realize()
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jf = TinyJit(f)
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prev = None
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for _ in range(5):
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o = jf(a, b, c, d, e).numpy()
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if prev is not None: np.testing.assert_allclose(o, prev, atol=1e-4, rtol=1e-5)
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prev = o
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graph_t = Device[Device.DEFAULT].graph.func if isinstance(Device[Device.DEFAULT].graph, functools.partial) else Device[Device.DEFAULT].graph
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# Checking that 2 graphs are inited.
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assert isinstance(jf.jit_cache[0].prg, graph_t)
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assert isinstance(jf.jit_cache[1].prg, graph_t)
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def test_jit_const_inputs(self):
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@TinyJit
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def g(x,y,z): return (x+y+z).realize()
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for i in range(5):
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np.testing.assert_equal(g(Tensor([i]*3), Tensor.ones(3), Tensor.zeros(3)).numpy(), np.array([i+1]*3))
|
|
|
|
def test_jitted_clone(self):
|
|
def f(a): return a.clone().realize()
|
|
jf = TinyJit(f)
|
|
for _ in range(5):
|
|
a = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
|
|
ja = jf(a)
|
|
np.testing.assert_allclose(a.numpy(), ja.numpy(), atol=1e-4, rtol=1e-5)
|
|
|
|
@unittest.skipIf(not_support_multi_device(), "no multi")
|
|
def test_jitted_transfers(self):
|
|
d0, d1 = f"{Device.DEFAULT}:0", f"{Device.DEFAULT}:1"
|
|
|
|
def f(a, b):
|
|
x = a.to(d1)
|
|
y = b.to(d1)
|
|
return x.realize(), y.realize()
|
|
|
|
jf = TinyJit(f)
|
|
for _ in range(5):
|
|
a = Tensor.randn(10, 10, device=d0).realize()
|
|
b = Tensor.randn(10, 10, device=d0).realize()
|
|
xc, yc = jf(a, b)
|
|
np.testing.assert_allclose(a.numpy(), xc.numpy(), atol=1e-4, rtol=1e-5)
|
|
np.testing.assert_allclose(b.numpy(), yc.numpy(), atol=1e-4, rtol=1e-5)
|
|
|
|
def test_jit_several_devs(self):
|
|
d0, d1 = f"{Device.DEFAULT}:0", "CPU"
|
|
|
|
def f(a, b):
|
|
x = a.to(d0).realize()
|
|
y = b.to(d0).realize()
|
|
return x+y.realize(), x*y.realize()
|
|
|
|
jf = TinyJit(f)
|
|
for _ in range(5):
|
|
a = Tensor.randn(10, 10, device=d1).realize()
|
|
b = Tensor.randn(10, 10, device=d1).realize()
|
|
zc, wc = jf(a, b)
|
|
np.testing.assert_allclose((a.numpy()+b.numpy()), zc.numpy(), atol=1e-4, rtol=1e-5)
|
|
np.testing.assert_allclose((a.numpy()*b.numpy()), wc.numpy(), atol=1e-4, rtol=1e-5)
|
|
|
|
@unittest.skipIf(not_support_multi_device(), "no multi")
|
|
def test_jitted_view(self):
|
|
d0, d1 = f"{Device.DEFAULT}:0", f"{Device.DEFAULT}:1"
|
|
|
|
def f(a):
|
|
x1 = a.sum(axis=(1,))
|
|
x = (x1 + 5).bitcast(dtypes.int32)
|
|
y = x.to(d1)
|
|
return y.realize()
|
|
|
|
jf = TinyJit(f)
|
|
for _ in range(5):
|
|
a = Tensor.randn(10, 1000, device=d0).realize()
|
|
xc = jf(a)
|
|
np.testing.assert_allclose((a.numpy().sum(axis=(1,)) + 5).view(np.int32), xc.numpy(), atol=1e-4, rtol=5e-5)
|
|
|
|
def test_jit_output_clone(self):
|
|
@TinyJit
|
|
def f(x:Tensor) -> Tensor: return (x + 1).realize()
|
|
|
|
f(Tensor([0.0]))
|
|
f(Tensor([0.0]))
|
|
|
|
a = f(Tensor([1.0])).clone().realize()
|
|
b = f(Tensor([2.0]))
|
|
assert abs((a - b).item()) > 0.5
|
|
|
|
@unittest.skip("Pending multioutput implementation #3607")
|
|
class TestMultioutputJit(unittest.TestCase):
|
|
def _test(self, f):
|
|
for _ in range(5):
|
|
a, b = Tensor.randn(10, 10), Tensor.randn(10, 10)
|
|
out0, out1, out2 = f(a, b)
|
|
np.testing.assert_allclose(out0.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
|
|
np.testing.assert_allclose(out1.numpy(), a.numpy()-b.numpy(), atol=1e-4, rtol=1e-5)
|
|
np.testing.assert_allclose(out2.numpy(), a.numpy()*b.numpy(), atol=1e-4, rtol=1e-5)
|
|
|
|
def test_jit_multioutput_realize(self):
|
|
@TinyJit
|
|
def fxn(a, b): return (a+b).realize(), (a-b).realize(), (a*b).realize()
|
|
self._test(fxn)
|
|
assert_jit_cache_len(fxn, 3)
|
|
|
|
def test_jit_multioutput_norealize(self):
|
|
@TinyJit
|
|
def fxn(a, b): return a+b, a-b, a*b
|
|
self._test(fxn)
|
|
assert_jit_cache_len(fxn, 1)
|
|
|
|
def test_jit_multioutput_mix(self):
|
|
@TinyJit
|
|
def fxn(a, b): return a+b, a-b, (a*b).realize()
|
|
self._test(fxn)
|
|
assert_jit_cache_len(fxn, 2)
|
|
|
|
class TestJitInsideJit(unittest.TestCase):
|
|
def test_jit_jit_error(self):
|
|
@TinyJit
|
|
def f(t): return t + 1
|
|
|
|
@TinyJit
|
|
def g(t): return f(t) * 3
|
|
|
|
# NOTE: first does not raise
|
|
g(Tensor([1])).realize()
|
|
with self.assertRaisesRegex(RuntimeError, "having TinyJit inside another TinyJit is not supported"):
|
|
g(Tensor([1])).realize()
|
|
|
|
class TestCopyInsideJit(unittest.TestCase):
|
|
def test_copy_inside_jit(self):
|
|
@TinyJit
|
|
def add(x,y) -> Tensor: return x.to(Device.DEFAULT)+y
|
|
for _ in range(5):
|
|
# create a Tensor on CPU
|
|
a = Tensor.rand(16,16,device="CPU").realize()
|
|
b = Tensor.rand(16,16).realize()
|
|
out = add(a,b)
|
|
np.testing.assert_allclose(out.flatten().tolist(), [x+y for x,y in zip(a.flatten().tolist(), b.flatten().tolist())])
|
|
|
|
class TestJitPrune(unittest.TestCase):
|
|
def test_simple_prune(self):
|
|
weights = Tensor.rand(16).realize()
|
|
def w2(x) -> Tensor: return (weights*2).contiguous() + x
|
|
w2_noprune = TinyJit(w2)
|
|
w2_prune = TinyJit(w2, prune=True)
|
|
|
|
for _ in range(3):
|
|
a = Tensor.rand(16).realize()
|
|
out = w2_noprune(a)
|
|
np.testing.assert_allclose(out.tolist(), [x*2+y for x,y in zip(weights.tolist(), a.tolist())])
|
|
assert len(w2_noprune.captured.jit_cache) == 2
|
|
|
|
for _ in range(3):
|
|
a = Tensor.rand(16).realize()
|
|
out = w2_prune(a)
|
|
np.testing.assert_allclose(out.tolist(), [x*2+y for x,y in zip(weights.tolist(), a.tolist())])
|
|
assert len(w2_prune.captured.jit_cache) == 1
|
|
|
|
def test_prune_w_copy_correct(self):
|
|
weights = Tensor.rand(16).realize()
|
|
def w2(x) -> Tensor: return (weights*2).contiguous() + x.to(Device.DEFAULT)
|
|
w2_noprune = TinyJit(w2)
|
|
w2_prune = TinyJit(w2, prune=True)
|
|
|
|
for _ in range(3):
|
|
a = Tensor.rand(16, device="CPU").realize()
|
|
out = w2_noprune(a)
|
|
np.testing.assert_allclose(out.tolist(), [x*2+y for x,y in zip(weights.tolist(), a.tolist())])
|
|
|
|
for _ in range(3):
|
|
a = Tensor.rand(16, device="CPU").realize()
|
|
out = w2_prune(a)
|
|
np.testing.assert_allclose(out.tolist(), [x*2+y for x,y in zip(weights.tolist(), a.tolist())])
|
|
|
|
def test_prune_w_independent_copy_correct(self):
|
|
weights = Tensor.rand(16, device="CPU").realize()
|
|
def w2(x) -> Tensor: return (weights*2).contiguous().to(Device.DEFAULT) + x
|
|
w2_noprune = TinyJit(w2)
|
|
w2_prune = TinyJit(w2, prune=True)
|
|
|
|
for _ in range(3):
|
|
a = Tensor.rand(16).realize()
|
|
out = w2_noprune(a)
|
|
np.testing.assert_allclose(out.tolist(), [x*2+y for x,y in zip(weights.tolist(), a.tolist())])
|
|
|
|
for _ in range(3):
|
|
a = Tensor.rand(16).realize()
|
|
out = w2_prune(a)
|
|
np.testing.assert_allclose(out.tolist(), [x*2+y for x,y in zip(weights.tolist(), a.tolist())])
|
|
|
|
assert len(w2_prune.captured.jit_cache) == 1, "prune should have removed the copy"
|
|
|
|
class TestJitFree(unittest.TestCase):
|
|
def test_free_intermediates(self):
|
|
ext_tensor = Tensor([1,24,23,45,1])
|
|
@TinyJit
|
|
def fxn(x:Tensor):
|
|
out = (x*2+ext_tensor).reshape(5,1).expand(5, 100).contiguous()
|
|
return out.sum()
|
|
for i in range(5):
|
|
out = fxn(Tensor([i,1,2,3,4]))
|
|
self.assertEqual(out.item(), 11400+200*i)
|
|
pre_free = GlobalCounters.mem_used
|
|
fxn.captured.free_intermediates()
|
|
savings_after_free = pre_free - GlobalCounters.mem_used
|
|
|
|
# Different allocator implementations have different savings.
|
|
expected_savings = 8196 if hasattr(Device[Device.DEFAULT].allocator, '_offset') else 2024
|
|
|
|
self.assertEqual(savings_after_free, expected_savings)
|
|
out = fxn(Tensor([11,1,2,3,4]))
|
|
self.assertEqual(out.item(), 13600)
|
|
|
|
# Try one more time...
|
|
pre_free = GlobalCounters.mem_used
|
|
fxn.captured.free_intermediates()
|
|
fxn.captured.free_intermediates() # 2nd time to validate
|
|
savings_after_free = pre_free - GlobalCounters.mem_used
|
|
|
|
self.assertEqual(savings_after_free, expected_savings)
|
|
out = fxn(Tensor([11,1,2,3,4]))
|
|
self.assertEqual(out.item(), 13600)
|
|
|
|
def test_updated_not_freed(self):
|
|
x = Tensor([1]).realize()
|
|
@TinyJit
|
|
def fxn(y):
|
|
nonlocal x
|
|
x += y
|
|
return x
|
|
for _ in range(5): fxn(Tensor([1]))
|
|
self.assertEqual(x.item(), 6)
|
|
pre_free = GlobalCounters.mem_used
|
|
fxn.captured.free_intermediates()
|
|
savings_after_free = pre_free - GlobalCounters.mem_used
|
|
self.assertEqual(savings_after_free, 0)
|
|
fxn(Tensor([2]))
|
|
self.assertEqual(x.item(), 8)
|
|
|
|
def test_replan_buffers_memory_layout(self):
|
|
if not hasattr(Device[Device.DEFAULT].allocator, '_offset'): raise unittest.SkipTest("replan_buffers_memory_layout useless")
|
|
|
|
ext_tensor = Tensor([1,24,23,45,1])
|
|
ext_tensor_2 = Tensor([2,2,2,2,2])
|
|
@TinyJit
|
|
def fxn(x:Tensor):
|
|
out = (x*ext_tensor_2+ext_tensor).reshape(5,1).expand(5, 100).contiguous()
|
|
return out.sum()
|
|
for i in range(5):
|
|
out = fxn(Tensor([i,1,2,3,4]))
|
|
self.assertEqual(out.item(), 11400+200*i)
|
|
assert len(set([b.base for item in fxn.captured.jit_cache for b in item.bufs if b is not None])) == 4
|
|
fxn.captured.replan_buffers_memory_layout()
|
|
assert len(set([b.base for item in fxn.captured.jit_cache for b in item.bufs if b is not None])) == 2
|
|
|
|
out = fxn(Tensor([11,1,2,3,4]))
|
|
self.assertEqual(out.item(), 13600)
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|
|
|