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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/10/07 15:01:01 UTC
[GitHub] [incubator-tvm] leandron opened a new issue #6642: [Test] patterns for check into tests/python/contrib/test_tedd.py mismatch when running on CPU
leandron opened a new issue #6642:
URL: https://github.com/apache/incubator-tvm/issues/6642
It seems there are a couple tests in `tests/python/contrib/test_tedd.py` that mismatch when running on CPU only machines. These tests are part of `task_python-integration.sh`.
```
FAILED tests/python/contrib/test_tedd.py::test_itervar_relationship_graph - A...
FAILED tests/python/contrib/test_tedd.py::test_schedule_tree - AssertionError...
```
Interestingly, I noticed these tests pass on upstream CI, but I think the only reason it happens is because the `tlcpack/ci-cpu:v0.66` doesn't have `graphviz` installed, and hence won't do any checks, reporting `PASS`.
There are a few things I think we should do to fix this:
1. Install graphviz in one of the setup scripts on ci-cpu Docker image
2. Improve the pattern within the test, so that we have one that works on GPU and CPU Docker images
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[GitHub] [incubator-tvm] leandron commented on issue #6642: [Test] patterns for check into tests/python/contrib/test_tedd.py mismatch when running on CPU
Posted by GitBox <gi...@apache.org>.
leandron commented on issue #6642:
URL: https://github.com/apache/incubator-tvm/issues/6642#issuecomment-705007816
One example error message I was seeing:
The expected pattern is: `bk(kVectorized)`
The actual contents: `bk: int32(kVectorized)`
```
______________________________ test_schedule_tree ______________________________
def test_schedule_tree():
block_x = te.thread_axis("blockIdx.x")
thread_x = te.thread_axis("threadIdx.x")
n = te.var("n")
m = te.var("m")
l = te.var("l")
A = te.placeholder((n, m, l), name="A")
B = te.compute((n, m, l), lambda bi, bj, bk: A[bi, bj, bk] + 1, name="B")
r = te.reduce_axis((0, m), "r")
C = te.compute(
(
n,
m,
),
lambda ci, cj: te.sum(B[ci, cj, r], axis=r),
name="C",
)
s = te.create_schedule(C.op)
s.cache_read(A, "shared", [B])
s[B].vectorize(B.op.axis[-1])
s[C].reorder(C.op.reduce_axis[0], C.op.axis[0])
_, ki = s[C].split(C.op.reduce_axis[0], factor=16)
Cr = s.rfactor(C, ki)
s[Cr].compute_at(s[C], s[C].op.axis[-1])
s[C].bind(s[C].op.axis[0], block_x)
s[C].bind(s[C].op.axis[1], thread_x)
def verify():
from tvm.contrib import tedd
str = tedd.viz_schedule_tree(s, False, "", True)
findany(r"digraph \"Schedule Tree\"", str)
findany(r"subgraph cluster_legend", str)
# Check the A_shared stage, including memory scope, itervars,
# and compute
findany(
r"Stage_1.*A\.shared<br/>Scope: shared.+>0.+>"
r"ax0.*\(kDataPar\).+>1.+ax1.*\(kDataPar\).+>2.+>ax2.*\(kDataPar\).+>"
r"\[A[\[\(]ax0, ax1, ax2[\)\]]\]",
str
)
# Check itervars of types different from KDataPar
findany(r"bk\(kVectorized\)", str)
findany(r"r.outer\(kCommReduce\)", str)
findany(r"label=ROOT", str)
# Check the compute_at edge
findany(r"Stage_1.*\[color\=\"\#000000\"\]", str)
if checkdependency():
> verify()
tests/python/contrib/test_tedd.py:148:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/python/contrib/test_tedd.py:141: in verify
findany(r"bk\(kVectorized\)", str)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pattern = 'bk\\(kVectorized\\)'
str = 'digraph "Schedule Tree" {\n\tgraph [rankdir=BT]\n\tsubgraph cluster_legend {\n\t\tlabel=Legend\n\t\tlegend [label=<<T...Stage_4:IterVar_4_1 [color="#76448A"]\n\tStage_4 -> ROOT [color="#000000"]\n\tROOT [label=ROOT margin=0 shape=oval]\n}'
def findany(pattern, str):
matches = re.findall(pattern, str)
> assert len(matches) > 0, "Pattern not found.\nPattern: " + pattern + "\nString: " + str
E AssertionError: Pattern not found.
E Pattern: bk\(kVectorized\)
E String: digraph "Schedule Tree" {
E graph [rankdir=BT]
E subgraph cluster_legend {
E label=Legend
E legend [label=<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4"><TR><TD BGCOLOR="#FFFFFF"></TD><TD BGCOLOR="white">kDataPar</TD></TR><TR><TD BGCOLOR="#2980B9"></TD><TD BGCOLOR="white">kThreadIndex</TD></TR><TR><TD BGCOLOR="#FAD7A0"></TD><TD BGCOLOR="white">kCommReduce</TD></TR><TR><TD BGCOLOR="#D35400"></TD><TD BGCOLOR="white">kOrdered</TD></TR><TR><TD BGCOLOR="#ABB2B9"></TD><TD BGCOLOR="white">kOpaque</TD></TR><TR><TD BGCOLOR="#D2B4DE"></TD><TD BGCOLOR="white">kUnrolled</TD></TR><TR><TD BGCOLOR="#AED6F1"></TD><TD BGCOLOR="white">kVectorized</TD></TR><TR><TD BGCOLOR="#F5B7B1"></TD><TD BGCOLOR="white">kParallelized</TD></TR><TR><TD BGCOLOR="#A9DFBF"></TD><TD BGCOLOR="white">kTensorized</TD></TR></TABLE>> margin=0 shape=none]
E }
E Stage_0 [label=<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4"> <TR><TD BGCOLOR="lightgrey" COLSPAN="2" PORT="stage">A<br/>Scope: </TD></TR></TABLE>> margin=0 shape=none]
E Stage_1 [label=<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4"> <TR><TD BGCOLOR="lightgrey" COLSPAN="2" PORT="stage">A.shared<br/>Scope: shared</TD></TR><TR><TD PORT="IterVar_1_0" BGCOLOR="#FFFFFF">0</TD><TD BGCOLOR="white" PORT="itervar">ax0: int32(kDataPar)<br/>range(min=0, ext=n)</TD></TR><TR><TD PORT="IterVar_1_1" BGCOLOR="#FFFFFF">1</TD><TD BGCOLOR="white" PORT="itervar">ax1: int32(kDataPar)<br/>range(min=0, ext=m)</TD></TR><TR><TD PORT="IterVar_1_2" BGCOLOR="#FFFFFF">2</TD><TD BGCOLOR="white" PORT="itervar">ax2: int32(kDataPar)<br/>range(min=0, ext=(floordiv((m + 15), 16)*16))</TD></TR><TR><TD COLSPAN="2">[A[ax0, ax1, ax2]]</TD></TR></TABLE>> margin=0 shape=none]
E Stage_2 [label=<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4"> <TR><TD BGCOLOR="lightgrey" COLSPAN="2" PORT="stage">B<br/>Scope: </TD></TR><TR><TD PORT="IterVar_2_0" BGCOLOR="#FFFFFF">0</TD><TD BGCOLOR="white" PORT="itervar">bi: int32(kDataPar)<br/>range(min=0, ext=n)</TD></TR><TR><TD PORT="IterVar_2_1" BGCOLOR="#FFFFFF">1</TD><TD BGCOLOR="white" PORT="itervar">bj: int32(kDataPar)<br/>range(min=0, ext=m)</TD></TR><TR><TD PORT="IterVar_2_2" BGCOLOR="#AED6F1">2</TD><TD BGCOLOR="white" PORT="itervar">bk: int32(kVectorized)<br/>range(min=0, ext=(floordiv((m + 15), 16)*16))</TD></TR><TR><TD COLSPAN="2">[(A.shared[bi, bj, bk] + 1f)]</TD></TR></TABLE>> margin=0 shape=none]
E Stage_3 [label=<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4"> <TR><TD BGCOLOR="lightgrey" COLSPAN="2" PORT="stage">C.rf<br/>Scope: </TD></TR><TR><TD PORT="IterVar_3_0" BGCOLOR="#FFFFFF">0</TD><TD BGCOLOR="white" PORT="itervar">r.inner: int32(kDataPar)<br/>range(min=0, ext=16)</TD></TR><TR><TD PORT="IterVar_3_1" BGCOLOR="#FFFFFF">1</TD><TD BGCOLOR="white" PORT="itervar">ci: int32(kDataPar)<br/>range(min=blockIdx.x, ext=1)</TD></TR><TR><TD PORT="IterVar_3_2" BGCOLOR="#FFFFFF">2</TD><TD BGCOLOR="white" PORT="itervar">cj: int32(kDataPar)<br/>range(min=threadIdx.x, ext=1)</TD></TR><TR><TD PORT="IterVar_3_3" BGCOLOR="#FAD7A0">3</TD><TD BGCOLOR="white" PORT="itervar">r.outer: int32(kCommReduce)<br/>range(min=0, ext=floordiv((m + 15), 16))</TD></TR><TR><TD COLSPAN="2">[reduce(combiner=comm_reducer(<br/>result=[(x + y)], lhs=[x], rhs<br/>=[y], identity_element=[0f]), <br/>source=[B[ci, cj, (r.inner + (<br/>r.outer*16))]], init=[], axis=<br/>[iter_var(r.outer,
range(min=0<br/>, ext=floordiv((m + 15), 16)))<br/>], where=tir.likely(((r.inner <br/>+ (r.outer*16)) < m)), value_i<br/>ndex=0)]</TD></TR></TABLE>> margin=0 shape=none]
E Stage_4 [label=<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4"> <TR><TD BGCOLOR="lightgrey" COLSPAN="2" PORT="stage">C.repl<br/>Scope: </TD></TR><TR><TD PORT="IterVar_4_0" BGCOLOR="#FFFFFF">0</TD><TD BGCOLOR="white" PORT="itervar">ax0: int32(kDataPar)<br/><font color="#2980B9">(blockIdx.x: int32)</font><br/>range(min=0, ext=n)</TD></TR><TR><TD PORT="IterVar_4_1" BGCOLOR="#FFFFFF">1</TD><TD BGCOLOR="white" PORT="itervar">ax1: int32(kDataPar)<br/><font color="#2980B9">(threadIdx.x: int32)</font><br/>range(min=0, ext=m)</TD></TR><TR><TD PORT="IterVar_4_2" BGCOLOR="#FAD7A0">2</TD><TD BGCOLOR="white" PORT="itervar">r.inner.v: int32(kCommReduce)<br/>range(min=0, ext=16)</TD></TR><TR><TD COLSPAN="2">[reduce(combiner=comm_reducer(<br/>result=[(x + y)], lhs=[x], rhs<br/>=[y], identity_element=[0f]), <br/>source=[C.rf[r.inner.v, ax0, a<br/>x1]], init=[], axis=[iter_var(<br/>r.inner.v, range(min=0, ext=16<br/>))], where=(bool)1, value_inde<br/>x=0)]</TD></TR></TA
BLE>> margin=0 shape=none]
E Stage_0 -> ROOT [color="#000000"]
E Stage_1 -> ROOT [color="#000000"]
E Stage_2 -> ROOT [color="#000000"]
E Stage_3 -> Stage_4:IterVar_4_1 [color="#76448A"]
E Stage_4 -> ROOT [color="#000000"]
E ROOT [label=ROOT margin=0 shape=oval]
E }
E assert 0 > 0
E + where 0 = len([])
```
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[GitHub] [incubator-tvm] leandron commented on issue #6642: [Test] patterns for check into tests/python/contrib/test_tedd.py mismatch when running on CPU
Posted by GitBox <gi...@apache.org>.
leandron commented on issue #6642:
URL: https://github.com/apache/incubator-tvm/issues/6642#issuecomment-705014714
cc @yongfeng-nv to get any comments you might have
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[GitHub] [incubator-tvm] leandron commented on issue #6642: [Test] patterns for check into tests/python/contrib/test_tedd.py mismatch when running on CPU
Posted by GitBox <gi...@apache.org>.
leandron commented on issue #6642:
URL: https://github.com/apache/incubator-tvm/issues/6642#issuecomment-706362371
I consider this is closed now
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[GitHub] [incubator-tvm] leandron closed issue #6642: [Test] patterns for check into tests/python/contrib/test_tedd.py mismatch when running on CPU
Posted by GitBox <gi...@apache.org>.
leandron closed issue #6642:
URL: https://github.com/apache/incubator-tvm/issues/6642
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