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execution time breakdown #51

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115 changes: 88 additions & 27 deletions train/compute/pt/pytorch_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,9 @@ def train_cpu(
loss_f = nn.CrossEntropyLoss()

# model.train()
start_time = time.time()
events = {"start_all": [], "stop_all": [], "start_fwd": [], "stop_fwd": [], "start_bwd": [], "stop_bwd": [], "start_opt": [], "stop_opt":[]}
times = {"all": 0, "fwd": 0, "bwd": 0, "opt": 0}
events["start_all"].append(time.time())

for i in range(args.steps + args.warmups):
data = torch.randn(batch_size, input_size, device=device)
Expand All @@ -45,15 +47,26 @@ def train_cpu(
if data_type == "float16":
data = data.half()

events["start_all"].append(time.time())
optimizer.zero_grad()
events["start_fwd"].append(time.time())
output = model(data).float()
events["stop_fwd"].append(time.time())
loss = loss_f(output, target)
events["start_bwd"].append(time.time())
loss.backward()
events["stop_bwd"].append(time.time())
events["start_opt"].append(time.time())
optimizer.step()
events["stop_opt"].append(time.time())
events["stop_all"].append(time.time())
if i < args.warmups:
start_time = time.time()
for t in events.values():
t.clear()

return time.time() - start_time, loss
for key in ["all", "fwd", "bwd", "opt"]:
times[key] = sum([te-ts for ts, te in zip(events["start_"+key], events["stop_"+key])])
return times, loss


def train_gpu(
Expand All @@ -67,10 +80,11 @@ def train_gpu(
model = apex.fp16_utils.network_to_half(model)

# model.train()
times = {"all": 0, "fwd": 0, "bwd": 0, "opt": 0}
events = {"start_all": [], "stop_all": [], "start_fwd": [], "stop_fwd": [], "start_bwd": [], "stop_bwd": [], "start_opt": [], "stop_opt":[]}
torch.cuda.synchronize()
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
total_time = 0.0
for e in events.keys():
events[e] = torch.cuda.Event(enable_timing=True)

for i in range(args.steps + args.warmups):
data = torch.randn(batch_size, input_size, device=device)
Expand All @@ -81,20 +95,26 @@ def train_gpu(
if data_type == "float16":
data = data.half()

if i >= args.warmups:
start_event.record()
events["start_all"].record()

optimizer.zero_grad()
events["start_fwd"].record()
output = model(data).float()
events["stop_fwd"].record()
loss = loss_f(output, target)
events["start_bwd"].record()
loss.backward()
events["stop_bwd"].record()
events["start_opt"].record()
optimizer.step()
events["stop_opt"].record()
if i >= args.warmups:
end_event.record()
events["stop_all"].record()
torch.cuda.synchronize()
total_time += start_event.elapsed_time(end_event) * 1.0e-3
for key in ["all", "fwd", "bwd", "opt"]:
times[key] += events["start_"+key].elapsed_time(events["stop_"+key]) * 1.0e-3

return total_time, loss
return times, loss


def train_tpu(
Expand All @@ -105,7 +125,9 @@ def train_tpu(
loss_f = nn.CrossEntropyLoss().to(device)

# model.train()
start_time = time.time()
times = {"all": 0, "fwd": 0, "bwd": 0, "opt": 0}
events = {"start_all": [], "stop_all": [], "start_fwd": [], "stop_fwd": [], "start_bwd": [], "stop_bwd": [], "start_opt": [], "stop_opt":[]}
events["start_all"].append(time.time())

for i in range(args.steps + args.warmups):
data = torch.randn(batch_size, input_size, device=device)
Expand All @@ -114,24 +136,35 @@ def train_tpu(
)
# data, target = data.to(device), target.to(device)

events["start_all"].append(time.time())
optimizer.zero_grad()
events["start_fwd"].append(time.time())
output = model(data).float()
events["stop_fwd"].append(time.time())
loss = loss_f(output, target)
events["start_bwd"].append(time.time())
loss.backward()
events["stop_bwd"].append(time.time())
events["start_opt"].append(time.time())
optimizer.step()
xm.mark_step()
events["stop_opt"].append(time.time())
events["stop_all"].append(time.time())
if i < args.warmups:
start_time = time.time()
for t in events.values():
t.clear()

return time.time() - start_time, loss
for key in ["all", "fwd", "bwd", "opt"]:
times[key] = sum([te-ts for ts, te in zip(events["start_"+key], events["stop_"+key])])
return times, loss


def train(
model, device, optimizer, data_type, input_size, output_size, batch_size, args
):

if device.type == "cpu":
elap, loss = train_cpu(
times, loss = train_cpu(
model,
device,
optimizer,
Expand All @@ -143,7 +176,7 @@ def train(
)

elif device.type == "cuda":
elap, loss = train_gpu(
times, loss = train_gpu(
model,
device,
optimizer,
Expand All @@ -155,7 +188,7 @@ def train(
)

elif device.type == "xla":
elap, loss = train_tpu(
times, loss = train_tpu(
model,
device,
optimizer,
Expand All @@ -166,7 +199,7 @@ def train(
args,
)

return elap, loss
return times, loss


def run_single(args, layers_size, batch_size):
Expand Down Expand Up @@ -203,6 +236,14 @@ def run_single(args, layers_size, batch_size):
optimizer = apex.optimizers.FusedLAMB(
model.parameters(), lr=lr, set_grad_none=True
)
elif optimizer_type == "adam":
optimizer = apex.optimizers.FusedAdam(
model.parameters(), lr=lr, set_grad_none=True
)
elif optimizer_type == "adagrad":
optimizer = apex.optimizers.FusedAdagrad(
model.parameters(), lr=lr, set_grad_none=True
)
else:
assert 0, "Unsupported optimizer type"

Expand All @@ -225,10 +266,10 @@ def run_single(args, layers_size, batch_size):
else:
assert 0, "Unsupported optimizer type"

elap, loss = train(
times, loss = train(
model, dev, optimizer, data_type, layers_size[0], layers_size[-1], batch_size, args
)
return elap, loss
return times, loss


def run(args, dataset):
Expand All @@ -237,41 +278,61 @@ def run(args, dataset):
"--------------------------------------------------------------------------------"
)
print(
" #Layer Input Hidden Output Batch Time(s)/step QPS Rate(TF/s)"
" Num Layers Batch Time(s)/step: All FWD BWD OPT QPS (TF/s):FWD BWD OPT(GB/s)"
)
print(
"--------------------------------------------------------------------------------"
)

for i in range(len(dataset)):
layers_size, batch_size = dataset[i]
elap, loss = run_single(
times, loss = run_single(
args, layers_size, batch_size
)
elap = times["all"]
fwd_t = times["fwd"]
bwd_t = times["bwd"]
opt_t = times["opt"]

elap /= args.steps
fwd_t /= args.steps
bwd_t /= args.steps
opt_t /= args.steps

flops = 0
for i in range(len(layers_size)-1):
flops += layers_size[i] * layers_size[i+1]
params = flops
bytes_per_dtype = 4 if args.dtype == "float" else 2
params *= bytes_per_dtype
# how many bytes of optimizer states?
flops *= batch_size

# Forward 2x and Backward 4x
fwd_flops = flops * 2
bwd_flops = flops * 6
flops *= 6



QPS = batch_size / elap

# The hidden layer size could vary, but for now keeping for backward
# compatibility
print(
"{0:6}, {1:6}, {2:6}, {3:6}, {4:6}, {5:10.6f}, {6:8.1f}, {7:10.1f}".format(
"{0:6}, {1:6}, {2:.3f}, {3:.3f}, {4:.3f}, {5:.3f}, {6:8.1f}, {7:10.1f}, {8:.3f}, {9:.3f}, {10:.3f}".format(
len(layers_size),
layers_size[0],
layers_size[1],
layers_size[-1],
batch_size,
elap,
fwd_t,
bwd_t,
opt_t,
QPS,
flops / elap / 1.0e12,
fwd_flops / fwd_t / 1.0e12,
bwd_flops / bwd_t / 1.0e12,
params / opt_t / 1.0e9

)
)

Expand Down Expand Up @@ -304,7 +365,7 @@ def dash_separated_ints(value):
"--optimizer-type",
default="sgd",
help="Optimizer: SGD",
choices=["sgd", "lamb"],
choices=["sgd", "lamb", "adam", "adagrad"],
)
parser.add_argument(
"--dtype",
Expand Down