mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
bdd0c06f29
* unwrap_dtype maybe * uopgraph stuff that hardcoded None * test_ops passes * dtypes.py fixups * update test_linearizer and friends * more ast updates * test_beam and test_schedule too * add void type to uop [run_process_replay] * remove dumb casts * start making it green * more cast cleanups * more cls methods to fix * regenerate dataset * split UOp and NOp const * maybe that too * fix docs * update test_uop_symbolic * test_verify_ast * new sops with no diff * meh, type_ignore is alright * remove that assert --------- Co-authored-by: qazal <qazal.software@gmail.com>
70 lines
3.4 KiB
Python
70 lines
3.4 KiB
Python
import unittest
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from typing import List, cast
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import numpy as np
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from tinygrad.codegen.uopgraph import full_graph_rewrite, linearize_uop
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from tinygrad.device import Buffer, Device
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from tinygrad.dtype import PtrDType, dtypes
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from tinygrad.engine.realize import CompiledRunner
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from tinygrad.helpers import dedup, flatten, getenv, prod
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from tinygrad.renderer.cstyle import CStyleLanguage
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from tinygrad.ops import BinaryOps, UOp, UOps
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from tinygrad.renderer import Program
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from tinygrad.tensor import Tensor, _to_np_dtype
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from tinygrad.lazy import LazyBuffer
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def _test_uop_result(inputs:List[Tensor], stores:List[UOp], local_size=None):
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for x in inputs: x.realize()
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# NOTE: we only toposort the stores
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uops: List[UOp] = []
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def _recursive_add(uop:UOp) -> List[UOp]: return flatten([_recursive_add(x) for x in uop.src])+[uop]
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uops = dedup(flatten(_recursive_add(st) for st in stores))
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outbufs = [Buffer(Device.DEFAULT, sz:=(1 if local_size is None else prod(local_size)), (dtype:=u.src[2].dtype), \
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initial_value=np.zeros(sz, dtype=_to_np_dtype(dtype)).data) for u in uops if u.op is UOps.STORE]
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inbufs = [cast(LazyBuffer,x.lazydata).base.buffer for x in inputs]
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src = Device[Device.DEFAULT].renderer.render("test", uops)
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ei = CompiledRunner(Program("test", src, Device.DEFAULT, uops=uops, local_size=local_size))
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ei.exec(outbufs+inbufs)
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return [np.frombuffer(x.as_buffer(), _to_np_dtype(x.dtype)) for x in outbufs]
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@unittest.skipIf(not isinstance(Device[Device.DEFAULT].renderer, CStyleLanguage), "uops are for cstyle")
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class TestCStyleFailures(unittest.TestCase):
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def test_inline_const_alu(self):
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a = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int), (), 0)
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b = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int), (), 1)
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idx = UOp.const(dtypes.int, 0)
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ld = UOp(UOps.LOAD, dtypes.int, (b, idx))
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alu = ld.alu(BinaryOps.MAX, UOp.const(dtypes.int, dtypes.min(dtypes.int)))
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store = UOp.store(a, idx, alu)
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# CLANG doesn't use the max function
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ret = _test_uop_result([Tensor([1])], [store])[0]
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self.assertEqual(ret[0], 1)
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@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
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class TestPTXFailures(unittest.TestCase):
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def test_gated_store_with_alu(self):
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a = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int), (), 0)
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gate_alu = (lidx0:=UOp(UOps.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
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gated_alu_store = UOp(UOps.STORE, dtypes.void, (a, lidx0, UOp.const(dtypes.int, 1), gate_alu))
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sink = UOp(UOps.SINK, dtypes.void, (gated_alu_store,))
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uops = linearize_uop(full_graph_rewrite(sink, Device[Device.DEFAULT].renderer))
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ret = _test_uop_result([], uops, local_size=[4, 1, 1])[0]
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np.testing.assert_equal(ret, [0, 1, 1, 1])
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def test_gated_store_with_if(self):
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a = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int), (), 0)
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gate_alu = (lidx0:=UOp(UOps.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
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val = UOp.const(dtypes.int, 1)
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if_uop = UOp(UOps.IF, dtypes.void, (gate_alu, val))
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gated_alu_store = UOp(UOps.STORE, dtypes.void, (a, lidx0, val, if_uop))
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sink = UOp(UOps.SINK, dtypes.void, (gated_alu_store,))
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uops = linearize_uop(full_graph_rewrite(sink, Device[Device.DEFAULT].renderer))
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ret = _test_uop_result([], uops, local_size=[4, 1, 1])[0]
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if getenv("PTX"):
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with self.assertRaises(AssertionError):
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np.testing.assert_equal(ret, [0, 1, 1, 1])
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else: np.testing.assert_equal(ret, [0, 1, 1, 1])
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if __name__ == '__main__':
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unittest.main()
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