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pytorch/torch/_dynamo/backends/distributed.py

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import logging
import traceback
from dataclasses import dataclass, field
from typing import Any, List, Optional
import torch
from torch import fx
from torch._dynamo.output_graph import GraphCompileReason
from torch._dynamo.utils import detect_fake_mode
from torch._subclasses.fake_tensor import is_fake
from torch.fx.node import Node
# Regular log messages should go through 'log'.
# ddp_graph_log is a separate artifact logger reserved for dumping graphs.
# See docs/source/logging.rst for more info.
log = logging.getLogger(__name__)
ddp_graph_log = torch._logging.getArtifactLogger(__name__, "ddp_graphs")
def args_str(args):
# a debug helper
if torch.is_tensor(args):
return f"T[{args.shape}]"
elif isinstance(args, tuple):
return f"tuple({', '.join([args_str(x) for x in args])})"
elif isinstance(args, list):
return f"list({', '.join([args_str(x) for x in args])})"
else:
return str(args)
@dataclass
class Bucket:
size: int = 0
params: List[str] = field(default_factory=list)
nodes: List[fx.Node] = field(default_factory=list)
# param_ids is just used for unit testing
param_ids: List = field(default_factory=list)
# keep track of any buckets that were extended for logging purposes
opcount_increased_to_capture_external_output: int = 0
paramsize_before_opcount_increase: int = 0
def bucket_has_external_output(bucket: Bucket) -> bool:
nodes_in_bucket = set()
# we want to iterate in reverse order, but clumsi-luckily the bucket.nodes list was already created backwards
# so we don't reverse it here
for node in bucket.nodes:
# assume node.op != output, since those are filtered in the original iteration
nodes_in_bucket.add(node)
for user in node.users:
if user not in nodes_in_bucket:
return True
return False
def pretty_print_buckets(buckets: List[Bucket], bucket_bytes_cap: int):
headers = ("Index", "Size (b)", "Param Names")
rows = []
extended_buckets = []
for idx, bucket in enumerate(reversed(buckets)):
if len(bucket.params) > 0:
rows.append((idx, bucket.size, bucket.params[0]))
for param in bucket.params[1:]:
rows.append((None, None, param))
if bucket.opcount_increased_to_capture_external_output > 0:
extended_buckets.append(
(
idx,
bucket.opcount_increased_to_capture_external_output,
bucket.size - bucket.paramsize_before_opcount_increase,
)
)
if len(rows):
log.info(
"\nDDPOptimizer used bucket cap %s and created %d buckets. Enable debug logs for detailed bucket info.",
bucket_bytes_cap,
len(buckets),
)
if len(extended_buckets):
log.warning(
"Some buckets were extended beyond their requested parameter capacities"
" in order to ensure each subgraph has an output node, required for fx graph partitioning."
" This can be the case when a subgraph would have only contained nodes performing inplace mutation,"
" and returning no logical outputs. This should not be a problem, unless it results in too few graph"
" partitions for optimal DDP performance."
)
try:
from tabulate import tabulate
log.debug(
"\nDDPOptimizer produced the following bucket assignments:\n%s",
tabulate(rows, headers=headers, tablefmt="simple_grid"),
)
if len(extended_buckets):
log.warning(
"DDPOptimizer extended these buckets to ensure per-subgraph output nodes:\n%s",
tabulate(
extended_buckets,
headers=("Index", "Extra Ops", "Extra Param Size (b)"),
tablefmt="simple_grid",
),
)
except ImportError:
log.debug(
"Please `pip install tabulate` in order to display ddp bucket sizes and diagnostic information."
)
else:
log.debug("DDPOptimizer captured no parameters and did not split this graph.")
def has_higher_order_op(gm):
# Check if there is a higher order op in the graph
for node in gm.graph.nodes:
if node.op == "get_attr":
maybe_param = getattr(gm, node.target)
if isinstance(maybe_param, torch.fx.GraphModule):
return True
return False
class DDPOptimizer:
"""Note [DDPOptimizer]
DDPOptimizer applies when dynamo compiles models wrapped in DistributedDataParallel (DDP),
breaking the dynamo graph into chunks to compile separately, with the breaks aligning to
the boundaries of gradient-allreduce buckets chosen by DDP.
Background/Motivation
- DDP uses allreduce collectives to synchronize partial gradients computed on different workers
- DDP groups gradient allreduces into 'buckets' to optimize communication efficiency of all-reduce
- Parameters grouped into buckets are assumed to be adjacent in time, so they become ready
at around the same time during backward and thus can share the same allreduce efficiently
- Allreduces must overlap with backward compute for optimal training performance
- DDP schedules allreduces using 'hooks' fired from the c++ autograd engine in pytorch, which
operates when individual grads become 'ready'
- Dynamo+AOTAutograd produces a single fused graph that runs 'atomically' from the perspective of the
autograd engine, such that all gradients become 'ready' at the same time. Hooks fire after the whole
fused backward function executes, preventing any overlap of compute and communication
Algorithm
- DDPOptimizer starts off with an FX graph traced by dynamo which represents forward. It can traverse
this graph in reverse order to determine the true order that gradients will become ready during backward.
- Parameter sizes are counted in reverse order, up to a bucket size limit, at which point a new bucket is started
and a graph break introduced
- Each of the subgraphs is compiled by the compiler provided to dynamo by the user, and then fused back together
into an outer module that is returned to the user
Notes
- It would be better to enforce (by adding an API to DDP) that the bucket splits chosen here are used by DDP,
and that DDP does not need to detect or optimize bucket order by observing execution at runtime, as it does
in eager.
- If Dynamo can't capture a whole graph for the portion of the model wrapped by DDP, this algorithm will currently
produce splits that do not necessarily align with the buckets used by DDP. This should result in performance
degradation approaching the baseline case where graph-splits are not used, but not worse.
- If the backend compiler fails to compile a single subgraph, it will execute eagerly despite the rest of the
subgraphs being compiled
- DDP has a 'parameters_and_buffers_to_ignore' field, which DDPOptimizer attempts to honor by reading markers
left by DDP on individual parameters. In cases where other transformations, such as reparameterization, are
also used, the ignore markers could be lost. If DDPOptimizer fails to ignore a parameter ignored by DDP,
it is not catastrophic but could impact performance by choosing sub-optimal bucket splits.
- DDPOptimizer always ignores all buffers, regardless of their ignore flag, since buffers do not require gradients,
and therefore aren't allreduced by DDP. (They are broadcast during forward, but this is not covered by
DDPOptimizer)
Debugging
- Generally, it is easiest to debug DDPOptimizer in a single process program, using pdb.
- In many cases, the log messages are helpful (they show bucket size assignments)-
just configure torch._dynamo.config.log_level to info or debug.
- See `benchmarks/dynamo/distributed.py` for a simple harness that will run a toy model or a torchbench model
in a single process (or with torchrun, in multiple processes)
Args:
bucket_bytes_cap (int): Controls the size of buckets, in bytes, used to determine graphbreaks. Should be
set to match the equivalent parameter on the original DDP module.
backend_compile_fn (callable): A dynamo compiler function, to be invoked to compile each subgraph.
first_bucket_cap (int): Controls the size of the first bucket. Should match DDP's first bucket cap. DDP
special-cases the first bucket size since it is sometimes optimal to start a small allreduce early.
"""
def __init__(
self,
bucket_bytes_cap: int,
backend_compile_fn,
first_bucket_cap: Optional[int] = None,
):
if first_bucket_cap is not None:
self.first_bucket_cap = first_bucket_cap
elif torch.distributed.is_available():
# this constant comes from C10D lib which is not always built
self.first_bucket_cap = torch.distributed._DEFAULT_FIRST_BUCKET_BYTES
else:
self.first_bucket_cap = bucket_bytes_cap
self.bucket_bytes_cap = bucket_bytes_cap
assert (
self.first_bucket_cap <= self.bucket_bytes_cap
), "First bucket should be smaller/equal to other buckets to get comms warmed up ASAP"
self.backend_compile_fn = backend_compile_fn
def _ignore_parameter(self, parameter):
return hasattr(parameter, "_ddp_ignored") and parameter._ddp_ignored
def compile_fn(self, gm: fx.GraphModule, example_inputs: List[torch.Tensor]):
"""
Implements graph splitting, first determining a set of of buckets by counting
parameter sizes in reverse graph order, then invoking the user/backend compiler
to compile each subgraph. Finally, stiches compiled graphs into one graphmodule
and returns its callable.
"""
fake_mode = detect_fake_mode(example_inputs)
if fake_mode is None:
fake_mode = torch._subclasses.fake_tensor.FakeTensorMode()
if has_higher_order_op(gm):
# This indicates presence of a higher order op. For now, we
# have no way to break the higher order op into two buckets.
# Allowing higher order ops in the graph also requires
# changes in the split_module, becuase graph splitter
# currently assumes that all the args of all ops are
# tensors, but in the case of higher order ops, it could be
# a graph module. As a workaround, we are shortcircuiting
raise NotImplementedError(
"DDPOptimizer backend: Found a higher order op in the graph. "
"This is not supported. Please turn off DDP optimizer using "
"torch._dynamo.config.optimize_ddp=False. Note that this can "
"cause performance degradation because there will be one bucket "
"for the entire Dynamo graph. Please refer to this issue - "
"https://github.com/pytorch/pytorch/issues/104674."
)
# 1: compute the partition map according to DDP bucket logic
buckets = [Bucket()] # (size, param_names)
for node in reversed(gm.graph.nodes):
if node.op in ("output", "placeholder"):
continue
if (
buckets[0].size >= self.bucket_bytes_cap
or len(buckets) == 1
and buckets[0].size >= self.first_bucket_cap
):
if bucket_has_external_output(buckets[0]):
buckets.insert(0, Bucket())
else:
# continue building this bucket past the point of filling its parameter capacity,
# to increase chances it contains at least one node that is either a global output or
# passed as input to a subsequent graph
if buckets[0].opcount_increased_to_capture_external_output == 0:
buckets[0].paramsize_before_opcount_increase = buckets[0].size
buckets[0].opcount_increased_to_capture_external_output += 1
if node.op == "call_module":
target = gm.get_submodule(node.target)
for name, param in target.named_parameters():
if param.requires_grad and not self._ignore_parameter(param):
buckets[0].size += param.untyped_storage().nbytes()
buckets[0].params.append(f"{node.target}_{name}")
buckets[0].param_ids.append(id(param))
elif node.op == "get_attr":
maybe_param = getattr(gm, node.target)
if maybe_param.requires_grad and not self._ignore_parameter(
maybe_param
):
buckets[0].size += maybe_param.untyped_storage().nbytes()
buckets[0].params.append(node.target)
buckets[0].param_ids.append(id(maybe_param))
# All nodes have to be mapped to a bucket, even if they don't have their own params
# Ignored params still end up in buckets, we just don't count them towards the capacity
buckets[0].nodes.append(node)
if len(buckets) > 1 and buckets[0].size == 0:
# we collected a small preamble graph with ops that don't include parameters, fuse it back
buckets[1].nodes.extend(buckets[0].nodes)
assert len(buckets[0].params) == 0, "Params should be empty if size is 0"
del buckets[0]
# stash buckets for testing/debugging purposes
self.buckets = buckets
pretty_print_buckets(buckets, self.bucket_bytes_cap)
if len(buckets) == 1:
# bypass split/fuse logic if there is only one bucket
return self.backend_compile_fn(gm, example_inputs)
# 2: partition the graphmodule according to bucket capacity
partition_map = {}
for idx, b in enumerate(buckets):
for node in b.nodes:
partition_map[node] = idx
split_gm = fx.passes.split_module.split_module(
gm, None, lambda node: partition_map[node]
)
debug_str = (
f"\n---orig graph---\n{gm.graph}\n"
+ f"\n---split graph---\n{split_gm.graph}\n"
)
for name, module in split_gm.named_modules():
if "." not in name and len(name):
# only print the submod graphs, not their children
debug_str += f"\n---{name} graph---\n{module.graph}\n"
debug_str += "\n---------------\n"
ddp_graph_log.debug(debug_str)
# 3: Replace submodules with lazily compiling submodule
class SubmoduleReplacer(torch.fx.interpreter.Interpreter):
def __init__(self, module, compiler):
super().__init__(module)
self.compiler = compiler
def lazily_compiled_submod(self, input_mod):
"""
Create a wrapper around submodules which:
- lazily compiles each of the partitioned submodules using the user-provided compiler
- unpacks singleton tuples/lists into flat arg
"""
class LazilyCompiledModule(torch.nn.Module):
def __init__(self, submod, compiler, unwrap_singleton_tuple):
super().__init__()
self.submod = submod
self.compiler = compiler
self.compiled = False
self.unwrap_singleton_tuple = unwrap_singleton_tuple
def forward(self, *args):
if not self.compiled:
assert (
fake_mode
), "fake mode must have been available when creating lazy submod"
fake_args = []
for arg in args:
if isinstance(arg, torch.Tensor) and not is_fake(arg):
fake_args.append(
torch._dynamo.utils.to_fake_tensor(
arg, fake_mode
)
)
else:
fake_args.append(arg)
# First trace with fake args
new_submod = self.compiler(self.submod, tuple(fake_args))
del self.submod
self.submod = new_submod
self.compiled = True
self.compiler = None
x = self.submod(*args)
# we must let 'input_mod' return a tuple, to make AOT happy.
# (aot_autograd compile_fn literally requires that the output of a graph it compiles is a tuple).
# however, we don't acutally want this tuple to be returned, since the fx logic that calls the submod
# will again wrap outputs from the submod in a tuple. So we unwrap it, and count on it being re-wrapped
if self.unwrap_singleton_tuple and isinstance(x, (tuple, list)):
return x[0]
return x
unwrap_singleton_tuple = False
for sn in input_mod.graph.nodes:
if sn.op == "output":
if not isinstance(sn.args[0], tuple):
unwrap_singleton_tuple = True
sn.args = (sn.args,)
input_mod.recompile()
input_mod.compile_subgraph_reason = GraphCompileReason(
"DDPOptimizer intentional graph-break (See Note [DDPOptimizer])."
" Set `torch._dynamo.config.optimize_ddp = False` to disable.",
[
# it's close to useless to get a real stacktrace here, and quite verbose.
traceback.FrameSummary(__file__, 0, DDPOptimizer),
],
)
wrapper = LazilyCompiledModule(
input_mod,
self.compiler,
unwrap_singleton_tuple,
)
return wrapper
# We replace the submodules with lazy submodules which compile
# the corresponding submodules when they are run with real values
# Always returns `None` - we do not need to propagate values in order
# to replace submodules.
def run_node(self, n: Node) -> Any:
if n.op == "call_module":
real_mod = self.fetch_attr(n.target)
ddp_graph_log.debug(
"\n---%s graph---\n%s", n.target, real_mod.graph
)
assert len(n.kwargs) == 0, "We assume only args for these modules"
lazily_compiled_submod = self.lazily_compiled_submod(real_mod)
# We update the original (outer) graph with a call into the compiled module
# instead of the uncompiled one.
self.module.delete_submodule(n.target)
n.target = "compiled_" + n.target
self.module.add_submodule(n.target, lazily_compiled_submod)
submod_compiler = SubmoduleReplacer(split_gm, self.backend_compile_fn)
submod_compiler.run(*example_inputs)
split_gm.recompile()
ddp_graph_log.debug(
"\n---final graph---\n%s\n---------------\n", split_gm.graph
)
return split_gm
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