#%matplotlib import subprocess, csv, re, datetime, argparse, os from subprocess import STDOUT, check_output from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from pylab import * import random def parse_args(): parser = argparse.ArgumentParser(description="GEMM performance tools") parser.add_argument('--bert', action='store_true', help='Run GEMM performance comparisons on BERT model') parser.add_argument( '--gemm', action='store_true', help='Run performance comparison on a range of GEMM problem sizes') args = parser.parse_args() return args class CSVFile: def __init__(self, path="output.csv"): self.path = path def write_row(self, row=[]): with open(self.path, "a+") as f: cw = csv.writer(f) cw.writerow(row) def get_device_name(): out = subprocess.run("rocminfo", capture_output=True, check=True, shell=True) matches = re.findall("gfx\d*[a-z]*", str(out.stdout)) return matches[0] def run_perf(model, batch_size, int8=False, use_ck=False, use_large_k=False, disable_fusion=False): env_vars = "" if use_ck: env_vars += "MIGRAPHX_ENABLE_CK=1 " if use_large_k: env_vars += "MIGRAPHX_USE_LARGE_K=1 " if disable_fusion: env_vars += "MIGRAPHX_DISABLE_CK_FUSION=1 " int8_str = "--int8" if int8 else "" cmd = f"{env_vars} ../build/bin/driver perf {model} --fill1 input_ids --input-dim @input_ids {batch_size} 384 --batch {batch_size} --fp16 {int8_str} --exhaustive-tune" out = subprocess.run(cmd, capture_output=True, check=True, shell=True) summary = re.findall("Summary.*", str(out.stdout))[0].replace("\\n", "\n") total_time = re.findall("Total time: \d+\.\d*", summary)[0] total_time = total_time.replace("Total time: ", "") ck_gemm_time = re.findall("ck_gemm_kernel: \d+\.\d*", summary) if ck_gemm_time: ck_gemm_time = re.findall("\d+\.\d*", ck_gemm_time[0])[0] else: ck_gemm_time = "0.0" rb_gemm_time = re.findall("gpu::quant_gemm: \d+\.\d*|gpu::gemm: \d+\.\d*", summary) if rb_gemm_time: rb_gemm_time = re.findall("\d+\.\d*", rb_gemm_time[0])[0] else: rb_gemm_time = "0.0" gemm_pack_time = re.findall("gpu::int8_gemm_pack_a: \d+\.\d*", summary) if gemm_pack_time: gemm_pack_time = re.findall("\d+\.\d*", gemm_pack_time[0])[0] else: gemm_pack_time = "0.0" gemm_times = [ck_gemm_time, rb_gemm_time, gemm_pack_time] total_gemm_time = [str(sum(map(float, gemm_times)))] gemm_times.extend(total_gemm_time) print(cmd) print(total_time + "ms") with open("perf_summaries.txt", "a+") as f: f.write(cmd + "\n") f.write(summary + "\n\n") return [total_time] + gemm_times def run_ck_perf(model, batch_size, int8=False, use_large_k=False): # CK with fusions total_time = run_perf(model, batch_size, int8, True, use_large_k, False)[0] # CK without fusions gemm_times = run_perf(model, batch_size, int8, True, use_large_k, True) return [total_time] + gemm_times[1:] def run_bert_perf(): device_id = get_device_name() model = "/code/bert_base_cased_1_fp16_gpu.onnx" cf = CSVFile() cf.write_row([str(datetime.datetime.now())]) cf.write_row([device_id]) cf.write_row([model]) headers = [ "", "Total Time (ms)", "CK GEMM Time (ms)", "RB GEMM Time (ms)", "GEMM Pack Time (ms)", "Total GEMM Time (ms)" ] batch_size = "1" # int8: quantize = True label = f"Int8 / BatchSize: {batch_size}" if quantize else f"FP16 / BatchSize: {batch_size}" cf.write_row([label]) cf.write_row(headers) # CK Only cf.write_row(["CK"] + run_ck_perf(model, batch_size, quantize, True)) # CK + rocBLAS (k>2048) cf.write_row(["CK + rocBLAS(k>2048)"] + run_ck_perf(model, batch_size, quantize, False)) # rocBLAS Only cf.write_row(["rocBLAS"] + run_perf(model, batch_size, quantize)) cf.write_row() # fp16: quantize = False label = f"Int8 / BatchSize: {batch_size}" if quantize else f"FP16 / BatchSize: {batch_size}" cf.write_row([label]) cf.write_row(headers) # CK Only cf.write_row(["CK"] + run_ck_perf(model, batch_size, quantize, True)) # CK + rocBLAS (k>2048) cf.write_row(["CK + rocBLAS(k>2048)"] + run_ck_perf(model, batch_size, quantize, False)) # rocBLAS Only cf.write_row(["rocBLAS"] + run_perf(model, batch_size, quantize)) cf.write_row() batch_size = "64" # int8: quantize = True label = f"Int8 / BatchSize: {batch_size}" if quantize else f"FP16 / BatchSize: {batch_size}" cf.write_row([label]) cf.write_row(headers) # CK Only cf.write_row(["CK"] + run_ck_perf(model, batch_size, quantize, True)) # CK + rocBLAS (k>2048) cf.write_row(["CK + rocBLAS(k>2048)"] + run_ck_perf(model, batch_size, quantize, False)) # rocBLAS Only cf.write_row(["rocBLAS"] + run_perf(model, batch_size, quantize)) cf.write_row() # fp16: quantize = False label = f"Int8 / BatchSize: {batch_size}" if quantize else f"FP16 / BatchSize: {batch_size}" cf.write_row([label]) cf.write_row(headers) # CK Only cf.write_row(["CK"] + run_ck_perf(model, batch_size, quantize, True)) # CK + rocBLAS (k>2048) cf.write_row(["CK + rocBLAS(k>2048)"] + run_ck_perf(model, batch_size, quantize, False)) # rocBLAS Only cf.write_row(["rocBLAS"] + run_perf(model, batch_size, quantize)) cf.write_row() def gemm_perf(b, m, n, k, fp16): print(f"{b}, {m}, {n}, {k}:", end=" ") model = "../test/onnx/matmul_half.onnx" if fp16 else "../test/onnx/matmul_int8.onnx" #rocBLAS run cmd = f"MIGRAPHX_ENABLE_CK=0 ../build/bin/driver perf {model} --input-dim @1 {b} {m} {k} @2 {b} {k} {n}" out = subprocess.run(cmd, capture_output=True, check=True, shell=True) summary = re.findall("Summary.*", str(out.stdout))[0].replace("\\n", "\n") # print(summary) total_time = re.findall("Total time: \d+\.\d*", summary)[0] total_time = total_time.replace("Total time: ", "") rb_time = total_time cmd = f"../build/bin/driver perf {model} --input-dim @1 {b} {m} {k} @2 {b} {k} {n} --exhaustive-tune" try: out = subprocess.run(cmd.split(), capture_output=True, check=True, timeout=300, env=dict(os.environ, MIGRAPHX_ENABLE_CK="1")) except: print("-69.0") return -69.0 summary = re.findall("Summary.*", str(out.stdout))[0].replace("\\n", "\n") # print(summary) total_time = re.findall("Total time: \d+\.\d*", summary)[0] total_time = total_time.replace("Total time: ", "") ck_time = total_time diff = float(ck_time) - float(rb_time) print(f"{diff}") return diff def run_gemm_perf(): batches = [1] sizes = [64, 256, 384, 768, 1024, 2048, 2304, 3072] results = [(b, m, n, k, gemm_perf(b, m, n, k, False)) for b in batches for m in sizes for n in sizes for k in sizes] print(results) with open("gemm_results.txt", "w+") as f: for r in results: f.write(f"{r[0]}, {r[1]}, {r[2]}, {r[3]}, {r[4]}\n") if __name__ == "__main__": args = parse_args() if args.bert: run_bert_perf() if args.gemm: run_gemm_perf()