def train_loop_fn(loader, epoch):
tracker = xm.RateTracker()
model.train()
for step, (data, target) in enumerate(loader):
optimizer.zero_grad()
with autocast():
output = model(data)
loss = loss_fn(output, target)
scaler.scale(loss).backward()
gradients = xm._fetch_gradients(optimizer)
xm.all_reduce("sum", gradients, scale=1.0 / xm.xrt_world_size())
scaler.step(optimizer)
scaler.update()
xm.mark_step()
tracker.add(FLAGS.batch_size)
if lr_scheduler:
lr_scheduler.step()
import resource
print(f" CPU Usage After: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss}")
if step % FLAGS.log_steps == 0:
# _train_update(device, step, loss, tracker, epoch, writer)
xm.add_step_closure(
_train_update, args=(device, step, loss, tracker, epoch, writer)
)
def train_loop_fn(loader, epoch):
if FLAGS.fine_grained_metrics:
epoch_start_time = time.time()
step_latency_tracker, bwd_latency_tracker, fwd_latency_tracker = [], [], []
else:
tracker = xm.RateTracker()
model.train()
for step, (data, target) in enumerate(loader):
if FLAGS.fine_grained_metrics:
step_start_time = time.time()
optimizer.zero_grad()
if FLAGS.fine_grained_metrics:
fwd_start_time = time.time()
with autocast():
output = model(data)
loss = loss_fn(output, target)
if FLAGS.fine_grained_metrics:
fwd_end_time = time.time()
fwd_latency = fwd_end_time - fwd_start_time
bwd_start_time = time.time()
scaler.scale(loss).backward()
gradients = xm._fetch_gradients(optimizer)
xm.all_reduce("sum", gradients, scale=1.0 / xm.xrt_world_size())
scaler.step(optimizer)
scaler.update()
xm.mark_step()
if lr_scheduler:
lr_scheduler.step()
if FLAGS.fine_grained_metrics:
bwd_end_time = time.time()
bwd_latency = bwd_end_time - bwd_start_time
step_latency = bwd_end_time - step_start_time
step_latency_tracker.append(step_latency)
bwd_latency_tracker.append(bwd_latency)
fwd_latency_tracker.append(fwd_latency)
else:
tracker.add(FLAGS.batch_size)
if step % FLAGS.log_steps == 0:
if FLAGS.fine_grained_metrics:
print('FineGrainedMetrics :: Epoch={} Step={} Rate(DataPoints/s)[p50]={:.1f} BatchSize={} Step(s/Batch)[p50]={:.2f} Fwd(s/Batch)[p50]={:.4f} Bwd(s/Batch)[p50]={:.4f}'.format(\
epoch, step, FLAGS.batch_size/p50(step_latency_tracker), FLAGS.batch_size, p50(step_latency_tracker), p50(bwd_latency_tracker), p50(fwd_latency_tracker)))
else:
# _train_update(device, step, loss, tracker, epoch, writer)
xm.add_step_closure(_train_update,
args=(device, step, loss, tracker,
epoch, writer))
if FLAGS.fine_grained_metrics:
epoch_end_time = time.time()
epoch_latency = epoch_end_time - epoch_start_time
print('FineGrainedMetrics :: Epoch={} Epoch(s)={:.} Rate(DataPoints/s)[p50]={:.1f} BatchSize={} Step(s/Batch)[p50]={:.2f} Fwd(s/Batch)[p50]={:.4f} Bwd(s/Batch)[p50]={:.4f}'.format(\
epoch, epoch_latency, FLAGS.batch_size/p50(step_latency_tracker), FLAGS.batch_size, p50(step_latency_tracker), p50(bwd_latency_tracker), p50(fwd_latency_tracker)))
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
for step, (data, target) in enumerate(loader):
optimizer.zero_grad()
with autocast():
output = model(data)
loss = loss_fn(output, target)
scaler.scale(loss).backward()
gradients = xm._fetch_gradients(optimizer)
xm.all_reduce('sum', gradients, scale=1.0 / xm.xrt_world_size())
scaler.step(optimizer)
scaler.update()
tracker.add(flags.batch_size)
if step % flags.log_steps == 0:
xm.add_step_closure(_train_update,
args=(device, step, loss, tracker, writer))
def loop_with_amp(model, input, positions, target, causal_mask, optimizer,
xla_enabled, autocast, scaler):
with autocast():
loss = model(input, positions, target, batch_mask=causal_mask)
if xla_enabled:
scaler.scale(loss).backward()
gradients = xm._fetch_gradients(optimizer)
xm.all_reduce("sum", gradients, scale=1.0 / xm.xrt_world_size())
scaler.step(optimizer)
scaler.update()
xm.mark_step()
else:
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
return loss
def loop_with_amp(model, input_ids, attention_mask, labels, optim, xla_enabled, autocast, scaler):
with autocast():
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs[0]
if xla_enabled:
scaler.scale(loss).backward()
gradients = xm._fetch_gradients(optim)
xm.all_reduce("sum", gradients, scale=1.0 / xm.xrt_world_size())
scaler.step(optim)
scaler.update()
xm.mark_step()
else:
scaler.scale(loss).backward()
scaler.step(optim)
scaler.update()
return loss, optim
def _run_autocast_outofplace(self,
op,
args,
run_as_type,
out_type=None,
module=torch,
add_kwargs=None):
# helper to cast args
def cast(val, to_type):
if isinstance(val, torch.Tensor):
return val.to(to_type) if val.is_floating_point() else val
elif isinstance(val, collections.abc.Iterable):
return type(val)(cast(v, to_type) for v in val)
else:
return val
if add_kwargs is None:
add_kwargs = {}
self.assertFalse(torch.is_autocast_enabled())
with autocast():
self.assertTrue(torch.is_autocast_enabled())
out_type = out_type if out_type is not None else run_as_type
output = output_method = None
# Try module.* variant, if requested:
if module is not None and hasattr(module, op):
output = getattr(module, op)(*args, **add_kwargs)
if isinstance(output, torch.Tensor):
self.assertTrue(
out_type == output.dtype,
"autocast for torch.{} produced {}, should produce {}".
format(op, output.dtype, out_type))
# Try Tensor.* variant:
if hasattr(torch.Tensor, op):
output_method = getattr(args[0], op)(*args[1:], **add_kwargs)
if isinstance(output_method, torch.Tensor):
self.assertTrue(
out_type == output_method.dtype,
"autocast for torch.{} produced {}, should produce torch.{}"
.format(op, output_method.dtype, out_type))
self.assertTrue((output is not None) or (
output_method is not None
), "{} not found as an attribute on either Tensor or the requested module {}"
.format(op, module))
# Accounts for ops that return Tensors, iterables, and other non-Tensors.
# For example, lstm_cell returns a tuple and equal returns bool.
def compare(first, second):
if isinstance(first, torch.Tensor):
return torch.equal(first, second)
elif isinstance(first, collections.abc.Iterable):
return all(compare(f, s) for f, s in zip(first, second))
else:
return first == second
# If both torch.* and Tensor.* variants were found, check outputs are identical
if (output is not None) and (output_method is not None):
self.assertTrue(type(output) == type(output_method))
comparison = compare(output, output_method)
self.assertTrue(
comparison,
"torch.{0} result did not match Tensor.{0} result".format(
op))
# Compare numerics to Python-side "autocasting" that (we expect) does the same thing
# as the C++-side autocasting, and should be bitwise accurate.
output_to_compare = output if output is not None else output_method
with autocast(enabled=False):
self.assertFalse(torch.is_autocast_enabled())
if module is not None and hasattr(module, op):
control = getattr(module, op)(*cast(args, run_as_type),
**add_kwargs)
else:
control = getattr(args[0].to(run_as_type),
op)(*cast(args[1:], run_as_type),
**add_kwargs)
self.assertTrue(type(output_to_compare) == type(control))
comparison = compare(output_to_compare, control)
self.assertTrue(
comparison,
"torch.{} result did not match control".format(op))
self.assertTrue(torch.is_autocast_enabled())
self.assertFalse(torch.is_autocast_enabled())
