Add dispatch-level config file generator for manual annotation (#1566)

shark/shark_generate_model_config.py

@@ -1,22 +1,86 @@
+import re
 import json
-from collections import OrderedDict
+import torch_mlir
+from iree.compiler import compile_str
+from shark.shark_importer import import_with_fx, get_f16_inputs
 
 
 class GenerateConfigFile:
     def __init__(
         self,
         model,
         num_sharding_stages: int,
-        sharding_stages_id: list[str] = None,
+        sharding_stages_id: list[str],
+        model_input=None,
+        config_file_path="model_config.json",
     ):
         self.model = model
         self.num_sharding_stages = num_sharding_stages
         self.sharding_stages_id = sharding_stages_id
         assert self.num_sharding_stages == len(
             self.sharding_stages_id
         ), "Number of sharding stages should be equal to the list of their ID"
+        self.model_input = model_input
+        self.config_file_path = config_file_path
 
-    def generate_json(self):
+    def split_into_dispatches(
+        self,
+        backend,
+        fx_tracing_required=True,
+        f16_model=False,
+        torch_mlir_tracing=False,
+    ):
+        graph_for_compilation = self.model
+        if fx_tracing_required:
+            graph_for_compilation = import_with_fx(
+                self.model,
+                self.model_input,
+                is_f16=f16_model,
+                f16_input_mask=[False, False],
+                mlir_type="torchscript",
+            )
+
+        module = torch_mlir.compile(
+            graph_for_compilation,
+            (self.model_input),
+            torch_mlir.OutputType.LINALG_ON_TENSORS,
+            use_tracing=torch_mlir_tracing,
+            verbose=False,
+        )
+        module = module.operation.get_asm(large_elements_limit=4)
+        compiled_module_str = str(
+            compile_str(
+                str(module),
+                target_backends=[backend],
+                extra_args=[
+                    "--compile-to=flow",
+                    "--mlir-elide-elementsattrs-if-larger=4",
+                ],
+            )
+        )
+
+        substring_start_idx = [
+            m.start()
+            for m in re.finditer("flow.dispatch @", compiled_module_str)
+        ]
+        dispatch_list = dict()
+
+        # dispatch_no is the 'i'th index of a dispatch out of n total dispatches of a model
+        # dispatch_id is the unique id of a dispatch, multiple instances of the same dispatch
+        # can occur in a model
+        for dispatch_no, substring_idx in enumerate(substring_start_idx):
+            dispatch_idx = (
+                compiled_module_str[substring_idx:]
+                .split(":")[0]
+                .split("@")[-1]
+            )
+            key = "dispatch_no_" + str(dispatch_no)
+            dispatch_list[key] = {n: "None" for n in self.sharding_stages_id}
+            dispatch_list[key]["dispatch_id"] = dispatch_idx
+
+        self.generate_json(dispatch_list)
+
+    def split_into_layers(self):
         model_dictionary = dict()
 
         for name, m in self.model.named_modules():
@@ -34,5 +98,8 @@ def generate_json(self):
             layer_dict = {n: "None" for n in self.sharding_stages_id}
             model_dictionary[name] = layer_dict
 
-        with open("model_config.json", "w") as outfile:
-            json.dump(model_dictionary, outfile)
+        self.generate_json(model_dictionary)
+
+    def generate_json(self, artifacts):
+        with open(self.config_file_path, "w") as outfile:
+            json.dump(artifacts, outfile)