Merge branch 'main' into fix-odr-violation-due-to-mismatched-gemm-definition

Author: manuelcandales

.github/workflows/apple.yml

@@ -37,7 +37,7 @@ jobs:
         id: set_version
         shell: bash
         run: |
-          VERSION="0.4.0.$(TZ='PST8PDT' date +%Y%m%d)"
+          VERSION="0.5.0.$(TZ='PST8PDT' date +%Y%m%d)"
           echo "version=$VERSION" >> "$GITHUB_OUTPUT"
 
   build-demo-ios:

CMakeLists.txt

@@ -1,4 +1,5 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright 2024-2025 Arm Limited and/or its affiliates.
 # All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
@@ -819,6 +820,14 @@ if(EXECUTORCH_BUILD_EXECUTOR_RUNNER)
     list(APPEND _executor_runner_libs quantized_ops_lib)
   endif()
 
+  if(EXECUTORCH_ENABLE_EVENT_TRACER)
+    if(EXECUTORCH_BUILD_DEVTOOLS)
+      list(APPEND _executor_runner_libs etdump flatccrt)
+    else()
+      message(SEND_ERROR "Use of 'EXECUTORCH_ENABLE_EVENT_TRACER' requires 'EXECUTORCH_BUILD_DEVTOOLS' to be enabled.")
+    endif()
+  endif()
+
   add_executable(executor_runner ${_executor_runner__srcs})
   if(CMAKE_BUILD_TYPE STREQUAL "Release")
     if(APPLE)

CONTRIBUTING.md

@@ -44,6 +44,38 @@ Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
 disclosure of security bugs. In those cases, please go through the process
 outlined on that page and do not file a public issue.
 
+### Issue Labels
+
+#### Module/Partner Labels
+
+[Labels beginning with `module:`](https://github.com/pytorch/executorch/labels?q=%22module%3A+%22)
+indicate the area that the issue relates to. The ExecuTorch oncall will
+typically add this label.
+
+[Labels beginning with `partner:`](https://github.com/pytorch/executorch/labels?q=%22partner%3A+%22)
+indicate the ExecuTorch partner who owns the issue. The ExecuTorch oncall will
+typically add this label.
+
+#### Lifecycle Labels
+
+The ExecuTorch oncall will triage new issues. If the issue requires more
+information from the issue's author, oncall will add the `need-user-input` label
+and wait for the author to respond.
+
+Once the issue contains enough information, the oncall will:
+- Ensure that the title is descriptive
+- Add one of the labels:
+  - `bug`: The issue describes an unexpected problem
+  - `feature`: The issue describes a request for new functionality
+  - `rfc`: The issue describes a proposed change to functionality
+- Add one `module:` label or one `partner:` label, as described above
+- Add the `triaged` label
+
+After this point, the oncall has finished the triage process, and the
+module owner or partner is responsible for resolving the issue. (See
+https://github.com/pytorch/executorch/issues/7679 for the mapping of labels to
+owners.)
+
 ### Claiming Issues
 We'd love your help closing out [open
 issues](https://github.com/pytorch/executorch/issues?q=sort%3Aupdated-desc+is%3Aissue+is%3Aopen)

backends/arm/_passes/fold_qdq_with_annotated_qparams_pass.py

@@ -105,21 +105,6 @@ def fold_and_annotate_arg(
         for arg in arg_list:
             if not isinstance(arg, Node):
                 return
-            """
-             Make sure arg has requires_grad set to False
-             For parameters that are not quantized, sometimes (i.e. convolution)
-             the Parameter(FakeTensor(...)) has requires_grad set to True, which
-             causes the retracing of the graph to fail with:
-
-             E       RuntimeError: isDifferentiableType(variable.scalar_type()) INTERNAL ASSERT FAILED at "/Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/functions/utils.h":74, please report a bug to PyTorch.
-             E
-             E       While executing %aten_convolution_default : [num_users=1] = call_function[target=executorch.exir.dialects.edge._ops.aten.convolution.default](args = (%quantized_decomposed_quantize_per_tensor_default, %b__frozen_param0, %p__param_constant1, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
-             E       Original traceback:
-             E         File "/Users/perast01/src/executorch/backends/arm/test/ops/test_conv2d.py", line 110, in forward
-             E           x = conv(x)
-            """
-            if arg.op == "placeholder":
-                arg.meta["val"].requires_grad = False
 
             arg_quant_params = None
             if arg.target == dq_op:

backends/arm/test/ops/test_cat.py

@@ -1,5 +1,5 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
-# Copyright 2024 Arm Limited and/or its affiliates.
+# Copyright 2024-2025 Arm Limited and/or its affiliates.
 # All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
@@ -33,6 +33,8 @@ class Cat(torch.nn.Module):
                 ),
                 -1,
             ),
+            ((torch.randn(1, 2, 4, 4), torch.randn(1, 2, 4, 1)), 3),
+            ((torch.randn(1, 2, 4, 4), torch.randn(1, 2, 4, 4)), 0),
             ((torch.randn(2, 2, 4, 4), torch.randn(2, 2, 4, 1)), 3),
             (
                 (
@@ -47,8 +49,8 @@ class Cat(torch.nn.Module):
         def __init__(self):
             super().__init__()
 
-        def forward(self, tensors: tuple[torch.Tensor, ...], dim: int) -> torch.Tensor:
-            return torch.cat(tensors, dim=dim)
+        def forward(self, t: tuple[torch.Tensor, ...], dim: int) -> torch.Tensor:
+            return torch.cat(t, dim=dim)
 
     def _test_cat_tosa_MI_pipeline(
         self, module: torch.nn.Module, test_data: Tuple[tuple[torch.Tensor, ...], int]
@@ -134,22 +136,38 @@ def test_cat_tosa_BI(self, operands: tuple[torch.Tensor, ...], dim: int):
         test_data = (operands, dim)
         self._test_cat_tosa_BI_pipeline(self.Cat(), test_data)
 
-    # Mismatch in provided number of inputs and model signature, MLETORCH 519
-    @parameterized.expand(Cat.test_parameters)
+    @parameterized.expand(Cat.test_parameters[:-3])
     @pytest.mark.corstone_fvp
-    @conftest.expectedFailureOnFVP
     def test_cat_u55_BI(self, operands: tuple[torch.Tensor, ...], dim: int):
         test_data = (operands, dim)
         self._test_cat_ethosu_BI_pipeline(
             self.Cat(), common.get_u55_compile_spec(), test_data
         )
 
-    # Mismatch in provided number of inputs and model signature, MLETORCH 519
-    @parameterized.expand(Cat.test_parameters)
+    # MLETORCH-630 Cat does not work on FVP with batch>1
+    @parameterized.expand(Cat.test_parameters[-3:])
     @pytest.mark.corstone_fvp
     @conftest.expectedFailureOnFVP
+    def test_cat_u55_BI_xfails(self, operands: tuple[torch.Tensor, ...], dim: int):
+        test_data = (operands, dim)
+        self._test_cat_ethosu_BI_pipeline(
+            self.Cat(), common.get_u55_compile_spec(), test_data
+        )
+
+    @parameterized.expand(Cat.test_parameters[:-3])
+    @pytest.mark.corstone_fvp
     def test_cat_u85_BI(self, operands: tuple[torch.Tensor, ...], dim: int):
         test_data = (operands, dim)
         self._test_cat_ethosu_BI_pipeline(
             self.Cat(), common.get_u85_compile_spec(), test_data
         )
+
+    # MLETORCH-630 Cat does not work on FVP with batch>1
+    @parameterized.expand(Cat.test_parameters[-3:])
+    @pytest.mark.corstone_fvp
+    @conftest.expectedFailureOnFVP
+    def test_cat_u85_BI_xfails(self, operands: tuple[torch.Tensor, ...], dim: int):
+        test_data = (operands, dim)
+        self._test_cat_ethosu_BI_pipeline(
+            self.Cat(), common.get_u85_compile_spec(), test_data
+        )

backends/arm/test/ops/test_expand.py

@@ -37,15 +37,17 @@ class Expand(torch.nn.Module):
         test_parameters = [
             (torch.rand(1), (2,)),
             (torch.randn(1, 4), (1, -1)),
-            (torch.rand(1, 1, 2, 2), (4, 3, -1, 2)),
             (torch.randn(1), (2, 2, 4)),
-            (torch.rand(3, 2, 4, 1), (-1, -1, -1, 3)),
+            (torch.randn(1, 1, 1, 5), (1, 4, -1, -1)),
             (torch.randn(1, 1, 192), (1, -1, -1)),
+            (torch.randn(1, 1), (1, 2, 2, 4)),
+            (torch.randn(1, 1), (2, 2, 2, 4)),
             (torch.randn(10, 1, 1, 97), (-1, 4, -1, -1)),
+            (torch.rand(1, 1, 2, 2), (4, 3, -1, 2)),
         ]
 
-        def forward(self, x: torch.Tensor, multiples: Sequence):
-            return x.expand(multiples)
+        def forward(self, x: torch.Tensor, m: Sequence):
+            return x.expand(m)
 
     def _test_expand_tosa_MI_pipeline(self, module: torch.nn.Module, test_data: Tuple):
         (
@@ -113,20 +115,34 @@ def test_expand_tosa_MI(self, test_input, multiples):
     def test_expand_tosa_BI(self, test_input, multiples):
         self._test_expand_tosa_BI_pipeline(self.Expand(), (test_input, multiples))
 
-    # Mismatch in provided number of inputs and model signature, MLETORCH 519
-    @parameterized.expand(Expand.test_parameters)
+    @parameterized.expand(Expand.test_parameters[:-3])
     @pytest.mark.corstone_fvp
-    @conftest.expectedFailureOnFVP
     def test_expand_u55_BI(self, test_input, multiples):
         self._test_expand_ethosu_BI_pipeline(
             common.get_u55_compile_spec(), self.Expand(), (test_input, multiples)
         )
 
-    # Mismatch in provided number of inputs and model signature, MLETORCH 519
-    @parameterized.expand(Expand.test_parameters)
+    # MLETORCH-629: Expand does not work on FVP with batch>1
+    @parameterized.expand(Expand.test_parameters[-3:])
     @pytest.mark.corstone_fvp
     @conftest.expectedFailureOnFVP
+    def test_expand_u55_BI_xfails(self, test_input, multiples):
+        self._test_expand_ethosu_BI_pipeline(
+            common.get_u55_compile_spec(), self.Expand(), (test_input, multiples)
+        )
+
+    @parameterized.expand(Expand.test_parameters[:-3])
+    @pytest.mark.corstone_fvp
     def test_expand_u85_BI(self, test_input, multiples):
         self._test_expand_ethosu_BI_pipeline(
             common.get_u85_compile_spec(), self.Expand(), (test_input, multiples)
         )
+
+    # MLETORCH-629: Expand does not work on FVP with batch>1
+    @parameterized.expand(Expand.test_parameters[-3:])
+    @pytest.mark.corstone_fvp
+    @conftest.expectedFailureOnFVP
+    def test_expand_u85_BI_xfails(self, test_input, multiples):
+        self._test_expand_ethosu_BI_pipeline(
+            common.get_u85_compile_spec(), self.Expand(), (test_input, multiples)
+        )

backends/arm/test/ops/test_full.py

@@ -143,20 +143,16 @@ def test_full_tosa_MI(self, test_tensor: Tuple):
     def test_full_tosa_BI(self, test_tensor: Tuple):
         self._test_full_tosa_BI_pipeline(self.AddVariableFull(), test_tensor)
 
-    # Mismatch in provided number of inputs and model signature, MLETORCH 519
     @parameterized.expand(AddVariableFull.test_parameters)
     @pytest.mark.corstone_fvp
-    @conftest.expectedFailureOnFVP
     def test_full_u55_BI(self, test_tensor: Tuple):
         self._test_full_tosa_u55_pipeline(
             self.AddVariableFull(),
             test_tensor,
         )
 
-    # Mismatch in provided number of inputs and model signature, MLETORCH 519
     @parameterized.expand(AddVariableFull.test_parameters)
     @pytest.mark.corstone_fvp
-    @conftest.expectedFailureOnFVP
     def test_full_u85_BI(self, test_tensor: Tuple):
         self._test_full_tosa_u85_pipeline(
             self.AddVariableFull(),

backends/arm/test/runner_utils.py

@@ -65,16 +65,7 @@ def get_input_names(program: ExportedProgram) -> list[str]:
     Returns:
         A list of strings with the names of the model input.
     """
-    input_names = []
-
-    # E.g. bias and weights are 'placeholders' as well. This is used to
-    # get only the use inputs.
-    usr_inputs = program.graph_signature.user_inputs
-    for node in program.graph.nodes:
-        if node.op == "placeholder" and node.name in usr_inputs:
-            input_names.append(node.name)
-
-    return input_names
+    return [spec.arg.name for spec in program.graph_signature.input_specs]
 
 
 def get_input_quantization_params(
@@ -334,13 +325,16 @@ def run_corstone(
 
 
 def prep_data_for_save(
-    data: torch.Tensor,
+    data,
     input_name: str,
     quant_param: Optional[QuantizationParams] = None,
 ):
-    data_np = np.array(data.detach(), order="C").astype(
-        torch_to_numpy_dtype_dict[data.dtype]
-    )
+    if isinstance(data, torch.Tensor):
+        data_np = np.array(data.detach(), order="C").astype(
+            torch_to_numpy_dtype_dict[data.dtype]
+        )
+    else:
+        data_np = np.array(data)
     if quant_param is not None:
         assert quant_param.node_name in input_name, (
             f"The quantization params name '{quant_param.node_name}' does not "

backends/qualcomm/CMakeLists.txt

@@ -1,4 +1,5 @@
 # Copyright (c) Qualcomm Innovation Center, Inc.
+# Copyright 2025 Arm Limited and/or its affiliates.
 # All rights reserved
 #
 # This source code is licensed under the BSD-style license found in the
@@ -199,11 +200,6 @@ target_link_libraries(
 #
 target_link_options_shared_lib(qnn_executorch_backend)
 
-#
-# add compile option
-#
-target_compile_options(executorch PUBLIC -DET_EVENT_TRACER_ENABLED)
-
 #
 # add sources
 #