[Tuning] Allow multiprocessing spawn to work (on macOS llvm at least)

python/tvm/autotvm/measure/measure_methods.py

@@ -24,29 +24,29 @@
 
 import contextlib
 import logging
-import shutil
 import os
+import shutil
+import tempfile
 import threading
 import time
 import typing
-from random import getrandbits
 from collections import namedtuple
-import tempfile
+from random import getrandbits
 
 import tvm._ffi
 import tvm.ir.transform
-from tvm import nd, rpc as _rpc
-from tvm.error import TVMError
+from tvm import nd
+from tvm import rpc as _rpc
+from tvm.contrib import ndk, nvcc, stackvm, tar
 from tvm.driver import build
-from tvm.contrib import nvcc, ndk, tar, stackvm
+from tvm.error import TVMError
 from tvm.target import Target
 
-from ..utils import get_const_tuple
 from ..env import AutotvmGlobalScope
 from ..task.space import InstantiationError
-
-from .measure import MeasureResult, MeasureErrorNo, Builder, Runner
+from ..utils import get_const_tuple
 from .local_executor import LocalExecutor
+from .measure import Builder, MeasureErrorNo, MeasureResult, Runner
 
 logger = logging.getLogger("autotvm")
 
@@ -393,8 +393,8 @@ def __init__(
 
     def set_task(self, task):
         # pylint: disable=import-outside-toplevel
-        from ...rpc.tracker import Tracker
         from ...rpc.server import Server
+        from ...rpc.tracker import Tracker
 
         self.task = task
         tracker = Tracker(port=9000, port_end=10000, silent=True)
@@ -605,26 +605,17 @@ def run_through_rpc(
     return MeasureResult(costs, errno, tstamp - tic + build_result.time_cost, tstamp)
 
 
-def default_module_loader(pre_load_function=None):
-    """Returns a default function that can be passed as module_loader to run_through_rpc.
-
-    Parameters
-    ----------
-    pre_load_function : Optional[Function[tvm.rpc.Session, tvm.runtime.Module]]
-        Invoked after a session is established and before the default code-loading RPC calls are
-        issued. Allows performing pre-upload actions, e.g. resetting the remote runtime environment.
+class DefaultModuleLoader:
+    """See default_module_loader(). A pickleable emulation of the original function closure."""
 
-    Returns
-    -------
-    ModuleLoader :
-        A function that can be passed as module_loader to run_through_rpc.
-    """
+    def __init__(self, pre_load_function=None) -> None:
+        self.pre_load_function = pre_load_function
 
     @contextlib.contextmanager
-    def default_module_loader_mgr(remote_kwargs, build_result):
+    def __call__(self, remote_kwargs, build_result):
         remote = request_remote(**remote_kwargs)
-        if pre_load_function is not None:
-            pre_load_function(remote, build_result)
+        if self.pre_load_function is not None:
+            self.pre_load_function(remote, build_result)
 
         remote.upload(build_result.filename)
         try:
@@ -636,7 +627,25 @@ def default_module_loader_mgr(remote_kwargs, build_result):
             remote.remove(os.path.splitext(build_result.filename)[0] + ".so")
             remote.remove("")
 
-    return default_module_loader_mgr
+
+def default_module_loader(pre_load_function=None):
+    """Returns a default function that can be passed as module_loader to run_through_rpc.
+
+    Parameters
+    ----------
+    pre_load_function : Optional[Function[tvm.rpc.Session, tvm.runtime.Module]]
+        Invoked after a session is established and before the default code-loading RPC calls are
+        issued. Allows performing pre-upload actions, e.g. resetting the remote runtime environment.
+
+    Returns
+    -------
+    DefaultModuleLoader :
+        A callable that can be passed as module_loader to run_through_rpc.
+    """
+
+    # This was a function with a closure before but that couldn't be pickled!
+    # We need pickle to work for using python's multiprocessing on some platforms.
+    return DefaultModuleLoader(pre_load_function)
 
 
 def request_remote(device_key, host=None, port=None, priority=1, timeout=60):

python/tvm/target/target.py

@@ -15,14 +15,15 @@
 # specific language governing permissions and limitations
 # under the License.
 """Target data structure."""
+import json
 import os
 import re
-import json
 import warnings
-import tvm._ffi
 
-from tvm.runtime import Object
+import tvm._ffi
 from tvm._ffi import register_func as _register_func
+from tvm.runtime import Object
+
 from . import _ffi_api
 
 

src/target/target.cc

@@ -418,9 +418,6 @@ Target::Target(const Map<String, ObjectRef>& config) {
 
 Target::Target(Target target, Target host) {
   ObjectPtr<TargetNode> n = make_object<TargetNode>(*target.get());
-  CHECK(!n->host.defined() || n->host.same_as(host))
-      << "ValueError: Adding a host to a target whose host field has been defined";
-  // add target host into host field
   n->host = std::move(host);
   data_ = std::move(n);
 }

tests/python/integration/test_tuning.py

@@ -18,22 +18,19 @@
 Test the tuner
 """
 import logging
+import multiprocessing as mp
 import sys
 import textwrap
 import time
 
 import pytest
-
 import tvm
 import tvm.relay
-from tvm import te
-
-from tvm import autotvm
+import tvm.testing
+from tvm import autotvm, te
 from tvm.autotvm.tuner import RandomTuner
 from tvm.target import Target
 
-import tvm.testing
-
 
 def setup_module():
     @autotvm.template("testing/conv2d_no_batching")
@@ -140,62 +137,84 @@ def get_sample_task(target=tvm.target.cuda(), target_host=None):
     return task, target
 
 
+def run_test_with_all_multiprocessing(func, *args, **kwargs):
+    """Check all multiprocessing methods work for the tuning test.
+
+    In the past fork() had the most support at detriment to spawn() and forkserver().
+    As fork() is unavailable or unsafe on some platforms it is good to check all
+    available methods.
+    """
+    for multiprocessing_method in mp.get_all_start_methods():
+        old_start_method = mp.get_start_method()
+        try:
+            mp.set_start_method(multiprocessing_method, force=True)
+            func(*args, **kwargs)
+        finally:
+            mp.set_start_method(old_start_method, force=True)
+
+
 @tvm.testing.parametrize_targets("cuda", "opencl")
 def test_tuning_gpu(target, dev):
-    # init task
-    task, target = get_sample_task(target, None)
-    logging.info("task config space: %s", task.config_space)
+    def runner(target, dev):
+        # init task
+        task, target = get_sample_task(target, None)
+        logging.info("task config space: %s", task.config_space)
 
-    measure_option = autotvm.measure_option(autotvm.LocalBuilder(), autotvm.LocalRunner())
+        measure_option = autotvm.measure_option(autotvm.LocalBuilder(), autotvm.LocalRunner())
 
-    results = []
+        results = []
 
-    tuner = RandomTuner(task)
-    tuner.tune(
-        n_trial=20,
-        measure_option=measure_option,
-        callbacks=(lambda _tuner, _inputs, rs: results.extend(rs),),
-    )
+        tuner = RandomTuner(task)
+        tuner.tune(
+            n_trial=20,
+            measure_option=measure_option,
+            callbacks=(lambda _tuner, _inputs, rs: results.extend(rs),),
+        )
+
+        assert len(results) == 20
 
-    assert len(results) == 20
+        successful_results = [r for r in results if r.error_no == autotvm.MeasureErrorNo.NO_ERROR]
+        assert len(successful_results) > 0, f"No successful tuning runs: {results!r}"
 
-    successful_results = [r for r in results if r.error_no == autotvm.MeasureErrorNo.NO_ERROR]
-    assert len(successful_results) > 0, f"No successful tuning runs: {results!r}"
+    run_test_with_all_multiprocessing(runner, target, dev)
 
 
 def test_tuning_cpu():
-    ir_mod = tvm.parser.fromtext(
-        textwrap.dedent(
+    def runner():
+        ir_mod = tvm.parser.fromtext(
+            textwrap.dedent(
+                """
+            #[version = "0.0.5"]
+            def @main(%a : Tensor[(1, 3, 32, 32), float32], %b : Tensor[(3, 3, 5, 5), float32]) {
+                nn.conv2d(%a, %b, data_layout="NCHW", kernel_layout="OIHW")
+            }
             """
-        #[version = "0.0.5"]
-        def @main(%a : Tensor[(1, 3, 32, 32), float32], %b : Tensor[(3, 3, 5, 5), float32]) {
-               nn.conv2d(%a, %b, data_layout="NCHW", kernel_layout="OIHW")
-        }
-        """
+            )
         )
-    )
-    tasks = autotvm.task.relay_integration.extract_from_program(
-        ir_mod, {}, tvm.target.create("llvm")
-    )
-    assert len(tasks) == 1, f"Extracted != 1 task from program: {tasks!r}"
+        tasks = autotvm.task.relay_integration.extract_from_program(
+            ir_mod, {}, tvm.target.create("llvm")
+        )
+        assert len(tasks) == 1, f"Extracted != 1 task from program: {tasks!r}"
 
-    task = tasks[0]
+        task = tasks[0]
 
-    measure_option = autotvm.measure_option(autotvm.LocalBuilder(), autotvm.LocalRunner())
+        measure_option = autotvm.measure_option(autotvm.LocalBuilder(), autotvm.LocalRunner())
 
-    results = []
+        results = []
 
-    tuner = RandomTuner(task)
-    tuner.tune(
-        n_trial=20,
-        measure_option=measure_option,
-        callbacks=(lambda _tuner, _inputs, rs: results.extend(rs),),
-    )
+        tuner = RandomTuner(task)
+        tuner.tune(
+            n_trial=20,
+            measure_option=measure_option,
+            callbacks=(lambda _tuner, _inputs, rs: results.extend(rs),),
+        )
+
+        assert len(results) == 20
 
-    assert len(results) == 20
+        successful_results = [r for r in results if r.error_no == autotvm.MeasureErrorNo.NO_ERROR]
+        assert len(successful_results) > 0, f"No successful tuning runs: {results!r}"
 
-    successful_results = [r for r in results if r.error_no == autotvm.MeasureErrorNo.NO_ERROR]
-    assert len(successful_results) > 0, f"No successful tuning runs: {results!r}"
+    run_test_with_all_multiprocessing(runner)
 
 
 if __name__ == "__main__":

tests/python/unittest/test_target_target.py

@@ -16,9 +16,10 @@
 # under the License.
 import json
 import sys
+
 import pytest
 import tvm
-from tvm.target import cuda, rocm, mali, intel_graphics, arm_cpu, vta, bifrost, Target
+from tvm.target import Target, arm_cpu, bifrost, cuda, intel_graphics, mali, rocm, vta
 
 
 @tvm.target.generic_func
@@ -210,17 +211,6 @@ def test_target_host_single_string_with_tag():
     assert tgt.host.attrs["registers_per_block"] == 32768
 
 
-def test_target_host_warning():
-    """
-    Confirm that constructing a target with invalid
-    attributes fails as expected.
-    """
-    with pytest.raises(
-        ValueError, match="Adding a host to a target whose host field has been defined"
-    ):
-        tvm.target.Target("cuda --host nvidia/jetson-nano", "llvm")
-
-
 def test_target_host_merge_0():
     tgt = tvm.target.Target(tvm.target.Target("cuda --host nvidia/jetson-nano"), None)
     assert tgt.kind.name == "cuda"
@@ -240,10 +230,10 @@ def test_target_host_merge_1():
 
 
 def test_target_host_merge_2():
-    with pytest.raises(
-        ValueError, match="Adding a host to a target whose host field has been defined"
-    ):
-        tvm.target.Target(tvm.target.Target("cuda --host llvm"), tvm.target.Target("llvm"))
+    """Test picking the same host is ok."""
+    tgt = tvm.target.Target(tvm.target.Target("cuda --host llvm"), tvm.target.Target("llvm"))
+    assert tgt.kind.name == "cuda"
+    assert tgt.host.kind.name == "llvm"
 
 
 @pytest.mark.skip(reason="Causing infinite loop because of pytest and handle issue")