2024-11-20 nightly release (f40daea)

Author: pytorchbot

.github/scripts/check_labels.py

@@ -45,15 +45,15 @@ def main() -> None:
 
     try:
         if not has_required_labels(pr):
-            print(LABEL_ERR_MSG)
+            print(LABEL_ERR_MSG, flush=True)
             add_label_err_comment(pr)
             if args.exit_non_zero:
-                sys.exit(1)
+                raise RuntimeError("PR does not have required labels")
         else:
             delete_all_label_err_comments(pr)
     except Exception as e:
         if args.exit_non_zero:
-            sys.exit(1)
+            raise RuntimeError(f"Error checking labels: {e}") from e
 
     sys.exit(0)
 

.github/scripts/github_utils.py

@@ -72,10 +72,10 @@ def gh_fetch_url(
     headers: Optional[Dict[str, str]] = None,
     data: Union[Optional[Dict[str, Any]], str] = None,
     method: Optional[str] = None,
-    reader: Callable[[Any], Any] = lambda x: x.read(),
+    reader: Callable[[Any], Any] = json.load,
 ) -> Any:
     return gh_fetch_url_and_headers(
-        url, headers=headers, data=data, reader=json.load, method=method
+        url, headers=headers, data=data, reader=reader, method=method
     )[1]
 
 
@@ -169,7 +169,7 @@ def gh_post_commit_comment(
 
 def gh_delete_comment(org: str, repo: str, comment_id: int) -> None:
     url = f"{GITHUB_API_URL}/repos/{org}/{repo}/issues/comments/{comment_id}"
-    gh_fetch_url(url, method="DELETE")
+    gh_fetch_url(url, method="DELETE", reader=lambda x: x.read())
 
 
 def gh_fetch_merge_base(org: str, repo: str, base: str, head: str) -> str:

.github/workflows/android-perf.yml

@@ -136,7 +136,7 @@ jobs:
       fail-fast: false
     with:
       runner: linux.4xlarge
-      docker-image: executorch-ubuntu-22.04-clang12-android
+      docker-image: executorch-ubuntu-22.04-qnn-sdk
       submodules: 'true'
       timeout: 60
       upload-artifact: android-models

.github/workflows/trunk.yml

@@ -302,7 +302,7 @@ jobs:
       fail-fast: false
     with:
       runner: linux.2xlarge
-      docker-image: executorch-ubuntu-22.04-clang12-android
+      docker-image: executorch-ubuntu-22.04-qnn-sdk
       submodules: 'true'
       ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
       timeout: 900

.gitmodules

@@ -1,9 +1,9 @@
 [submodule "backends/arm/third-party/ethos-u-core-driver"]
 	path = backends/arm/third-party/ethos-u-core-driver
-	url = https://review.mlplatform.org/ml/ethos-u/ethos-u-core-driver
+	url = https://git.mlplatform.org/ml/ethos-u/ethos-u-core-driver.git/
 [submodule "backends/arm/third-party/serialization_lib"]
 	path = backends/arm/third-party/serialization_lib
-	url = https://review.mlplatform.org/tosa/serialization_lib
+	url = https://git.mlplatform.org/tosa/serialization_lib.git/
 [submodule "backends/vulkan/third-party/Vulkan-Headers"]
 	path = backends/vulkan/third-party/Vulkan-Headers
 	url = https://github.com/KhronosGroup/Vulkan-Headers

backends/arm/operators/op_add.py

@@ -82,6 +82,7 @@ def define_node(
 
         if needs_rescale:
             # Scale output back to 8 bit
+            # pyre-ignore
             tqutils.rescale_node_back_to_int8(node, add_output, scale, tosa_graph)
 
 

backends/cadence/aot/TARGETS

@@ -131,3 +131,51 @@ python_library(
         "//executorch/exir/dialects:lib",
     ],
 )
+
+python_library(
+    name = "graph_builder",
+    srcs = [
+        "graph_builder.py",
+    ],
+    typing = True,
+    deps = [
+        "fbcode//caffe2:torch",
+        "fbcode//executorch/exir:pass_base",
+    ],
+)
+
+python_library(
+    name = "fuse_ops",
+    srcs = [
+        "fuse_ops.py",
+    ],
+    typing = True,
+    deps = [
+        "//caffe2:torch",
+        ":compiler_utils",
+        "//executorch/backends/cadence/aot:pass_utils",
+        "//executorch/backends/cadence/aot:utils",
+        "//executorch/exir:pass_base",
+        "//executorch/exir/dialects:lib",
+        "//executorch/exir/dialects/edge:lib",
+        "//executorch/exir/passes:lib",
+        "//executorch/exir/passes:spec_prop_pass",
+    ],
+)
+
+python_unittest(
+    name = "test_graph_builder",
+    srcs = [
+        "tests/test_graph_builder.py",
+    ],
+    typing = True,
+    deps = [
+        "//caffe2:torch",
+        "//executorch/backends/cadence/aot:graph_builder",
+        "//executorch/backends/cadence/aot:pass_utils",
+        "//executorch/exir:pass_base",
+        "//executorch/exir/dialects:lib",
+        "//later:lib",
+        ":ops_registrations"
+    ],
+)

backends/cadence/aot/compiler.py

@@ -196,7 +196,26 @@ def export_to_edge(
 # Export the model and lower it to an EdgeProgramManager (in edge IR), and
 # apply passes specific to Cadence DSP execution. Return both to print the
 # differences.
-def export_to_cadence_edge_executorch(
+def export_to_cadence(
+    model: torch.nn.Module,
+    inputs: tuple[object, ...],
+    dump_graphs: bool = False,
+    output_dir: Optional[str] = None,
+    opt_level: int = 1,
+) -> EdgeProgramManager:
+    edge_prog_manager = export_to_edge(model, inputs)
+    cadence_passes = get_cadence_passes(opt_level)
+
+    # Run a couple required passes for quant/dequant ops
+    cadence_prog_manager = edge_prog_manager.transform(
+        cast(
+            list[Callable[[torch.fx.GraphModule], Optional[PassResult]]], cadence_passes
+        )
+    )
+    return cadence_prog_manager
+
+
+def export_to_executorch_gen_etrecord(
     model: torch.nn.Module,
     inputs: tuple[object, ...],
     dump_graphs: bool = False,

backends/cadence/aot/export_example.py

@@ -16,7 +16,7 @@
 
 from executorch.backends.cadence.aot.compiler import (
     convert_pt2,
-    export_to_cadence_edge_executorch,
+    export_to_executorch_gen_etrecord,
     fuse_pt2,
 )
 
@@ -86,8 +86,8 @@ def export_model(
     quantized_model = fuse_pt2(converted_model, quantizer)
 
     # Get edge program after Cadence specific passes
-    exec_prog: ExecutorchProgramManager = export_to_cadence_edge_executorch(
-        quantized_model, example_inputs, working_dir
+    exec_prog: ExecutorchProgramManager = export_to_executorch_gen_etrecord(
+        quantized_model, example_inputs, output_dir=working_dir
     )
 
     logging.info("Final exported graph:\n")