[TESTS] Refactor tests to run on either the GPU or CPU.

Much of the time spent in testing is duplicated work between CPU and GPU
test nodes. The main reason is that there is no way to control which
TVM devices are enabled at runtime, so tests that use LLVM will run on
both GPU and CPU nodes.

This patch adds an environment variable, TVM_TEST_DEVICES, which
controls which TVM devices should be used by tests. Devices not in
TVM_TEST_DEVICES can still be used, so tests must be careful to check
that the desired device is enabled with `tvm.testing.device_enabled` or
by enumerating all devices with `tvm.testing.enabled_devices`. All
tests have been retrofitted with these checks.

This patch also provides the decorator `@tvm.testing.gpu` to mark a test
as possibly using the gpu. Tests that require the gpu can use
`@tvm.testing.requires_gpu`. Tests without these flags will not be run
on GPU nodes.

Author: tkonolige

apps/extension/tests/test_ext.py

@@ -17,6 +17,7 @@
 import tvm_ext
 import tvm
 import tvm._ffi.registry
+import tvm.testing
 from tvm import te
 import numpy as np
 
@@ -32,7 +33,7 @@ def test_ext_dev():
     B = te.compute((n,), lambda *i: A(*i) + 1.0, name='B')
     s = te.create_schedule(B.op)
     def check_llvm():
-        if not tvm.runtime.enabled("llvm"):
+        if not tvm.testing.device_enabled("llvm"):
             return
         f = tvm.build(s, [A, B], "ext_dev", "llvm")
         ctx = tvm.ext_dev(0)
@@ -77,7 +78,7 @@ def test_extern_call():
     s = te.create_schedule(B.op)
 
     def check_llvm():
-        if not tvm.runtime.enabled("llvm"):
+        if not tvm.testing.device_enabled("llvm"):
             return
         f = tvm.build(s, [A, B], "llvm")
         ctx = tvm.cpu(0)

conftest.py

@@ -0,0 +1,29 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm.testing
+from pytest import ExitCode
+
+def pytest_configure(config):
+    print("enabled targets:", "; ".join(map(lambda x: x[0], tvm.testing.enabled_targets())))
+    print("pytest marker:", config.option.markexpr)
+
+def pytest_sessionfinish(session, exitstatus):
+    # Don't exit with an error if we select a subset of tests that doesn't
+    # include anything
+    if session.config.option.markexpr != '':
+        if exitstatus == ExitCode.NO_TESTS_COLLECTED:
+            session.exitstatus = ExitCode.OK

docs/contribute/code_guide.rst

@@ -82,6 +82,20 @@ Python Code Styles
 - Stick to language features as in ``python 3.5``
 
 
+Writing Python Tests
+--------------------
+We use `pytest <https://docs.pytest.org/en/stable/>`_ for all python testing. ``tests/python`` contains all the tests.
+
+If you want your test to run over a variety of targets, use the :py:func:`tvm.testing.parametrize_targets` decorator. For example:
+
+.. code:: python
+
+  @tvm.testing.parametrize_targets
+  def test_mytest(target, ctx):
+    ...
+
+will run `test_mytest` with `target="llvm"`, `target="cuda"`, and few others. This also ensures that your test is run on the correct hardware by the CI. If you only want to test against a couple targets use `@tvm.testing.parametrize_targets("target_1", "target_2")`. If you want to test on a single target, use the associated decorator from :py:func:`tvm.testing`. For example, CUDA tests use the `@tvm.testing.requires_cuda` decorator.
+
 Handle Integer Constant Expression
 ----------------------------------
 We often need to handle constant integer expressions in TVM. Before we do so, the first question we want to ask is that is it really necessary to get a constant integer. If symbolic expression also works and let the logic flow, we should use symbolic expression as much as possible. So the generated code works for shapes that are not known ahead of time.

python/tvm/relay/testing/config.py renamed to pytest.ini

@@ -14,18 +14,13 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-"""Configuration about tests"""
-from __future__ import absolute_import as _abs
-
-import os
-import tvm
-
-
-def ctx_list():
-    """Get context list for testcases"""
-    device_list = os.environ.get("RELAY_TEST_TARGETS", "")
-    device_list = (device_list.split(",") if device_list
-                   else ["llvm", "cuda"])
-    device_list = set(device_list)
-    res = [(device, tvm.context(device, 0)) for device in device_list]
-    return [x for x in res if x[1].exist]
+[pytest]
+markers =
+    gpu: mark a test as requiring a gpu
+    tensorcore: mark a test as requiring a tensorcore
+    cuda: mark a test as requiring cuda
+    opencl: mark a test as requiring opencl
+    rocm: mark a test as requiring rocm
+    vulkan: mark a test as requiring vulkan
+    metal: mark a test as requiring metal
+    llvm: mark a test as requiring llvm

python/tvm/relay/testing/__init__.py

@@ -25,6 +25,7 @@
 import tvm.relay as relay
 import tvm.relay.op as op
 from tvm.relay import Prelude
+from tvm.testing import enabled_targets
 
 from . import mlp
 from . import resnet
@@ -41,7 +42,6 @@
 from . import temp_op_attr
 from . import synthetic
 
-from .config import ctx_list
 from .init import create_workload
 from .nat import add_nat_definitions, count, make_nat_value, make_nat_expr
 from .py_converter import to_python, run_as_python
@@ -125,7 +125,7 @@ def check_grad(func,
     if test_inputs is None:
         test_inputs = inputs
 
-    for target, ctx in ctx_list():
+    for target, ctx in enabled_targets():
         intrp = relay.create_executor(ctx=ctx, target=target)
 
         # Get analytic gradients.