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indexing fusion 2 (#5888)

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* arange fusion

* kernels that fuse

* tests
This commit is contained in:
qazal
2024-08-03 13:13:39 +03:00
committed by GitHub
parent af59b2eea9
commit 65fa86901a
4 changed files with 94 additions and 18 deletions
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test/test_arange.py
+4 -4
View File
@@ -23,7 +23,7 @@ class TestIndexing(unittest.TestCase):
needle = Tensor.zeros(16384, dtype=dtypes.int).contiguous()
needle[1337] = 1
needle.realize()
with Context(NOOPT=1, FUSE_AS_ONE_KERNEL=1):
with Context(NOOPT=1, FUSE_ARANGE=1):
GlobalCounters.reset()
# TODO: it should work without these reshapes
out = ((Tensor.arange(1,16385).reshape(16384,1)-1)*needle.reshape(16384,1)).sum()
@@ -38,7 +38,7 @@ class TestIndexing(unittest.TestCase):
idxs = Tensor([0,3,5,6]).realize()
real_index = dataset.numpy()[idxs.numpy()]
print("*** indexing ***")
with Context(NOOPT=1, FUSE_AS_ONE_KERNEL=1):
with Context(NOOPT=1, FUSE_ARANGE=1):
GlobalCounters.reset()
rng = Tensor.ones(4, 256, 16384, dtype=dtypes.int)._cumsum(axis=-1, _first_zero=True).reshape(4, 256, 16384, 1)
idxs = idxs.reshape(4,1,1,1).expand(4, 256, 16384, 1)
@@ -72,12 +72,12 @@ class TestIndexing(unittest.TestCase):
idxs = Tensor([0,3,5,6]).realize()
real_index = dataset.numpy()[idxs.numpy()]
print("*** indexing ***")
with Context(NOOPT=1, FUSE_AS_ONE_KERNEL=1):
with Context(NOOPT=1, FUSE_ARANGE=1):
GlobalCounters.reset()
X = dataset[idxs]
assert X.shape == (4,256)
sched = X.schedule()
assert len(sched) == 1
assert len(sched) == 2
run_schedule(sched)
assert GlobalCounters.global_ops < 4*16384, f"too many ops {GlobalCounters.global_ops} != {4*16384}"
np.testing.assert_allclose(real_index, X.numpy())
test/test_schedule.py
+75 -10
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@@ -9,7 +9,7 @@ from tinygrad import nn, dtypes
from tinygrad.device import Device
from tinygrad.tensor import Tensor
from tinygrad.ops import BinaryOps, MetaOps, ReduceOps, UnaryOps
from tinygrad.helpers import DEBUG, flatten, getenv
from tinygrad.helpers import DEBUG, FUSE_ARANGE, flatten, getenv
from tinygrad.codegen.kernel import Kernel
from tinygrad.engine.schedule import create_schedule
from tinygrad.engine.realize import run_schedule
@@ -1268,36 +1268,41 @@ class TestSchedule(unittest.TestCase):
class TestIndexing(unittest.TestCase):
def check_schedule(self, xt:Tensor, cnt:int):
s = xt.schedule()
kernel_cnt = len([si for si in s if si.ast.op is MetaOps.KERNEL])
run_schedule(s)
self.assertEqual(kernel_cnt, cnt)
with Context(FUSE_ARANGE=getenv("FUSE_ARANGE", 1)):
s = xt.schedule()
kernel_cnt = len([si for si in s if si.ast.op is MetaOps.KERNEL])
run_schedule(s)
if FUSE_ARANGE: self.assertEqual(kernel_cnt, cnt)
def test_simple_indexing(self):
X = Tensor.randn(10, 10).realize()
idxs = Tensor([0, 2]).realize()
xt = X[idxs]
self.check_schedule(xt, 3)
self.check_schedule(xt, 2)
np.testing.assert_equal(xt.numpy(), X.numpy()[idxs.numpy()])
@unittest.expectedFailure
def test_simple_indexing_alt(self):
X = Tensor.arange(16).reshape(4, 4)
xt = X[[1, 2], [1, 2]]
self.check_schedule(xt, 5)
np.testing.assert_equal(xt.numpy(), (np.arange(16).reshape(4, 4))[[1, 2], [1, 2]])
@unittest.expectedFailure
def test_advanced_indexing(self):
X = Tensor.arange(10)+1
xt = X[[0]]
self.check_schedule(xt, 3)
np.testing.assert_equal(xt.numpy(), (np.arange(10)+1)[[0]])
@unittest.expectedFailure
def test_advanced_indexing_alt(self):
X = Tensor.arange(6).reshape(3, 2)+1
xt = X[[Tensor([2]), Tensor([1])]]
self.check_schedule(xt, 6)
np.testing.assert_equal(xt.numpy(), 6)
@unittest.expectedFailure
def test_advanced_simple_indexing_combined(self):
X = Tensor.arange(16).reshape(4, 4)
xt = X[1:2, [1, 2]]
@@ -1308,7 +1313,7 @@ class TestIndexing(unittest.TestCase):
Tensor.manual_seed(0)
x = Tensor.randn(10, 20).realize()
out = x.argmax(1)
self.check_schedule(out, 3)
self.check_schedule(out, 2)
np.testing.assert_allclose(out.numpy(), np.argmax(x.numpy(), 1))
def test_arange_push_through_expand(self):
@@ -1316,22 +1321,82 @@ class TestIndexing(unittest.TestCase):
a = Tensor.arange(4,)
b = Tensor.randn(4, 4).realize()
out = a+b
self.check_schedule(out, 2)
self.check_schedule(out, 1)
np.testing.assert_allclose(out.numpy(), np.arange(4)+b.numpy())
def test_argmin(self):
Tensor.manual_seed(0)
x = Tensor.randn(4, 32).realize()
out = x.argmin(-1)
self.check_schedule(out, 3)
self.check_schedule(out, 2)
np.testing.assert_equal(out.numpy(), x.numpy().argmin(axis=-1))
def test_argmax(self):
Tensor.manual_seed(0)
x = Tensor.randn(4, 32).realize()
out = x.argmax(-1)
self.check_schedule(out, 3)
self.check_schedule(out, 2)
np.testing.assert_equal(out.numpy(), x.numpy().argmax(axis=-1))
def test_arange_transposed(self):
Tensor.manual_seed(0)
x = Tensor.randint(4, 1)
a = (Tensor.arange(4,)*x).T
self.check_schedule(a, 2)
np.testing.assert_equal(a.numpy(), (np.arange(4)*x.numpy()).T)
def test_arange_transposed_descendants(self):
Tensor.manual_seed(0)
x = Tensor.randint(4, 1)
a = (Tensor.arange(4,)*x).T
b = Tensor.randint(4, 4).realize()
out = a+b
self.check_schedule(out, 2)
np.testing.assert_equal(out.numpy(), (np.arange(4)*x.numpy()).T+b.numpy())
def test_arange_index(self):
Tensor.manual_seed(0)
x = Tensor.randn(5, 2).realize()
a = Tensor.arange(10)
out = (x + a[2]).sum()
self.check_schedule(out, 1)
np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum())
def test_arange_index_contiguous(self):
Tensor.manual_seed(0)
x = Tensor.randn(5, 2).realize()
a = Tensor.arange(10).contiguous()
out = (x + a[2]).sum()
self.check_schedule(out, 2)
np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum())
def test_arange_index_child(self):
Tensor.manual_seed(0)
x = Tensor.randn(5, 2).realize()
a = Tensor.arange(10)+1
out = (x + a[2]).sum()
self.check_schedule(out, 1)
np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum())
def test_arange_index_contiguous_child(self):
Tensor.manual_seed(0)
x = Tensor.randn(5, 2).realize()
a = (Tensor.arange(10)+1).contiguous()
out = (x + a[2]).sum()
self.check_schedule(out, 2)
np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum())
def test_arange_childless(self):
a = Tensor.arange(4)
self.check_schedule(a, 1)
np.testing.assert_equal(a.numpy(), np.arange(4))
def test_arange_group_childless(self):
Tensor.manual_seed(0)
x = Tensor.randint(4)
a = Tensor.arange(4)+x
self.check_schedule(a, 1)
np.testing.assert_equal(a.numpy(), np.arange(4)+x.numpy())
if __name__ == '__main__':
unittest.main(verbosity=2)
tinygrad/engine/schedule.py
+14 -3
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@@ -4,7 +4,7 @@ from dataclasses import dataclass
from typing import Tuple, List, Dict, Optional, Set, DefaultDict, Union, cast, get_args
from tinygrad.ops import MetaOps, BufferOps, LazyOp, Op, ReduceOps, ConstBuffer, MemBuffer, UNSAFE_PAD_OPS, UnaryOps, reduce_st
from tinygrad.engine.graph import log_lazybuffer, realized_lazybuffer
from tinygrad.helpers import GRAPH, DEBUG, MULTIOUTPUT, SAVE_SCHEDULE, FUSE_CONV_BW, FUSE_AS_ONE_KERNEL, GlobalCounters, colored, prod, dedup,\
from tinygrad.helpers import GRAPH, DEBUG, MULTIOUTPUT, SAVE_SCHEDULE, FUSE_CONV_BW, FUSE_ARANGE, GlobalCounters, colored, prod, dedup,\
all_int, merge_dicts, getenv, Metadata
from tinygrad.shape.symbolic import Variable, sint
from tinygrad.dtype import ConstType, ImageDType, dtypes
@@ -169,7 +169,7 @@ def _recurse_lb(buf:LazyBuffer, realizes:Dict[LazyBuffer, None], allbufs:Dict[La
# this was causing "test_lil_model" to fail
if buf.base.op is UnaryOps.CAST and isinstance(buf.base.srcs[0].dtype, ImageDType) and isinstance(buf.base.arg, ImageDType):
simple_pads[buf.base] = None # don't realize image to image casts. this is part of a larger problem
elif not FUSE_AS_ONE_KERNEL: realizes[buf.base] = None
else: realizes[buf.base] = None
# check all other pads for safe fusion
elif any(v.mask is not None for v in buf.st.views): simple_pads[buf.base] = None
return _recurse_lb(buf.base, realizes, allbufs, simple_pads, children, assign_targets, double_reduces)
@@ -243,6 +243,7 @@ def _graph_schedule(outs:List[LazyBuffer], seen:Set[LazyBuffer]):
# find all reduces, and pair them to a elementwise op. if they can't be cleanly paired, force realize the reduce (or a contig child)
reduce_for_op: Dict[LazyBuffer, LazyBuffer] = {}
reduce_of_const: List[LazyBuffer] = []
for r in allbufs:
if r.op not in ReduceOps or r in realizes: continue
@@ -279,8 +280,9 @@ def _graph_schedule(outs:List[LazyBuffer], seen:Set[LazyBuffer]):
if tr.op is UnaryOps.CAST and tr.arg.itemsize > tr.srcs[0].dtype.itemsize:
tr = tr.srcs[0].base
reduce_for_op[tr] = r
if not FUSE_AS_ONE_KERNEL: realizes[tr] = None
realizes[tr] = None
else: reduce_for_op.update((tr, r) for tr in group)
if FUSE_ARANGE and r.op is ReduceOps.SUM and r.srcs[0].base.op is MetaOps.CONST: reduce_of_const.append(r)
# fuse double reduces with no other child
if FUSE_CONV_BW:
@@ -288,6 +290,15 @@ def _graph_schedule(outs:List[LazyBuffer], seen:Set[LazyBuffer]):
top_reduce = reduceop.base.srcs[0].base
if len(children[top_reduce]) == 1: del realizes[top_reduce]
def _can_fold_reduce(r:LazyBuffer, group:Dict[LazyBuffer, None]) -> bool:
if DEBUG_ARANGE:=(getenv("DEBUG_ARANGE")): print(f"checking {r} {group=}")
if any(tr.forced_realize or tr in outs for tr in group): return False
if DEBUG_ARANGE: print(colored(f"folding {r}", "green"))
return True
for r in reduce_of_const:
if _can_fold_reduce(r, group:={tr:None for tr,rop in reduce_for_op.items() if rop is r}):
for tr in group: del realizes[tr]
output_groups: DefaultDict[LazyBuffer, List[LazyBuffer]] = defaultdict(list)
for buf in realizes:
if buf.realized is not None or buf.op is MetaOps.CONST or buf in seen: continue
tinygrad/helpers.py
+1 -1
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@@ -107,7 +107,7 @@ WINO, THREEFRY, CAPTURING, TRACEMETA = ContextVar("WINO", 0), ContextVar("THREEF
GRAPH, GRAPHPATH, SAVE_SCHEDULE, RING = ContextVar("GRAPH", 0), getenv("GRAPHPATH", "/tmp/net"), ContextVar("SAVE_SCHEDULE", 0), ContextVar("RING", 1)
MULTIOUTPUT, PROFILE, PROFILEPATH = ContextVar("MULTIOUTPUT", 1), ContextVar("PROFILE", 0), ContextVar("PROFILEPATH", temp("tinygrad_profile.json"))
USE_TC, TC_OPT, TRANSCENDENTAL = ContextVar("TC", 1), ContextVar("TC_OPT", 0), ContextVar("TRANSCENDENTAL", 1)
FUSE_AS_ONE_KERNEL, FUSE_CONV_BW = ContextVar("FUSE_AS_ONE_KERNEL", 0), ContextVar("FUSE_CONV_BW", 0)
FUSE_ARANGE, FUSE_CONV_BW = ContextVar("FUSE_ARANGE", 0), ContextVar("FUSE_CONV_BW", 0)
@dataclass(frozen=True)
class Metadata: