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tinygrad/test/test_linearizer.py

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import numpy as np
import unittest
from tinygrad.codegen.kernel import Opt, OptOps, KernelOptError, tensor_cores
from tinygrad.codegen.linearizer import Linearizer, UOp, UOps, expand_node, expand_idxs
from tinygrad.device import Device, Buffer
from tinygrad.ops import BinaryOps, BufferOps, MemBuffer, ConstBuffer, LazyOp, LoadOps, TernaryOps, ReduceOps, UnaryOps
from tinygrad.shape.shapetracker import ShapeTracker
from tinygrad.shape.view import View
from tinygrad.shape.symbolic import MulNode, Variable, NumNode, Node
from tinygrad.tensor import Tensor
from tinygrad.engine.jit import CacheCollector
from tinygrad.engine.schedule import create_schedule
from tinygrad.engine.realize import run_schedule
from tinygrad.helpers import prod, Context, getenv
from tinygrad.dtype import DType, dtypes
from tinygrad.codegen.uops import UOpGraph
class TestLinearizer(unittest.TestCase):
def test_arg_dedup(self):
a, b = Tensor.randn(4), Tensor.randn(4)
np_a, np_b = a.numpy(), b.numpy()
CacheCollector.start()
c = ((a.shrink(((0, 2),)) - a.shrink(((2, 4),))) - (b.shrink(((0, 2),)) - b.shrink(((2, 4),)))).realize()
rawbufs = CacheCollector.finish()[0].rawbufs
assert len(rawbufs) == 3 and set(rawbufs[1:]) == {a.lazydata.base.realized, b.lazydata.base.realized}
np_c = (np_a[:2] - np_a[2:]) - (np_b[:2] - np_b[2:])
np.testing.assert_allclose(np_c, c.numpy(), atol=1e-4, rtol=1e-4)
def test_load_removed(self):
a = Tensor.rand(1).realize()
b = Tensor.rand(1).realize()
ta = Tensor.where(Tensor(True), a, b).numpy()
tb = Tensor.where(Tensor(False), a, b).numpy()
np.testing.assert_equal(a.numpy(), ta)
np.testing.assert_equal(b.numpy(), tb)
def test_multioutput(self):
dtype, st = dtypes.int, ShapeTracker.from_shape((8,))
a = LazyOp(BufferOps.LOAD, arg=MemBuffer(idx=2, dtype=dtype, st=st))
b = LazyOp(BufferOps.LOAD, arg=MemBuffer(idx=3, dtype=dtype, st=st))
out0 = LazyOp(BufferOps.STORE, (LazyOp(op=BinaryOps.ADD, src=(a,b)),), MemBuffer(idx=0, dtype=dtype, st=st))
out1 = LazyOp(BufferOps.STORE, (LazyOp(op=BinaryOps.MUL, src=(a,b)),), MemBuffer(idx=1, dtype=dtype, st=st))
lin = Linearizer(out0, out1)
lin.linearize()
stores = [u for u in lin.uops if u.uop is UOps.STORE]
mutable_bufs = [u for u in lin.uops if u.uop is UOps.DEFINE_GLOBAL and u.arg[-1]]
assert len(mutable_bufs) == len(stores) == 2
assert [u.arg[0] for u in mutable_bufs] == [0, 1]
def test_load_dedup(self):
# for different leaves in the AST, the same loads may occur.
a = Tensor.randn(4).realize()
# these are of size 3 to avoid float4 coalesce
r = a[:-1] + a[1:]
k = Linearizer(*create_schedule([r.lazydata])[-1].ast)
k.upcast()
k.linearize()
num_loads = len([uop for uop in k.uops if uop.uop is UOps.LOAD])
assert num_loads <= 4, "more load uops than needed"
assert num_loads >= 4, "unexpected number of uops, maybe this test needs updating?"
def test_load_cache_const_bufs(self):
# make sure const buffers are differentiated from local and mem buffers
ST, DT = ShapeTracker(views=(View(shape=((1,)), strides=(0, 0), offset=0, mask=None, contiguous=False),)), dtypes.int
VAL = LazyOp(op=BufferOps.CONST, src=(), arg=ConstBuffer(val=2, dtype=DT, st=ST))
# data1[0] + VAL
a = LazyOp(op=BinaryOps.ADD, src=(LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=1, dtype=DT, st=ST)), VAL))
# (literal const 1) + VAL
b = LazyOp(op=BinaryOps.ADD, src=(LazyOp(op=BufferOps.CONST, src=(), arg=ConstBuffer(val=1, dtype=DT, st=ST)), VAL))
ast = LazyOp(op=BufferOps.STORE, src=(LazyOp(op=BinaryOps.ADD, src=(a,b)),), arg=MemBuffer(idx=0, dtype=DT, st=ST))
lin = Linearizer(ast)
lin.linearize()
a_bufs = [u.uop for u in lin.uops.uops[-2].vin[0].vin]
b_bufs = [u.uop for u in lin.uops.uops[-2].vin[1].vin]
assert a_bufs == [UOps.LOAD, UOps.CONST]
assert b_bufs == [] # [UOps.CONST, UOps.CONST] will be folded
def test_upcast_cse(self):
# when upcasting, within a subtree, there may be common expressions.
a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize()
r = a.expand([2]) + b.expand([2])
k = Linearizer(*create_schedule([r.lazydata])[-1].ast)
k.upcast()
k.linearize()
num_ops = len([uop for uop in k.uops if uop.uop is UOps.ALU])
assert num_ops <= 1, "more alu uops than needed"
def test_reduce_upcast(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.supports_float4:
self.skipTest("device does not support upcast")
x, w = Tensor.randn((1,1,3)).realize(), Tensor.randn((1,1,2)).realize()
r = Tensor.conv2d(x,w,padding=1).relu()
k = Linearizer(*create_schedule([r.lazydata])[-1].ast)
k.upcast()
k.upcast()
k.linearize()
accs = [u for u in k.uops if u.uop is UOps.DEFINE_ACC]
stores = [u for u in k.uops if u.uop is UOps.STORE]
assert len(accs) == 1
assert len(stores) == 1
assert stores[0].vin[-1].dtype == accs[0].dtype == dtypes.float.vec(4)
def test_upcast_with_locals(self):
if not (opts:=Device[Device.DEFAULT].compiler.compiler_opts).has_local or not opts.has_shared or not opts.supports_float4:
self.skipTest("device does not support upcasted reduce with locals")
x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
r = (x@y).relu()
k = Linearizer(*create_schedule([r.lazydata])[-1].ast)
k.hand_coded_optimizations()
k.linearize()
accs = [u for u in k.uops if u.uop is UOps.DEFINE_ACC]
stores = [u for u in k.uops if u.uop is UOps.STORE]
# the first store is to lds and can be upcasted
assert accs[0].dtype == stores[0].vin[-1].dtype == dtypes.float.vec(4)
assert stores[0].vin[0].uop is UOps.DEFINE_LOCAL
# the second store is to gds with no upcasts
assert accs[1].dtype == stores[1].vin[-1].dtype == dtypes.float
assert stores[1].vin[0].uop is UOps.DEFINE_GLOBAL
def test_zero_fold(self):
a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize()
r = Tensor.stack([a, b])
k = Linearizer(*create_schedule([r.lazydata])[-1].ast)
k.upcast()
k.linearize()
num_ops = len([uop for uop in k.uops if uop.uop is UOps.ALU])
assert num_ops == 0, "more alu uops than needed"
def test_constant_fold(self):
a, b = Tensor(2), Tensor(3)
r = a * b
k = Linearizer(*create_schedule([r.lazydata])[-1].ast)
k.linearize()
num_ops = len([uop for uop in k.uops if uop.uop in [UOps.LOAD, UOps.ALU]])
assert num_ops <= 0, "more load or alu uops than needed"
def test_sum_acc_dtype(self):
for tensor_dtype, acc_dtype in (
(dtypes.bool, dtypes.int), (dtypes.int16, dtypes.int), (dtypes.float16, dtypes.float), (dtypes.bfloat16, dtypes.float)):
a = Tensor([1, 2, 3], dtype=tensor_dtype).sum()
k = Linearizer(*create_schedule([a.lazydata])[-1].ast)
k.linearize()
local = [uop for uop in k.uops if uop.uop is UOps.DEFINE_ACC]
assert local[0].dtype == acc_dtype
def test_arg_acc_dtype(self):
def helper_arg_acc_dtype(c: Tensor, expected_dtype:DType):
k = Linearizer(*create_schedule([c.lazydata])[-1].ast)
k.linearize()
local = [uop for uop in k.uops if uop.uop is UOps.DEFINE_ACC]
assert local[0].dtype == expected_dtype
tests = (
(dtypes.float16, None, dtypes.float),
(dtypes.bfloat16, None, dtypes.float),
(dtypes.float, None, dtypes.float),
(dtypes.float16, dtypes.float16, dtypes.float16),
(dtypes.bfloat16, dtypes.bfloat16, dtypes.bfloat16),
(dtypes.float, dtypes.float16, dtypes.float16),
)
for tensor_dtype, acc_dtype, expected_dtype in tests:
a, b = Tensor.rand(8, 8, dtype=tensor_dtype), Tensor.rand(8, 8, dtype=tensor_dtype)
helper_arg_acc_dtype(a.sum(acc_dtype=acc_dtype), expected_dtype)
helper_arg_acc_dtype(a.matmul(b, acc_dtype=acc_dtype), expected_dtype)
d, w = Tensor.rand(4, 8, 8, 8, dtype=tensor_dtype), Tensor.rand(8, 8, 2, 2, dtype=tensor_dtype)
helper_arg_acc_dtype(d.conv2d(w, acc_dtype=acc_dtype), expected_dtype)
def test_tensor_cores(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_tensor_cores:
self.skipTest("device doesn't have tensor cores")
for tc in tensor_cores[Device[Device.DEFAULT].compiler.compiler_opts.device]:
if getenv("EMULATE_CUDA") and (tc.dtype_in == dtypes.bfloat16 or tc.dtype_out == dtypes.bfloat16): continue
a, b = Tensor.rand(tc.dims[1], tc.dims[2], dtype=tc.dtype_in), Tensor.rand(tc.dims[2], tc.dims[0], dtype=tc.dtype_in)
np_a, np_b = a.numpy(), b.numpy()
r = a.matmul(b, acc_dtype=tc.dtype_out)
sched = create_schedule([r.lazydata])
realized_ast = sched[-1].ast[0]
run_schedule(sched)
out = r.numpy()
k = Linearizer(realized_ast)
k.apply_tensor_cores(1)
k.linearize()
assert len([uop for uop in k.uops if uop.uop is UOps.WMMA]) == 1, "tensor core not triggered"
assert len([x for x in k.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
np_c = np_a @ np_b
(tc_atol, tc_rtol) = (1e-2, 1e-3) if tc.dtype_out == dtypes.half else (5e-3, 1e-4)
np.testing.assert_allclose(np_c, out, atol=tc_atol, rtol=tc_rtol)
def test_limit_dims_to_max_5d_global(self):
t = Tensor.empty(3, 4, 5, 6, 7).pad(((1, 1), (1, 1), (1, 1), (1, 1), (1, 1))) + 1
sched = [si for si in create_schedule([t.lazydata]) if si.ast[0].op not in LoadOps]
assert len(sched) == 1
lin = Linearizer(*sched[0].ast)
assert lin.full_shape[:lin.global_dims] == (5, 6, 7, 8, 9)
lin.limit_dims_to_max(global_max=[16, 16, 16], local_max=[16, 16, 16])
def test_sum_collapse(self):
t = Tensor.ones(256,256).sum()
sched = [si for si in create_schedule([t.lazydata]) if si.ast[0].op not in LoadOps]
assert len(sched) == 1
lin = Linearizer(*sched[0].ast)
assert not any(u.uop is UOps.LOOP for u in lin.linearize().uops), "found loop in sum collapse"
def test_assign_fold(self):
a = Tensor.ones(4, 4).contiguous().realize()
m = Tensor.ones(4, 4).shrink(((1, 2), None)).pad(((1, 2), None))
a.assign(a+m)
a.realize()
np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.])
def test_where_fold(self):
a = Tensor.ones(4, 4).contiguous().realize()
b = a.shrink(((1, 2), None)).pad(((1, 2), None))
a.assign(b.where(2, a))
sched = create_schedule([a.lazydata])
assert len(sched) == 1
sched_copy = sched[:]
run_schedule(sched)
np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.])
lin = Linearizer(*sched_copy[-1].ast)
lin.hand_coded_optimizations()
lin.linearize()
assert not any(u.arg == TernaryOps.WHERE for u in lin.uops), "found where where where should be folded"
def test_simplify_uop(self):
def helper_test_simplify(uop, dtype, vin, arg=None):
ast = LazyOp(BufferOps.CONST, (),
ConstBuffer(42, dtypes.float, ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),))))
ast = LazyOp(BufferOps.STORE, (ast,),
MemBuffer(0, dtypes.float, ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),))))
lin = Linearizer(ast) # this is a dummy ast
lin.uops = UOpGraph()
return lin.uops.add(uop, dtype, vin, arg, cachable=False)
c0 = UOp(UOps.CONST, dtypes.float, vin=(), arg=0.0)
assert helper_test_simplify(UOps.ALU, dtypes.float, vin=(UOp(UOps.CONST, dtypes.bool, vin=(), arg=True), c0, c0), arg=TernaryOps.WHERE) == c0
c0 = UOp(UOps.CONST, dtypes.float, vin=(), arg=0.0)
c1 = UOp(UOps.CONST, dtypes.float, vin=(), arg=1.0)
assert helper_test_simplify(UOps.ALU, dtypes.float, vin=(UOp(UOps.CONST, dtypes.bool, vin=(), arg=True), c0, c1),
arg=TernaryOps.WHERE).arg == c0.arg
def test_phi_simplification(self):
def helper(t, max_ops=0):
sched = create_schedule([t.lazydata])
assert len(sched) == 1
k = Linearizer(*sched[0].ast)
k.hand_coded_optimizations()
uops = list(k.linearize().uops)
# ignore kernel optimized IF/LOOP statements for now
if if_op:=next((u for u in uops if u.uop is UOps.IF), None):
uops = uops[:uops.index(if_op)]
assert len(set([u.uop for u in uops if u.uop in {UOps.LOOP, UOps.SPECIAL}])) == 1, "has either specials or loops, not both"
assert len([u for u in uops if u.uop is UOps.PHI]) == 0, "PHI should have been simplified"
assert len([u for u in uops if u.arg is BinaryOps.MAX]) <= max_ops, "no unnecessary MAX ops"
helper(Tensor.arange(5.5, (3.5*300), 3.5))
helper(Tensor.arange(-1, -100, -5))
helper(Tensor.arange(-3.2, 6.7, 0.64))
helper(Tensor.arange(256), max_ops=2)
helper(Tensor.arange(255), max_ops=0)
def helper_realized_ast(r:Tensor):
s = create_schedule([r.lazydata])
run_schedule(s[:-1]) # run all kernels except the last one
# now all input LazyBuffers buffers in s[-1] should be realized
# allocate an output buffer
output_buffer = Buffer((out:=s[-1].outputs[0]).device, out.size, out.dtype).allocate()
return s[-1].ast[0], [output_buffer] + list(s[-1].inputs)
@unittest.skipUnless(Device[Device.DEFAULT].compiler.compiler_opts.supports_float4, "need backends that support float4")
class TestFloat4(unittest.TestCase):
@staticmethod
def count_float4(k):
return (len([uop for uop in k.uops if uop.uop is UOps.LOAD and uop.dtype == dtypes.float.vec(4)]),
len([uop for uop in k.uops if uop.uop is UOps.STORE and len(uop.vin) == 3 and uop.vin[2].dtype == dtypes.float.vec(4)]))
# TODO: express opts below as auto opts
def test_float4_basic(self):
a = Tensor.rand(2, 8).realize()
b = Tensor.rand(2, 8).realize()
c = a + b
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.hand_coded_optimizations()
k.linearize()
assert TestFloat4.count_float4(k) == (2, 1)
def test_float4_multidim(self):
a = Tensor.rand(2, 8).realize()
b = Tensor.rand(2, 8).realize()
c = a + b
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.shift_to(0, 4) # float4 dimension
k.shift_to(0, 2, insert_before=k.shape_len-1)
k.upcast()
k.upcast()
k.local_dims += 1
k.linearize()
assert TestFloat4.count_float4(k) == (4, 2)
def test_float4_unaligned_load(self):
a = Tensor.rand(9).realize().shrink(((1, 9),))
b = Tensor.rand(9).realize().shrink(((1, 9),))
c = a + b
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.hand_coded_optimizations() # implicit trigger float4 dim
k.linearize()
assert TestFloat4.count_float4(k) == (0, 1)
def test_float4_multidim_unaligned_load(self):
a = Tensor.rand(2, 9).realize().shrink(((0, 2), (1, 9),))
b = Tensor.rand(2, 9).realize().shrink(((0, 2), (1, 9),))
c = a + b
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.shift_to(len(k.full_unupcasted_shape)-1, 4) # manual trigger float4 dim
k.upcast()
k.shift_to(len(k.full_unupcasted_shape)-1, 2, insert_before=k.shape_len-1)
k.upcast()
k.local_dims += 1
k.linearize()
assert TestFloat4.count_float4(k) == (0, 2)
def test_float4_sometimes_unaligned(self):
a = Tensor.rand(1, 1, 8).realize()
b = Tensor.rand(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
c = a.conv2d(b)
# only the first and last conv dot products are aligned in a, and b is never aligned, so no
# float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.upcast()
k.linearize()
assert TestFloat4.count_float4(k) == (0, 0)
def test_float4_multidim_sometimes_unaligned(self):
a = Tensor.rand(1, 1, 7).realize()
b = Tensor.rand(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
c = a.conv2d(b)
# the first conv dot product is aligned in a. If we upcast the output and reduce
# dimension, then we could do float4 for only that one set of loads, but we currently
# don't.
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.upcast()
k.upcast()
k.linearize()
assert TestFloat4.count_float4(k) == (0, 1)
def test_float4_noncontiguous(self):
a = Tensor.rand(4, 2).realize()
b = Tensor.rand(4, 2).realize()
c = a + b
# we will upcast the top axis of sz 4. they should not be coalesced into float4,
# since the top axis is not contiguous.
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.shift_to(0, 4, top=True) # top axes are float4 axes
k.upcast()
k.linearize()
assert TestFloat4.count_float4(k) == (0, 0)
def test_float4_expand(self):
a = Tensor.rand(9).realize().shrink(((1, 9),))
b = Tensor.rand(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
c = a + b
# we will upcast the top axis of sz 4. they should not be coalesced into float4,
# since the top axis is not contiguous.
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.shift_to(0, 4) # float4 axis
k.upcast()
k.linearize()
assert TestFloat4.count_float4(k) == (0, 1)
def test_float4_heterogeneous(self):
a = Tensor.rand(8).realize()
b = Tensor.rand(9).realize().shrink(((1, 9),))
c = a + b
# should float4 b but not a
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.shift_to(0, 4) # float4 axis
k.upcast()
k.linearize()
assert TestFloat4.count_float4(k) == (1, 1)
class TestHandCodedOpts(unittest.TestCase):
def test_masked_upcast(self):
layer_1 = Tensor.cat(*[Tensor.rand(5) for _ in range(4)])
layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.rand(6, 20))
s = create_schedule([layer_2.lazydata])[-1]
k = Linearizer(*s.ast)
k.hand_coded_optimizations()
assert len(k.bufs) == 6 # make sure all ops are done in one kernel
# masked upcast should upcast masked axis of size 7
# masked upcast should not upcast large (20) last axis
# float4/other hcopt shouldn't upcast last axis, since we already have 7 upcast, and the last axis is not very contiguous
assert k.upcasted == 1 and k.full_shape[-1] == 7
@unittest.skipIf(Device.DEFAULT == "WEBGPU", "Failing because of custom kernel splitting to circumvent the 8 buffer limit")
def test_masked_upcast_wino(self):
monster = Tensor.stack([Tensor.stack([Tensor.rand(16) for _ in range(6)]) for _ in range(6)])
s = create_schedule([monster.lazydata])[-1]
k = Linearizer(*s.ast)
k.hand_coded_optimizations()
assert len(k.bufs) == 37 # make sure all ops are done in one kernel
# should upcast the two Tensor.stacks
assert k.upcasted >= 2 and k.full_shape[k.shape_len-k.upcasted:k.shape_len].count(6) == 2
def test_masked_upcast_wino_full(self):
with Context(WINO=1):
x,w = Tensor.rand(1,4,8,8, requires_grad=True).realize(), Tensor.rand(4,4,3,3, requires_grad=True).realize()
out = Tensor.conv2d(x,w, padding=1)
upcasts = []
wino_schedule = create_schedule([out.lazydata])
# collect upcasts of tile transform kernels
for i, si in enumerate(wino_schedule):
k = Linearizer(*si.ast)
k.hand_coded_optimizations()
if k.reduceop is not None: continue # not a tile transform kernel (there is a gemm reduce kernel)
if len(k.bufs) < 36: continue # not a tile transform kernel (there's a permute kernel at the end)
upcasts.append(tuple(k.full_shape[k.shape_len - k.upcasted:k.shape_len]))
assert len(upcasts) == 3 # 3 transformation matrices
assert len(wino_schedule) <= 4 # 4 kernels
# this test case's inputs are too small, so one of the 4-stacks became a local, which is fine i guess
assert upcasts.count((6, 6)) == 2 #and upcasts.count((4, 4)) == 1
out.mean().backward()
backward_schedule = create_schedule([x.grad.lazydata, w.grad.lazydata])
for si in backward_schedule:
k = Linearizer(*si.ast)
k.hand_coded_optimizations()
k.linearize()
if len(k.bufs) < 20: continue # not a tile transform kernel
# heuristic number to make sure that at least some upcasts but not too many upcasts are being done
assert 6 <= prod(k.full_shape[k.shape_len - k.upcasted:k.shape_len]) <= 216
assert len(backward_schedule) <= 13 # just the current number, but it could be better
def test_masked_upcast_many(self):
layer_1 = Tensor.cat(Tensor.rand(3, 4), Tensor.rand(4, 4))
layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.rand(6, 7, 4))
layer_3 = Tensor.cat(layer_2.unsqueeze(0), Tensor.rand(6, 7, 7, 4))
s = create_schedule([layer_3.lazydata])[-1]
k = Linearizer(*s.ast)
k.hand_coded_optimizations()
assert len(k.bufs) == 5 # make sure all ops are done in one kernel
# check that we don't do too many upcasts
assert prod(k.full_shape[k.shape_len-k.upcasted:k.shape_len]) <= 49
def test_matvec(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local:
self.skipTest("Only devices with locals")
N = 128
a = Tensor.rand(1, N).realize()
b = Tensor.rand(N, N).realize()
c = a @ b
s = create_schedule([c.lazydata])[0]
k = Linearizer(*s.ast)
k.hand_coded_optimizations()
assert k.group_for_reduces == 1
assert k.local_dims == 1
assert k.upcasted == 1
def helper_linearizer_opt(r:Tensor, opts=[], apply_tc=False, atol=1e-4, rtol=1e-4, color_sizes=[]):
wanna_output = None
realized_ast, real_bufs = helper_realized_ast(r)
def check_opt(opts, create_k, to_prg, expected_color_size):
k = create_k()
if apply_tc:
assert k.apply_tensor_cores(1, opts), "no tensor core triggered"
else:
for opt in opts:
k.apply_opt(opt)
if expected_color_size is not None:
assert (cs:=[(x,y) for x,y in zip(k.colors(), k.full_shape)]) == expected_color_size, f"expected={expected_color_size} got={cs}"
prg = to_prg(k)
real_bufs[0].copyin(np.zeros((real_bufs[0].size, ), dtype=real_bufs[0].dtype.np).data) # Zero to check that all values are filled
prg.exec(real_bufs)
np.testing.assert_allclose(wanna_output, np.frombuffer(real_bufs[0].as_buffer(), real_bufs[0].dtype.np), atol=atol, rtol=rtol)
# Get baseline, which is not optimized at all.
k = Linearizer(realized_ast)
prg = Device[Device.DEFAULT].to_program(k)
prg.exec(real_bufs)
wanna_output = np.frombuffer(real_bufs[0].as_buffer(), real_bufs[0].dtype.np).copy()
# Check correctness of handcoded optimiztions.
k = Linearizer(realized_ast)
k.hand_coded_optimizations()
prg = Device[Device.DEFAULT].to_program(k)
real_bufs[0].copyin(np.zeros((real_bufs[0].size, ), dtype=real_bufs[0].dtype.np).data) # Zero to check that all values are filled
prg.exec(real_bufs)
np.testing.assert_allclose(wanna_output, np.frombuffer(real_bufs[0].as_buffer(), real_bufs[0].dtype.np), atol=atol, rtol=rtol)
for i, x in enumerate(opts): # Check custom transformations if any.
check_opt(x, lambda: Linearizer(realized_ast), Device[Device.DEFAULT].to_program, color_sizes[i] if i < len(color_sizes) else None)
class TestKernelOpts(unittest.TestCase):
def test_local_and_grouped_reduce(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.has_shared:
self.skipTest("Only Compiled uses linearizer with locals and shared")
N = 128
Tensor.manual_seed(1882)
a = Tensor.rand(4, 4, N, N)
b = Tensor.rand(4, 4, N)
r = (b.sqrt() + ((a+1).sum(axis=3).exp()))
helper_linearizer_opt(r, [
[Opt(OptOps.LOCAL, 0, 2)],
[Opt(OptOps.LOCAL, 0, 8)],
[Opt(OptOps.LOCAL, 0, 16)], # Checking how it works with locals
[Opt(OptOps.GROUPTOP, 0, 2)],
[Opt(OptOps.GROUPTOP, 0, 32)],
[Opt(OptOps.GROUPTOP, 0, 64)], # Checking how it works with grouped reduce
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2)],
[Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.GROUPTOP, 0, 16)],
[Opt(OptOps.LOCAL, 0, 32), Opt(OptOps.GROUPTOP, 0, 2)],
# Checking how it works with locals + grouped reduce
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 64)],
# Checking how it works with locals + grouped reduce + upcasts
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.UPCAST, 0, 8), Opt(OptOps.UNROLL, 1, 4)],
])
def test_upcasts(self):
N = 16
Tensor.manual_seed(1772)
a = Tensor.rand(N, N)
b = Tensor.rand(N, N)
r = (a+b).sqrt() * ((a+1).exp())
helper_linearizer_opt(r, [
[Opt(OptOps.UPCAST, 0, 2)],
[Opt(OptOps.UPCAST, 0, 4)],
[Opt(OptOps.UPCAST, 0, 8)], # Checking how it works with upcasts
])
def test_full_upcast(self):
Tensor.manual_seed(1772)
a = Tensor.rand(4)
b = Tensor.rand(4)
r = (a+b).sqrt() * ((a+1).exp())
helper_linearizer_opt(r, [
[Opt(OptOps.UPCAST, 0, 4)], # Checking how it works with upcasts
])
def test_matmul(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.has_shared:
self.skipTest("Only Compiled uses linearizer with locals and shared")
N = 128
Tensor.manual_seed(1552)
a = Tensor.rand(N, N)
b = Tensor.rand(N, N)
r = a@b
helper_linearizer_opt(r, [
[Opt(OptOps.UPCAST, 0, 2)],
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # Checking how it works with upcasts
[Opt(OptOps.LOCAL, 0, 2)],
[Opt(OptOps.LOCAL, 1, 32)],
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4)],
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 32)],
[Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.LOCAL, 1, 8)], # Checking how it works with locals
[Opt(OptOps.GROUPTOP, 0, 2)],
[Opt(OptOps.GROUPTOP, 0, 32)],
[Opt(OptOps.GROUPTOP, 0, 32), Opt(OptOps.UNROLL, 0, 4)], # Checking how it works with grouped_reduce
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 32)],
[Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 32)],
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 4)], # Checking how it works with local+grouped_reduce
# Checking all together
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4),
Opt(OptOps.UPCAST, 1, 2)],
# Full global upcast + local
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 8)],
])
def test_double_reduce(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.has_shared:
self.skipTest("Only Compiled uses linearizer with locals and shared")
N = 128
Tensor.manual_seed(1552)
a = Tensor.rand(8, N, 8, N)
r = a.sum(axis=(1,3))
helper_linearizer_opt(r, [
# openCL / GPU=1 is 256 max threads
[Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)],
[Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 1, 32)], # Checking how it works with 1 grouped_reduce.
[Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 2)],
[Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2)],
[Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 64)], # Checking how it works with 2 grouped_reduces.
[Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2), Opt(OptOps.UNROLL, 0, 4)],
[Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 2, 4)], # Checking how it works with 2 grouped_reduces + upcasts.
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4)],
# Checking how it works with 2 grouped_reduces + upcasts + locals.
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 1, 4)],
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2)],
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)], # Checking how it works with 2 grouped_reduces + upcasts + locals.
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
Opt(OptOps.UPCAST, 0, 2)], # No globals
])
def test_invalid_tensor_core_extra_opts(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_tensor_cores:
self.skipTest("device doesn't have tensor cores")
if Device.DEFAULT not in tensor_cores:
self.skipTest("No tensor cores for device")
N = 128
Tensor.manual_seed(1552)
a = Tensor.rand(N, N)
b = Tensor.rand(N, N)
realized_ast, _ = helper_realized_ast(a@b)
invalid_opts = [
[Opt(OptOps.LOCAL, 2, 2)],
[Opt(OptOps.UPCAST, 2, 2)],
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 2, 2)],
]
for x in invalid_opts:
k = Linearizer(realized_ast)
with self.assertRaises(AssertionError):
assert k.apply_tensor_cores(use_tensor_cores=1, extra_opts=x), "no valid tensor core" # for METAL in runners
def test_buf_index_not_found_tensor_core(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_tensor_cores:
self.skipTest("device doesn't have tensor cores")
if Device.DEFAULT not in tensor_cores:
self.skipTest("No tensor cores for device")
ast = LazyOp(op=BufferOps.STORE, src=(LazyOp(op=ReduceOps.SUM, src=(LazyOp(op=BinaryOps.MUL, src=(LazyOp(op=UnaryOps.CAST, src=(LazyOp(op=BinaryOps.CMPEQ, src=(LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=1, dtype=dtypes.int, st=ShapeTracker(views=(View(shape=(1243, 256), strides=(0, 1), offset=0, mask=None, contiguous=False),)))), LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=2, dtype=dtypes.int, st=ShapeTracker(views=(View(shape=(1243, 256), strides=(1, 0), offset=0, mask=None, contiguous=False),))))), arg=None),), arg=(dtypes.float, False)), LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=3, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(1243, 256), strides=(1, 0), offset=0, mask=None, contiguous=False),))))), arg=None),), arg=(0,)),), arg=MemBuffer(idx=0, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(1, 256), strides=(0, 1), offset=0, mask=None, contiguous=True),)))) # noqa: E501
k = Linearizer(ast, opts=Device[Device.DEFAULT].compiler.compiler_opts)
with self.assertRaises(KernelOptError):
k.apply_opt(Opt(OptOps.TC, 0, 1))
def test_tensor_core_opts(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_tensor_cores:
self.skipTest("device doesn't have tensor cores")
if Device.DEFAULT not in tensor_cores:
self.skipTest("No tensor cores for device")
N = 128
Tensor.manual_seed(1552)
for tc in tensor_cores[Device[Device.DEFAULT].compiler.compiler_opts.device]:
a, b = Tensor.rand(N, N, dtype=tc.dtype_in), Tensor.rand(N, N, dtype=tc.dtype_in)
r = a.matmul(b, acc_dtype=tc.dtype_out)
(atol, rtol) = ((0.25, 0.01) if tc.dtype_out == dtypes.half else (3e-2, 1e-3)) if tc.dtype_in == dtypes.half else (1e-4, 1e-4)
helper_linearizer_opt(r, [
[],
[Opt(OptOps.UPCAST, 0, 4)],
[Opt(OptOps.UPCAST, 1, 4)],
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # check upcasts
[Opt(OptOps.UNROLL, 0, 2)], # check unroll
[Opt(OptOps.UNROLL, 0, 0)], # check full unroll of reduce with locals
[Opt(OptOps.LOCAL, 0, 4)], # check local
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2)], # check combo of unroll and local
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2)],
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)],
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.LOCAL, 0, 2)],
[Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4)], # check permutations
[Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4)],
[Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)],
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
# [Opt(OptOps.GROUP, 0, 2)] # doesn't work because group_for_reduce dims become early locals (conflicting with TC)
], apply_tc=True, atol=atol, rtol=rtol)
def test_padto_matmul(self):
if Device.DEFAULT in ["CUDA", "RHIP"]: self.skipTest("super slow on CUDA and RHIP because of the big grid dims")
N = 17 * 17
Tensor.manual_seed(289)
a = Tensor.rand(N, N)
b = Tensor.rand(N, N)
helper_linearizer_opt(a@b, [
[Opt(OptOps.PADTO, 0, 32)],
[Opt(OptOps.PADTO, 1, 32)],
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32)],
# can optimize further post PADTO
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 2),],
])
def test_padto_max(self):
N = 17 * 17
a = -Tensor.ones(N, N)
helper_linearizer_opt(a.max(0), [
[Opt(OptOps.PADTO, 0, 32)],
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
])
helper_linearizer_opt(a.max(1), [
[Opt(OptOps.PADTO, 0, 32)],
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
])
# cannot pad a reduce axis
with self.assertRaises(KernelOptError):
helper_linearizer_opt(a.max(), [[Opt(OptOps.PADTO, 0, 32)],])
with self.assertRaises(KernelOptError):
helper_linearizer_opt(a.max(0), [[Opt(OptOps.PADTO, 1, 32)],])
def test_padto_where(self):
N = 17 * 17
a = (Tensor.empty(N, N).max(axis=0, keepdim=True) > 1).where(1, 0)
helper_linearizer_opt(a.max(0), [
[Opt(OptOps.PADTO, 0, 32)],
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
])
def test_color_shapes_with_local(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.has_shared:
self.skipTest("Only Compiled uses linearizer with locals and shared")
N = 32
Tensor.manual_seed(1552)
a = Tensor.rand(N, N)
b = Tensor.rand(N, N)
r = a@b
opts_shapes = [
([Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("red",32)]),
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",2),("red",16)]),
# check to ensure local_dims are stable for full UNROLL of first_reduce
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
([Opt(OptOps.UNROLL, 0, 0),Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
# check behavior for full UNROLL on an existing GROUP
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",16),("magenta",2)]),
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
([Opt(OptOps.GROUP, 0, 0),Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
([Opt(OptOps.GROUP, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",32),("blue",32),("red",16),("magenta",2)]),
]
helper_linearizer_opt(r, [x[0] for x in opts_shapes], color_sizes=[x[1] for x in opts_shapes])
class TestLinearizerHelper(unittest.TestCase):
def test_num_node_expand(self):
a = NumNode(42)
assert expand_node(a) == [a]
def test_variable_expand(self):
a = Variable("a", 5, 7)
assert expand_node(a) == [a]
def test_variable_expand_expr_none(self):
a = Variable("_uidx0", 5, 7)
assert expand_node(a) == [NumNode(5), NumNode(6), NumNode(7)]
def test_mul_node_expand(self):
a = Variable("_uidx0", 5, 7)
m = MulNode(a, 3)
assert expand_node(m) == [NumNode(15), NumNode(18), NumNode(21)]
b = Variable("b", 1, 3)
n = MulNode(b, 3)
assert expand_node(n) == [Variable("b", 1, 3)*3]
def test_sum_node_expand(self):
a = Variable("_uidx0", 1, 3)
b = Variable("b", 5, 7)
s1 = a + b
assert expand_node(s1) == [Node.sum([NumNode(i),b]) for i in range(1,4)]
def test_multi_expand(self):
a = Variable("a", 1, 3)
b = Variable("b", 14, 17)
s1 = a + b
# expand increments earlier variables faster than later variables (as specified in the argument)
# this behavior was just copied from before, no idea why this should be true
assert expand_node(s1, (a, b)) == [NumNode(x + y) for x in range(b.min, b.max + 1) for y in range(a.min, a.max + 1)]
def test_expand_nonpresent_var(self):
a = Variable("a", 1, 3)
n = NumNode(3) * Variable("b", 1, 3)
assert expand_node(n, (a,)) == [n, n, n]
def test_expand_idxs(self):
uidx0 = Variable("_uidx0", 0, 6)
uidx1 = Variable("_uidx1", 0, 1)
idxs = (uidx0 // 5, uidx0 * 5, uidx1)
assert expand_idxs(idxs) == (uidx0, NumNode(0), uidx1)
class TestLinearizerUOptimize(unittest.TestCase):
@unittest.skipUnless(Device[Device.DEFAULT].compiler.compiler_opts.supports_float4, "device doesn't support float4")
def test_grouped_store_phis(self):
x, y = Tensor.randn(64,64), Tensor.randn(64,64)
out = x.matmul(y)
k = Linearizer(*create_schedule([out.lazydata])[-1].ast)
k.hand_coded_optimizations()
k.linearize()
# check that the float4 cast collapses
store_vals = [u.vin[-1] for u in k.uops if u.uop is UOps.STORE]
for val in store_vals:
assert val.dtype == dtypes.float.vec(4) and val.uop is not UOps.CAST
@unittest.skipUnless(Device[Device.DEFAULT].compiler.compiler_opts.supports_float4, "device doesn't support float4")
def test_grouped_store_values(self):
x = Tensor.randn((4,3,6,6)).realize()
out = x.flip((0,1)).contiguous()
k = Linearizer(*create_schedule([out.lazydata])[-1].ast)
k.hand_coded_optimizations()
k.linearize()
store_val = [u.vin[-1] for u in k.uops if u.uop is UOps.STORE][0]
assert store_val.dtype == dtypes.float.vec(4) and store_val.uop is not UOps.CAST
def test_grouped_store_locals_and_globals(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.has_shared or \
not Device[Device.DEFAULT].compiler.compiler_opts.supports_float4:
self.skipTest("Only Compiled uses linearizer with locals, shared, and float4")
x, y = Tensor.rand(128, 128), Tensor.rand(128, 128)
out = x@y
opts = [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8),
Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 2)] # upcast accs in both reduces
k = Linearizer(*create_schedule([out.lazydata])[-1].ast)
for opt in opts: k.apply_opt(opt)
k.linearize()
local_stores = [u for u in k.uops if u.uop is UOps.STORE and u.vin[0].uop is UOps.DEFINE_LOCAL]
barrier = [u for u in k.uops if u.uop is UOps.BARRIER][0]
global_stores = [u for u in k.uops if u.uop is UOps.STORE and u.vin[0].uop is UOps.DEFINE_GLOBAL]
# check that the float4 cast collapses for all stores
for store in local_stores+global_stores:
assert store.vin[-1].dtype == dtypes.float.vec(2) and store.vin[-1].uop is not UOps.CAST
# check the children's vins
assert barrier.vin == tuple(local_stores)
assert len([u for u in k.uops if u.uop is UOps.IF and u.vin[-1] == barrier]) == 1
def test_grouped_store_local_only(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.has_shared or \
not Device[Device.DEFAULT].compiler.compiler_opts.supports_float4:
self.skipTest("Only Compiled uses linearizer with locals, shared, and float4")
x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
r = (x@y).relu()
k = Linearizer(*create_schedule([r.lazydata])[-1].ast)
k.hand_coded_optimizations()
k.linearize()
stores = [u for u in k.uops if u.uop is UOps.STORE]
# the float4 value stores directly in lds and we skip upcast
assert stores[0].vin[-1].dtype == dtypes.float.vec(4)
assert stores[0].vin[-1].uop is not UOps.CAST
# the global store doesn't change
assert stores[1].vin[-1].dtype == dtypes.float
def test_skip_unmatching_upcasts(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.supports_float4:
self.skipTest("Needs locals and float4")
ast = LazyOp(op=BufferOps.STORE, src=(LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=1, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(1, 240, 0, 0), offset=0, mask=None, contiguous=False),)))),), arg=MemBuffer(idx=0, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(40, 1, 0, 0), offset=0, mask=None, contiguous=True),)))) # noqa: E501
opts = [
Opt(op=OptOps.UPCAST, axis=1, amt=4), Opt(op=OptOps.LOCAL, axis=0, amt=16),
Opt(op=OptOps.LOCAL, axis=1, amt=2), Opt(op=OptOps.UPCAST, axis=3, amt=2)
]
k = Linearizer(ast)
for opt in opts: k.apply_opt(opt)
k.linearize()
out = [u for u in k.uops if u.uop is UOps.STORE][0]
assert out.vin[-1].uop is UOps.CAST and out.vin[-1].dtype == dtypes.float.vec(4)
def test_skip_unmatching_upcasts_with_gep(self):
if not Device[Device.DEFAULT].compiler.compiler_opts.has_local or not Device[Device.DEFAULT].compiler.compiler_opts.supports_float4:
self.skipTest("Needs locals and float4")
ast = LazyOp(op=BufferOps.STORE, src=(LazyOp(op=BufferOps.LOAD, src=(), arg=MemBuffer(idx=1, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(1, 8, 0, 0), offset=0, mask=None, contiguous=False),)))),), arg=MemBuffer(idx=0, dtype=dtypes.float, st=ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(32, 1, 0, 0), offset=0, mask=None, contiguous=True),)))) # noqa: E501
opts = [Opt(op=OptOps.LOCAL, axis=1, amt=4), Opt(op=OptOps.UPCAST, axis=2, amt=2), Opt(op=OptOps.LOCAL, axis=1, amt=8),
Opt(op=OptOps.UPCAST, axis=2, amt=0), Opt(op=OptOps.UPCAST, axis=1, amt=4), Opt(op=OptOps.LOCAL, axis=0, amt=8),
Opt(op=OptOps.UPCAST, axis=1, amt=0), Opt(op=OptOps.UPCAST, axis=0, amt=2)]
k = Linearizer(ast)
for opt in opts: k.apply_opt(opt)
k.linearize()
out = [u for u in k.uops if u.uop is UOps.STORE][0]
assert out.vin[-1].uop is UOps.CAST and out.vin[-1].dtype == dtypes.float.vec(2)
if __name__ == '__main__':
unittest.main()
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