Merge branch 'main' into gh/JulianCloudNTH/51/orig

Author: JCNTH

backends/transforms/test/common.py

@@ -0,0 +1,69 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any, Callable, ParamSpec, TypeVar
+
+import pytest
+
+xfail_type = str | tuple[str, type[Exception]]
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+Decorator = Callable[[Callable[_P, _R]], Callable[_P, _R]]
+
+
+def parametrize(
+    arg_name: str,
+    test_data: dict[str, Any],
+    xfails: dict[str, xfail_type] | None = None,
+    skips: dict[str, str] | None = None,
+    strict: bool = True,
+    flakies: dict[str, int] | None = None,
+) -> Decorator:
+    """Backend-neutral version of pytest.mark.parametrize with some syntactic
+    sugar and added xfail functionality.
+
+    - test_data is expected as a dict of (id, test_data) pairs
+    - allows specifying a dict of (id, failure_reason) pairs to mark specific
+      tests as xfail. failure_reason can be str or tuple[str, type[Exception]].
+      Strings set the reason for failure, the exception type sets the expected
+      error.
+    """
+    xfails = xfails or {}
+    skips = skips or {}
+    flakies = flakies or {}
+
+    def decorator_func(func: Callable[_P, _R]) -> Callable[_P, _R]:
+        pytest_testsuite = []
+        for id, test_parameters in test_data.items():
+            if id in flakies:
+                marker = (pytest.mark.flaky(reruns=flakies[id]),)
+            elif id in skips:
+                # fail markers do not work with 'buck' based ci, so use skip instead
+                marker = (pytest.mark.skip(reason=skips[id]),)
+            elif id in xfails:
+                xfail_info = xfails[id]
+                reason = ""
+                raises = None
+                if isinstance(xfail_info, str):
+                    reason = xfail_info
+                elif isinstance(xfail_info, tuple):
+                    reason, raises = xfail_info
+                else:
+                    raise RuntimeError(
+                        "xfail info needs to be str, or tuple[str, type[Exception]]"
+                    )
+                marker = (
+                    pytest.mark.xfail(reason=reason, raises=raises, strict=strict),
+                )
+            else:
+                marker = ()
+
+            pytest_param = pytest.param(test_parameters, id=id, marks=marker)
+            pytest_testsuite.append(pytest_param)
+        decorator = pytest.mark.parametrize(arg_name, pytest_testsuite)
+        return decorator(func)
+
+    return decorator_func

backends/transforms/test/test_to_contiguous_channels_last_pass.py

@@ -8,7 +8,7 @@
 
 import pytest
 import torch
-from executorch.backends.arm.test import common
+from executorch.backends.transforms.test import common
 from executorch.exir import to_edge_transform_and_lower
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass
@@ -695,6 +695,6 @@ def test_permute_view_counts(case: PermuteCountTestCase) -> None:
 @pytest.mark.skip(
     reason="Proof of concept - currently no permute-view passes implemented."
 )
-@common.parametrize("case", cases_channels_last, xfail=xfails)
+@common.parametrize("case", cases_channels_last, xfails=xfails)
 def test_permute_view_counts_channels_last(case: PermuteCountTestCase) -> None:
     run_test(case)

extension/llm/apple/ExecuTorchLLM/Exported/ExecuTorchLLMMultimodalRunner.mm

@@ -170,8 +170,8 @@ - (BOOL)isLoaded {
 - (BOOL)loadWithError:(NSError**)error {
   if (![self isLoaded]) {
     _runner = llm::create_multimodal_runner(
-      _modelPath.UTF8String,
-      llm::load_tokenizer(_tokenizerPath.UTF8String)
+      _modelPath.UTF8String ?: "",
+      llm::load_tokenizer(_tokenizerPath.UTF8String ?: "")
     );
     if (!_runner) {
       if (error) {
@@ -206,7 +206,7 @@ - (BOOL)generateWithInputs:(NSArray<ExecuTorchLLMMultimodalInput *> *)inputs
   for (ExecuTorchLLMMultimodalInput *input in inputs) {
     switch (input.type) {
       case ExecuTorchLLMMultimodalInputTypeText:
-        nativeInputs.emplace_back(llm::MultimodalInput(input.text.UTF8String));
+        nativeInputs.emplace_back(llm::MultimodalInput(input.text.UTF8String ?: ""));
         break;
       case ExecuTorchLLMMultimodalInputTypeImage: {
         ExecuTorchLLMImage *image = input.image;

extension/llm/apple/ExecuTorchLLM/Exported/ExecuTorchLLMTextRunner.mm

@@ -44,7 +44,7 @@ - (instancetype)initWithModelPath:(NSString*)modelPath
     _tokenizerPath = [tokenizerPath copy];
     _specialTokens = std::make_unique<std::vector<std::string>>();
     for (NSString *token in specialTokens) {
-      _specialTokens->emplace_back(token.UTF8String);
+      _specialTokens->emplace_back(token.UTF8String ?: "");
     }
   }
   return self;
@@ -57,8 +57,8 @@ - (BOOL)isLoaded {
 - (BOOL)loadWithError:(NSError**)error {
   if (![self isLoaded]) {
     _runner = llm::create_text_llm_runner(
-      _modelPath.UTF8String,
-      llm::load_tokenizer(_tokenizerPath.UTF8String, std::move(_specialTokens))
+      _modelPath.UTF8String ?: "",
+      llm::load_tokenizer(_tokenizerPath.UTF8String ?: "", std::move(_specialTokens))
     );
     if (!_runner) {
       if (error) {
@@ -89,7 +89,7 @@ - (BOOL)generateWithPrompt:(NSString*)prompt
     return NO;
   }
   auto status = _runner->generate(
-    prompt.UTF8String,
+    prompt.UTF8String ?: "",
     config.nativeConfig,
     [callback](const std::string& token) {
       if (callback) {