def gemm(A, B):
b_fac = 4
assert (A.shape[1] == B.shape[0])
assert (A.shard_sizes[1] == B.shard_sizes[0])
shard_sizes = (A.shard_sizes[0], B.shard_sizes[1])
num_tree_levels = max(
int(np.ceil(np.log2(A.num_blocks(1)) / np.log2(b_fac))), 1)
Temp = BigMatrix(f"matmul_test_Temp({A.key},{B.key})",
shape=(A.shape[0], B.shape[1], B.shape[0],
num_tree_levels),
shard_sizes=[A.shard_sizes[0], B.shard_sizes[1], 1, 1],
write_header=True,
safe=False,
parent_fn=constant_zeros)
C_sharded = BigMatrix("matmul_test_C",
shape=(A.shape[0], B.shape[1]),
shard_sizes=shard_sizes,
write_header=True)
config = npw.config.default()
t = time.time()
p0 = lpcompile_for_execution(GEMM, inputs=["A", "B"], outputs=["Out"])
print("tree depth", np.ceil(np.log(B.num_blocks(1)) / np.log(4)))
p1 = p0(A, B, A.num_blocks(0), A.num_blocks(1), B.num_blocks(1), Temp,
C_sharded)
e = time.time()
c_time = e - t
program = lp.LambdaPackProgram(p1, config=config)
return program, {
"outputs": [C_sharded],
"intermediates": [Temp],
"compile_time": c_time
}
def test_if_run(self):
X = np.random.randn(64)
shard_sizes = (int(X.shape[0]/8),)
X_sharded = BigMatrix("if_test", shape=X.shape,
shard_sizes=shard_sizes, write_header=True)
O_sharded = BigMatrix("if_test_output", shape=X.shape,
shard_sizes=shard_sizes, write_header=True)
X_sharded.free()
shard_matrix(X_sharded, X)
f = frontend.lpcompile(f1_if)
p = f(X_sharded, O_sharded, X_sharded.num_blocks(0))
num_cores = 1
executor = fs.ProcessPoolExecutor(num_cores)
config = npw.config.default()
p_ex = lp.LambdaPackProgram(p, config=config)
p_ex.start()
all_futures = []
for i in range(num_cores):
all_futures.append(executor.submit(
job_runner.lambdapack_run, p_ex, pipeline_width=1, idle_timeout=5, timeout=60))
p_ex.wait()
time.sleep(5)
p_ex.free()
for i in range(X_sharded.num_blocks(0)):
Ob = O_sharded.get_block(i)
Xb = X_sharded.get_block(i)
if ((i % 2) == 0):
assert(np.allclose(Ob, 1*Xb))
else:
assert(np.allclose(Ob, 2*Xb))
def cholesky(X, truncate=0):
S = BigMatrix("Cholesky.Intermediate({0})".format(X.key),
shape=(X.num_blocks(1) + 1, X.shape[0], X.shape[0]),
shard_sizes=(1, X.shard_sizes[0], X.shard_sizes[0]),
bucket=X.bucket,
write_header=True,
parent_fn=constant_zeros)
#S.free()
O = BigMatrix("Cholesky({0})".format(X.key),
shape=(X.shape[0], X.shape[0]),
shard_sizes=(X.shard_sizes[0], X.shard_sizes[0]),
write_header=True,
parent_fn=constant_zeros)
t = time.time()
p0 = lpcompile_for_execution(CHOLESKY, inputs=["I"], outputs=["O"])
p1 = p0(O, X, S, int(np.ceil(X.shape[0] / X.shard_sizes[0])), truncate)
e = time.time()
c_time = e - t
config = npw.config.default()
program = lp.LambdaPackProgram(p1, config=config)
return program, {
"outputs": [O],
"intermediates": [S],
"compile_time": c_time
}
def test_matmul(self):
size = 4
shard_size = 2
np.random.seed(0)
A = np.random.randn(size, size)
B = np.random.randn(size, size)
C = np.dot(A, B)
shard_sizes = (shard_size, shard_size)
A_sharded = BigMatrix("matmul_test_A",
shape=A.shape,
shard_sizes=shard_sizes,
write_header=True)
A_sharded.free()
shard_matrix(A_sharded, A)
B_sharded = BigMatrix("matmul_test_B",
shape=B.shape,
shard_sizes=shard_sizes,
write_header=True)
B_sharded.free()
shard_matrix(B_sharded, B)
Temp = BigMatrix("matmul_test_Temp",
shape=[A.shape[0], B.shape[1], B.shape[0], 100],
shard_sizes=[
A_sharded.shard_sizes[0],
B_sharded.shard_sizes[1], 1, 1
],
write_header=True)
C_sharded = BigMatrix("matmul_test_C",
shape=C.shape,
shard_sizes=shard_sizes,
write_header=True)
b_fac = 2
config = npw.config.default()
compiled_matmul = frontend.lpcompile(matmul)
program = compiled_matmul(A_sharded, B_sharded,
A_sharded.num_blocks(0),
A_sharded.num_blocks(1),
B_sharded.num_blocks(1), b_fac, Temp,
C_sharded)
program_executable = lp.LambdaPackProgram(program, config=config)
program_executable.start()
job_runner.lambdapack_run(program_executable,
pipeline_width=1,
idle_timeout=5,
timeout=60)
executor = fs.ThreadPoolExecutor(1)
all_futures = [
executor.submit(job_runner.lambdapack_run,
program_executable,
pipeline_width=1,
idle_timeout=5,
timeout=60)
]
program_executable.wait()
program_executable.free()
C_remote = C_sharded.numpy()
assert (np.allclose(C, C_remote))
def qr(A):
b_fac = 2
N = A.shape[0]
N_blocks = A.num_blocks(0)
b_fac = 2
shard_size = A.shard_sizes[0]
num_tree_levels = max(
int(np.ceil(np.log2(A.num_blocks(0)) / np.log2(b_fac))), 1) + 1
Vs = BigMatrix("Vs",
shape=(2 * N, 2 * N, num_tree_levels),
shard_sizes=(shard_size, shard_size, 1),
write_header=True,
parent_fn=constant_zeros,
safe=False)
Ts = BigMatrix("Ts",
shape=(2 * N, 2 * N, num_tree_levels),
shard_sizes=(shard_size, shard_size, 1),
write_header=True,
parent_fn=constant_zeros,
safe=False)
Rs = BigMatrix("Rs",
shape=(2 * N, 2 * N, num_tree_levels),
shard_sizes=(shard_size, shard_size, 1),
write_header=True,
parent_fn=constant_zeros,
safe=False)
Ss = BigMatrix("Ss",
shape=(2 * N, 2 * N, 2 * N, num_tree_levels * shard_size),
shard_sizes=(shard_size, shard_size, 1, 1),
write_header=True,
parent_fn=constant_zeros,
safe=False)
print("Rs", Rs.shape)
print("Ss", Ss.shape)
print("Ts", Ts.shape)
print("Vs", Vs.shape)
t = time.time()
p0 = lpcompile_for_execution(QR, inputs=["I"], outputs=["Rs"])
p1 = p0(A, Vs, Ts, Rs, Ss, N_blocks, 0)
e = time.time()
c_time = e - t
config = npw.config.default()
program = lp.LambdaPackProgram(p1, config=config)
return program, {
"outputs": [Rs, Vs, Ts],
"intermediates": [Ss],
"compile_time": c_time
}
def tsqr(X, truncate=0):
b_fac = 2
assert (X.shard_sizes[1] == X.shape[1])
shard_size = X.shard_sizes[0]
shard_sizes = X.shard_sizes
num_tree_levels = max(
int(np.ceil(np.log2(X.num_blocks(0)) / np.log2(b_fac))), 1)
R_sharded = BigMatrix("tsqr_R({0})".format(X.key),
shape=(num_tree_levels * shard_size, X.shape[0]),
shard_sizes=shard_sizes,
write_header=True,
safe=False)
T_sharded = BigMatrix("tsqr_T({0})".format(X.key),
shape=(num_tree_levels * shard_size * b_fac,
X.shape[0]),
shard_sizes=(shard_size * b_fac, shard_size),
write_header=True,
safe=False)
V_sharded = BigMatrix("tsqr_V({0})".format(X.key),
shape=(num_tree_levels * shard_size * b_fac,
X.shape[0]),
shard_sizes=(shard_size * b_fac, shard_size),
write_header=True,
safe=False)
t = time.time()
p0 = lpcompile_for_execution(TSQR, inputs=["A"], outputs=["Rs"])
config = npw.config.default()
N_blocks = X.num_blocks(0)
p1 = p0(X, V_sharded, T_sharded, R_sharded, N_blocks)
e = time.time()
c_time = e - t
program = lp.LambdaPackProgram(p1, config=config)
return program, {
"outputs": [R_sharded, V_sharded, T_sharded],
"intermediates": [],
"compile_time": c_time
}
def run_experiment(problem_size, shard_size, pipeline, num_priorities, lru,
eager, truncate, max_cores, start_cores, trial,
launch_granularity, timeout, log_granularity,
autoscale_policy, standalone, warmup, verify, matrix_exists,
read_limit, write_limit):
# set up logging
invoke_executor = fs.ThreadPoolExecutor(1)
logger = logging.getLogger()
region = wc.default()["account"]["aws_region"]
print("REGION", region)
for key in logging.Logger.manager.loggerDict:
logging.getLogger(key).setLevel(logging.CRITICAL)
logger.setLevel(logging.DEBUG)
arg_bytes = pickle.dumps(
(problem_size, shard_size, pipeline, num_priorities, lru, eager,
truncate, max_cores, start_cores, trial, launch_granularity, timeout,
log_granularity, autoscale_policy, read_limit, write_limit))
arg_hash = hashlib.md5(arg_bytes).hexdigest()
log_file = "{0}.log".format(arg_hash)
fh = logging.FileHandler(log_file)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
logger.info("Logging to {0}".format(log_file))
if standalone:
extra_env = {
"AWS_ACCESS_KEY_ID": os.environ["AWS_ACCESS_KEY_ID"],
"AWS_SECRET_ACCESS_KEY": os.environ["AWS_ACCESS_KEY_ID"],
"OMP_NUM_THREADS": "1",
"AWS_DEFAULT_REGION": region
}
config = wc.default()
config['runtime']['s3_bucket'] = 'numpywrenpublic'
key = "pywren.runtime/pywren_runtime-3.6-numpywren-standalone.tar.gz"
config['runtime']['s3_key'] = key
pwex = pywren.standalone_executor(config=config)
else:
extra_env = {"AWS_DEFAULT_REGION": region}
config = wc.default()
config['runtime']['s3_bucket'] = 'numpywrenpublic-us-east-1'
key = "pywren.runtime/pywren_runtime-3.6-numpywren-08-25-2018.tar.gz"
config['runtime']['s3_key'] = key
pwex = pywren.default_executor(config=config)
if (not matrix_exists):
X = np.random.randn(problem_size, 1)
shard_sizes = [shard_size, 1]
X_sharded = BigMatrix("cholesky_test_{0}_{1}".format(
problem_size, shard_size),
shape=X.shape,
shard_sizes=shard_sizes,
write_header=True,
autosqueeze=False,
bucket="numpywrentop500test",
hash_keys=False)
shard_matrix(X_sharded, X)
print("Generating PSD matrix...")
t = time.time()
print(X_sharded.shape)
XXT_sharded = binops.gemm(pwex,
X_sharded,
X_sharded.T,
overwrite=False)
e = time.time()
print("GEMM took {0}".format(e - t))
else:
X_sharded = BigMatrix("cholesky_test_{0}_{1}".format(
problem_size, shard_size),
autosqueeze=False,
hash_keys=False,
bucket="numpywrentop500test")
key_name = binops.generate_key_name_binop(X_sharded, X_sharded.T,
"gemm")
XXT_sharded = BigMatrix(key_name,
hash_keys=False,
bucket="numpywrentop500test")
XXT_sharded.lambdav = problem_size * 10
if (verify):
A = XXT_sharded.numpy()
print("Computing local cholesky")
L = np.linalg.cholesky(A)
t = time.time()
instructions, trailing, L_sharded = compiler._chol(XXT_sharded,
truncate=truncate)
pipeline_width = args.pipeline
if (lru):
cache_size = 5
else:
cache_size = 0
pywren_config = pwex.config
config = npw.config.default()
program = lp.LambdaPackProgram(instructions,
executor=pywren.lambda_executor,
pywren_config=pywren_config,
num_priorities=num_priorities,
eager=eager,
config=config,
write_limit=write_limit,
read_limit=read_limit)
warmup_start = time.time()
if (warmup):
warmup_sleep = 170
def warmup_fn(x):
program.incr_up(1)
time.sleep(warmup_sleep)
program.decr_up(1)
print("Warming up...")
futures = pwex.map(warmup_fn, range(max_cores))
last_spinup = time.time()
while (True):
if ((time.time() - last_spinup) > 0.75 * warmup_sleep):
print("Calling pwex.map..")
futures = pwex.map(warmup_fn, range(max_cores))
last_spinup = time.time()
time.sleep(2)
if (program.get_up() is None):
up_workers = 0
else:
up_workers = int(program.get_up())
print("{0} workers alive".format(up_workers))
if (up_workers >= max_cores):
time.sleep(warmup_sleep)
break
warmup_end = time.time()
print("Warmup took {0} seconds".format(warmup_end - warmup_start))
e = time.time()
print("Program compile took {0} seconds".format(e - t))
print("program.hash", program.hash)
REDIS_CLIENT = program.control_plane.client
done_counts = []
ready_counts = []
post_op_counts = []
not_ready_counts = []
running_counts = []
sqs_invis_counts = []
sqs_vis_counts = []
up_workers_counts = []
busy_workers_counts = []
read_objects = []
write_objects = []
all_read_timeouts = []
all_write_timeouts = []
all_redis_timeouts = []
times = [time.time()]
flops = [0]
reads = [0]
writes = [0]
print("LRU", lru)
print("eager", eager)
exp = {}
exp["redis_done_counts"] = done_counts
exp["redis_ready_counts"] = ready_counts
exp["redis_post_op_counts"] = post_op_counts
exp["redis_not_ready_counts"] = not_ready_counts
exp["redis_running_counts"] = running_counts
exp["sqs_invis_counts"] = sqs_invis_counts
exp["sqs_vis_counts"] = sqs_vis_counts
exp["busy_workers"] = busy_workers_counts
exp["up_workers"] = up_workers_counts
exp["times"] = times
exp["lru"] = lru
exp["priority"] = num_priorities
exp["eager"] = eager
exp["truncate"] = truncate
exp["max_cores"] = max_cores
exp["problem_size"] = problem_size
exp["shard_size"] = shard_size
exp["pipeline"] = pipeline
exp["flops"] = flops
exp["reads"] = reads
exp["writes"] = writes
exp["read_objects"] = read_objects
exp["write_objects"] = write_objects
exp["read_timeouts"] = all_read_timeouts
exp["write_timeouts"] = all_write_timeouts
exp["redis_timeouts"] = all_redis_timeouts
exp["trial"] = trial
exp["launch_granularity"] = launch_granularity
exp["log_granularity"] = log_granularity
exp["autoscale_policy"] = autoscale_policy
exp["standalone"] = standalone
exp["program"] = program
exp["time_steps"] = 1
exp["failed"] = False
program.start()
t = time.time()
logger.info("Starting with {0} cores".format(start_cores))
invoker = fs.ThreadPoolExecutor(1)
all_future_futures = invoker.submit(lambda: pwex.map(
lambda x: job_runner.lambdapack_run(program,
pipeline_width=pipeline_width,
cache_size=cache_size,
timeout=timeout),
range(start_cores),
extra_env=extra_env))
# print(all_future_futures.result())
all_futures = [all_future_futures]
# print([f.result() for f in all_futures])
start_time = time.time()
last_run_time = start_time
print(program.program_status())
print("QUEUE URLS", len(program.queue_urls))
total_lambda_epochs = start_cores
try:
while (program.program_status() == lp.PS.RUNNING):
time.sleep(log_granularity)
curr_time = int(time.time() - start_time)
p = program.get_progress()
if (p is None):
print("no progress...")
continue
else:
p = int(p)
times.append(int(time.time()))
max_pc = p
waiting = 0
running = 0
for i, queue_url in enumerate(program.queue_urls):
client = boto3.client('sqs')
attrs = client.get_queue_attributes(
QueueUrl=queue_url,
AttributeNames=[
'ApproximateNumberOfMessages',
'ApproximateNumberOfMessagesNotVisible'
])['Attributes']
waiting += int(attrs["ApproximateNumberOfMessages"])
running += int(attrs["ApproximateNumberOfMessagesNotVisible"])
sqs_invis_counts.append(running)
sqs_vis_counts.append(waiting)
busy_workers = REDIS_CLIENT.get("{0}_busy".format(program.hash))
if (busy_workers == None):
busy_workers = 0
else:
busy_workers = int(busy_workers)
up_workers = program.get_up()
if (up_workers == None):
up_workers = 0
else:
up_workers = int(up_workers)
up_workers_counts.append(up_workers)
busy_workers_counts.append(busy_workers)
logger.debug("{2}: Up Workers: {0}, Busy Workers: {1}".format(
up_workers, busy_workers, curr_time))
if ((curr_time % INFO_FREQ) == 0):
logger.info("Waiting: {0}, Currently Processing: {1}".format(
waiting, running))
logger.info("{2}: Up Workers: {0}, Busy Workers: {1}".format(
up_workers, busy_workers, curr_time))
current_gflops = program.get_flops()
if (current_gflops is None):
current_gflops = 0
else:
current_gflops = int(current_gflops) / 1e9
flops.append(current_gflops)
current_gbytes_read = program.get_read()
if (current_gbytes_read is None):
current_gbytes_read = 0
else:
current_gbytes_read = int(current_gbytes_read) / 1e9
reads.append(current_gbytes_read)
current_gbytes_write = program.get_write()
if (current_gbytes_write is None):
current_gbytes_write = 0
else:
current_gbytes_write = int(current_gbytes_write) / 1e9
writes.append(current_gbytes_write)
gflops_rate = flops[-1] / (times[-1] - times[0])
greads_rate = reads[-1] / (times[-1] - times[0])
gwrites_rate = writes[-1] / (times[-1] - times[0])
b = XXT_sharded.shard_sizes[0]
current_objects_read = (current_gbytes_read * 1e9) / (b * b * 8)
current_objects_write = (current_gbytes_write * 1e9) / (b * b * 8)
read_objects.append(current_objects_read)
write_objects.append(current_objects_write)
read_rate = read_objects[-1] / (times[-1] - times[0])
write_rate = write_objects[-1] / (times[-1] - times[0])
avg_workers = np.mean(up_workers_counts)
smooth_len = 10
if (len(flops) > smooth_len + 5):
gflops_rate_5_min_window = (flops[-1] - flops[-smooth_len]) / (
times[-1] - times[-smooth_len])
gread_rate_5_min_window = (reads[-1] - reads[-smooth_len]) / (
times[-1] - times[-smooth_len])
gwrite_rate_5_min_window = (
writes[-1] - writes[-smooth_len]) / (times[-1] -
times[-smooth_len])
read_rate_5_min_window = (read_objects[-1] -
read_objects[-smooth_len]) / (
times[-1] - times[-smooth_len])
write_rate_5_min_window = (write_objects[-1] -
write_objects[-smooth_len]) / (
times[-1] - times[-smooth_len])
workers_5_min_window = np.mean(up_workers_counts[-smooth_len:])
else:
gflops_rate_5_min_window = "N/A"
gread_rate_5_min_window = "N/A"
gwrite_rate_5_min_window = "N/A"
workers_5_min_window = "N/A"
read_rate_5_min_window = "N/A"
write_rate_5_min_window = "N/A"
read_timeouts = int(REDIS_CLIENT.get("s3.timeouts.read"))
write_timeouts = int(REDIS_CLIENT.get("s3.timeouts.write"))
redis_timeouts = int(REDIS_CLIENT.get("redis.timeouts"))
all_read_timeouts.append(read_timeouts)
all_write_timeouts.append(write_timeouts)
all_redis_timeouts.append(redis_timeouts)
read_timeouts_fraction = read_timeouts / current_objects_read
write_timeouts_fraction = write_timeouts / current_objects_write
print("=======================================")
print("Max PC is {0}".format(max_pc))
print("Waiting: {0}, Currently Processing: {1}".format(
waiting, running))
print("{2}: Up Workers: {0}, Busy Workers: {1}".format(
up_workers, busy_workers, curr_time))
print(
"{0}: Total GFLOPS {1}, Total GBytes Read {2}, Total GBytes Write {3}"
.format(curr_time, current_gflops, current_gbytes_read,
current_gbytes_write))
print(
"{0}: Average GFLOPS rate {1}, Average GBytes Read rate {2}, Average GBytes Write rate {3}, Average Worker Count {4}"
.format(curr_time, gflops_rate, greads_rate, gwrites_rate,
avg_workers))
print("{0}: Average read txns/s {1}, Average write txns/s {2}".
format(curr_time, read_rate, write_rate))
print(
"{0}: smoothed GFLOPS rate {1}, smoothed GBytes Read rate {2}, smoothed GBytes Write rate {3}, smoothed Worker Count {4}"
.format(curr_time, gflops_rate_5_min_window,
gread_rate_5_min_window, gwrite_rate_5_min_window,
workers_5_min_window))
print("{0}: smoothed read txns/s {1}, smoothed write txns/s {2}".
format(curr_time, read_rate_5_min_window,
write_rate_5_min_window))
print(
"{0}: Read timeouts: {1}, Write timeouts: {2}, Redis timeouts: {3} "
.format(curr_time, read_timeouts, write_timeouts,
redis_timeouts))
print(
"{0}: Read timeouts fraction: {1}, Write timeouts fraction: {2}"
.format(curr_time, read_timeouts_fraction,
write_timeouts_fraction))
print("=======================================")
time_since_launch = time.time() - last_run_time
if (autoscale_policy == "dynamic"):
if (time_since_launch > launch_granularity and
up_workers < np.ceil(waiting * 0.5 / pipeline_width)
and up_workers < max_cores):
cores_to_launch = int(
min(
np.ceil(waiting / pipeline_width) - up_workers,
max_cores - up_workers))
logger.info(
"launching {0} new tasks....".format(cores_to_launch))
new_future_futures = invoker.submit(
lambda: pwex.map(lambda x: job_runner.lambdapack_run(
program,
pipeline_width=pipeline_width,
cache_size=cache_size,
timeout=timeout),
range(cores_to_launch),
extra_env=extra_env))
last_run_time = time.time()
# check if we OOM-erred
# [x.result() for x in all_futures]
all_futures.extend(new_future_futures)
elif (autoscale_policy == "constant_timeout"):
if (time_since_launch > (0.85 * timeout)):
cores_to_launch = max_cores
logger.info(
"launching {0} new tasks....".format(cores_to_launch))
new_future_futures = invoker.submit(
lambda: pwex.map(lambda x: job_runner.lambdapack_run(
program,
pipeline_width=pipeline_width,
cache_size=cache_size,
timeout=timeout),
range(cores_to_launch),
extra_env=extra_env))
last_run_time = time.time()
# check if we OOM-erred
# [x.result() for x in all_futures]
all_futures.append(new_future_futures)
else:
raise Exception("unknown autoscale policy")
exp["time_steps"] += 1
if (verify):
L_sharded_local = L_sharded.numpy()
print("max diff", np.max(np.abs(L_sharded_local - L)))
except KeyboardInterrupt:
exp["failed"] = True
program.stop()
pass
except Exception as e:
traceback.print_exc()
exp["failed"] = True
program.stop()
raise
pass
print(program.program_status())
exp["all_futures"] = all_futures
exp_bytes = dill.dumps(exp)
client = boto3.client('s3')
client.put_object(Key="lambdapack/{0}/runtime.pickle".format(program.hash),
Body=exp_bytes,
Bucket=program.bucket)
print("=======================")
print("=======================")
print("Execution Summary:")
print("Executed Program ID: {0}".format(program.hash))
print("Program Success: {0}".format((not exp["failed"])))
print("Problem Size: {0}".format(exp["problem_size"]))
print("Shard Size: {0}".format(exp["shard_size"]))
print("Total Execution time: {0}".format(times[-1] - times[0]))
print("Average Flop Rate (GFlop/s): {0}".format(exp["flops"][-1] /
(times[-1] - times[0])))
with open("/tmp/last_run", "w+") as f:
f.write(program.hash)
def test_cholesky_multi_repeats(self):
''' Insert repeated instructions into PC queue avoid double increments '''
print("RUNNING MULTI")
np.random.seed(1)
size = 256
shard_size = 30
repeats = 15
total_repeats = 150
np.random.seed(2)
print("Generating X")
X = np.random.randn(size, 128)
print("Generating A")
A = X.dot(X.T) + size*np.eye(X.shape[0])
shard_sizes = (shard_size, shard_size)
A_sharded = BigMatrix("cholesky_test_A_{0}".format(
int(time.time())), shape=A.shape, shard_sizes=shard_sizes, write_header=True)
A_sharded.free()
shard_matrix(A_sharded, A)
instructions, trailing, L_sharded = compiler._chol(A_sharded)
all_nodes = instructions.unroll_program()
L_sharded.free()
pwex = pywren.default_executor()
executor = pywren.lambda_executor
config = npw.config.default()
pywren_config = pwex.config
program = lp.LambdaPackProgram(
instructions, executor=executor, pywren_config=pywren_config, config=config, eager=True)
print("PROGRAM HASH", program.hash)
cores = 1
program.start()
jobs = []
for c in range(cores):
p = mp.Process(target=job_runner.lambdapack_run, args=(
program,), kwargs={'timeout': 3600, 'pipeline_width': 5})
jobs.append(p)
p.start()
np.random.seed(0)
try:
while(program.program_status() == lp.PS.RUNNING):
sqs = boto3.resource(
'sqs', region_name=program.control_plane.region)
time.sleep(0.5)
waiting = 0
running = 0
for i, queue_url in enumerate(program.queue_urls):
client = boto3.client('sqs')
print("Priority {0}".format(i))
attrs = client.get_queue_attributes(QueueUrl=queue_url, AttributeNames=[
'ApproximateNumberOfMessages', 'ApproximateNumberOfMessagesNotVisible'])['Attributes']
print(attrs)
waiting += int(attrs["ApproximateNumberOfMessages"])
running += int(attrs["ApproximateNumberOfMessagesNotVisible"])
print("SQS QUEUE STATUS Waiting {0}, Running {1}".format(
waiting, running))
for i in range(repeats):
p = program.get_progress()
if (p is None):
continue
else:
p = int(p)
pc = int(np.random.choice(min(p, len(all_nodes)), 1))
node = all_nodes[pc]
queue = sqs.Queue(program.queue_urls[0])
total_repeats -= 1
if (total_repeats > 0):
print("Malicilously enqueueing node ",
pc, node, total_repeats)
queue.send_message(MessageBody=json.dumps(node))
time.sleep(1)
# for p in jobs:
# p.join()
except:
pass
print("Program status")
print(program.program_status())
for node in all_nodes:
edge_sum = lp.get(program.control_plane.client,
program._node_edge_sum_key(*node))
if (edge_sum == None):
edge_sum = 0
edge_sum = int(edge_sum)
parents = program.program.get_parents(*node)
children = program.program.get_children(*node)
indegree = len(parents)
node_status = program.get_node_status(*node)
redis_str = "Node: {0}, Edge Sum: {1}, Indegree: {2}, Node Status {3}".format(
node, edge_sum, indegree, node_status)
if (edge_sum != indegree):
print(redis_str)
for p in parents:
p_status = program.get_node_status(*p)
edge_key = program._edge_key(p[0], p[1], node[0], node[1])
edge_value = lp.get(program.control_plane.client, edge_key)
child_str = "Parent Node: {0}, Parent Status: {1}, Edge Key: {2}".format(
p, p_status, edge_value)
print(child_str)
#assert(edge_sum == indegree)
program.free()
L_npw = L_sharded.numpy()
L = np.linalg.cholesky(A)
z = np.argmax(np.abs(L - L_npw))
assert(np.allclose(L_npw, L))
def test_cholesky_multi_failures(self):
''' Insert repeated instructions into PC queue avoid double increments '''
print("RUNNING MULTI")
np.random.seed(1)
size = 256
shard_size = 64
failures = 4
np.random.seed(1)
print("Generating X")
X = np.random.randn(size, 128)
print("Generating A")
A = X.dot(X.T) + size*np.eye(X.shape[0])
shard_sizes = (shard_size, shard_size)
A_sharded = BigMatrix("cholesky_test_A", shape=A.shape,
shard_sizes=shard_sizes, write_header=True)
A_sharded.free()
shard_matrix(A_sharded, A)
instructions, trailing, L_sharded = compiler._chol(A_sharded)
pwex = pywren.default_executor()
executor = pywren.lambda_executor
pywren_config = pwex.config
config = npw.config.default()
program = lp.LambdaPackProgram(
instructions, executor=executor, pywren_config=pywren_config, config=config, eager=False)
cores = 16
program.start()
jobs = []
for c in range(cores):
p = mp.Process(target=job_runner.lambdapack_run, args=(
program,), kwargs={'timeout': 3600, 'pipeline_width': 4})
jobs.append(p)
p.start()
np.random.seed(0)
while(program.program_status() == lp.PS.RUNNING):
sqs = boto3.resource(
'sqs', region_name=program.control_plane.region)
waiting = 0
running = 0
for i, queue_url in enumerate(program.queue_urls):
client = boto3.client('sqs')
print("Priority {0}".format(i))
attrs = client.get_queue_attributes(QueueUrl=queue_url, AttributeNames=[
'ApproximateNumberOfMessages', 'ApproximateNumberOfMessagesNotVisible'])['Attributes']
print(attrs)
waiting += int(attrs["ApproximateNumberOfMessages"])
running += int(attrs["ApproximateNumberOfMessagesNotVisible"])
print("SQS QUEUE STATUS Waiting {0}, Running {1}".format(
waiting, running))
time.sleep(10)
if (np.random.random() > 0.65):
for i in range(failures):
core = int(np.random.choice(cores, 1)[0])
print("Maliciously Killing a job!")
jobs[core].terminate()
p = mp.Process(target=job_runner.lambdapack_run, args=(
program,), kwargs={'timeout': 3600, 'pipeline_width': 4})
p.start()
jobs[core] = p
for p in jobs:
p.join()
print("Program status")
print(program.program_status())
program.free()
L_npw = L_sharded.numpy()
L = np.linalg.cholesky(A)
print(L_npw)
print(L)
print("MAX ", np.max(np.abs(L - L_npw)))
assert(np.allclose(L_npw, L))
def run_experiment(problem_size, shard_size, pipeline, priority, lru, eager,
truncate, max_cores, start_cores, trial, launch_granularity,
timeout, log_granularity, autoscale_policy,
failure_percentage, max_failure_events, failure_time):
# set up logging
logger = logging.getLogger()
for key in logging.Logger.manager.loggerDict:
logging.getLogger(key).setLevel(logging.CRITICAL)
logger.setLevel(logging.DEBUG)
arg_bytes = pickle.dumps(
(problem_size, shard_size, pipeline, priority, lru, eager, truncate,
max_cores, start_cores, trial, launch_granularity, timeout,
log_granularity, autoscale_policy, failure_percentage,
max_failure_events, failure_time))
arg_hash = hashlib.md5(arg_bytes).hexdigest()
log_file = "failure_experiments/{0}.log".format(arg_hash)
fh = logging.FileHandler(log_file)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
logger.info("Logging to {0}".format(log_file))
X = np.random.randn(problem_size, 1)
pwex = pywren.default_executor()
shard_sizes = [shard_size, 1]
X_sharded = BigMatrix("cholesky_test_{0}_{1}".format(
problem_size, shard_size),
shape=X.shape,
shard_sizes=shard_sizes,
write_header=True)
shard_matrix(X_sharded, X)
print("Generating PSD matrix...")
XXT_sharded = binops.gemm(pwex, X_sharded, X_sharded.T, overwrite=False)
XXT_sharded.lambdav = problem_size * 10
instructions, L_sharded, trailing = lp._chol(XXT_sharded)
pipeline_width = args.pipeline
if (priority):
num_priorities = 5
else:
num_priorities = 1
if (lru):
cache_size = 5
else:
cache_size = 0
REDIS_CLIENT = redis.StrictRedis(REDIS_ADDR,
port=REDIS_PORT,
password=REDIS_PASS,
db=0,
socket_timeout=5)
if (truncate is not None):
instructions = instructions[:truncate]
config = pwex.config
program = lp.LambdaPackProgram(instructions,
executor=pywren.lambda_executor,
pywren_config=config,
num_priorities=num_priorities,
eager=eager)
redis_env = {
"REDIS_ADDR": os.environ.get("REDIS_ADDR", ""),
"REDIS_PASS": os.environ.get("REDIS_PASS", "")
}
done_counts = []
ready_counts = []
post_op_counts = []
not_ready_counts = []
running_counts = []
sqs_invis_counts = []
sqs_vis_counts = []
up_workers_counts = []
busy_workers_counts = []
times = []
flops = []
reads = []
writes = []
failure_times = []
exp = {}
exp["redis_done_counts"] = done_counts
exp["redis_ready_counts"] = ready_counts
exp["redis_post_op_counts"] = post_op_counts
exp["redis_not_ready_counts"] = not_ready_counts
exp["redis_running_counts"] = running_counts
exp["sqs_invis_counts"] = sqs_invis_counts
exp["sqs_vis_counts"] = sqs_vis_counts
exp["busy_workers"] = busy_workers_counts
exp["up_workers"] = up_workers_counts
exp["times"] = times
exp["lru"] = lru
exp["priority"] = priority
exp["eager"] = eager
exp["truncate"] = truncate
exp["max_cores"] = max_cores
exp["problem_size"] = problem_size
exp["shard_size"] = shard_size
exp["pipeline"] = pipeline
exp["flops"] = flops
exp["reads"] = reads
exp["writes"] = writes
exp["trial"] = trial
exp["launch_granularity"] = launch_granularity
exp["log_granularity"] = log_granularity
exp["autoscale_policy"] = autoscale_policy
exp["failure_times"] = failure_times
logger.info("Longest Path: {0}".format(program.longest_path))
program.start()
t = time.time()
logger.info("Starting with {0} cores".format(start_cores))
failure_keys = [
"{0}_failure_{1}_{2}".format(program.hash, i, 0)
for i in range(start_cores)
]
all_futures = pwex.map(lambda x: job_runner.lambdapack_run_with_failures(
failure_keys[x],
program,
pipeline_width=pipeline_width,
cache_size=cache_size,
timeout=timeout),
range(start_cores),
extra_env=redis_env)
start_time = time.time()
last_run_time = start_time
last_failure = time.time()
num_failure_events = 0
while (program.program_status() == lp.PS.RUNNING):
curr_time = int(time.time() - start_time)
max_pc = program.get_max_pc()
times.append(int(time.time()))
time.sleep(log_granularity)
waiting = 0
running = 0
for i, queue_url in enumerate(program.queue_urls):
client = boto3.client('sqs')
attrs = client.get_queue_attributes(
QueueUrl=queue_url,
AttributeNames=[
'ApproximateNumberOfMessages',
'ApproximateNumberOfMessagesNotVisible'
])['Attributes']
waiting += int(attrs["ApproximateNumberOfMessages"])
running += int(attrs["ApproximateNumberOfMessagesNotVisible"])
sqs_invis_counts.append(running)
sqs_vis_counts.append(waiting)
busy_workers = REDIS_CLIENT.get("{0}_busy".format(program.hash))
if (busy_workers == None):
busy_workers = 0
else:
busy_workers = int(busy_workers)
up_workers = program.get_up()
if (up_workers == None):
up_workers = 0
else:
up_workers = int(up_workers)
up_workers_counts.append(up_workers)
busy_workers_counts.append(busy_workers)
logger.debug("Waiting: {0}, Currently Processing: {1}".format(
waiting, running))
logger.debug("{2}: Up Workers: {0}, Busy Workers: {1}".format(
up_workers, busy_workers, curr_time))
if ((curr_time % INFO_FREQ) == 0):
logger.info("Max PC is {0}".format(max_pc))
logger.info("Waiting: {0}, Currently Processing: {1}".format(
waiting, running))
logger.info("{2}: Up Workers: {0}, Busy Workers: {1}".format(
up_workers, busy_workers, curr_time))
#print("{5}: Not Ready: {0}, Ready: {1}, Running: {4}, Post OP: {2}, Done: {3}".format(not_ready_count, ready_count, post_op_count, done_count, running_count, curr_time))
current_gflops = program.get_flops()
if (current_gflops is None):
current_gflops = 0
else:
current_gflops = int(current_gflops) / 1e9
flops.append(current_gflops)
current_gbytes_read = program.get_read()
if (current_gbytes_read is None):
current_gbytes_read = 0
else:
current_gbytes_read = int(current_gbytes_read) / 1e9
reads.append(current_gbytes_read)
current_gbytes_write = program.get_write()
if (current_gbytes_write is None):
current_gbytes_write = 0
else:
current_gbytes_write = int(current_gbytes_write) / 1e9
writes.append(current_gbytes_write)
#print("{0}: Total GFLOPS {1}, Total GBytes Read {2}, Total GBytes Write {3}".format(curr_time, current_gflops, current_gbytes_read, current_gbytes_write))
time_since_launch = time.time() - last_run_time
if (autoscale_policy == "dynamic"):
if (time_since_launch > launch_granularity
and up_workers < np.ceil(waiting * 0.5 / pipeline_width)
and up_workers < max_cores):
cores_to_launch = int(
min(
np.ceil(waiting / pipeline_width) - up_workers,
max_cores - up_workers))
logger.info(
"launching {0} new tasks....".format(cores_to_launch))
_failure_keys = [
"{0}_failure_{1}_{2}".format(program.hash, i, curr_time)
for i in range(cores_to_launch)
]
new_futures = pwex.map(
lambda x: job_runner.lambdapack_run_with_failures(
_failure_keys[x],
program,
pipeline_width=pipeline_width,
cache_size=cache_size,
timeout=timeout),
range(cores_to_launch),
extra_env=redis_env)
last_run_time = time.time()
# check if we OOM-erred
# [x.result() for x in all_futures]
all_futures.extend(new_futures)
elif (autoscale_policy == "constant_timeout"):
if (time_since_launch > (0.75 * timeout)):
cores_to_launch = max_cores
logger.info(
"launching {0} new tasks....".format(cores_to_launch))
_failure_keys = [
"{0}_failure_{1}_{2}".format(program.hash, i, curr_time)
for i in range(cores_to_launch)
]
new_futures = pwex.map(
lambda x: job_runner.lambdapack_run_with_failures(
_failure_keys[x],
program,
pipeline_width=pipeline_width,
cache_size=cache_size,
timeout=timeout),
range(cores_to_launch),
extra_env=redis_env)
last_run_time = time.time()
failure_keys += _failure_keys
# check if we OOM-erred
# [x.result() for x in all_futures]
all_futures.extend(new_futures)
else:
raise Exception("unknown autoscale policy")
if ((time.time() - last_failure) > failure_time
and num_failure_events < max_failure_events):
logging.info("Killing some jobs")
idxs = np.random.choice(len(failure_keys),
int(failure_percentage *
len(failure_keys)),
replace=False)
num_failure_events += 1
last_failure = time.time()
failure_times.append(last_failure)
for i in idxs:
logging.info("Killing: job {0}".format(i))
REDIS_CLIENT.set(failure_keys[i], 1)
exp["all_futures"] = all_futures
for pc in range(program.num_inst_blocks):
run_count = REDIS_CLIENT.get("{0}_{1}_start".format(program.hash, pc))
if (run_count is None):
run_count = 0
else:
run_count = int(run_count)
if (run_count != 1):
logger.info("PC: {0}, Run Count: {1}".format(pc, run_count))
e = time.time()
logger.info(program.program_status())
logger.info("PROGRAM STATUS " + str(program.program_status()))
logger.info("PROGRAM HASH " + str(program.hash))
logger.info("Took {0} seconds".format(e - t))
exp["total_runtime"] = e - t
exp["num_failure_events"] = num_failure_events
# collect in
executor = fs.ThreadPoolExecutor(72)
futures = []
for i in range(0, program.num_inst_blocks, 1):
futures.append(executor.submit(program.get_profiling_info, i))
res = fs.wait(futures)
profiled_blocks = [f.result() for f in futures]
serializer = serialize.SerializeIndependent()
byte_string = serializer([profiled_blocks])[0][0]
exp["profiled_block_pickle_bytes"] = byte_string
read, write, total_flops, bins, instructions, runtimes = lp.perf_profile(
profiled_blocks, num_bins=100)
flop_rate = sum(total_flops) / max(bins)
exp["flop_rate"] = flop_rate
print("Average Flop rate of {0}".format(flop_rate))
# save other stuff
try:
os.mkdir("failure_experiments/")
except FileExistsError:
pass
exp_bytes = pickle.dumps(exp)
dump_path = "failure_experiments/{0}.pickle".format(arg_hash)
print("Dumping experiment pickle to {0}".format(dump_path))
with open(dump_path, "wb+") as f:
f.write(exp_bytes)
def bdfac(A, truncate=0):
b_fac = 2
N = A.shape[0]
N_blocks = A.num_blocks(0)
b_fac = 2
shard_size = A.shard_sizes[0]
num_tree_levels = max(
int(np.ceil(np.log2(A.num_blocks(0)) / np.log2(b_fac))), 1) + 1
V_QR = BigMatrix("V_QR",
shape=(2 * N, num_tree_levels, 2 * N),
shard_sizes=(1, 1, shard_size),
write_header=True,
safe=False)
T_QR = BigMatrix("T_QR",
shape=(2 * N, num_tree_levels, 2 * N),
shard_sizes=(1, 1, shard_size),
write_header=True,
safe=False)
R_QR = BigMatrix("R_QR",
shape=(2 * N, num_tree_levels, 2 * N),
parent_fn=constant_zeros,
shard_sizes=(shard_size, 1, shard_size),
write_header=True,
safe=False)
S_QR = BigMatrix("S_QR",
shape=(2 * N, num_tree_levels, 2 * N, 2 * N),
parent_fn=constant_zeros,
shard_sizes=(1, 1, shard_size, shard_size),
write_header=True,
safe=False)
V_LQ = BigMatrix("V_LQ",
shape=(2 * N, num_tree_levels, 2 * N),
shard_sizes=(1, 1, shard_size),
write_header=True,
safe=False)
T_LQ = BigMatrix("T_LQ",
shape=(2 * N, num_tree_levels, 2 * N),
shard_sizes=(1, 1, shard_size),
write_header=True,
safe=False)
L_LQ = BigMatrix("L_LQ",
shape=(2 * N, num_tree_levels, 2 * N),
parent_fn=constant_zeros_ext,
shard_sizes=(1, 1, shard_size),
write_header=True,
safe=False)
S_LQ = BigMatrix("S_LQ",
shape=(2 * N, num_tree_levels, 2 * N, 2 * N),
parent_fn=constant_zeros_ext,
shard_sizes=(1, 1, shard_size, shard_size),
write_header=True,
safe=False)
t = time.time()
p0 = lpcompile_for_execution(BDFAC, inputs=["I"], outputs=["R_QR", "L_LQ"])
p1 = p0(A, V_QR, T_QR, S_QR, R_QR, V_LQ, T_LQ, S_LQ, L_LQ, N_blocks,
truncate)
e = time.time()
c_time = e - t
config = npw.config.default()
program = lp.LambdaPackProgram(p1, config=config)
return program, {
"outputs": [L_LQ, R_QR],
"intermediates": [S_LQ, S_QR, T_QR, V_QR, V_LQ, T_LQ],
"compile_time": c_time
}
