[SHARK] Add a compile API to use for quick testing of inference

shark/examples/shark_inference/mega_test.py

@@ -1,10 +1,7 @@
 import torch
 import torch_mlir
 from shark.shark_inference import SharkInference
-from apps.stable_diffusion.src.utils import (
-    compile_through_fx,
-    args,
-)
+from shark.shark_compile import shark_compile_through_fx
 from MEGABYTE_pytorch import MEGABYTE
 
 import os
@@ -37,23 +34,22 @@ def forward(self, input):
 
 
 megaModel = MegaModel()
-input = [torch.randint(0, 16000, (1, 1024, 4))]
+inputs = [torch.randint(0, 16000, (1, 1024, 4))]
 
 # CURRENTLY IT BAILS OUT HERE BECAUSE OF MISSING OP LOWERINGS :-
 # 1. aten.alias
-shark_module, _ = compile_through_fx(
-    megaModel,
-    inputs=input,
+shark_module, _ = shark_compile_through_fx(
+    model=megaModel,
+    inputs=inputs,
     extended_model_name="mega_shark",
-    debug=False,
-    generate_vmfb=True,
+    is_f16=False,
+    f16_input_mask=None,
     save_dir=os.getcwd(),
+    debug=False,
+    generate_or_load_vmfb=True,
     extra_args=[],
-    base_model_id=None,
-    model_name="mega_shark",
-    precision=None,
-    return_mlir=True,
     device="cuda",
+    mlir_dialect="tm_tensor",
 )
 # logits = model(x)
 
@@ -63,10 +59,10 @@ def print_output_info(output, msg):
     print("\n\t", output.shape)
 
 
-ans = shark_module("forward", input)
+ans = shark_module("forward", inputs)
 print_output_info(torch.from_numpy(ans), "SHARK's output")
 
-ans = megaModel.forward(*input)
+ans = megaModel.forward(*inputs)
 print_output_info(ans, "ORIGINAL Model's output")
 
 # and sample from the logits accordingly

shark/shark_compile.py

@@ -0,0 +1,99 @@
+import os
+import tempfile
+from shark.shark_inference import SharkInference
+from shark.shark_importer import import_with_fx
+
+
+def load_vmfb(extended_model_name, device, mlir_dialect, extra_args=[]):
+    vmfb_path = os.path.join(os.getcwd(), extended_model_name + ".vmfb")
+    shark_module = None
+    if os.path.isfile(vmfb_path):
+        shark_module = SharkInference(
+            None,
+            device=device,
+            mlir_dialect=mlir_dialect,
+        )
+        print(f"loading existing vmfb from: {vmfb_path}")
+        shark_module.load_module(vmfb_path, extra_args=extra_args)
+    return shark_module
+
+
+def compile_module(
+    shark_module, extended_model_name, generate_vmfb, extra_args=[]
+):
+    if generate_vmfb:
+        vmfb_path = os.path.join(os.getcwd(), extended_model_name + ".vmfb")
+        if os.path.isfile(vmfb_path):
+            print(f"loading existing vmfb from: {vmfb_path}")
+            shark_module.load_module(vmfb_path, extra_args=extra_args)
+        else:
+            print(
+                "No vmfb found. Compiling and saving to {}".format(vmfb_path)
+            )
+            path = shark_module.save_module(
+                os.getcwd(), extended_model_name, extra_args
+            )
+            shark_module.load_module(path, extra_args=extra_args)
+    else:
+        shark_module.compile(extra_args)
+    return shark_module
+
+
+def shark_compile_through_fx(
+    model,
+    inputs,
+    extended_model_name,
+    is_f16=False,
+    f16_input_mask=None,
+    save_dir=tempfile.gettempdir(),
+    debug=False,
+    generate_or_load_vmfb=True,
+    extra_args=[],
+    device=None,
+    mlir_dialect="tm_tensor",
+):
+    if generate_or_load_vmfb:
+        shark_module = load_vmfb(
+            extended_model_name=extended_model_name,
+            device=device,
+            mlir_dialect=mlir_dialect,
+            extra_args=extra_args,
+        )
+        if shark_module:
+            return (
+                shark_module,
+                None,
+            )
+
+    from shark.parser import shark_args
+
+    if "cuda" in device:
+        shark_args.enable_tf32 = True
+
+    (
+        mlir_module,
+        _,
+    ) = import_with_fx(
+        model=model,
+        inputs=inputs,
+        is_f16=is_f16,
+        f16_input_mask=f16_input_mask,
+        debug=debug,
+        model_name=extended_model_name,
+        save_dir=save_dir,
+    )
+
+    shark_module = SharkInference(
+        mlir_module,
+        device=device,
+        mlir_dialect=mlir_dialect,
+    )
+    return (
+        compile_module(
+            shark_module,
+            extended_model_name,
+            generate_vmfb=generate_or_load_vmfb,
+            extra_args=extra_args,
+        ),
+        mlir_module,
+    )