refactor(mge/func): change `conv_execution_strategy` to `execution_strategy`

GitOrigin-RevId: 7aef9935ee918ddb967e12fb84453a3b2c918e1b

imperative/python/megengine/functional/debug_param.py

@@ -8,23 +8,31 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import os
 
-_conv_execution_strategy = os.getenv("MEGENGINE_CONV_EXECUTION_STRATEGY", "HEURISTIC")
+from ..logger import get_logger
+from ..utils.deprecation import deprecated
 
+_execution_strategy = os.getenv("MEGENGINE_EXECUTION_STRATEGY", "HEURISTIC")
 
-def get_conv_execution_strategy() -> str:
+if os.getenv("MEGENGINE_CONV_EXECUTION_STRATEGY") != None:
+    get_logger().warning(
+        "Environment variable `MEGENGINE_CONV_EXECUTION_STRATEGY` is deprecated, please use `MEGENGINE_EXECUTION_STRATEGY`"
+    )
+
+
+def get_execution_strategy() -> str:
     """
-    Returns the execuation strategy of :class:`~.Conv2d`.
+    Returns the execution strategy of :class:`~.Conv2d` and :func:'~.matmul'
 
-    See :func:`~.set_conv_execution_strategy` for possible return values
+    See :func:`~.set_execution_strategy` for possible return values
     """
-    return _conv_execution_strategy
+    return _execution_strategy
 
 
-def set_conv_execution_strategy(option: str):
+def set_execution_strategy(option: str):
     """
-    Sets the execuation strategy of :class:`~.Conv2d`.
+    Sets the execution strategy of :class:`~.Conv2d` and :func:'~.matmul'
 
-    :param option: Decides how :class:`~.Conv2d` algorithm is chosen.
+    :param option: Decides how :class:`~.Conv2d` and :func:'~.matmul' algorithms are chosen.
         Available values:
 
         * 'HEURISTIC' uses heuristic to choose the fastest algorithm.
@@ -35,7 +43,7 @@ def set_conv_execution_strategy(option: str):
 
         The default strategy is 'HEURISTIC'.
 
-        It can also be set through the environment variable 'MEGENGINE_CONV_EXECUTION_STRATEGY'.
+        It can also be set through the environment variable 'MEGENGINE_EXECUTION_STRATEGY'.
     """
     valid_option = (
         "HEURISTIC",
@@ -47,5 +55,15 @@ def set_conv_execution_strategy(option: str):
     if not option in valid_option:
         raise ValueError("Valid option can only be one of {}".format(valid_option))
 
-    global _conv_execution_strategy  # pylint: disable=global-statement
-    _conv_execution_strategy = option
+    global _execution_strategy  # pylint: disable=global-statement
+    _execution_strategy = option
+
+
+@deprecated(version="1.3", reason="use get_execution_strategy() instead")
+def get_conv_execution_strategy() -> str:
+    return get_execution_strategy()
+
+
+@deprecated(version="1.3", reason="use set_execution_strategy() instead")
+def set_conv_execution_strategy(option: str):
+    return set_execution_strategy(option)

imperative/python/megengine/functional/nn.py

@@ -22,7 +22,7 @@ from ..jit.tracing import is_tracing
 from ..random import uniform
 from ..tensor import Tensor
 from ..utils.tuple_function import _pair, _pair_nonzero
-from .debug_param import get_conv_execution_strategy
+from .debug_param import get_execution_strategy
 from .distributed import all_reduce_sum
 from .elemwise import exp, floor, log, log1p, maximum, minimum, relu
 from .math import argsort, matmul, max, prod, sum
@@ -149,7 +149,7 @@ def conv2d(
         pad_w=pad_w,
         dilate_h=dilate_h,
         dilate_w=dilate_w,
-        strategy=get_conv_execution_strategy(),
+        strategy=get_execution_strategy(),
         mode=conv_mode,
         compute_mode=compute_mode,
         sparse=sparse_type,
@@ -217,7 +217,7 @@ def conv_transpose2d(
         pad_w=pad_w,
         dilate_h=dilate_h,
         dilate_w=dilate_w,
-        strategy=get_conv_execution_strategy(),
+        strategy=get_execution_strategy(),
     )
     weight, inp = utils.convert_inputs(weight, inp)
     (output,) = apply(op, weight, inp)
@@ -282,7 +282,7 @@ def deformable_conv2d(
         pad_w=pad_w,
         dilate_h=dilate_h,
         dilate_w=dilate_w,
-        strategy=get_conv_execution_strategy(),
+        strategy=get_execution_strategy(),
         mode=conv_mode,
         compute_mode=compute_mode,
         sparse=sparse_type,
@@ -1658,7 +1658,7 @@ def conv1d(
         pad_w=0,
         dilate_h=dilate_h,
         dilate_w=1,
-        strategy=get_conv_execution_strategy(),
+        strategy=get_execution_strategy(),
         mode=conv_mode,
         compute_mode=compute_mode,
         sparse=sparse_type,

imperative/python/megengine/functional/quantized.py

@@ -12,7 +12,7 @@ from ..core._imperative_rt.core2 import apply
 from ..core.ops import builtin
 from ..tensor import Tensor
 from ..utils.tuple_function import _pair, _pair_nonzero
-from .debug_param import get_conv_execution_strategy
+from .debug_param import get_execution_strategy
 
 
 def conv_bias_activation(
@@ -65,7 +65,7 @@ def conv_bias_activation(
         dilate_w=dw,
         dtype=dtype,
         format="NCHW",
-        strategy=get_conv_execution_strategy(),
+        strategy=get_execution_strategy(),
         nonlineMode=nonlinear_mode,
         mode=conv_mode,
         compute_mode=compute_mode,
@@ -125,7 +125,7 @@ def batch_conv_bias_activation(
         dilate_w=dw,
         dtype=dtype,
         format="NCHW",
-        strategy=get_conv_execution_strategy(),
+        strategy=get_execution_strategy(),
         nonlineMode=nonlinear_mode,
         mode=conv_mode,
         compute_mode=compute_mode,

imperative/python/test/integration/test_correctness_mnistnet.py

@@ -20,7 +20,7 @@ import megengine.functional as F
 from megengine import jit
 from megengine.core._trace_option import set_symbolic_shape
 from megengine.core.tensor.utils import make_shape_tuple
-from megengine.functional.debug_param import set_conv_execution_strategy
+from megengine.functional.debug_param import set_execution_strategy
 from megengine.jit import SublinearMemoryConfig
 from megengine.module import (
     AdaptiveAvgPool2d,
@@ -242,7 +242,7 @@ def test_correctness():
     else:
         model_name = "mnist_model_with_test_cpu.mge"
     model_path = os.path.join(os.path.dirname(__file__), model_name)
-    set_conv_execution_strategy("HEURISTIC_REPRODUCIBLE")
+    set_execution_strategy("HEURISTIC_REPRODUCIBLE")
 
     run_train(model_path, False, False, max_err=1e-5)
     run_train(model_path, True, False, max_err=1e-5)
@@ -265,7 +265,7 @@ def test_correctness_use_adaptive_pooling():
     else:
         model_name = "mnist_model_with_test_cpu.mge"
     model_path = os.path.join(os.path.dirname(__file__), model_name)
-    set_conv_execution_strategy("HEURISTIC_REPRODUCIBLE")
+    set_execution_strategy("HEURISTIC_REPRODUCIBLE")
 
     run_train(model_path, False, False, max_err=1e-5, use_adaptive_pooling=True)
     run_train(model_path, True, False, max_err=1e-5, use_adaptive_pooling=True)

imperative/python/test/integration/test_dp_correctness.py

@@ -21,7 +21,7 @@ import megengine.autodiff as ad
 import megengine.distributed as dist
 import megengine.functional as F
 from megengine.device import get_default_device, set_default_device
-from megengine.functional.debug_param import set_conv_execution_strategy
+from megengine.functional.debug_param import set_execution_strategy
 from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
 from megengine.optimizer import SGD
 from megengine.tensor import Tensor
@@ -198,5 +198,5 @@ def run_test(
 def test_dp_correctness():
     model_name = "mnist_model_with_test.mge"
     model_path = os.path.join(os.path.dirname(__file__), model_name)
-    set_conv_execution_strategy("HEURISTIC_REPRODUCIBLE")
+    set_execution_strategy("HEURISTIC_REPRODUCIBLE")
     run_test(model_path, False, False, max_err=1e-5)

src/opr/impl/search_policy/algo_chooser.cpp

@@ -336,8 +336,10 @@ void AlgoChooser<Opr>::profile(ExeContext& ctx, bool require_reproducible) {
                       rst.workspace, rst.time);
         prof_rst.push_back(rst);
     }
-    mgb_assert(!prof_rst.empty(), "no usable convolution algorithm %s",
-               str_on_inp_shape.c_str());
+    std::string msg = ssprintf("no usable %s algorithm %s",
+                                ctx.mgb_opr()->dyn_typeinfo()->name,
+                                str_on_inp_shape.c_str());
+    mgb_assert(!prof_rst.empty(), "%s", msg.c_str());
 
     FixedTensorLayouts origin_layouts = ctx.layouts();
     typename Opr::Param origin_param = ctx.megdnn_opr()->param();