mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
a0604193a2
When nn module inlining is enabled, modules are replaced with the underlying function calls in the output fx graph.
example:
```
class GraphModule(torch.nn.Module):
def forward(self, L_x_: "f32[1024, 1024]"):
l_x_ = L_x_
# File: /data/users/lsakka/pytorch/pytorch/test/dynamo/test_structured_trace.py:284 in forward, code: return self.layers(x)
l__self___layers_0: "f32[1024, 1024]" = self.L__self___layers_0(l_x_); l_x_ = None
l__self___layers_1: "f32[1024, 1024]" = self.L__self___layers_1(l__self___layers_0); l__self___layers_0 = None
return (l__self___layers_1,)
```
will be
```
class GraphModule(torch.nn.Module):
def forward(self, L_self_layers_0_weight: "f32[1024, 1024]", L_self_layers_0_bias: "f32[1024]", L_x_: "f32[1024, 1024]", L_self_layers_1_weight: "f32[1024, 1024]", L_self_layers_1_bias: "f32[1024]"):
l_self_layers_0_weight = L_self_layers_0_weight
l_self_layers_0_bias = L_self_layers_0_bias
l_x_ = L_x_
l_self_layers_1_weight = L_self_layers_1_weight
l_self_layers_1_bias = L_self_layers_1_bias
# File: /data/users/lsakka/pytorch/pytorch/torch/nn/modules/linear.py:116 in forward, code: return F.linear(input, self.weight, self.bias)
input_1: "f32[1024, 1024]" = torch._C._nn.linear(l_x_, l_self_layers_0_weight, l_self_layers_0_bias); l_x_ = l_self_layers_0_weight = l_self_layers_0_bias = None
input_2: "f32[1024, 1024]" = torch._C._nn.linear(input_1, l_self_layers_1_weight, l_self_layers_1_bias); input_1 = l_self_layers_1_weight = l_self_layers_1_bias = None
return (input_2,)
```
The DDP optimizer when performing splitting, does not handle the inlined graph since it does not handle function calls since earlier we did not have function calls with params as inputs. (but calls to modules instead).
This diff addresses that, it uses the example_value in the arguments to determine Parameter arguments of a function call
and the Parameter properties.
This address #https://github.com/pytorch/pytorch/issues/127552
running the optimizer on the code above with inlining yields to the following splitting:
```
---submod_0 graph---
graph():
%l_x_ : torch.Tensor [num_users=1] = placeholder[target=l_x_]
%l_self_layers_0_weight : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=l_self_layers_0_weight]
%l_self_layers_0_bias : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=l_self_layers_0_bias]
%linear : [num_users=1] = call_function[target=torch._C._nn.linear](args = (%l_x_, %l_self_layers_0_weight, %l_self_layers_0_bias), kwargs = {})
return linear
---submod_1 graph---
graph():
%input_1 : [num_users=1] = placeholder[target=input_1]
%l_self_layers_1_weight : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=l_self_layers_1_weight]
%l_self_layers_1_bias : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=l_self_layers_1_bias]
%linear : [num_users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %l_self_layers_1_weight, %l_self_layers_1_bias), kwargs = {})
return linear
---final graph---
graph():
%l_self_layers_0_weight : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=L_self_layers_0_weight]
%l_self_layers_0_bias : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=L_self_layers_0_bias]
%l_x_ : torch.Tensor [num_users=1] = placeholder[target=L_x_]
%l_self_layers_1_weight : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=L_self_layers_1_weight]
%l_self_layers_1_bias : torch.nn.parameter.Parameter [num_users=1] = placeholder[target=L_self_layers_1_bias]
%submod_0 : [num_users=1] = call_module[target=compiled_submod_0](args = (%l_x_, %l_self_layers_0_weight, %l_self_layers_0_bias), kwargs = {})
%submod_1 : [num_users=1] = call_module[target=compiled_submod_1](args = (%submod_0, %l_self_layers_1_weight, %l_self_layers_1_bias), kwargs = {})
return (submod_1,)
---------------
```
where as without inlining it uses to be
```
---submod_0 graph---
graph():
%l_x_ : torch.Tensor [num_users=1] = placeholder[target=l_x_]
%l__self___layers_0 : [num_users=1] = call_module[target=L__self___layers_0](args = (%l_x_,), kwargs = {})
return l__self___layers_0
/data/users/lsakka/pytorch/pytorch/torch/_inductor/compile_fx.py:133: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
warnings.warn(
---submod_1 graph---
graph():
%l__self___layers_0 : [num_users=1] = placeholder[target=l__self___layers_0]
%l__self___layers_1 : [num_users=1] = call_module[target=L__self___layers_1](args = (%l__self___layers_0,), kwargs = {})
return l__self___layers_1
---final graph---
graph():
%l_x_ : torch.Tensor [num_users=1] = placeholder[target=L_x_]
%submod_0 : [num_users=1] = call_module[target=compiled_submod_0](args = (%l_x_,), kwargs = {})
%submod_1 : [num_users=1] = call_module[target=compiled_submod_1](args = (%submod_0,), kwargs = {})
return (submod_1,)
---------------
```
TESTING:
(1) running
``` TORCHDYNAMO_INLINE_INBUILT_NN_MODULES=1 pytest test/distributed/test_dynamo_distributed.py -k ```
result in reduction in failures from 6 to 2 with this PR.
The two remaining are FSDP related which does not sounds trivial and have so many details. will leave them for future work.
Co-authored-by: Animesh Jain <anijain@umich.edu>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128034
Approved by: https://github.com/anijain2305, https://github.com/wconstab
656 lines
30 KiB
Python
656 lines
30 KiB
Python
# mypy: ignore-errors
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import logging
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import traceback
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from dataclasses import dataclass, field
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from typing import Any, List, Optional
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from unittest import mock
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import torch
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from torch import fx
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from torch._dynamo.output_graph import GraphCompileReason
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from torch._dynamo.utils import deepcopy_to_fake_tensor, detect_fake_mode
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from torch._logging import trace_structured
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from torch.fx.node import Node
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# Regular log messages should go through 'log'.
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# ddp_graph_log is a separate artifact logger reserved for dumping graphs.
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# See docs/source/logging.rst for more info.
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log = logging.getLogger(__name__)
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ddp_graph_log = torch._logging.getArtifactLogger(__name__, "ddp_graphs")
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def args_str(args):
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# a debug helper
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if torch.is_tensor(args):
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return f"T[{args.shape}]"
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elif isinstance(args, tuple):
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return f"tuple({', '.join([args_str(x) for x in args])})"
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elif isinstance(args, list):
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return f"list({', '.join([args_str(x) for x in args])})"
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else:
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return str(args)
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@dataclass
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class Bucket:
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size: int = 0
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params: List[str] = field(default_factory=list)
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nodes: List[fx.Node] = field(default_factory=list)
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# param_ids is just used for unit testing
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param_ids: List = field(default_factory=list)
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# keep track of any buckets that were extended for logging purposes
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opcount_increased_to_capture_external_output: int = 0
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paramsize_before_opcount_increase: int = 0
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def bucket_has_external_output(bucket: Bucket) -> bool:
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nodes_in_bucket = set()
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# we want to iterate in reverse order, but clumsi-luckily the bucket.nodes list was already created backwards
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# so we don't reverse it here
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for node in bucket.nodes:
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# assume node.op != output, since those are filtered in the original iteration
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nodes_in_bucket.add(node)
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for user in node.users:
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if user not in nodes_in_bucket:
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return True
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return False
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def pretty_print_buckets(buckets: List[Bucket], bucket_bytes_cap: int):
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headers = ("Index", "Size (b)", "Param Names")
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rows = []
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extended_buckets = []
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for idx, bucket in enumerate(reversed(buckets)):
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if len(bucket.params) > 0:
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rows.append((idx, bucket.size, bucket.params[0]))
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for param in bucket.params[1:]:
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rows.append((None, None, param))
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if bucket.opcount_increased_to_capture_external_output > 0:
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extended_buckets.append(
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(
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idx,
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bucket.opcount_increased_to_capture_external_output,
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bucket.size - bucket.paramsize_before_opcount_increase,
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)
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)
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if len(rows):
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log.info(
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"\nDDPOptimizer used bucket cap %s and created %d buckets. Enable debug logs for detailed bucket info.",
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bucket_bytes_cap,
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len(buckets),
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)
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if len(extended_buckets):
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log.warning(
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"Some buckets were extended beyond their requested parameter capacities"
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" in order to ensure each subgraph has an output node, required for fx graph partitioning."
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" This can be the case when a subgraph would have only contained nodes performing inplace mutation,"
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" and returning no logical outputs. This should not be a problem, unless it results in too few graph"
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" partitions for optimal DDP performance."
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)
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try:
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from tabulate import tabulate
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log.debug(
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"\nDDPOptimizer produced the following bucket assignments:\n%s",
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tabulate(rows, headers=headers, tablefmt="simple_grid"),
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)
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if len(extended_buckets):
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log.warning(
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"DDPOptimizer extended these buckets to ensure per-subgraph output nodes:\n%s",
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tabulate(
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extended_buckets,
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headers=("Index", "Extra Ops", "Extra Param Size (b)"),
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tablefmt="simple_grid",
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),
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)
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except ImportError:
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log.debug(
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"Please `pip install tabulate` in order to display ddp bucket sizes and diagnostic information."
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)
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else:
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log.debug("DDPOptimizer captured no parameters and did not split this graph.")
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def has_higher_order_op(gm):
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# Check if there is a higher order op in the graph
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for node in gm.graph.nodes:
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if node.op == "get_attr":
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maybe_param = getattr(gm, node.target)
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if isinstance(maybe_param, torch.fx.GraphModule):
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return True
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return False
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# 3 (lazy compile): Replace submodules with lazily compiling submodule
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class SubmoduleReplacer(torch.fx.interpreter.Interpreter):
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def __init__(self, module, compiler):
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super().__init__(module)
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self.compiler = compiler
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def lazily_compiled_submod(self, input_mod):
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"""
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Create a wrapper around submodules which:
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- lazily compiles each of the partitioned submodules using the user-provided compiler
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- unpacks singleton tuples/lists into flat arg
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"""
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class LazilyCompiledModule(torch.nn.Module):
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def __init__(self, submod, compiler, unwrap_singleton_tuple):
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super().__init__()
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self.submod = submod
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self.compiler = compiler
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self.compiled = False
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self.unwrap_singleton_tuple = unwrap_singleton_tuple
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def forward(self, *args):
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if not self.compiled:
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# First compile with args as example_inputs
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# These args will be fakeified if using Inductor/AOTAutograd
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new_submod = self.compiler(self.submod, args)
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del self.submod
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self.submod = new_submod
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self.compiled = True
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self.compiler = None
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x = self.submod(*args)
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# we must let 'input_mod' return a tuple, to make AOT happy.
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# (aot_autograd compile_fn literally requires that the output of a graph it compiles is a tuple).
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# however, we don't acutally want this tuple to be returned, since the fx logic that calls the submod
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# will again wrap outputs from the submod in a tuple. So we unwrap it, and count on it being re-wrapped
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if self.unwrap_singleton_tuple and isinstance(x, (tuple, list)):
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return x[0]
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return x
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unwrap_singleton_tuple = False
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for sn in input_mod.graph.nodes:
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if sn.op == "output":
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if not isinstance(sn.args[0], tuple):
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unwrap_singleton_tuple = True
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sn.args = (sn.args,)
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input_mod.recompile()
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input_mod.compile_subgraph_reason = GraphCompileReason(
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"DDPOptimizer intentional graph-break (See Note [DDPOptimizer])."
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" Set `torch._dynamo.config.optimize_ddp = False` to disable.",
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[
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# it's close to useless to get a real stacktrace here, and quite verbose.
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traceback.FrameSummary(__file__, 0, DDPOptimizer),
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],
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)
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wrapper = LazilyCompiledModule(
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input_mod,
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self.compiler,
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unwrap_singleton_tuple,
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)
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return wrapper
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# We replace the submodules with lazy submodules which compile
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# the corresponding submodules when they are run with real values
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# Always returns `None` - we do not need to propagate values in order
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# to replace submodules.
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def run_node(self, n: Node) -> Any:
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if n.op == "call_module":
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real_mod = self.fetch_attr(n.target)
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ddp_graph_log.debug("\n---%s graph---\n%s", n.target, real_mod.graph)
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assert len(n.kwargs) == 0, "We assume only args for these modules"
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lazily_compiled_submod = self.lazily_compiled_submod(real_mod)
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# We update the original (outer) graph with a call into the compiled module
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# instead of the uncompiled one.
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self.module.delete_submodule(n.target)
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n.target = "compiled_" + n.target
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self.module.add_submodule(n.target, lazily_compiled_submod)
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# 3 (no lazy compile): compile each of the partitioned submodules using the user-provided compiler
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class SubmodCompiler(torch.fx.interpreter.Interpreter):
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def __init__(self, module, compiler, fake_mode):
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super().__init__(module)
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self.compiler = compiler
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self.fake_mode = fake_mode
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def compile_submod(self, input_mod, args, kwargs):
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"""
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Compile the submodule,
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using a wrapper to make sure its output is always a tuple,
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which is required by AotAutograd based compilers
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"""
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assert len(kwargs) == 0, "We assume only args for these modules"
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class WrapperModule(torch.nn.Module):
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def __init__(self, submod, unwrap_singleton_tuple):
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super().__init__()
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self.submod = submod
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self.unwrap_singleton_tuple = unwrap_singleton_tuple
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def forward(self, *args):
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x = self.submod(*args)
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# TODO(whc)
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# for some reason the isinstance check is necessary if I split one node per submod
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# - even though I supposedly wrapped the output in a tuple in those cases, the real
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# compiled module was still returning a tensor
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if self.unwrap_singleton_tuple and isinstance(x, (tuple, list)):
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return x[0]
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return x
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unwrap_singleton_tuple = False
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for sn in input_mod.graph.nodes:
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if sn.op == "output":
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if not isinstance(sn.args[0], tuple):
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unwrap_singleton_tuple = True
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sn.args = (sn.args,)
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input_mod.recompile()
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input_mod.compile_subgraph_reason = GraphCompileReason(
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"DDPOptimizer intentional graph-break (See Note [DDPOptimizer])."
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" Set `torch._dynamo.config.optimize_ddp = False` to disable.",
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[
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# it's close to useless to get a real stacktrace here, and quite verbose.
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traceback.FrameSummary(__file__, 0, DDPOptimizer),
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],
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)
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wrapper = WrapperModule(
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self.compiler(input_mod, args),
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unwrap_singleton_tuple,
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)
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return wrapper
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# Note:
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#
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# The way distributed works today around fake tensors can be somewhat confusing.
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# Some of these codepaths are shared in both runtime, and compile time. The presence
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# of a fake_mode, read off of fake tensor inputs, dictates how we will operate.
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#
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# A few things to keep in mind:
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#
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# 1) We invoke `compile_submod` with a real module. The output of that gets stored
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# on the graph via `self.module.add_submodule(n.target, compiled_submod_real)`.
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#
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# 2) When running a call_module targeted node, if we have a fake_mode, we fakify the
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# module we got from self.fetch_attr(n.target). Regardless of fake_mode, we then execute it.
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#
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# 3) Fake tensors should always be around during compile time.
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#
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# 4) Fake tensors should never be around at runtime.
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#
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# 5) We end up with a compilation mode that takes a real submodule and fake tensors,
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# to match what aot_autograd expects. See Note: [Fake Modules and AOTAutograd]
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def run_node(self, n: Node) -> Any:
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args, kwargs = self.fetch_args_kwargs_from_env(n)
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new_args = []
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assert self.fake_mode
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for arg in args:
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if isinstance(arg, torch.Tensor) and not isinstance(
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arg, torch._subclasses.FakeTensor
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):
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new_args.append(torch._dynamo.utils.to_fake_tensor(arg, self.fake_mode))
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else:
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new_args.append(arg)
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log.debug("run_node %s, %s got args %s", n.op, n.target, args_str(args))
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assert isinstance(args, tuple)
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assert isinstance(kwargs, dict)
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if n.op == "call_module":
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real_mod = self.fetch_attr(n.target)
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if self.fake_mode:
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curr_submod = deepcopy_to_fake_tensor(real_mod, self.fake_mode)
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else:
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curr_submod = real_mod
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ddp_graph_log.debug("\n---%s graph---\n%s", n.target, curr_submod.graph)
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# When calling the compiler on the submod, inputs (new_args) are expected to
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# be FakeTensors already since Dynamo would have made them FakeTensors in the
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# non-DDP flow. However, the parameters are _not_ expected to be FakeTensors,
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# since this wrapping happens during compilation
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# Note: Returning Fake Tensors on First AOT Autograd Call
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#
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# Inductor will optimize strides of outputs when it deems it profitable.
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# For instance, converting to channels last. When we split the graph here
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# into multiple inductor compilations, we need to make sure that the
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# output strides of one compilation is appropriately passed to the subsequent
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# compilations. However, the mapping from inductor output to dynamo output
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# is non-trivial due to aot_autograd's deduping, de-aliasing, mutation, re-writing,
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# subclass handling, etc. In order to replay all this logic we set a flag such that
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# the first invocation of inductor in aot_autograd will return Fake Tensors with
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# appropriate strides. Then, all of aot autograd's runtime logic is replayed.
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# This gives us the appropriately strided outputs here which will reflect runtime strides.
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class FakeifyFirstAOTInvocationGuard:
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def __init__(self):
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self.tc = torch._guards.TracingContext.try_get()
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assert self.tc
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torch._guards.TracingContext.try_get().fakify_first_call = True
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def __del__(self):
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self.tc.fakify_first_call = False
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# For aot_eager and other backends, tracing context is not set
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has_tracing_context = torch._guards.TracingContext.try_get() is not None
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if has_tracing_context:
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g = FakeifyFirstAOTInvocationGuard()
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from torch._dynamo.utils import counters
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init = counters["aot_autograd"]["total"]
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compiled_submod_real = self.compile_submod(real_mod, new_args, kwargs)
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# TODO - better way of doing this?
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# Only aot autograd handles fakifying first call
|
|
invoked_aot_autograd = init != counters["aot_autograd"]["total"]
|
|
|
|
# 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, compiled_submod_real)
|
|
|
|
# Finally, we have to produce inputs for use compiling the next submodule,
|
|
# and these need to be FakeTensors, so we execute the module under fake_mode
|
|
# Because parameters are not fake we patch fake tensor mode to allow non fake inputs
|
|
with self.fake_mode, mock.patch.object(
|
|
self.fake_mode, "allow_non_fake_inputs", True
|
|
):
|
|
if has_tracing_context and invoked_aot_autograd:
|
|
out = compiled_submod_real(*new_args, **kwargs)
|
|
# output should be fake or subclass
|
|
assert all(
|
|
(not isinstance(t, torch.Tensor) or type(t) is not torch.Tensor)
|
|
for t in (out if isinstance(out, (list, tuple)) else [out])
|
|
)
|
|
return out
|
|
else:
|
|
return curr_submod(*new_args, **kwargs)
|
|
else:
|
|
# placeholder or output nodes don't need to get compiled, just executed
|
|
return getattr(self, n.op)(n.target, new_args, kwargs)
|
|
|
|
|
|
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 set TORCH_LOGS env to include any of 'dynamo', 'distributed', or 'dist_ddp'.
|
|
- 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 add_param(self, bucket, param, name):
|
|
bucket.size += param.untyped_storage().nbytes()
|
|
bucket.params.append(name)
|
|
bucket.param_ids.append(id(param))
|
|
|
|
def add_module_params_to_bucket(self, mod, bucket, processed_modules, prefix):
|
|
processed_modules.add(mod)
|
|
for name, param in mod.named_parameters():
|
|
if param.requires_grad and not self._ignore_parameter(param):
|
|
self.add_param(bucket, param, f"{prefix}_{name}")
|
|
|
|
def add_param_args(self, bucket, node):
|
|
for arg in node.args:
|
|
if not isinstance(arg, torch.fx.node.Node):
|
|
continue
|
|
if arg.op != "placeholder":
|
|
continue
|
|
param = arg.meta["example_value"]
|
|
if (
|
|
isinstance(param, torch.nn.Parameter)
|
|
and param.requires_grad
|
|
and not self._ignore_parameter(param)
|
|
):
|
|
self.add_param(bucket, param, arg.target)
|
|
|
|
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.
|
|
"""
|
|
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)
|
|
processed_modules = set()
|
|
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_function":
|
|
self.add_param_args(buckets[0], node)
|
|
|
|
elif node.op == "call_module":
|
|
target_mod = gm.get_submodule(node.target)
|
|
if target_mod not in processed_modules:
|
|
self.add_module_params_to_bucket(
|
|
target_mod, buckets[0], processed_modules, node.target
|
|
)
|
|
elif node.op == "call_method":
|
|
if isinstance(node.args[0].target, str):
|
|
target_mod = None
|
|
try:
|
|
target_mod = gm.get_submodule(node.args[0].target)
|
|
except AttributeError:
|
|
pass
|
|
if target_mod is not None and target_mod not in processed_modules:
|
|
self.add_module_params_to_bucket(
|
|
target_mod, buckets[0], processed_modules, node.target
|
|
)
|
|
# This handles situations like tmp = torch.mm(x, self.weight.t())
|
|
# t: "f32[512, 512]" = l_self_seq_2_weight.t(); l_self_seq_2_weight = None
|
|
# tmp: "f32[512, 512]" = torch.mm(input_2, t); input_2 = t = None
|
|
self.add_param_args(buckets[0], node)
|
|
|
|
elif node.op == "get_attr":
|
|
maybe_param = getattr(gm, node.target)
|
|
if (
|
|
isinstance(maybe_param, torch.nn.Parameter)
|
|
and maybe_param.requires_grad
|
|
and not self._ignore_parameter(maybe_param)
|
|
):
|
|
self.add_param(buckets[0], maybe_param, node.target)
|
|
|
|
# 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)
|
|
|
|
trace_structured(
|
|
"optimize_ddp_split_graph",
|
|
payload_fn=lambda: split_gm.print_readable(print_output=False),
|
|
)
|
|
for name, module in split_gm.named_modules():
|
|
if "." not in name and len(name):
|
|
trace_structured(
|
|
"optimize_ddp_split_child",
|
|
lambda: {"name": name},
|
|
payload_fn=lambda: module.print_readable(print_output=False),
|
|
)
|
|
|
|
# NOTE, we want to enable `optimize_ddp_lazy_compile` by default as soon as possible,
|
|
# becuase it will fix stride mismatch errors (see motivation: https://github.com/pytorch/pytorch/pull/114154).
|
|
# However, lazy compile currently causes shape mismatch in other cases (`test_graph_split_inductor_transpose`)
|
|
# and we need to fix them before we can enable it by default.
|
|
if not torch._dynamo.config.optimize_ddp_lazy_compile:
|
|
# Today, optimize_ddp=True and keep_output_stride=False can lead to silent
|
|
# correctness issues. The problem is that ddp_optimizer works by partitioning
|
|
# the dynamo graph, sending each subgraph through aot autograd to inductor,
|
|
# and creates example inputs by eagerly interpreting each subgraph to get
|
|
# an output that with the same metadata that we'd get from eager mode.
|
|
# This is a problem though, for torch._inductor.config.keep_output_stride.
|
|
# The above config can cause the outputs of the first graph to have
|
|
# **different** strides from eager, causing the inputs that we pass
|
|
# to the second graph to be wrong.
|
|
# To really fix this, we would need to faithfully ask inductor
|
|
# what the outputs to each graph it expects are.
|
|
fake_mode = detect_fake_mode(example_inputs)
|
|
if fake_mode is None:
|
|
fake_mode = torch._subclasses.fake_tensor.FakeTensorMode()
|
|
|
|
if torch._dynamo.config.optimize_ddp_lazy_compile:
|
|
submod_compiler = SubmoduleReplacer(split_gm, self.backend_compile_fn)
|
|
else:
|
|
submod_compiler = SubmodCompiler(
|
|
split_gm, self.backend_compile_fn, fake_mode
|
|
)
|
|
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
|