[4] move pt_operator_library to shared BUCK file (#80170)

Summary:
Move pt_operator_library to pt_ops.bzl and make it shared with OSS BUCK build

This will replace D36912042. I will update all load statements in future diffs.

Test Plan: sandcaslte, OSS CI

Differential Revision: D37390060

Pull Request resolved: https://github.com/pytorch/pytorch/pull/80170
Approved by: https://github.com/JacobSzwejbka

pt_ops.bzl

@@ -1,3 +1,114 @@
+load("//tools/build_defs:expect.bzl", "expect")
+load("//tools/build_defs:fb_xplat_genrule.bzl", "fb_xplat_genrule")
+load("//tools/build_defs:type_defs.bzl", "is_list", "is_string")
+
+# @lint-ignore BUCKRESTRICTEDSYNTAX
+IS_OSS = read_config("pt", "is_oss", "0") == "1"  # True for OSS BUCK build, and False for internal BUCK build
+
+USED_PT_BACKENDS = [
+    "CPU",
+    "QuantizedCPU",
+    "SparseCPU",  # brings ~20 kb size regression
+]
+
+def pt_operator_library(
+        name,
+        ops = [],
+        exported_deps = [],
+        check_decl = True,
+        train = False,
+        model = None,
+        include_all_operators = False,
+        **kwargs):
+    (model_name, model_versions, model_assets, model_traced_backends) = validate_and_extract_model_information(
+        name,
+        model,
+    )
+
+    ops = [op.strip() for op in ops]
+
+    # If ops are specified, then we are in static selective build mode, so we append
+    # base ops to this list to avoid additional special case logic in subsequent code.
+    if len(ops) > 0:
+        ops.extend(PT_BASE_OPS)
+
+    labels = kwargs.pop("labels", [])
+    visibility = kwargs.pop("visibility", ["PUBLIC"])
+
+    fb_xplat_genrule(
+        name = name,
+        out = "model_operators.yaml",
+        cmd = (
+            "$(exe {root}:gen_operators_yaml) " +
+            "{optionally_root_ops} " +
+            "{optionally_training_root_ops} " +
+            "--rule_name {rule_name} " +
+            "--output_path \"${{OUT}}\" " +
+            "--model_name {model_name} " +
+            "--dep_graph_yaml_path {dep_graph_yaml} " +
+            "--models_yaml_path {models_yaml} " +
+            "{optionally_model_versions} " +
+            "{optionally_model_assets} " +
+            "{optionally_model_traced_backends} " +
+            "{optionally_include_all_operators}"
+        ).format(
+            root = "//" if IS_OSS else "//xplat/caffe2",
+            rule_name = name,
+            model_name = model_name,
+            dep_graph_yaml = "none" if IS_OSS else "$(location //xplat/caffe2:pytorch_op_deps)/fb/pytorch_op_deps.yaml ",
+            models_yaml = "none" if IS_OSS else "$(location //xplat/pytorch_models:all_mobile_model_configs)/build/all_mobile_model_configs.yaml ",
+            optionally_root_ops = "--root_ops " + (",".join(ops)) if len(ops) > 0 else "",
+            optionally_training_root_ops = "--training_root_ops " + (",".join(ops)) if len(ops) > 0 and train else "",
+            optionally_model_versions = "--model_versions " + (",".join(model_versions)) if model_versions != None else "",
+            optionally_model_assets = "--model_assets " + (",".join(model_assets)) if model_assets != None else "",
+            optionally_model_traced_backends = "--model_traced_backends " + (",".join(model_traced_backends)) if model_traced_backends != None else "",
+            optionally_include_all_operators = "--include_all_operators " if include_all_operators else "",
+        ),
+        labels = labels + [
+            "pt_operator_library",
+            "supermodule:android/default/pytorch",
+            "supermodule:ios/default/public.pytorch",
+        ] + (["pt_train_operator_library"] if train else []),
+        visibility = visibility,
+        **kwargs
+    )
+
+def validate_and_extract_model_information(name, model):
+    model_name = name
+    model_versions = None
+    model_assets = None
+    model_traced_backends = None
+
+    if model != None:
+        model_name = model.get("name")
+        expect(model_name != None, "Expected Model Name to be present")
+        model_versions = model.get("versions")
+        expect(is_list(model_versions), "Expected model versions to be a list of string")
+        for ver in model_versions or []:
+            expect(is_string(ver), "Expected version '{}' to be string".format(str(ver)))
+        model_assets = model.get("assets")
+        expect(
+            model_assets == None or is_list(model_assets),
+            "Expected model assets to be a list of string if specified",
+        )
+        for asset_name in model_assets or []:
+            expect(is_string(asset_name), "Expected asset_name '{}' to be string".format(str(asset_name)))
+        model_traced_backends = model.get("traced_backends")
+        expect(
+            model_traced_backends == None or is_list(model_traced_backends),
+            "Expected model traced backends to be a list of string if specified",
+        )
+
+        if model_traced_backends != None:
+            for backend in model_traced_backends:
+                expect(is_string(backend), "Expected backend name '{}' to be string".format(str(backend)))
+                expect(
+                    backend in USED_PT_BACKENDS,
+                    "Expected backend name ({}) to be in set: {}".format(backend, ",".join(USED_PT_BACKENDS)),
+                )
+
+    return (model_name, model_versions, model_assets, model_traced_backends)
+
 # This file keeps a list of PyTorch operators used by any targets in
 # @fbsource//xplat/...
 # The purpose of the list is to avoid generating large number of unused