Contents
pytorch_saving_loading
pytorch_saving_loading_instructions
Supported versions: 1.7.1, 1.6.0
This API document assumes you use the following import statements in your training scripts.
import smdistributed.modelparallel.torch as smp
Tip
Refer to Modify a PyTorch Training Script to learn how to use the following API in your PyTorch training script.
A sub-class of torch.nn.Module
which specifies the model to be partitioned. Accepts a torch.nn.Module
object module
which is the model to be partitioned. The returned DistributedModel
object internally manages model parallelism and data parallelism. Only one model in the training script can be wrapped with smp.DistributedModel
.
Example:
model = smp.DistributedModel(model)
Important: The __call__
and backward
method calls on the smp.DistributedModel
object (in the following example, the object is model
) can only be made inside a smp.step
-decorated function.
Since DistributedModel
is a torch.nn.Module
, a forward pass can be performed by calling the DistributedModel
object on the input tensors.
predictions = model(inputs) # model is a smp.DistributedModel object
For a backward pass, one needs to call the backward function on the DistributedModel
object, with tensors and gradients as arguments, replacing the PyTorch operations torch.Tensor.backward
or torch.autograd.backward
.
The API for model.backward
is very similar to torch.autograd.backward
. For example, the following backward
calls:
torch.autograd.backward(loss) or loss.backward()
should be replaced with:
model.backward(loss) # loss is a tensor with only one element as its data
Similarly, for non-scalar tensors, replace the following backward
call containing incoming gradient arguments:
torch.autograd.backward(outputs, out_grads)
with the following line:
model.backward(outputs, out_grads)
In these examples, all __call__
and backward
method calls on the model objects (model(inputs)
and model.backward(loss)
) must be made inside a smp.step
-decorated function.
Using DDP
If DDP is enabled, do not not place a PyTorch DistributedDataParallel
wrapper around the DistributedModel
because the DistributedModel
wrapper will also handle data parallelism.
Unlike the original DDP wrapper, when you use DistributedModel
, model parameters and buffers are not immediately broadcast across processes when the wrapper is called. Instead, the broadcast is deferred to the first call of the smp.step
-decorated function when the partition is done.
Parameters
module
(torch.nn.Module
): Module to be distributed (data parallelism and model parallelism).trace_device
("cpu"
or"gpu"
) (default:"gpu"
) Whether to perform the tracing step on the GPU or CPU. The tracing step gathers information on the order of execution of modules, the shapes of intermediate outputs, and execution times, to be used by the partitioning algorithm. Iftrace_device
is set to GPU, accurate module execution times can be gathered during tracing for potentially improved partitioning decision. However, if the model is too large to fit in a single GPU, thentrace_device
should be set to CPU.trace_execution_times
(bool
) (default:False
): IfTrue
, the library profiles the execution time of each module during tracing, and uses it in the partitioning decision. This improves the partitioning decision, but it might make the tracing slower. It may also introduce some degree of non-determinism in partitioning results, because of the inherent randomness in module execution times. Must beFalse
iftrace_device
is"cpu"
.overlapping_allreduce
(bool
) (default:True
): This is only applicable for hybrid data parallelism/model parallelism use cases (whenddp
is set toTrue
while launching training). The library uses this flag to decide whether to do overlapping allreduce whenever a parameter gradients are ready. This leads to overlapping of communication and computation and can improve performance. If this is set toFalse
, allreduce is performed at the end of the step.backward_passes_per_step
(int
) (default: 1): This is only applicable for hybrid data parallelism/model parallelism use cases (whenddp
is set toTrue
in config). This parameter indicates the number of backward passes to perform before calling allreduce on DDP. This allows accumulating updates over multiple mini-batches before reducing and applying them.average_grads_across_microbatches
(bool
) (default:True
): Whether or not the computed gradients should be averaged across microbatches. IfFalse
, the computed gradients will be summed across microbatches, but not divided by the number of microbatches. In typical use case where the computed loss is averaged over the mini-batch, this should be left asTrue
. If you use a loss function that only sums the per-sample loss across the batch (and not divide by the batch size), then this must be set toFalse
for correctness.bucket_cap_mb
(default: 25):DistributedDataParallel
buckets parameters into multiple buckets so that gradient reduction of each bucket can potentially overlap with backward computation.bucket_cap_mb
controls the bucket size in MegaBytes (MB).trace_memory_usage
(default: False): When set to True, the library attempts to measure memory usage per module during tracing. If this is disabled, memory usage will be estimated through the sizes of tensors returned from the module.broadcast_buffers
(default: True): Flag to be used withddp=True
. This parameter is forwarded to the underlyingDistributedDataParallel
wrapper. Please see: broadcast_buffer.gradient_as_bucket_view (PyTorch 1.7.1 only)
(default: False): To be used withddp=True
. This parameter is forwarded to the underlyingDistributedDataParallel
wrapper. Please see gradient_as_bucket_view.
Properties
partitioned
: IsTrue
if the model is partitioned,False
otherwise. Initialized toFalse
whenDistributedModel
is first created. It becomes beTrue
during the first call tosmp.step
-decorated function. Once the model is partitioned, the local parameters or localstate_dict
can be fetched using the following methods.
Methods
backward(tensors, grad_tensors)
Triggers a distributed backward pass across model partitions. Example usage provided in the previous section. The API is very similar to https://pytorch.org/docs/stable/autograd.html#torch.autograd.backward. retain_grad
and create_graph
flags are not supported.
local_buffers( )
Returns an iterator over buffers for the modules in the partitioned model that have been assigned to the current process.
local_named_buffers( )
Returns an iterator over buffers for the modules in the partitioned model that have been assigned to the current process. This yields both the name of the buffer as well as the buffer itself.
local_parameters( )
Returns an iterator over parameters for the modules in the partitioned model that have been assigned to the current process.
local_named_parameters( )
Returns an iterator over parameters for the modules in the partitioned model that have been assigned to the current process. This yields both the name of the parameter as well as the parameter itself.
local_modules( )
Returns an iterator over the modules in the partitioned model that have been assigned to the current process.
local_named_modules( )
Returns an iterator over the modules in the partitioned model that have been assigned to the current process. This yields both the name of the module as well as the module itself.
local_state_dict( )
Returns the state_dict
that contains local parameters that belong to the current mp_rank
. This state_dict
contains a key _smp_is_partial
to indicate this is a partial state_dict
, which indicates whether the state_dict
contains elements corresponding to only the current partition, or to the entire model.
state_dict( )
Returns the state_dict
that contains parameters for the entire model. It first collects the local_state_dict
and gathers and merges the local_state_dict
from all mp_rank
s to create a full state_dict
. Please note that this needs to be called on all ranks with dp_rank()==0
to ensure the gather happens properly. If it is only called on all such ranks, it can hang.
load_state_dict( )
Same as the torch.module.load_state_dict()
, except: It first gathers and merges the state_dict
s across mp_rank
s, if they are partial. The actual loading happens after the model partition so that each rank knows its local parameters.
register_post_partition_hook(hook)
Registers a callable hook
to be executed after the model is partitioned. This is useful in situations where an operation needs to be executed after the model partition during the first call to smp.step
, but before the actual execution of the first forward pass. Returns a RemovableHandle
object handle
, which can be used to remove the hook by calling handle.remove()
.
cpu( )
Allgathers parameters and buffers across all mp_rank
s and moves them to the CPU.
join( )
Available for PyTorch 1.7.1 only
A context manager to be used in conjunction with an instance of smp.DistributedModel
to be able to train with uneven inputs across participating processes. This is only supported when ddp=True
for smp.DistributedModel
. This will use the join with the wrapped DistributedDataParallel
instance. For more information, see: join in the PyTorch documentation.
Parameters - optimizer
An optimizer wrapper for saving/loading optimizer states. This wrapper returns optimizer
with the following methods overridden:
state_dict( )
Returns the state_dict
that contains optimizer state for the entire model. It first collects the local_state_dict
and gathers and merges the local_state_dict
from all mp_rank
s to create a full state_dict
.
load_state_dict( )
Same as the torch.optimizer.load_state_dict()
, except:
- It first gathers and merges the local
state_dict
s if they are partial.- The actual loading happens after the model partition so that each rank knows its local parameters.
local_state_dict( )
Returns the state_dict
that contains the local optimizer state that belongs to the current mp_rank
. This state_dict
contains a key _smp_is_partial
to indicate this is a partial state_dict
, which indicates whether the state_dict
contains elements corresponding to only the current partition, or to the entire model.
smp.partition(index)
Inputs
index
(int) - The index of the partition.
A context manager which places all modules defined inside into the partition with ID index
. The index
argument must be less than the number of partitions.
Use smp.partition
to implement manual partitioning. If "auto_partition"
is True
, then the smp.partition
contexts are ignored. Any module that is not placed in any smp.partition
context is placed in the default_partition
defined through the SageMaker Python SDK.
When smp.partition
contexts are nested, the innermost context overrides the rest (see the following example). In PyTorch, manual partitioning should be done inside the module __init__
, and the partition assignment applies to the modules that are created inside the smp.partition
context.
Example:
class Model(torch.nn.Module):
def __init__(self):
with smp.partition(1):
self.child0 = Child0() # child0 on partition 1
with smp.partition(2):
self.child1 = Child1() # child1 on partition 2
self.child2 = Child2() # child2 on partition 1
self.child3 = Child3() # child3 on default_partition
smp.get_world_process_group( )
Returns a torch.distributed
ProcessGroup
that consists of all processes, which can be used with the torch.distributed
API. Requires "ddp": True
in SageMaker Python SDK parameters.
smp.get_mp_process_group( )
Returns a torch.distributed
ProcessGroup
that consists of the processes in the MP_GROUP
which contains the current process, which can be used with the torch.distributed
API. Requires "ddp": True
in SageMaker Python SDK parameters.
smp.get_dp_process_group( )
Returns a torch.distributed
ProcessGroup
that consists of the processes in the DP_GROUP
which contains the current process, which can be used with the torch.distributed
API. Requires "ddp": True
in SageMaker Python SDK parameters.
smp.is_initialized( )
Returns True
if smp.init
has already been called for the process, and False
otherwise.
smp.is_tracing( )
Returns True
if the current process is running the tracing step, and False
otherwise.
smp.nn.FusedLayerNorm
Apex Fused Layer Norm is currently not supported by the library. smp.nn.FusedLayerNorm
replaces apex
FusedLayerNorm
and provides the same functionality. This requires apex
to be installed on the system.
smp.optimizers.FusedNovoGrad
Fused Novo Grad optimizer is currently not supported by the library. smp.optimizers.FusedNovoGrad
replaces apex
FusedNovoGrad
optimizer and provides the same functionality. This requires apex
to be installed on the system.
smp.optimizers.FusedLamb
FusedLamb optimizer currently doesn’t work with the library. smp.optimizers.FusedLamb
replaces apex
FusedLamb
optimizer and provides the same functionality. This requires apex
to be installed on the system.
smp.amp.GradScaler
Torch AMP Gradscaler currently doesn’t work with the library. smp.amp.GradScaler
replaces torch.amp.GradScaler
and provides the same functionality.
smp.save( )
Saves an object. This operation is similar to torch.save()
, except it has an additional keyword argument, partial
, and accepts only string type for the argument f
(file). If partial=True
, each mp_rank
saves a separate checkpoint file and the library adds an mp_rank
index to your saved file.
Parameters
obj
(dict): A saved object.f
(str): A string containing a file name.partial
(bool, default=True
): When set toTrue
, eachmp_rank
saves a separate checkpoint file and the library adds anmp_rank
index to the saved file. If you want to be able to load and further train a model that you save withsmp.save()
, you must setpartial=True
.pickle_module
(picklemodule, default = module"pickle"
from"/opt/conda/lib/python3.6/pickle.py"
): A module used for pickling metadata and objects.pickle_protocol
(int, default=2): Can be specified to override the defaultprotocol.
smp.load( )
Loads an object saved with smp.save()
from a file.
Similar to, torch.load(), except it has an additional keyword argument, partial
, and accepts only string type for the argument f
(file). If partial=True
, then each mp_rank
loads a separate checkpoint file.
Parameters
f
(string): A string containing a file name.map_location
(function): A function torch.device, a string, or a dict specifying how to remap storage locations.pickle_module
(pickle module): A module used for unpickling metadata and objects (has to match thepickle_module
used to serialize file).pickle_load_args
(Python 3 only): Optional keyword arguments passed topickle_module.load()
andpickle_module.Unpickler()
.partial
(bool, default=True
): When set toTrue
, eachmp_rank
loads the checkpoint corresponding to themp_rank
. Should be used when loading a model trained with the library.
The library can save partial or full checkpoints.
- For partial checkpoints, each
mp_rank
saves its own checkpoint file with only the parameters that belong to that rank. - For full checkpoints, the library saves a single checkpoint that contains entire model parameters.
When saving using smp.save()
, each rank only holds its own parameters. If you want to save the full model, there will be some communication between the ranks to create the full model. If you save checkpoints often, you should save partial checkpoints for best performance.
When loading using smp.load()
, the library can load either partial or | full checkpoints or full checkpoints saved by a non-model-parallel model. If you want to resume training with a non-model-parallel model or do inference, you need a full checkpoint.
The following is an example of how you can save and load a checkpoint:
# Original model and optimizer
model = MyModel(...)
optimizer = MyOpt(...)
# model parallel wrapper
model = smp.DistributedModel(model)
optimizer = smp.DistributedOptimizer(optimizer)
# To save, always save on dp_rank 0 to avoid data racing
if partial:
# To save the partial model on each mp rank
# the library will create `checkpoint.pt_{mprank}` for each mp rank
if save_partial_model:
if smp.dp_rank() == 0:
model_dict = model.local_state_dict() # save the partial model
opt_dict = optimizer.local_state_dict() # save the partial optimizer state
smp.save(
{"model_state_dict": model_dict, "optimizer_state_dict": opt_dict},
f"/checkpoint.pt",
partial=True,
)
# To save the full model
if save_full_model:
if smp.dp_rank() == 0:
model_dict = model.state_dict() # save the full model
opt_dict = optimizer.state_dict() # save the full optimizer state
smp.save(
{"model_state_dict": model_dict, "optimizer_state_dict": opt_dict},
"/checkpoint.pt",
partial=False,
)
# To load, load on all ranks.
# The only difference for partial/full loading is the partial flag in smp.load
# Load partial checkpoint
if partial_checkpoint:
checkpoint = smp.load("/checkpoint.pt", partial=True)
model.load_state_dict(checkpoint["model_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
# Load full checkpoint
if full_checkpoint:
checkpoint = smp.load("/checkpoint.pt", partial=False)
model.load_state_dict(checkpoint["model_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])