Add simple script to benchmark LoRA fragment

Requires that Xonsh is installed.

tools/mlir_bench/lora-runner.xsh

@@ -0,0 +1,72 @@
+#!/usr/bin/env xonsh
+
+# xonsh can be installed with `pip install xonsh`
+# xonsh can then be run by invoking `python -m xonsh`
+# this script in particular can be invoked with `python -m xonsh lora-benchmark.xsh`
+
+import openvino as ov
+from openvino.runtime.op import Constant
+from openvino_devtools.builder import OpFactory, outputs_to_nodes
+import numpy as np
+from pprint import pprint
+import re
+
+
+SIZES = [8, 16, 32, 64, 128, 256, 512, 1024]
+ITERATIONS = 100
+
+BENCH_RUNNER="tpp-run"
+BENCH_FLAGS=f"-entry-point-result=void -e entry -seed 123 -n {ITERATIONS}".split()
+
+
+def build_lora_model(x_dyn_dim):
+    opset = OpFactory('opset13')
+
+    #t40 = opset.Parameter({'shape': [-1, -1, 2048], 'element_type': 'f32'}, output_names=[{'x'}])  # Input data
+    t40 = opset.Parameter({'shape': [x_dyn_dim, 2048], 'element_type': 'f32'}, output_names=[{'x'}])  # Input data
+    t52 = opset.Parameter({'shape': [1, 8], 'element_type': 'f32'}, output_names=[{'alpha'}])  # LoRA alpha parameter
+
+    t48 = Constant(np.random.rand(2048, 2048).astype(np.float32))   #  -> f32[2048,2048]  # Original weight matrix W (usually it is compressed to bf16/f16/u8/u4 and represented as a sub-graph)
+    t50 = Constant(np.random.rand(8, 2048).astype(np.float32))  #  -> f32[8,2048]   # LoRA matrix A
+    t54 = Constant(np.random.rand(2048, 8).astype(np.float32))  #  -> f32[2048,8]   # LoRA matrix B
+
+    t49 = opset.MatMul([t40, t48], {'transpose_a': False, 'transpose_b': True})  # f32[?,?,2048], f32[2048,2048] -> f32[?,?,2048]
+    t51 = opset.MatMul([t40, t50], {'transpose_a': False, 'transpose_b': True})  # f32[?,?,2048], f32[8,2048] -> f32[?,?,8]
+    t53 = opset.Multiply([t51, t52], {'auto_broadcast': 'numpy'})  # f32[?,?,8], f32[1,8] -> f32[?,?,8]
+    t55 = opset.MatMul([t53, t54], {'transpose_a': False, 'transpose_b': True})  # f32[?,?,8], f32[2048,8] -> f32[?,?,2048]
+    t56 = opset.Add([t49, t55], {'auto_broadcast': 'numpy'})  # f32[?,?,2048], f32[?,?,2048] -> f32[?,?,2048]
+    t57 = opset.Result([t56], {})  # f32[?,?,2048] -> f32[?,?,2048]
+
+    parameters = [t40, t52]
+    results = [t57]
+    sinks = []
+    return ov.Model(outputs_to_nodes(results), outputs_to_nodes(sinks), outputs_to_nodes(parameters))
+
+
+def main():
+    no_mlir_averages = []
+    mlir_averages = []
+    no_ov_averages = []
+    for size in SIZES:
+        model_xml = f"lora.{size}.xml"
+        model = build_lora_model(size)
+        ov.save_model(model, model_xml)
+
+        def do_it(env_str):
+            out = $(env @(env_str) benchmark_app -m @(model_xml) -d CPU -niter @(ITERATIONS) -hint none -nstreams 1 -nthreads 1)
+            match = re.search(r"Average: +(\d.*) ms", out)
+            return float(match.group(1))
+        no_mlir_averages.append(do_it("OV_MLIR=0"))
+        mlir_averages.append(do_it("OV_MLIR=1"))
+
+        raw_kernel_secs = $(env OV_MLIR=1 OV_MLIR_TPP=1 OV_MLIR_DEBUG=1 benchmark_app -m @(model_xml) -d CPU -niter 1 -hint none -nstreams 1 -nthreads 1 2>&1 | awk '/Source MLIR:/{flag=1; next} /Target LLVM:/{flag=0} flag' | grep -vE '^[-]+$' | tpp-run @(BENCH_FLAGS))
+        no_ov_averages.append(float(raw_kernel_secs) * 1000)
+
+    print("SIZES", SIZES)
+    print("OV NO-MLIR", no_mlir_averages)
+    print("OV MLIR", mlir_averages)
+    print("NO-OV MLIR", no_ov_averages)
+
+
+if __name__ == "__main__":
+    main()