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ed194a1d3b
* add constant fold * err, it's just zero folding * self store fold + caching * prints and more folds * simpler winograd kernels * remove childless uops
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
import numpy as np
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import unittest
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from tinygrad.codegen.linearizer import Linearizer, UOps
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from tinygrad.ops import Compiled, Device
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from tinygrad.tensor import Tensor
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from tinygrad.jit import CacheCollector
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class TestLinearizer(unittest.TestCase):
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def test_arg_dedup(self):
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if not isinstance(Device[Device.DEFAULT], Compiled):
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self.skipTest("Only Compiled supports cache")
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a, b = Tensor.randn(4), Tensor.randn(4)
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np_a, np_b = a.numpy(), b.numpy()
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CacheCollector.start()
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c = ((a.shrink(((0, 2),)) - a.shrink(((2, 4),))) - (b.shrink(((0, 2),)) - b.shrink(((2, 4),)))).realize()
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rawbufs = CacheCollector.finish()[0][1]
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assert len(rawbufs) == 3 and set(rawbufs[1:]) == {a.lazydata.realized, b.lazydata.realized}
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np_c = (np_a[:2] - np_a[2:]) - (np_b[:2] - np_b[2:])
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np.testing.assert_allclose(np_c, c.numpy())
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def test_load_dedup(self):
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# for different leaves in the AST, the same loads may occur.
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if not isinstance(Device[Device.DEFAULT], Compiled):
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self.skipTest("Only Compiled uses linearizer")
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a = Tensor.randn(4).realize()
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# these are of size 3 to avoid float4 coalesce
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r = a[:-1] + a[1:]
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ast = r.lazydata.op
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r = r.realize() # realize an output buffer
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k = Linearizer(ast, r.lazydata, Device[Device.DEFAULT].linearizer_opts)
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k.process()
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k.upcast()
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k.linearize()
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num_loads = len([uop for uop in k.uops if uop.uop == UOps.LOAD])
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assert num_loads <= 4, "more load uops than needed"
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assert num_loads >= 4, "unexpected number of uops, maybe this test needs updating?"
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def test_upcast_cse(self):
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# when upcasting, within a subtree, there may be common expressions.
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if not isinstance(Device[Device.DEFAULT], Compiled):
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self.skipTest("Only Compiled uses linearizer")
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a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize()
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r = a.expand([2]) + b.expand([2])
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ast = r.lazydata.op
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r = r.realize() # realize an output buffer
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k = Linearizer(ast, r.lazydata, Device[Device.DEFAULT].linearizer_opts)
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k.process()
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k.upcast()
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k.linearize()
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num_ops = len([uop for uop in k.uops if uop.uop == UOps.ALU])
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assert num_ops <= 1, "more alu uops than needed"
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def test_zero_fold(self):
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if not isinstance(Device[Device.DEFAULT], Compiled):
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self.skipTest("Only Compiled uses linearizer")
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a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize()
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r = Tensor.stack([a, b])
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ast = r.lazydata.op
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r = r.realize() # realize an output buffer
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k = Linearizer(ast, r.lazydata, Device[Device.DEFAULT].linearizer_opts)
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k.process()
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k.upcast()
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k.linearize()
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num_ops = len([uop for uop in k.uops if uop.uop == UOps.ALU])
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assert num_ops == 0, "more alu uops than needed"
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@unittest.skip("constant folding not supported yet")
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def test_constant_fold(self):
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if not isinstance(Device[Device.DEFAULT], Compiled):
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self.skipTest("Only Compiled uses linearizer")
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a, b = Tensor(2), Tensor(3)
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r = a * b
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ast = r.lazydata.op
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r = r.realize() # realize an output buffer
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k = Linearizer(ast, r.lazydata, Device[Device.DEFAULT].linearizer_opts)
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k.process()
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k.linearize()
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num_ops = len([uop for uop in k.uops if uop.uop in [UOps.LOAD, UOps.ALU]])
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assert num_ops <= 0, "more load or alu uops than needed"
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if __name__ == '__main__':
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unittest.main()
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