[RELEASE ONLY CHANGES] Finalize 1.4 release dependencies and version

.ci/scripts/test_backend.sh

@@ -113,7 +113,12 @@ GOLDEN_DIR="${ARTIFACT_DIR}/golden-artifacts"
 export GOLDEN_ARTIFACTS_DIR="${GOLDEN_DIR}"
 
 EXIT_CODE=0
-PYTEST_ARGS=(-c /dev/null -n auto)
+PYTEST_WORKERS=auto
+if [[ "$FLOW" == *qnn* && "$SUITE" == "models" ]]; then
+    # Concurrent QNN model exports can exhaust a linux.2xlarge runner.
+    PYTEST_WORKERS=2
+fi
+PYTEST_ARGS=(-c /dev/null -n "$PYTEST_WORKERS")
 if [[ ${#PYTEST_RETRY_ARGS[@]} -gt 0 ]]; then
     PYTEST_ARGS+=("${PYTEST_RETRY_ARGS[@]}")
 fi

.ci/scripts/test_model.sh

@@ -127,6 +127,14 @@ test_model() {
       return
   fi
 
+  if [[ "${MODEL_NAME}" == "yolo26" ]]; then
+      # Install yolo26 requirements (ultralytics). torch/torchvision are already
+      # installed and satisfy ultralytics, so only-if-needed leaves them as-is.
+      # No pytorch extra-index-url: it is unused here and would broaden pip's
+      # version resolution across all deps.
+      "${PYTHON_EXECUTABLE}" -m pip install --upgrade-strategy only-if-needed -r examples/models/yolo26/requirements.txt
+  fi
+
   # Export a basic .pte and run the model.
   "${PYTHON_EXECUTABLE}" -m examples.portable.scripts.export --model_name="${MODEL_NAME}" "${STRICT}"
   run_portable_executor_runner

.ci/scripts/test_model_e2e.sh

@@ -284,7 +284,7 @@ elif [[ "$MODEL_NAME" == *whisper* ]] || [ "$MODEL_NAME" = "voxtral_realtime" ];
     fi
   fi
   pip install datasets soundfile
-  pip install torchcodec==0.11.0 --extra-index-url https://download.pytorch.org/whl/test/cpu
+  pip install torchcodec==0.15.0 --index-url https://download.pytorch.org/whl/cpu
   python -c "from datasets import load_dataset;import soundfile as sf;sample = load_dataset('distil-whisper/librispeech_long', 'clean', split='validation')[0]['audio'];sf.write('${MODEL_DIR}/$AUDIO_FILE', sample['array'][:sample['sampling_rate']*30], sample['sampling_rate'])"
 fi
 

.ci/scripts/test_wheel_package_qnn.sh

@@ -167,8 +167,8 @@ PY
 # )
   echo "=== [$LABEL] Install torch==${TORCH_VERSION} ==="
 
-  # Install torch based on the pinned PyTorch version, preferring the PyTorch test index
-  "$PIPBIN" install torch=="${TORCH_VERSION}" --extra-index-url "https://download.pytorch.org/whl/test"
+  # Install torch based on the pinned PyTorch version.
+  "$PIPBIN" install --no-cache-dir torch=="${TORCH_VERSION}" --index-url "https://download.pytorch.org/whl/cpu"
   "$PIPBIN" install wheel
 
   # Install torchao based on the pinned commit from third-party/ao submodule

backends/arm/scripts/aot_arm_compiler.py

@@ -925,12 +925,16 @@ def _to_edge_cortex_m(
 
     def _to_channels_last(x):
         if isinstance(x, torch.Tensor):
-            if x.dim() == 4 and not x.is_contiguous(memory_format=torch.channels_last):
-                logging.warning(
-                    "Converting input tensor with shape %s to channels_last",
-                    list(x.shape),
-                )
-                return x.to(memory_format=torch.channels_last)
+            if x.dim() == 4:
+                # Singleton channels can satisfy both contiguity checks while
+                # retaining NCHW strides, so always request the target format.
+                channels_last = x.to(memory_format=torch.channels_last)
+                if channels_last.stride() != x.stride():
+                    logging.warning(
+                        "Converting input tensor with shape %s to channels_last",
+                        list(x.shape),
+                    )
+                return channels_last
             return x
         elif isinstance(x, tuple):
             return tuple(_to_channels_last(t) for t in x)
@@ -979,7 +983,7 @@ def _to_channels_last(x):
     )
     edge._edge_programs["forward"] = pass_manager.transform()
 
-    return model_quant, edge
+    return model_quant, edge, example_inputs
 
 
 def _to_edge_no_delegate(
@@ -1078,7 +1082,7 @@ def main() -> None:  # noqa: C901
                 "(this target does not use delegated ops)."
             )
             args.delegate = False
-        model_quant, edge = _to_edge_cortex_m(
+        model_quant, edge, example_inputs = _to_edge_cortex_m(
             exported_program,
             args,
             model,

backends/arm/test/models/test_llama.py

@@ -208,6 +208,11 @@ def test_llama_tosa_FP():
         pipeline.run()
 
 
+@pytest.mark.xfail(
+    reason="index_put into a preserved fp32 mutable KV cache (torchao pytorch/ao#4466) is "
+    "not delegatable by the INT backend, so the cache round-trip forms a partition "
+    "dependency cycle. Same root cause as the xfailed static-cache tests: MLETORCH-1971."
+)
 def test_llama_tosa_INT():
     llama_model, llama_inputs, llama_meta = TestLlama().prepare_model()
 
@@ -229,6 +234,11 @@ def test_llama_tosa_INT():
         pipeline.run()
 
 
+@pytest.mark.xfail(
+    reason="index_put into a preserved fp32 mutable buffer (torchao pytorch/ao#4466) is "
+    "not delegatable by the INT backend, so the KV-cache round-trip forms a partition "
+    "dependency cycle. Same root cause as the xfailed static-cache tests: MLETORCH-1971."
+)
 def test_llama_tosa_INT_static():
     llama_model, llama_inputs, _ = TestLlama().prepare_model_hf_static()
     if llama_model is None or llama_inputs is None:
@@ -270,6 +280,11 @@ def test_llama_vgf_no_quant():
 
 
 @common.SkipIfNoModelConverter
+@pytest.mark.xfail(
+    reason="The KV cache stays fp32 (torchao pytorch/ao#4466), so attention reads it as "
+    "float while the query is quantized: MATMUL rejects the int8/float32 operand pair. "
+    "Same root cause as the xfailed static-cache tests: MLETORCH-1971."
+)
 def test_llama_vgf_quant():
     llama_model, llama_inputs, llama_meta = TestLlama().prepare_model()
 

docs/source/getting-started.md

@@ -160,7 +160,7 @@ For a full example of running a model on Android, see the [DeepLabV3AndroidDemo]
 #### Installation
 ExecuTorch supports both iOS and macOS via C++, as well as hardware backends for CoreML, MPS, and CPU. The iOS runtime library is provided as a collection of .xcframework targets and are made available as a Swift PM package.
 
-To get started with Xcode, go to File > Add Package Dependencies. Paste the URL of the ExecuTorch repo into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format “swiftpm-”, (e.g. “swiftpm-0.6.0”).  The ExecuTorch dependency can also be added to the package file manually. See [Using ExecuTorch on iOS](using-executorch-ios.md) for more information.
+To get started with Xcode, go to File > Add Package Dependencies. Paste the URL of the ExecuTorch repo into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format “swiftpm-”, (e.g. “swiftpm-1.4.0”).  The ExecuTorch dependency can also be added to the package file manually. See [Using ExecuTorch on iOS](using-executorch-ios.md) for more information.
 
 #### Runtime APIs
 Models can be loaded and run from Objective-C using the C++ APIs.

docs/source/raspberry_pi_llama_tutorial.md

@@ -57,7 +57,7 @@ First, clone the ExecuTorch repository with the Raspberry Pi support:
 
 ```bash
 # Create project directory
-mkdir ~/executorch-rpi && cd ~/executorch-rpi &&  git clone -b release/1.0 https://github.com/pytorch/executorch.git &&
+mkdir ~/executorch-rpi && cd ~/executorch-rpi &&  git clone -b release/1.4 https://github.com/pytorch/executorch.git &&
 cd executorch
 ```
 

docs/source/using-executorch-ios.md

@@ -28,7 +28,7 @@ The prebuilt ExecuTorch runtime, backend, and kernels are available as a [Swift
 
 #### Xcode
 
-In Xcode, go to `File > Add Package Dependencies`. Paste the URL of the [ExecuTorch repo](https://github.com/pytorch/executorch) into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format "swiftpm-<version>", (e.g. "swiftpm-1.0.0"), or a branch name in format "swiftpm-<version>.<year_month_date>" (e.g. "swiftpm-1.1.0-20251101") for a [nightly build](https://ossci-ios.s3.amazonaws.com/list.html) on a specific date.
+In Xcode, go to `File > Add Package Dependencies`. Paste the URL of the [ExecuTorch repo](https://github.com/pytorch/executorch) into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format "swiftpm-<version>", (e.g. "swiftpm-1.4.0"), or a branch name in format "swiftpm-<version>.<year_month_date>" (e.g. "swiftpm-1.5.0-20260801") for a [nightly build](https://ossci-ios.s3.amazonaws.com/list.html) on a specific date.
 
 ![](_static/img/swiftpm_xcode1.png)
 
@@ -61,7 +61,7 @@ let package = Package(
   ],
   dependencies: [
     // Use "swiftpm-<version>.<year_month_day>" branch name for a nightly build.
-    .package(url: "https://github.com/pytorch/executorch.git", branch: "swiftpm-1.0.0")
+    .package(url: "https://github.com/pytorch/executorch.git", branch: "swiftpm-1.4.0")
   ],
   targets: [
     .target(

examples/models/mlperf_tiny/ds_cnn.py

@@ -83,6 +83,4 @@ def get_eager_model(self) -> torch.nn.Module:
         return DSCNNKWS().eval()
 
     def get_example_inputs(self):
-        return (
-            (torch.rand(1, 1, 49, 10) * 2 - 1).to(memory_format=torch.channels_last),
-        )
+        return (torch.rand(1, 1, 49, 10) * 2 - 1,)

examples/models/moshi/mimi/install_requirements.sh

@@ -8,7 +8,7 @@
 set -x
 
 sudo apt install ffmpeg -y
-pip install torchcodec==0.11.0 --extra-index-url https://download.pytorch.org/whl/test/cpu
+pip install torchcodec==0.15.0 --index-url https://download.pytorch.org/whl/cpu
 pip install moshi==0.2.11
 pip install bitsandbytes soundfile einops
 # Run llama2/install requirements for torchao deps

install_requirements.py

@@ -16,7 +16,7 @@
 # This will be dynamically set based on CUDA availability and CUDA backend enabled/disabled.
 TORCH_URL_BASE = "https://download.pytorch.org/whl/test"
 TORCHAO_URL_BASE = "https://download.pytorch.org/whl/nightly"
-TORCHAO_NIGHTLY_VERSION = "0.18.0.dev20260715"
+TORCHAO_NIGHTLY_VERSION = "0.18.0"
 
 # Since ExecuTorch often uses main-branch features of pytorch, only the nightly
 # pip versions will have the required features.

setup.py

@@ -184,11 +184,12 @@ def _base_dependencies() -> List[str]:
         "packaging",
         "pandas>=2.2.2; python_version >= '3.10'",
         "parameterized",
-        "pytorch-tokenizers",
+        "pytorch-tokenizers>=1.4.0",
         "pyyaml",
         "ruamel.yaml",
         "sympy",
         "tabulate",
+        "torchao>=0.18.0",
         # See also third-party/TARGETS for buck's typing-extensions version.
         "typing-extensions>=4.10.0",
         # Keep this version in sync with: ./backends/apple/coreml/scripts/install_requirements.sh

version.txt

@@ -1 +1 @@
-1.4.0a0
+1.4.0