Adds verify utility for kernels

This is a helper script verify
e2e numerical equivalence of
two MLIR programs.

common_tools/verify.py

@@ -0,0 +1,122 @@
+import os
+import tempfile
+import numpy as np
+from typing import List
+from multiprocessing import Pool, cpu_count, Manager
+import argparse
+from utils import *
+
+def compile_attention_mlir_file(mlir_file : str, target_device: str, hip_target: str, extra_flags: list):
+    vmfb_file = os.path.splitext(mlir_file)[0] + ".vmfb"
+    exec_args = [
+    "iree-compile",
+    # Input file
+    f"{mlir_file}",
+    # Output file
+    "-o",
+    f"{vmfb_file}",
+    # Target Device: hip
+    f"--iree-hal-target-device={target_device}",
+    # Device: MI300x
+    f"--iree-hip-target={hip_target}",
+    ] + extra_flags
+    ret_value, stdout, stderr = run_iree_command(exec_args)
+    if ret_value == 0:
+        print(f"Successfully compiled {mlir_file} to {vmfb_file}")
+    else:
+        print(f"Failed to compile {mlir_file}")
+        return None
+    return vmfb_file
+
+def get_output_npy(vmfb_file : str, inputs : List[str], target_device: str):
+    with tempfile.NamedTemporaryFile(suffix='.npy', delete=False) as out:
+        exec_args = [
+            "iree-run-module",
+            f"--device={target_device}",
+            "--device_allocator=caching",
+            f"--module={vmfb_file}",
+            "--function=main",
+            f"--input=@{inputs[0].name}",
+            f"--input=@{inputs[1].name}",
+            f"--input=@{inputs[2].name}",
+            f"--output=@{out.name}"
+        ]
+        ret_value, stdout, stderr = run_iree_command(exec_args)
+        if ret_value == 0:
+            print(f"Successfully ran {vmfb_file}")
+            return out
+        else:
+            print(f"Failed to run {vmfb_file}")
+            print(stderr.decode("utf-8"))
+            out.close()
+            return None
+
+
+def create_test_inputs(shapes : List[List[int]]):
+    inputfiles = list()
+    for shape in shapes:
+        with tempfile.NamedTemporaryFile(suffix='.npy', delete=False) as temp:
+            arr = np.random.randn(*shape).astype(np.float16)
+            np.save(temp, arr)
+            inputfiles.append(temp)
+    return inputfiles
+
+def get_shapes(shape_strs : List[List[str]]) -> List[List[int]]:
+    shapes = list()
+    for shape_str in shape_strs:
+        shape_str_split = shape_str.split('x')
+        shape_int_split = [int(dim) for dim in shape_str_split]
+        shapes.append(shape_int_split)
+    return shapes
+
+def close_files(files):
+    for file in files:
+        file.close()
+        os.remove(file.name)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="""Verify numerical results of a mlir program with a reference mlir program.
+                       NOTE: the main function should accept same typed tensors for this to work."""
+    )
+    parser.add_argument("--mlir-uut", help="The unit under test", type=str, required=True)
+    parser.add_argument("--mlir-ref", help="The reference mlir file", type=str, required=True)
+    parser.add_argument("--input-shape", help="input shape(s)", type=str, action='append', required=True)
+    parser.add_argument("--device", help="iree device", type=str, default="hip")
+    parser.add_argument("--hip-target", help="iree hip target", type=str, default="gfx942")
+    parser.add_argument("--extra-compiler-flags", help="extra iree-compile flags", type=str, action='append', default="--iree-codegen-gpu-native-math-precision")
+
+    args = parser.parse_args()
+    mlir_uut_file = args.mlir_uut
+    extra_compiler_flags = args.extra_compiler_flags
+    if type(extra_compiler_flags) == str:
+        extra_compiler_flags = [extra_compiler_flags]
+    mlir_uut_vmfb = compile_attention_mlir_file(mlir_uut_file, args.device, args.hip_target, extra_compiler_flags)
+    mlir_ref_file = args.mlir_ref
+    mlir_ref_vmfb = compile_attention_mlir_file(mlir_ref_file, args.device, args.hip_target, extra_compiler_flags)
+
+    shape_strs = args.input_shape
+    shapes = get_shapes(shape_strs)
+    inputfiles = create_test_inputs(shapes)
+
+    mlir_uut_out_file = get_output_npy(mlir_uut_vmfb, inputfiles, args.device)
+    mlir_ref_out_file = get_output_npy(mlir_ref_vmfb, inputfiles, args.device)
+
+    uut_out = np.load(mlir_uut_out_file.name)
+    print("uut:")
+    print(uut_out)
+
+    ref_out = np.load(mlir_ref_out_file.name)
+    print("ref:")
+    print(ref_out)
+
+    diff = uut_out - ref_out
+    max_diff = np.max(diff)
+    avg_diff = np.mean(diff)
+    print(f"max_diff:{max_diff}")
+    print(f"avg_diff:{avg_diff}")
+    print("diff:")
+    print(diff)
+
+    close_files([mlir_uut_out_file, mlir_ref_out_file])
+    close_files(inputfiles)