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Add benchmarks for ops, eager vs. inductor
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| import argparse | ||
| import torch | ||
| import sys | ||
| sys.path.append(sys.path[0]) | ||
| from int8_ops import ( | ||
| double_quant_eager, | ||
| double_quant, | ||
| transform_eager, | ||
| transform, | ||
| igemmlt, | ||
| mm_dequant_eager, | ||
| mm_dequant, | ||
| extract_outliers_eager, | ||
| extract_outliers, | ||
| ) | ||
| import time | ||
|
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| parser = argparse.ArgumentParser(description="Benchmarks for bnb int8 ops, eager vs inductor") | ||
| parser.add_argument("--num-active", default=20, type=int, help="number of active iterations for benchmark") | ||
| parser.add_argument("--num-warmup", default=10, type=int, help="number of warmup iterations for benchmark") | ||
| parser.add_argument("--all", action="store_true", help="Run all benchmarks") | ||
| parser.add_argument("--double-quant", action="store_true", help="Run benchmark for the double_quant op") | ||
| parser.add_argument("--transform", action="store_true", help="Run benchmark for the transform op") | ||
| parser.add_argument("--mm-dequant", action="store_true", help="Run benchmark for the mm_dequant op") | ||
| parser.add_argument("--extract-outliers", action="store_true", help="Run benchmark for the extract_outliers op") | ||
| parser.add_argument("--profile", action="store_true", help="Run all benchmarks with PyTorch profiler") | ||
| args = parser.parse_args() | ||
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| def trace_handler(prof): | ||
| print(prof.key_averages().table( | ||
| sort_by="cpu_time_total", row_limit=-1)) | ||
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| def run_benchmark(func_name, func_eager, func_inductor, *func_args, **func_kwargs): | ||
| with torch.no_grad(): | ||
| # Eager | ||
| for _ in range(args.num_warmup): | ||
| func_eager(*func_args, **func_kwargs) | ||
| t0 = time.time() | ||
| for _ in range(args.num_active): | ||
| func_eager(*func_args, **func_kwargs) | ||
| latency_eager = (time.time() - t0) / args.num_active | ||
| latency_eager = round(latency_eager * 1000, 3) | ||
| # Inductor | ||
| for _ in range(args.num_warmup): | ||
| func_inductor(*func_args, **func_kwargs) | ||
| t0 = time.time() | ||
| for _ in range(args.num_active): | ||
| func_inductor(*func_args, **func_kwargs) | ||
| latency_inductor = (time.time() - t0) / args.num_active | ||
| latency_inductor = round(latency_inductor * 1000, 3) | ||
| print(f"--- Benchmkark for {func_name} ---") | ||
| if args.profile: | ||
| print("\nProfiling for eager") | ||
| with torch.no_grad(), torch.profiler.profile( | ||
| activities=[torch.profiler.ProfilerActivity.CPU], | ||
| schedule=torch.profiler.schedule( | ||
| wait=0, warmup=3, active=1, repeat=0), | ||
| on_trace_ready=trace_handler | ||
| ) as p: | ||
| for _ in range(4): | ||
| func_eager(*func_args, **func_kwargs) | ||
| p.step() | ||
| print("\nProfiling for inductor") | ||
| with torch.no_grad(), torch.profiler.profile( | ||
| activities=[torch.profiler.ProfilerActivity.CPU], | ||
| schedule=torch.profiler.schedule( | ||
| wait=0, warmup=3, active=1, repeat=0), | ||
| on_trace_ready=trace_handler | ||
| ) as p: | ||
| for _ in range(4): | ||
| func_inductor(*func_args, **func_kwargs) | ||
| p.step() | ||
| print("\n--- Summary ---") | ||
| print(f"Eager latency: {latency_eager} ms, inductor latency: {latency_inductor} ms, " | ||
| f"speedup: {round(latency_eager - latency_inductor, 3)} ms ({round(latency_eager / latency_inductor - 1, 4) * 100}%)") | ||
| print("----------\n") | ||
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| if args.double_quant or args.all: | ||
| A = torch.rand(4096, 4096) * 3.0 | ||
| threshold = 3.0 | ||
| func_args = (A,) | ||
| func_kwargs = {'threshold': threshold} | ||
| run_benchmark('double_quant', double_quant_eager, double_quant, *func_args, **func_kwargs) | ||
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| if args.transform or args.all: | ||
| A = torch.rand(4096, 4096) | ||
| func_args = (A,) | ||
| func_kwargs = {'transpose': True} | ||
| run_benchmark('transform', transform_eager, transform, *func_args, **func_kwargs) | ||
|
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| if args.mm_dequant or args.all: | ||
| shapeA, shapeB = (4096, 4096), (4096, 4096) | ||
| A = torch.rand(shapeA) | ||
| A_min, A_max = A.aminmax(dim=-1) | ||
| A_stats = torch.max(A_max, A_min.neg()) | ||
| A_scale = A_stats / 127 | ||
| A_int8 = torch.round(A / A_scale.unsqueeze(-1)).to(torch.int8) | ||
| B = torch.randn(shapeB) | ||
| B_min, B_max = B.aminmax(dim=-1) | ||
| B_stats = torch.max(B_max, B_min.neg()) | ||
| B_scale = B_stats / 127 | ||
| B_int8 = torch.round(B / B_scale.unsqueeze(-1)).to(torch.int8) | ||
| bias = torch.randn(shapeB[0]) | ||
| C_i32, _ = igemmlt(A_int8, B_int8) | ||
| func_args = (C_i32, None, A_stats, B_stats) | ||
| func_kwargs = {'bias': bias} | ||
| run_benchmark('mm_dequant', mm_dequant_eager, mm_dequant, *func_args, **func_kwargs) | ||
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| if args.extract_outliers or args.all: | ||
| shapeA = (4096, 4096 * 4) | ||
| idx = torch.unique(torch.randint(0, shapeA[1], size=(10,)).int()) | ||
| A = torch.randint(-128, 127, size=shapeA).to(torch.int8) | ||
| func_args = (A, None, idx) | ||
| func_kwargs = {} | ||
| run_benchmark('extract_outliers', extract_outliers_eager, extract_outliers, *func_args, **func_kwargs) |
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