Merge branch 'main' into change-1018030

Author: zingo

backends/arm/test/models/test_mobilenet_v3_arm.py

@@ -46,7 +46,7 @@ def test_mv3_tosa_BI():
         aten_op=[],
         exir_op=[],
         use_to_edge_transform_and_lower=True,
-        atol=0.3,
+        atol=0.5,
         qtol=1,
     )
     pipeline.run()
@@ -63,7 +63,7 @@ def test_mv3_u55_BI():
         exir_ops=[],
         run_on_fvp=True,
         use_to_edge_transform_and_lower=True,
-        atol=0.3,
+        atol=0.5,
         qtol=1,
     )
     pipeline.run()
@@ -80,7 +80,7 @@ def test_mv3_u85_BI():
         exir_ops=[],
         run_on_fvp=True,
         use_to_edge_transform_and_lower=True,
-        atol=0.3,
+        atol=0.5,
         qtol=1,
     )
     pipeline.run()

backends/arm/test/models/test_torch_functions.py

@@ -101,6 +101,7 @@ def forward(self, *args):
         "Requires dynamic output shape.",
         "topk": "NotImplementedError: No registered serialization name for <class 'torch.return_types.topk'> found",
         "sort": "NotImplementedError: No registered serialization name for <class 'torch.return_types.sort'> found",
+        "norm": "An error occurred when running the 'KeepDimsFalseToSqueezePass' pass after the following passes:",
     },
 )
 def test_torch_fns_MI(test_data):
@@ -129,6 +130,7 @@ def test_torch_fns_MI(test_data):
         "topk": "NotImplementedError: No registered serialization name for <class 'torch.return_types.topk'> found",
         "sort": "NotImplementedError: No registered serialization name for <class 'torch.return_types.sort'> found",
         "t": "MLETORCH-855: Issue with Quantization folding.",
+        "norm": "An error occurred when running the 'KeepDimsFalseToSqueezePass' pass after the following passes:",
     },
     strict=False,
 )

backends/arm/test/ops/test_sigmoid_16bit.py

@@ -81,7 +81,7 @@ def forward(self, x):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         Sigmoid(), (test_data(),), Sigmoid.aten_op, Sigmoid.exir_op
@@ -97,7 +97,7 @@ def test_sigmoid_tosa_BI(test_data):
         "ramp": "AssertionError: Output 0 does not match reference output. MLETORCH-787"
     },
 )
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         SigmoidAddSigmoid(), (test_data(),), Sigmoid.aten_op, Sigmoid.exir_op
@@ -110,6 +110,7 @@ def test_sigmoid_add_sigmoid_tosa_BI(test_data):
     "test_data",
     test_data_suite,
 )
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         Sigmoid(), (test_data(),), "TOSA-0.80+BI+u55", {Sigmoid.exir_op: 1}
@@ -122,6 +123,7 @@ def test_sigmoid_tosa_u55(test_data):
     "test_data",
     test_data_suite,
 )
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         SigmoidAddSigmoid(),
@@ -135,7 +137,7 @@ def test_sigmoid_add_sigmoid_tosa_u55(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(
@@ -152,7 +154,7 @@ def test_sigmoid_tosa_u85(test_data):
         "ramp": "AssertionError: Output 0 does not match reference output.",
     },
 )
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_add_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(

backends/arm/test/ops/test_sigmoid_32bit.py

@@ -97,7 +97,7 @@ def forward(self, x):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         Sigmoid(),
@@ -110,7 +110,7 @@ def test_sigmoid_tosa_BI(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         SigmoidAddSigmoid(),
@@ -123,6 +123,7 @@ def test_sigmoid_add_sigmoid_tosa_BI(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         Sigmoid(), (test_data(),), "TOSA-0.80+BI+u55", {Sigmoid.exir_op: 1}
@@ -132,6 +133,7 @@ def test_sigmoid_tosa_u55(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         SigmoidAddSigmoid(),
@@ -145,7 +147,7 @@ def test_sigmoid_add_sigmoid_tosa_u55(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(
@@ -162,7 +164,7 @@ def test_sigmoid_tosa_u85(test_data):
         "ramp": "AssertionError: Output 0 does not match reference output.",
     },
 )
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_add_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(

docs/source/getting-started.md

@@ -99,6 +99,7 @@ print("Comparing against original PyTorch module")
 print(torch.allclose(output[0], eager_reference_output, rtol=1e-3, atol=1e-5))
 ```
 
+For complete examples of exporting and running the model, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/python).
 
 <hr/>
 
@@ -178,6 +179,7 @@ target_link_libraries(
           xnnpack_backend)
 ```
 
+
 #### Runtime APIs
 Both high-level and low-level C++ APIs are provided. The low-level APIs are platform independent, do not dynamically allocate memory, and are most suitable for resource-constrained embedded systems. The high-level APIs are provided as a convenience wrapper around the lower-level APIs, and make use of dynamic memory allocation and standard library constructs to reduce verbosity.
 
@@ -194,8 +196,8 @@ using namespace ::executorch::extension;
 Module module("/path/to/model.pte");
 
 // Create an input tensor.
-float input[1 * 3 * 256 * 256];
-auto tensor = from_blob(input, {1, 3, 256, 256});
+float input[1 * 3 * 224 * 224];
+auto tensor = from_blob(input, {1, 3, 224, 224});
 
 // Perform an inference.
 const auto result = module.forward(tensor);
@@ -208,6 +210,8 @@ if (result.ok()) {
 
 For more information on the C++ APIs, see [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md) and [Managing Tensor Memory in C++](extension-tensor.md).
 
+For complete examples of building and running C++ application, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp).
+
 <hr/>
 
 ## Next Steps