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exp_bert.py
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exp_bert.py
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import numpy as np
from pathlib import Path
from fitter import FitterPool, ModelFnPool
from util import Viewer, Util
import random,os
suffix = "pdf"
algo = "text_classification_fp16"
class Experiment:
def run_experiment(machine_tag, network = None):
mem_dir = "{}/{}/results/mem_results.json".format(algo,machine_tag)
ips_dir = "{}/{}/results/speed_results.json".format(algo,machine_tag)
cnt = 1
ips_archived, mem_archived = False, False
while True:
mem_dir_arc = "{}/{}/results/mem_archive_{}.json".format(algo,machine_tag,cnt)
ips_dir_arc = "{}/{}/results/speed_archive_{}.json".format(algo,machine_tag,cnt)
if (not os.path.exists(mem_dir_arc)) and (not os.path.exists(ips_dir_arc)):
break
cnt += 1
if Path(mem_dir).is_file():
os.rename(mem_dir, mem_dir_arc)
mem_archived = True
if Path(ips_dir).is_file():
os.rename(ips_dir, ips_dir_arc)
ips_archived = True
cmd = '''cd ./text_classification_fp16/{}/ &&
python exp_mem_speed.py --mode linear_scan --layer_num 24 &&
python exp_mem_speed.py --mode linear_scan --layer_num 24 --get_mem'''.format(machine_tag)
ret = os.system(cmd)
if ret!=0:
print("[Error] Failed to run new experiments, restoring experiment data")
if ret!=0 and mem_archived:
if Path(mem_dir).is_file():
os.remove(mem_dir)
os.rename(mem_dir_arc, mem_dir)
if ret!=0 and ips_archived:
if Path(ips_dir).is_file():
os.remove(ips_dir)
os.rename(ips_dir_arc, ips_dir)
return ret
def sample_dict(dic, percentenge):
sample_data = {}
sample_keys = random.sample(list(dic.keys()), int(len(dic) * percentenge))
for k in sample_keys:
sample_data[k] = dic[k]
return sample_data
def plot_helper(cond, mem_dir, ips_dir, offset = None):
mem = Util.load_data(mem_dir, "batch_size", "peak", cond)
for k in mem:
mem[k] /= 1000
btime = Util.load_data(ips_dir, "batch_size", "batch_time", cond)
# use only 20% data to fit the model
mem_sample= Experiment.sample_dict(mem, 0.5)
btime_sample= Experiment.sample_dict(btime, 0.5)
mem_model,mem_score,alpha,beta = FitterPool.fit_leastsq_verbose(mem_sample, ModelFnPool.linear)
# mem_model, mem_score, alpha, beta = None, 0, 0, 0
btime_model,btime_score,gamma,delta = FitterPool.fit_leastsq_verbose(btime_sample, ModelFnPool.linear)
retry = 0
while offset is None and delta<0:
retry += 1
btime_sample= Experiment.sample_dict(btime, 0.4)
btime_model,btime_score,gamma,delta = FitterPool.fit_leastsq_verbose(btime_sample, ModelFnPool.linear)
if retry>3:
break
if delta<0 and offset: btime_model,btime_score,gamma,delta = FitterPool.fit_leastsq_verbose_offset(btime_sample, ModelFnPool.linear,offset)
ips_model = lambda bsize: bsize / btime_model(bsize)
# print("[predict mem] ", mem_model(np.array(list(mem.keys()))))
return mem, btime, mem_model, btime_model, ips_model, alpha, beta, gamma, delta, mem_score, btime_score
def do_plot(machine_tag,to_plot):
algo = "text_classification_fp16"
mem_dir = "{}/{}/results/mem_results.json".format(algo,machine_tag)
ips_dir = "{}/{}/results/speed_results.json".format(algo,machine_tag)
result_dir = "graphs/{}/{}/".format(algo,machine_tag)
if not Path(mem_dir).is_file() or not Path(ips_dir).is_file():
print("Error: No experiment data found. Pease run expriment from scratch with --run-new for {}@{}".format(algo,machine_tag))
return
Path(result_dir).mkdir(parents=True, exist_ok=True)
#print("----------------Org-------------------")
is_org = lambda obj : obj['algorithm'] == None and 'grad_acc' not in obj
org_mem, org_btime, org_mem_model, org_btime_model, org_ips_model,\
alpha, beta, gamma, delta, mem_score, btime_score = Experiment.plot_helper(is_org, mem_dir, ips_dir)
offset = delta
print("-----------------{}@{} Params-----------------".format(algo,machine_tag))
print ("{:<8} {:<10} {:<10} {:<10} {:<10} {:<12} {:<12}".\
format('Method','Alpha','Beta','Gamma','Delta','Mem R','Latency R'))
print ("{:<8} {:<10g} {:<10g} {:<10g} {:<10g} {:<12g} {:<12g}".format('Org',alpha,beta,gamma,delta,mem_score,btime_score))
#print("----------------Swap-------------------")
is_swap = lambda obj : obj['algorithm'] == "swap" and 'grad_acc' not in obj
swap_mem, swap_btime, swap_mem_model, swap_btime_model, swap_ips_model,\
alpha, beta, gamma, delta, mem_score, btime_score = Experiment.plot_helper(is_swap, mem_dir, ips_dir, offset)
print ("{:<8} {:<10g} {:<10g} {:<10g} {:<10g} {:<12g} {:<12g}".format('Swap',alpha,beta,gamma,delta,mem_score,btime_score))
#print("----------------Ckpt-------------------")
is_ckpt = lambda obj : obj['algorithm'] == "ckpt" and 'grad_acc' not in obj
ckpt_mem, ckpt_btime, ckpt_mem_model, ckpt_btime_model, ckpt_ips_model,\
alpha, beta, gamma, delta, mem_score, btime_score = Experiment.plot_helper(is_ckpt, mem_dir, ips_dir, offset)
print ("{:<8} {:<10g} {:<10g} {:<10g} {:<10g} {:<12g} {:<12g}".format('Ckpt',alpha,beta,gamma,delta,mem_score,btime_score))
#print("----------------Quantize-------------------")
is_quantize = lambda obj : obj['algorithm'] == "L1" and 'grad_acc' not in obj
quantize_mem, quantize_btime, quantize_mem_model, quantize_btime_model, quantize_ips_model,\
alpha, beta, gamma, delta, mem_score, btime_score = Experiment.plot_helper(is_quantize, mem_dir, ips_dir, offset)
print ("{:<8} {:<10g} {:<10g} {:<10g} {:<10g} {:<12g} {:<12g}".format('Quantize',alpha,beta,gamma,delta,mem_score,btime_score))
if to_plot:
import matplotlib
# matplotlib.rc('axes',edgecolor='silver')
import matplotlib.pyplot as plt
# plt.style.use(['grid'])
fig, axes = plt.subplots(4, 1, sharex=True)
fig.set_size_inches(4, 6)
# plot batch time
Viewer.plot_fit(axes[0],"org", org_btime_model, np.array(list(org_btime.keys())), np.array(
list(org_btime.values())), None, False)
sample_cnt = 5
x, y= Util.sample_data(list(swap_btime.keys()), sample_cnt), Util.sample_data(list(swap_btime.values()), sample_cnt)
Viewer.plot_fit(axes[1], "swap", swap_btime_model, np.array(x), np.array(y), None, False)
x, y= Util.sample_data(list(ckpt_btime.keys()), sample_cnt), Util.sample_data(list(ckpt_btime.values()), sample_cnt)
Viewer.plot_fit(axes[2],"ckpt", ckpt_btime_model, np.array(x), np.array(y), None, False)
x, y= Util.sample_data(list(quantize_btime.keys()), sample_cnt), Util.sample_data(list(quantize_btime.values()), sample_cnt)
Viewer.plot_fit(axes[3],"quantize", quantize_btime_model, np.array(x), np.array(y), None, False)
plt.xlabel("Batch Size", size=22)
for ax in axes:
# ax.legend(loc="lower right")
ax.tick_params(axis='x', labelsize=18)
ax.tick_params(axis='y', labelsize=18)
fig.text(0, 0.5, 'Time (s)', va='center', rotation='vertical', size=22)
plt.savefig(result_dir + "bert_batch_time.%s" % suffix, bbox_inches="tight")
plt.close()
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(4, 4)
# plot memory
x, y= Util.sample_data(list(org_mem.keys()), sample_cnt), Util.sample_data(list(org_mem.values()), sample_cnt)
Viewer.plot_fit(ax, "org", org_mem_model, np.array(x), np.array(y), None, False)
x, y= Util.sample_data(list(swap_mem.keys()), sample_cnt), Util.sample_data(list(swap_mem.values()), sample_cnt)
Viewer.plot_fit(ax, "swap", swap_mem_model, np.array(x), np.array(y), None, False)
x, y= Util.sample_data(list(ckpt_mem.keys()), sample_cnt), Util.sample_data(list(ckpt_mem.values()), sample_cnt)
Viewer.plot_fit(ax, "ckpt", ckpt_mem_model, np.array(x), np.array(y), None, False)
x, y= Util.sample_data(list(quantize_mem.keys()), sample_cnt), Util.sample_data(list(quantize_mem.values()), sample_cnt)
Viewer.plot_fit(ax, "quantize", quantize_mem_model, np.array(x), np.array(y), None, False)
plt.ylabel("Memory (GB)")
plt.xlabel("Batch Size")
Util.set_tick_label_size([ax])
plt.savefig(result_dir + "bert_mem.%s" % suffix, bbox_inches="tight")
plt.close()
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(4, 4)
Viewer.plot_fit(ax, "org", org_ips_model, np.array(list(org_btime.keys())), np.array(
[bsize / org_btime[bsize] for bsize in org_btime]), None, False)
print(swap_btime)
print(ckpt_btime)
Viewer.plot_fit(ax, "swap", swap_ips_model, np.array(list(swap_btime.keys())), np.array(
[bsize / swap_btime[bsize] for bsize in swap_btime]), None, False)
Viewer.plot_fit(ax, "ckpt", ckpt_ips_model, np.array(list(ckpt_btime.keys())), np.array(
[bsize / ckpt_btime[bsize] for bsize in ckpt_btime]), None, False)
Viewer.plot_fit(ax, "quantize", quantize_ips_model, np.array(list(quantize_btime.keys())), np.array(
[bsize / quantize_btime[bsize] for bsize in quantize_btime]), None, False)
# plt.savefig(result_dir + "bert_ips.%s" % suffix)
# plt.close()
# ax.set_yticks([40, 80, 120, 160])
plt.ylabel("Throughput (record/s)", size=22)
plt.xlabel("Batch Size", size=22)
plt.legend(prop={'size': 14})
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
print(result_dir)
plt.savefig(result_dir + "bert_ips.%s" % suffix, bbox_inches="tight")
plt.close()
if __name__ == "__main__": Experiment.do_plot("v100",True)