Improve CoreML logging with env variables

backends/apple/coreml/TARGETS

@@ -17,6 +17,7 @@ runtime.python_library(
     name = "backend",
     srcs = glob([
         "compiler/*.py",
+        "logging.py",
     ]),
     visibility = [
         "@EXECUTORCH_CLIENTS",
@@ -33,6 +34,7 @@ runtime.python_library(
     name = "partitioner",
     srcs = glob([
         "partition/*.py",
+        "logging.py",
     ]),
     visibility = [
         "@EXECUTORCH_CLIENTS",

backends/apple/coreml/compiler/coreml_preprocess.py

@@ -16,20 +16,20 @@
 
 import coremltools as ct
 import coremltools.optimize as cto
-
 from executorch.backends.apple.coreml import executorchcoreml
+from executorch.backends.apple.coreml.logging import get_coreml_log_level
 from executorch.exir.backend.backend_details import (
     BackendDetails,
     ExportedProgram,
     PreprocessResult,
 )
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.WARNING)
-
 from executorch.backends.apple.coreml.compiler.torch_ops import *  # noqa: F401, F403
 
+logger = logging.getLogger(__name__)
+logger.setLevel(get_coreml_log_level(default_level=logging.WARNING))
+
 
 class COMPILE_SPEC_KEYS(Enum):
     COMPUTE_UNITS = "compute_units"
@@ -409,6 +409,7 @@ def preprocess(
         edge_program: ExportedProgram,
         compile_specs: List[CompileSpec],
     ) -> PreprocessResult:
+        logger.info(f"Edge program: {edge_program}")
         model_type: CoreMLBackend.MODEL_TYPE = (
             CoreMLBackend.model_type_from_compile_specs(
                 compile_specs,

backends/apple/coreml/compiler/torch_ops.py

@@ -9,7 +9,7 @@
 # the op to the coremltools library.
 
 import torch as _torch
-from coremltools import _logger as logger
+from coremltools import _logger
 from coremltools.converters.mil.frontend import _utils
 from coremltools.converters.mil.frontend.torch.ops import (
     _get_inputs,
@@ -88,7 +88,7 @@ def dequantize_affine(context, node):
     out_np_dtype = None
     if len(inputs) > 7:
         out_np_dtype = NUM_TO_NUMPY_DTYPE[inputs[7].val]
-        logger.warning(
+        _logger.warning(
             f"Core ML ignores output_dtype {out_np_dtype} on torchao.dequantize_affine and instead uses the native precision."
         )
 

backends/apple/coreml/logging.py

@@ -0,0 +1,24 @@
+# Copyright © 2023 Apple Inc. All rights reserved.
+#
+# Please refer to the license found in the LICENSE file in the root directory of the source tree.
+
+import logging
+import os
+from typing import Optional
+
+
+def get_coreml_log_level(default_level: int) -> Optional[str]:
+    level_str = os.environ.get("ET_COREML_LOG_LEVEL", "").upper()
+    if level_str == "":
+        return default_level
+
+    level_map = {
+        "DEBUG": logging.DEBUG,
+        "INFO": logging.INFO,
+        "WARNING": logging.WARNING,
+        "ERROR": logging.ERROR,
+        "CRITICAL": logging.CRITICAL,
+    }
+    if level_str not in level_map:
+        raise ValueError(f"Invalid ET_COREML_LOG_LEVEL: {level_str}")
+    return level_map[level_str]

backends/apple/coreml/partition/coreml_partitioner.py

@@ -10,6 +10,8 @@
 import torch
 
 from executorch.backends.apple.coreml.compiler import CoreMLBackend
+
+from executorch.backends.apple.coreml.logging import get_coreml_log_level
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 
 from executorch.exir.backend.partitioner import (
@@ -23,7 +25,7 @@
 from torch.fx.passes.operator_support import OperatorSupportBase
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
+logger.setLevel(get_coreml_log_level(default_level=logging.INFO))
 
 
 class _OperatorsSupportedForCoreMLBackend(OperatorSupportBase):