# ruff: noqa: E501 import numpy as np import unittest from dataclasses import replace from tinygrad import Tensor, Context, Device, dtypes from tinygrad.ops import Ops, UOp # noqa: F401 # pylint: disable=unused-import from tinygrad.codegen.kernel import Kernel, Opt, OptOps from tinygrad.engine.realize import CompiledRunner, ExecItem, lower_schedule_item from tinygrad.engine.search import bufs_from_lin from tinygrad.shape.shapetracker import ShapeTracker, View # noqa: F401 # pylint: disable=unused-import N = 512 def create_gemm_model(model_path:str, batch_size=N, in_size=N, out_size=N, bias=False): import onnx from onnx import helper, numpy_helper, TensorProto # Define input and output input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [batch_size, in_size]) output_tensor = helper.make_tensor_value_info("output", TensorProto.FLOAT, [batch_size, out_size]) # Create random weights and bias W_data = np.random.randn(in_size, out_size).astype(np.float32) W_init = numpy_helper.from_array(W_data, name="W") if bias: B_data = np.random.randn(out_size).astype(np.float32) B_init = numpy_helper.from_array(B_data, name="B") gemm_node = helper.make_node("Gemm", inputs=["input", "W", "B"], outputs=["output"], alpha=1.0, beta=1.0, transB=0) graph_def = helper.make_graph([gemm_node], "SingleGemmGraph", [input_tensor], [output_tensor], initializer=[W_init, B_init]) else: gemm_node = helper.make_node("Gemm", inputs=["input", "W"], outputs=["output"], alpha=1.0, beta=1.0, transB=0) graph_def = helper.make_graph([gemm_node], "SingleGemmGraph", [input_tensor], [output_tensor], initializer=[W_init]) # Create and save the model model_def = helper.make_model(graph_def, producer_name="single_gemm_example") onnx.save_model(model_def, model_path) return model_path def sexec(out:Tensor, opts:list[Opt], replace_src=None, run_count=3): si = out.schedule()[-1] k = Kernel(si.ast, opts=Device[Device.DEFAULT].renderer) #opts = [Opt(op=OptOps.UPCAST, axis=0, arg=128)] #, Opt(op=OptOps.UNROLL, axis=0, arg=4)] for opt in opts: k.apply_opt(opt) prg = k.to_program() if replace_src is not None: old_name = prg.src.split("__attribute__((noinline)) void ")[1].split("(")[0] prg = replace(prg, src=replace_src + "/* DSP boilerplate */" + prg.src.split("/* DSP boilerplate */")[1].replace(old_name, "fxn")) ei = ExecItem(CompiledRunner(prg), [x.ensure_allocated() for x in si.bufs], si.metadata) for _ in range(run_count): ei.run(wait=True) def get_quantized_model(sz): from onnxruntime.quantization import quantize_static, QuantFormat, QuantType, CalibrationDataReader class FakeDataReader(CalibrationDataReader): def __init__(self): self.cnt = 0 def get_next(self) -> dict: self.cnt += 1 if self.cnt == 100: return None return {"input": np.random.uniform(size=(sz, sz)).astype(np.float32)} out_file = "/tmp/test_out.onnx" quantize_static(create_gemm_model("/tmp/test_in.onnx", sz, sz, sz), out_file, FakeDataReader(), quant_format=QuantFormat.QDQ, per_channel=False, reduce_range=False, activation_type=QuantType.QUInt8, weight_type=QuantType.QInt8, extra_options={"ActivationSymmetric": False}) return out_file @unittest.skipIf(Device.DEFAULT != "CPU", "only tests for CPU") class TestQuantizeOnnxCPU(unittest.TestCase): def test_quant_128(self, sz=128): try: import onnx except ImportError: raise unittest.SkipTest() from tinygrad.frontend.onnx import OnnxRunner out_file = get_quantized_model(sz) onnx_model = onnx.load(out_file) run_onnx = OnnxRunner(onnx_model) inp = Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32)) with Context(DONT_REALIZE_EXPAND=1, QUANTIZE=1): sched = run_onnx({"input":inp})["output"].schedule() ei = lower_schedule_item(sched[-2]) daccs = [u for u in ei.prg.p.uops if u.op is Ops.DEFINE_ACC] assert all(u.dtype.scalar() is dtypes.int for u in daccs) @unittest.skipIf(Device.DEFAULT != "DSP", "only tests for DSP") class TestQuantizeOnnx(unittest.TestCase): def test_quant_128(self): self.test_quant(128) def test_quant(self, sz=512): from examples.benchmark_onnx import load_onnx_model # divide is ~1500-2000 without reduce_range, 750-900 with it out_file = get_quantized_model(sz) run_onnx_jit, _ = load_onnx_model(out_file) with Context(DONT_REALIZE_EXPAND=1): run_onnx_jit(input=Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32))) def test_prequant_conv2d_1x1(self): X = Tensor(np.random.uniform(0, 255, size=(1, 32, 128, 128)).astype(np.uint8)) W = Tensor(np.random.uniform(0, 255, size=(64, 32, 1, 1)).astype(np.uint8)) out = X.conv2d(W, dtype=X.dtype) opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] sexec(out, opts) def test_prequant_gemm(self): N = 512 X = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8)) W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8)) out = X.matmul(W, dtype=X.dtype) opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] sexec(out, opts) # TODO: this has to work def test_prequant_gemm_intacc_early(self, xi=np.int8, wi=np.int8): N = 512 X = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(xi)) W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(wi)) with Context(DONT_REALIZE_EXPAND=1): # this divide is interesting and forces the accumulator to actually be an int out = (X.cast("int").matmul(W.cast("int"))//1000).cast("int8") opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] sexec(out, opts) def test_prequant_gemm_handcode(self): src = """typedef int int128 __attribute__((aligned(512),vector_size(512))); typedef int int32 __attribute__((aligned(128),vector_size(128))); typedef int int64 __attribute__((aligned(256),vector_size(256))); typedef unsigned char unsigned_char4 __attribute__((aligned(4),vector_size(4))); typedef signed char signed_char128 __attribute__((aligned(128),vector_size(128))); typedef unsigned char unsigned_char128 __attribute__((aligned(128),vector_size(128))); typedef unsigned char unsigned_char256 __attribute__((aligned(256),vector_size(256))); union V256 { unsigned_char256 vec256; struct { unsigned_char128 lo128; unsigned_char128 hi128; }; }; __attribute__((noinline)) void fxn(unsigned char* restrict __attribute__((align_value(128))) data0, unsigned char* restrict __attribute__((align_value(128))) data1, signed char* restrict __attribute__((align_value(128))) data2) { for (int ridx0 = 0; ridx0 < 512; ridx0++) { int alu0 = (ridx0<<9); for (int ridx1 = 0; ridx1 < 4; ridx1++) { int alu1 = (ridx1<<7); int32 acc0 = __builtin_HEXAGON_V6_vd0_128B(); int32 acc1 = __builtin_HEXAGON_V6_vd0_128B(); int32 acc2 = __builtin_HEXAGON_V6_vd0_128B(); int32 acc3 = __builtin_HEXAGON_V6_vd0_128B(); for (int ridx2 = 0; ridx2 < 128; ridx2++) { unsigned_char4 val0 = *((unsigned_char4*)((data1+(alu0+(ridx2<<2))))); int alu2 = (alu1+(ridx2<<11)); signed_char128 x0 = *((signed_char128*)((data2+alu2))); signed_char128 x1 = *((signed_char128*)((data2+(alu2+512)))); signed_char128 x2 = *((signed_char128*)((data2+(alu2+1024)))); signed_char128 x3 = *((signed_char128*)((data2+(alu2+1536)))); union V256 ss01; // ss01.lo128 = (x0[0], x1[0], x0[2], x1[2], x0[4], x1[4], ...) // ss01.hi128 = (x0[1], x1[1], x0[3], x1[3], x0[5], x1[5], ...) ss01.vec256 = __builtin_HEXAGON_V6_vshufoeb_128B(x1, x0); union V256 ss23; // ss23.lo128 = (x2[0], x3[0], x2[2], x3[2], x2[4], x3[4], ...) // ss23.hi128 = (x2[1], x3[1], x2[3], x3[3], x2[5], x3[5], ...) ss23.vec256 = __builtin_HEXAGON_V6_vshufoeb_128B(x3, x2); union V256 sslo; // sslo.lo128 = (x0[0], x1[0], x2[0], x3[0], x0[4], x1[4], ...) // sslo.hi128 = (x0[2], x1[2], x2[2], x3[2], x0[6], x1[6], ...) sslo.vec256 = __builtin_HEXAGON_V6_vdealvdd_128B(ss23.lo128, ss01.lo128, 2); union V256 sshi; // sshi.lo128 = (x0[1], x1[1], x2[1], x3[1], x0[5], x1[5], ...) // sshi.hi128 = (x0[3], x1[3], x2[3], x3[3], x0[7], x1[7], ...) sshi.vec256 = __builtin_HEXAGON_V6_vdealvdd_128B(ss23.hi128, ss01.hi128, 2); //unsigned_char128 w0 = (unsigned_char128){val0[0],val0[1],val0[2],val0[3],val0[0],val0[1],val0[2],val0[3],... unsigned_char128 w0 = __builtin_HEXAGON_V6_lvsplatw_128B(*((unsigned int*)&val0)); acc0 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc0, w0, sslo.lo128); acc1 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc1, w0, sshi.lo128); acc2 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc2, w0, sslo.hi128); acc3 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc3, w0, sshi.hi128); } acc0 /= 1000; acc1 /= 1000; acc2 /= 1000; acc3 /= 1000; // ','.join([f"acc{j}[{i}]" for i in range(32) for j in range(4)]) // acc0[0], acc0[1], acc0[2], ..... acc3[30], acc3[31] unsigned_char128 packed = __builtin_HEXAGON_V6_vpackhub_sat_128B(__builtin_HEXAGON_V6_vpackwh_sat_128B(acc3, acc2), __builtin_HEXAGON_V6_vpackwh_sat_128B(acc1, acc0)); packed = __builtin_HEXAGON_V6_vshuffb_128B(packed); packed = __builtin_HEXAGON_V6_vshuffb_128B(packed); // acc0[0], acc1[0], acc2[0], ..... acc2[31], acc3[31] *((unsigned_char128*)((data0+(alu0+alu1)))) = packed; } } }""" self.test_prequant_gemm_intacc(np.uint8, np.int8, src) def test_prequant_gemm_intacc_32(self): opts = [Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=0)] self.test_prequant_gemm_intacc(np.uint8, np.int8, N=32, opts=opts) def test_prequant_gemm_intacc_128(self): self.test_prequant_gemm_intacc(np.uint8, np.int8, N=128) def test_prequant_gemm_intacc_256(self): self.test_prequant_gemm_intacc(np.uint8, np.int8, N=256) def test_prequant_gemm_intacc(self, xi=np.uint8, wi=np.uint8, replace_src=None, N=512, clip=True, opts=None): X = Tensor(m1:=(np.random.uniform(0, 255, size=(N,N)).astype(xi))).realize() W = Tensor(m2:=(np.random.uniform(0, 255, size=(N,N)).astype(wi))).realize() # ugh, it's so broken with those casts. need DONT_REALIZE_EXPAND=1 python3 test/test_quantize_onnx.py TestQuantizeOnnx.test_prequant tg_dtype = dtypes.int8 if xi == np.int8 else dtypes.uint8 with Context(DONT_REALIZE_EXPAND=1): out = (X.int().matmul(W.int())//1000) if clip: out = out.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype)) out = out.cast(tg_dtype) opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] if opts is None else opts sexec(out, opts, replace_src, run_count=1) tout = out.numpy() mout = ((m1.astype(np.int32) @ m2.astype(np.int32)) / 1000) if clip: mout = mout.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype)) mout = mout.astype(xi) print(tout) print(mout) np.testing.assert_equal(tout, mout) def test_prequant_gemm_intacc_wi(self): self.test_prequant_gemm_intacc(wi=np.int8) def test_prequant_gemm_intacc_xiwi(self): self.test_prequant_gemm_intacc(xi=np.int8, wi=np.int8) def test_prequant_gemm_intacc_xiwi_noclip(self): self.test_prequant_gemm_intacc(xi=np.int8, wi=np.int8, clip=False) def test_prequant_gemv(self): N = 2048 # ugh, it's so broken with those casts. need DONT_REALIZE_EXPAND=1 python3 test/test_quantize_onnx.py TestQuantizeOnnx.test_prequant X = Tensor(np.random.uniform(0, 255, size=(1,N)).astype(np.uint8)).realize() W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8)).realize() #out = X.cast(dtypes.int) @ W.cast(dtypes.int) #out = X @ W out = X.matmul(W, dtype=X.dtype) opts = [Opt(op=OptOps.UPCAST, axis=0, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] sexec(out, opts) @unittest.skipIf(Device.DEFAULT != "DSP", "only tests for DSP") class TestDSPCache(unittest.TestCase): def test_cache_speed(self): # string becuase this breaks Python language server for syntax highlight for some reason ast = eval("""UOp(Ops.SINK, dtypes.void, arg=None, src=( UOp(Ops.STORE, dtypes.void, arg=None, src=( UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(25088), arg=0, src=()), UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 1), strides=(0, 896, 32, 1, 0), offset=0, mask=None, contiguous=True),)), src=()), UOp(Ops.CAST, dtypes.uchar, arg=None, src=( UOp(Ops.XOR, dtypes.int, arg=None, src=( UOp(Ops.MAX, dtypes.int, arg=None, src=( UOp(Ops.XOR, dtypes.int, arg=None, src=( UOp(Ops.MAX, dtypes.int, arg=None, src=( UOp(Ops.CAST, dtypes.int, arg=None, src=( UOp(Ops.ADD, dtypes.float, arg=None, src=( UOp(Ops.ADD, dtypes.float, arg=None, src=( UOp(Ops.MUL, dtypes.float, arg=None, src=( UOp(Ops.ADD, dtypes.float, arg=None, src=( UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4,)), src=( UOp(Ops.MUL, dtypes.float, arg=None, src=( UOp(Ops.MUL, dtypes.float, arg=None, src=( UOp(Ops.CAST, dtypes.float, arg=None, src=( UOp(Ops.CAST, dtypes.int, arg=None, src=( UOp(Ops.LOAD, dtypes.uchar, arg=None, src=( UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(150528), arg=1, src=()), UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 192), strides=(0, 5376, 192, 0, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)), UOp(Ops.CONST, dtypes.float, arg=0.012368360534310341, src=( x22:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 192), strides=(0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)), UOp(Ops.MUL, dtypes.float, arg=None, src=( UOp(Ops.CAST, dtypes.float, arg=None, src=( UOp(Ops.CAST, dtypes.int, arg=None, src=( UOp(Ops.LOAD, dtypes.char, arg=None, src=( UOp(Ops.DEFINE_GLOBAL, dtypes.char.ptr(6144), arg=2, src=()), UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(32, 48, 4), strides=(4, 128, 1), offset=0, mask=None, contiguous=False), View(shape=(1, 28, 28, 32, 192), strides=(0, 0, 0, 192, 1), offset=0, mask=None, contiguous=False))), src=()),)),)),)), UOp(Ops.CONST, dtypes.float, arg=0.007441135589033365, src=( x22,)),)),)),)), UOp(Ops.MUL, dtypes.float, arg=None, src=( UOp(Ops.CAST, dtypes.float, arg=None, src=( UOp(Ops.LOAD, dtypes.int, arg=None, src=( UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(32), arg=3, src=()), UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 1), strides=(0, 0, 0, 1, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)), UOp(Ops.CONST, dtypes.float, arg=9.203465015161783e-05, src=( x36:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 1), strides=(0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),)), UOp(Ops.CONST, dtypes.float, arg=33.812857328652136, src=( x36,)),)), UOp(Ops.CONST, dtypes.float, arg=0.4999999, src=( x36,)),)), UOp(Ops.CONST, dtypes.float, arg=136.0, src=( x36,)),)),)), UOp(Ops.CONST, dtypes.int, arg=0, src=( x36,)),)), x41:=UOp(Ops.CONST, dtypes.int, arg=-1, src=( x36,)),)), UOp(Ops.CONST, dtypes.int, arg=-256, src=( x36,)),)), x41,)),)),)),))""") opts = [Opt(op=OptOps.UNROLL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=32), Opt(op=OptOps.UPCAST, axis=0, arg=4)] with Context(DEVECTORIZE=0, QUANTIZE=1): k = Kernel(ast, opts=Device[Device.DEFAULT].renderer) for opt in opts: k.apply_opt(opt) prg = k.to_program() #print(prg.src) new_src = """ typedef int int32 __attribute__((aligned(128),vector_size(128))); typedef signed char signed_char128 __attribute__((aligned(128),vector_size(128))); typedef unsigned char unsigned_char8 __attribute__((aligned(8),vector_size(8))); typedef unsigned char unsigned_char4 __attribute__((aligned(4),vector_size(4))); typedef unsigned char unsigned_char128 __attribute__((aligned(128),vector_size(128))); __attribute__((noinline)) void r_196_24_8_32_4(unsigned char* restrict __attribute__((align_value(128))) data0, unsigned char* restrict __attribute__((align_value(128))) data1, signed char* restrict __attribute__((align_value( 128))) data2, int* restrict __attribute__((align_value(128))) data3) { int32 cast0 = (int32){0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}; int32 val0 = *((int32*)((data3+0))); for (int ridx0 = 0; ridx0 < 196; ridx0++) { int32 acc0 = cast0; int32 acc1 = cast0; int32 acc2 = cast0; int32 acc3 = cast0; __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768); __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768+192); __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768+384); __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768+576); for (int ridx1 = 0; ridx1 < 24; ridx1++) { signed_char128 val1 = *((signed_char128*)((data2+(ridx1<<8)))); signed_char128 val2 = *((signed_char128*)((data2+((1+(ridx1<<1))<<7)))); int alu0 = ((ridx0*768)+(ridx1<<3)); unsigned_char8 val3 = *((unsigned_char8*)((data1+alu0))); __builtin_HEXAGON_Y2_dcfetch(((data1+alu0)+16)); unsigned_char8 val4 = *((unsigned_char8*)((data1+(alu0+192)))); __builtin_HEXAGON_Y2_dcfetch(((data1+(alu0+192))+16)); unsigned_char8 val5 = *((unsigned_char8*)((data1+(alu0+384)))); __builtin_HEXAGON_Y2_dcfetch(((data1+(alu0+384))+16)); unsigned_char8 val6 = *((unsigned_char8*)((data1+(alu0+576)))); __builtin_HEXAGON_Y2_dcfetch(((data1+(alu0+576))+16)); unsigned_char4 alu5 = __builtin_shufflevector(val3, val3, 0, 1, 2, 3); unsigned_char4 alu6 = __builtin_shufflevector(val4, val4, 0, 1, 2, 3); unsigned_char4 alu7 = __builtin_shufflevector(val5, val5, 0, 1, 2, 3); unsigned_char4 alu8 = __builtin_shufflevector(val6, val6, 0, 1, 2, 3); acc0 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc0, val1, (*((unsigned int*)&alu5))); acc1 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc1, val1, (*((unsigned int*)&alu6))); acc2 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc2, val1, (*((unsigned int*)&alu7))); acc3 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc3, val1, (*((unsigned int*)&alu8))); unsigned_char4 alu9 = __builtin_shufflevector(val3, val3, 4, 5, 6, 7); unsigned_char4 alu10 = __builtin_shufflevector(val4, val4, 4, 5, 6, 7); unsigned_char4 alu11 = __builtin_shufflevector(val5, val5, 4, 5, 6, 7); unsigned_char4 alu12 = __builtin_shufflevector(val6, val6, 4, 5, 6, 7); acc0 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc0, val2, (*((unsigned int*)&alu9))); acc1 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc1, val2, (*((unsigned int*)&alu10))); acc2 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc2, val2, (*((unsigned int*)&alu11))); acc3 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc3, val2, (*((unsigned int*)&alu12))); } unsigned_char128 alu18 = __builtin_HEXAGON_V6_vpackhub_sat_128B(__builtin_HEXAGON_V6_vpackwh_sat_128B((((((acc3+val0)*203)+32767)/65536)+136), (((((acc2+val0)*203)+32767)/65536)+136)), __builtin_HEXAGON_V6_vpackwh_sat_128B((((((acc1+val0)*203)+32767)/65536)+136), (((((acc0+val0)*203)+32767)/65536)+136))); *((unsigned_char128*)((data0+(ridx0<<7)))) = alu18; } } """ prg = replace(prg, src=new_src+prg.src.split("/* DSP boilerplate */ ")[1]) rt = CompiledRunner(prg) #Device.default.compiler.disassemble(rt.lib) ei = ExecItem(rt, bufs_from_lin(k)) tm = ei.run(wait=True) print(f"final time {tm*1e6:.2f} us") if __name__ == "__main__": unittest.main()