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28bf8d86d8
* ast is tuple * run test_phi_simplification * update reason * more tc * beam * a few more * use test_opt directly
102 lines
7.3 KiB
Python
102 lines
7.3 KiB
Python
import unittest
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from tinygrad.codegen.kernel import Opt, OptOps
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from tinygrad.codegen.linearizer import Linearizer
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from tinygrad.engine.schedule import create_schedule
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from tinygrad.engine.search import time_linearizer, bufs_from_lin, actions, beam_search
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from tinygrad.device import Device, Buffer
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from tinygrad.ops import LazyOp, LoadOps, BufferOps, ReduceOps, BinaryOps, MemBuffer, ConstBuffer
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from tinygrad.tensor import Tensor
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from tinygrad.dtype import dtypes
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from tinygrad.helpers import Context, GlobalCounters
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from tinygrad.engine.realize import capturing
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.shape.view import View
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class TestTimeLinearizer(unittest.TestCase):
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def test_reasonable_time(self):
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si = [i for i in create_schedule([Tensor([1,2,3,4]).add(1).lazydata]) if i.ast[0].op not in LoadOps][0]
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out = Buffer(Device.DEFAULT, si.outputs[0].size, si.outputs[0].dtype).allocate()
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memops = {x.arg.idx:x.arg.st.real_size() for x in si.ast[0].lazyops if x.op is BufferOps.LOAD}
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rawbufs = [out] + [Buffer(Device.DEFAULT, memops[i], x.dtype).allocate() for i,x in enumerate(si.inputs, start=len(si.outputs))]
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tm = time_linearizer(Linearizer(*si.ast), rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
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assert tm > 0 and tm != float('inf')
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def test_bufs_from_lin(self):
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si = [i for i in create_schedule([Tensor([1,2,3,4]).add(1).lazydata]) if i.ast[0].op not in LoadOps][0]
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rawbufs = bufs_from_lin(lin:=Linearizer(*si.ast))
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assert len(rawbufs) == len(lin.membufs)
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assert all(r is not None for r in rawbufs)
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assert all(isinstance(r, Buffer) for r in rawbufs)
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assert all(r.size > 0 for r in rawbufs)
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def test_kernel_count(self):
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"""
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Ensure that the kernel count is not incremented by time_linearizer when clearing l2
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"""
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# ast of Tensor.zeros(16).contiguous().realize()
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ast = LazyOp(op=BufferOps.STORE, src=(LazyOp(op=BufferOps.CONST, src=(), arg=ConstBuffer(val=0.0, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(16,), strides=(0,), offset=0, mask=None, contiguous=False),)))),), arg=MemBuffer(idx=0, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(16,), strides=(1,), offset=0, mask=None, contiguous=True),)))) # noqa: E501
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lin = Linearizer(ast)
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bufs = bufs_from_lin(lin)
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kernel_count = GlobalCounters.kernel_count
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time_linearizer(lin, bufs, allow_test_size=False, cnt=2, disable_cache=True, clear_l2=True)
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assert GlobalCounters.kernel_count == kernel_count, "kernel count was incremented by time_linearizer"
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class TestBEAM(unittest.TestCase):
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def test_dynamic_beam(self):
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# TODO: make this infra globally usable
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class Capture:
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def __init__(self): self.captured = []
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def add(self, x): self.captured.append(x)
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capturing.append(Capture())
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kernel_count = GlobalCounters.kernel_count
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with Context(BEAM=1): Tensor.zeros(16).contiguous().realize()
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assert GlobalCounters.kernel_count == kernel_count + 1
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k_beam_1 = capturing[0].captured
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capturing.clear()
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capturing.append(Capture())
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kernel_count = GlobalCounters.kernel_count
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with Context(BEAM=0): Tensor.zeros(16).contiguous().realize()
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assert GlobalCounters.kernel_count == kernel_count + 1
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k_beam_0 = capturing[0].captured
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capturing.clear()
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self.assertNotEqual(k_beam_0[-1].prg.p.src, k_beam_1[-1].prg.p.src)
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def test_get_linearizer_actions(self):
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from test.test_linearizer import helper_realized_ast
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a = Tensor.rand(4, 3)
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b = Tensor.rand(3)
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realized_ast, _ = helper_realized_ast(a @ b)
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from tinygrad.engine.search import get_linearizer_actions
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lins = get_linearizer_actions(Linearizer(*realized_ast), False).values()
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# ensure amt=0 are not duplicated
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if Opt(OptOps.UPCAST, 0, 0) in actions:
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assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.UPCAST, axis=0, amt=4)]) == 0, "did not de-dup UPCAST"
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if Opt(OptOps.LOCAL, 0, 0) in actions:
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assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.LOCAL, axis=0, amt=4)]) == 0, "did not de-dup LOCAL"
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if Opt(OptOps.UNROLL, 0, 0) in actions:
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assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.UNROLL, axis=0, amt=3)]) == 0, "did not de-dup UNROLL"
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if Opt(OptOps.GROUP, 0, 0) in actions:
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assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.GROUP, axis=0, amt=3)]) == 0, "did not de-dup GROUP"
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if Opt(OptOps.GROUPTOP, 0, 0) in actions:
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assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.GROUPTOP, axis=0, amt=3)]) == 0, "did not de-dup GROUPTOP"
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def test_filter_global_buffer(self):
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# taken from https://github.com/tinygrad/tinygrad/issues/4612
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ast = LazyOp(op=BufferOps.STORE, src=(LazyOp(op=ReduceOps.MAX, src=(LazyOp(op=BinaryOps.MUL, src=(LazyOp(op=BinaryOps.ADD, src=(LazyOp(op=BinaryOps.ADD, src=(LazyOp(op=BinaryOps.ADD, src=(LazyOp(op=BinaryOps.ADD, src=(LazyOp(op=BinaryOps.ADD, src=(LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=1, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=0, mask=((0, 64128),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))))), LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=2, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-64128, mask=((64128, 128256),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False)))))), arg=None), LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=3, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-128256, mask=((128256, 192384),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False)))))), arg=None), LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=4, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-192384, mask=((192384, 256512),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False)))))), arg=None), LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=5, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-256512, mask=((256512, 320640),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False)))))), arg=None), LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=6, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-320640, mask=((320640, 384768),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False)))))), arg=None), LazyOp(op=BufferOps.CONST, src=(), arg=ConstBuffer(val=1.4285714285714286, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(1, 501, 256), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),))))), arg=None),), arg=(1,)),), arg=MemBuffer(idx=0, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(1, 1, 256), strides=(0, 0, 1), offset=0, mask=None, contiguous=True),)))) # noqa: E501
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lin = Linearizer(ast)
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bufs = bufs_from_lin(lin)
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best_lin = beam_search(lin, bufs, 3)
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assert best_lin
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# need disable_cache to trigger.
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tm = time_linearizer(best_lin, bufs, allow_test_size=False, cnt=2, disable_cache=True)
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assert tm
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if __name__ == '__main__':
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unittest.main()
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