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# Owner(s): ["oncall: distributed"]
import functools
import itertools
import os
import tempfile
import unittest
from enum import auto, Enum
from typing import Callable, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributed.fsdp._wrap_utils import _validate_frozen_params
from torch.distributed.fsdp.fully_sharded_data_parallel import (
BackwardPrefetch,
CPUOffload,
FullyShardedDataParallel as FSDP,
MixedPrecision,
ShardingStrategy,
)
from torch.distributed.fsdp.wrap import (
_or_policy,
_Policy,
_wrap_module_cls_individually,
always_wrap_policy,
CustomPolicy,
enable_wrap,
ModuleWrapPolicy,
size_based_auto_wrap_policy,
transformer_auto_wrap_policy,
wrap,
)
from torch.nn import TransformerDecoderLayer, TransformerEncoderLayer
from torch.nn.modules.batchnorm import _BatchNorm
from torch.testing._internal.common_cuda import TEST_MULTIGPU
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import (
_move_to_device,
DEVICEInitMode,
DummyProcessGroup,
FSDPInitMode,
FSDPTest,
TransformerWithSharedParams,
)
from torch.testing._internal.common_utils import (
FILE_SCHEMA,
find_free_port,
instantiate_parametrized_tests,
parametrize,
run_tests,
TEST_CUDA,
TestCase,
)
class BatchNormNet(nn.Module):
def __init__(self) -> None:
super().__init__()
self.lin = nn.Linear(10, 10, bias=False)
self.bn1 = nn.BatchNorm1d(10)
self.bn2 = nn.BatchNorm2d(10)
self.bn3 = nn.BatchNorm3d(10)
self.sync_bn = nn.SyncBatchNorm(10)
class LoraModel(nn.Module):
"""This is a toy LoRA decoder model."""
def __init__(self) -> None:
super().__init__()
self.embed_tokens = nn.Embedding(100, 32)
self.layers = nn.ModuleList([LoraDecoder() for _ in range(4)])
self.norm = nn.LayerNorm(32)
self.embed_tokens.weight.requires_grad_(False)
self.norm.weight.requires_grad_(False)
self.norm.bias.requires_grad_(False)
class LoraDecoder(nn.Module):
def __init__(self) -> None:
super().__init__()
self.attn = LoraAttention()
self.mlp = LoraMLP()
self.inp_layernorm = nn.LayerNorm(32)
self.post_attn_layernorm = nn.LayerNorm(32)
self.inp_layernorm.weight.requires_grad_(False)
self.inp_layernorm.bias.requires_grad_(False)
self.post_attn_layernorm.weight.requires_grad_(False)
self.post_attn_layernorm.bias.requires_grad_(False)
class LoraAttention(nn.Module):
def __init__(self) -> None:
super().__init__()
self.q_proj = nn.Linear(32, 32, bias=False)
self.lora_A = nn.Linear(32, 8, bias=False)
self.lora_B = nn.Linear(8, 32, bias=False)
self.k_proj = nn.Linear(32, 32, bias=False)
self.v_proj = nn.Linear(32, 32, bias=False)
self.o_proj = nn.Linear(32, 32, bias=False)
self.q_proj.weight.requires_grad_(False)
self.k_proj.weight.requires_grad_(False)
self.v_proj.weight.requires_grad_(False)
self.o_proj.weight.requires_grad_(False)
class LoraMLP(nn.Module):
def __init__(self) -> None:
super().__init__()
self.proj1 = nn.Linear(32, 128, bias=False)
self.proj2 = nn.Linear(128, 32, bias=False)
self.proj1.weight.requires_grad_(False)
self.proj2.weight.requires_grad_(False)
class WrapMethod(Enum):
FSDP_CTOR = auto()
# FSDP_CTOR is the supported way forward, but keep WRAP_API in case we miss
# any use cases and fix them to work with FSDP_CTOR over time.
WRAP_API = auto()
class TestFSDPWrap(FSDPTest):
"""
Tests main API for wrapping FSDP, which is to pass auto_wrap_policy into
FSDP constructor.
"""
def setUp(self) -> None:
super().setUp()
class NestedSequentialModel:
@staticmethod
def get_model(cuda=True):
sequential = nn.Sequential(
nn.Linear(5, 5),
nn.Linear(5, 5),
nn.Sequential(nn.Linear(5, 5), nn.Linear(5, 5)),
)
if cuda:
sequential = sequential.cuda()
return sequential
@staticmethod
def verify_model_all_wrapped(cls, model):
cls.assertTrue(isinstance(model, FSDP))
cls.assertTrue(isinstance(model.module[0], FSDP))
cls.assertTrue(isinstance(model.module[1], FSDP))
cls.assertTrue(isinstance(model.module[2], FSDP))
cls.assertTrue(isinstance(model.module[2].module[0], FSDP))
cls.assertTrue(isinstance(model.module[2].module[1], FSDP))
@staticmethod
def verify_model(cls, model):
cls.assertTrue(isinstance(model, FSDP))
cls.assertTrue(isinstance(model.module[0], nn.Linear))
cls.assertTrue(isinstance(model.module[1], nn.Linear))
cls.assertTrue(isinstance(model.module[2], FSDP))
# following modules were not wrapped by the policy.
cls.assertTrue(isinstance(model.module[2].module[0], nn.Linear))
cls.assertTrue(isinstance(model.module[2].module[1], nn.Linear))
def _get_linear(self, fin, fout):
return nn.Linear(fin, fout, bias=False)
def _get_already_wrapped_fsdp(
self, device_init_mode=DEVICEInitMode.DEVICE_BEFORE, nested=False
) -> FSDP:
fn_self = self
class MyModel(nn.Module):
def __init__(self, nested):
super().__init__()
# TODO: test the various init modes.
move_to_device = device_init_mode == DEVICEInitMode.DEVICE_BEFORE
# if nested=True, the FSDP module will be nested one layer deep
# and we should pick that up.
if nested:
self.lin1 = nn.Sequential(
_move_to_device(fn_self._get_linear(1, 1), move_to_device),
FSDP(
_move_to_device(fn_self._get_linear(1, 1), move_to_device)
),
)
else:
self.lin1 = FSDP(
_move_to_device(fn_self._get_linear(1, 1), move_to_device)
)
self.lin2 = FSDP(
_move_to_device(fn_self._get_linear(1, 1), move_to_device)
)
self.lin3 = FSDP(
_move_to_device(fn_self._get_linear(1, 1), move_to_device)
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
return self.lin3(self.lin2(self.lin1(input)))
model = MyModel(nested=nested)
return model
@skip_if_lt_x_gpu(2)
@parametrize("nested", [True, False])
@parametrize(
"device_init_mode", [DEVICEInitMode.DEVICE_AFTER, DEVICEInitMode.DEVICE_BEFORE]
)
def test_error_already_wrapped(self, nested, device_init_mode):
"""
Test that an error is raised if we attempt to wrap when submodules are
already FSDP.
"""
wrapped_fsdp = self._get_already_wrapped_fsdp(
nested=nested, device_init_mode=device_init_mode
)
if device_init_mode == DEVICEInitMode.DEVICE_AFTER:
wrapped_fsdp = wrapped_fsdp.cuda()
wrapped_module_name = "lin1.1" if nested else "lin1"
with self.assertRaisesRegex(
ValueError,
"FSDP auto wrapping requires modules to not already have FSDP "
f"applied but found {wrapped_module_name} in",
):
FSDP(wrapped_fsdp, auto_wrap_policy=size_based_auto_wrap_policy)
@skip_if_lt_x_gpu(2)
@parametrize("use_or_policy", [True, False])
def test_wrap_batchnorm_individually(self, use_or_policy):
def never_wrap_policy(*args, **kwargs):
return False
wrap_batchnorm_individually = functools.partial(
_wrap_module_cls_individually,
module_classes=[
_BatchNorm,
],
)
policy = (
functools.partial(
_or_policy, policies=[never_wrap_policy, wrap_batchnorm_individually]
)
if use_or_policy
else wrap_batchnorm_individually
)
model = BatchNormNet()
fsdp = FSDP(model, auto_wrap_policy=policy)
# Batchnorms should be wrapped
for layer in [fsdp.bn1, fsdp.bn2, fsdp.bn3, fsdp.sync_bn]:
self.assertTrue(isinstance(layer, FSDP))
self.assertFalse(isinstance(fsdp.lin, FSDP))
@skip_if_lt_x_gpu(2)
def test_bn_always_wrapped_individually(self):
"""
Ensures that by using _or_policy with _wrap_module_cls_individually, even
if the other policy results in a module containing a BN unit being
wrapped, the contained BN unit will still be individually wrapped.
"""
class MyModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.bn_container = BatchNormNet()
def wrap_bn_container(module, recurse, *args, **kwargs):
if recurse:
return True
return isinstance(module, BatchNormNet)
wrap_batchnorm_individually = functools.partial(
_wrap_module_cls_individually,
module_classes=[
_BatchNorm,
],
)
my_policy = functools.partial(
_or_policy, policies=[wrap_bn_container, wrap_batchnorm_individually]
)
mod = MyModule()
fsdp = FSDP(mod, auto_wrap_policy=my_policy)
# Wrapping should be FSDP(FSDP(BatchNormNet(FSDP(BN))))
# and not FSDP(FSDP(BatchNormNet(BN))) (in the latter the inner
# BN is not individually wrapped.)
for bn in [
fsdp.bn_container.bn1,
fsdp.bn_container.bn2,
fsdp.bn_container.bn3,
fsdp.bn_container.sync_bn,
]:
self.assertTrue(isinstance(bn, FSDP))
# if we just wrapped BN container, individual batchnorms are not
# wrapped.
mod = MyModule()
fsdp = FSDP(mod, auto_wrap_policy=wrap_bn_container)
self.assertTrue(isinstance(mod.bn_container, FSDP))
for bn in [
fsdp.bn_container.bn1,
fsdp.bn_container.bn2,
fsdp.bn_container.bn3,
fsdp.bn_container.sync_bn,
]:
self.assertFalse(isinstance(bn, FSDP))
@skip_if_lt_x_gpu(2)
@parametrize(
"cpu_offload",
[CPUOffload(offload_params=False), CPUOffload(offload_params=True)],
)
@parametrize(
"backward_prefetch",
[BackwardPrefetch.BACKWARD_POST, BackwardPrefetch.BACKWARD_PRE],
)
@parametrize("forward_prefetch", [False, True])
@parametrize(
"device_init_mode", [DEVICEInitMode.DEVICE_AFTER, DEVICEInitMode.DEVICE_BEFORE]
)
def test_main_wrap_api(
self,
cpu_offload: CPUOffload,
backward_prefetch: BackwardPrefetch,
forward_prefetch: bool,
device_init_mode: DEVICEInitMode,
):
if (
device_init_mode == DEVICEInitMode.DEVICE_AFTER
and cpu_offload.offload_params
):
# they don't work together, expected
return
move_to_device = device_init_mode == DEVICEInitMode.DEVICE_BEFORE
class Nested(nn.Module):
def __init__(self) -> None:
super().__init__()
self.nested_lin = _move_to_device(
nn.Linear(1, 1, bias=False), move_to_device
)
def forward(self, input):
return self.nested_lin(input)
class MyModel(nn.Module):
def __init__(self) -> None:
super().__init__()
self.lin1 = _move_to_device(nn.Linear(1, 1, bias=False), move_to_device)
self.lin2 = _move_to_device(nn.Linear(1, 1, bias=False), move_to_device)
self.lin3 = _move_to_device(nn.Linear(1, 1, bias=False), move_to_device)
self.lin4 = Nested()
def forward(self, input):
return self.lin4(self.lin3(self.lin2(self.lin1(input))))
model = MyModel()
wrapped_model = FSDP(
model,
auto_wrap_policy=functools.partial(
size_based_auto_wrap_policy,
min_num_params=0, # wrap all modules
),
cpu_offload=cpu_offload,
backward_prefetch=backward_prefetch,
forward_prefetch=forward_prefetch,
)
if device_init_mode == DEVICEInitMode.DEVICE_AFTER:
wrapped_model = wrapped_model.cuda()
modules_in_fsdp_graph_order = [
wrapped_model.module.lin1,
wrapped_model.module.lin2,
wrapped_model.module.lin3,
wrapped_model.module.lin4.module.nested_lin,
wrapped_model.module.lin4,
wrapped_model,
]
for module in modules_in_fsdp_graph_order:
self.assertTrue(isinstance(module, FSDP))
self._check_cpu_offload(module, cpu_offload)
self._check_backward_prefetch(module, backward_prefetch)
self._check_forward_prefetch(module, forward_prefetch)
# Run model a few times for sanity check.
optim = torch.optim.SGD(wrapped_model.parameters(), lr=1e-2, momentum=0.9)
inp = torch.ones(1).cuda()
for _ in range(6):
optim.zero_grad()
loss = wrapped_model(inp).sum()
loss.backward()
optim.step()
class TestAutoWrap(TestCase):
def setUp(self) -> None:
super().setUp()
# For all the tests here, we use a fake group
self.process_group = DummyProcessGroup(rank=0, size=1)
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
@parametrize("wrap_method", [WrapMethod.FSDP_CTOR, WrapMethod.WRAP_API])
def test_wrap(self, wrap_method):
if wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group):
layer = wrap(nn.Linear(5, 5))
else:
assert wrap_method == WrapMethod.FSDP_CTOR
layer = FSDP(
nn.Linear(5, 5),
process_group=self.process_group,
auto_wrap_policy=functools.partial(
size_based_auto_wrap_policy, min_num_params=1
),
)
self.assertTrue(isinstance(layer, FSDP))
self.assertEqual(layer.rank, self.process_group.rank())
self.assertEqual(layer.world_size, self.process_group.size())
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_wrap_disabled_outside_context(self):
pg = self.process_group
class MyModel(nn.Module):
def __init__(self) -> None:
super().__init__()
self.lin = wrap(nn.Linear(5, 5), process_group=pg)
model = MyModel()
with enable_wrap(wrapper_cls=FSDP, process_group=pg):
model = wrap(model)
self.assertTrue(isinstance(model, FSDP))
self.assertFalse(isinstance(model.lin, FSDP))
self.assertTrue(isinstance(model.lin, nn.Linear))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_wrap_override_defaults(self):
new_process_group = DummyProcessGroup(rank=0, size=2)
with enable_wrap(wrapper_cls=FSDP, process_group=self.process_group):
layer = wrap(nn.Linear(5, 5), process_group=new_process_group)
self.assertTrue(isinstance(layer, FSDP))
self.assertTrue(layer.process_group is new_process_group)
self.assertEqual(layer.rank, 0)
self.assertEqual(layer.world_size, 2)
@unittest.skipIf(not TEST_CUDA, "Test Requires CUDA")
def test_always_wrap(self):
"""
Test to ensure that if `always_wrap_policy` is
passed into FSDP, all submodules are wrapped.
"""
seq = TestFSDPWrap.NestedSequentialModel.get_model(cuda=True)
model = FSDP(
seq, process_group=self.process_group, auto_wrap_policy=always_wrap_policy
)
TestFSDPWrap.NestedSequentialModel.verify_model_all_wrapped(self, model)
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_transformer_auto_wrap_policy(self):
"""Tests the ``transformer_auto_wrap_policy``."""
auto_wrap_policy = functools.partial(
transformer_auto_wrap_policy,
transformer_layer_cls={TransformerEncoderLayer, TransformerDecoderLayer},
)
self._test_transformer_wrapping(auto_wrap_policy)
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_module_wrap_policy(self):
"""Tests the ``ModuleWrapPolicy``."""
auto_wrap_policy = ModuleWrapPolicy(
{TransformerEncoderLayer, TransformerDecoderLayer}
)
self._test_transformer_wrapping(auto_wrap_policy)
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_module_wrap_policy_callable(self):
"""Tests the ``ModuleWrapPolicy`` as a ``Callable``."""
auto_wrap_policy = ModuleWrapPolicy(
{TransformerEncoderLayer, TransformerDecoderLayer}
)
callable_policy = functools.partial(_or_policy, policies=[auto_wrap_policy])
self._test_transformer_wrapping(callable_policy)
def _test_transformer_wrapping(self, auto_wrap_policy: Union[Callable, _Policy]):
fsdp_kwargs = {"auto_wrap_policy": auto_wrap_policy}
fsdp_model = TransformerWithSharedParams.init(
self.process_group,
FSDPInitMode.RECURSIVE,
DEVICEInitMode.DEVICE_BEFORE,
fsdp_kwargs,
)
modules = list(fsdp_model.modules())
encoder_layers = set(fsdp_model.module.transformer.encoder.layers)
decoder_layers = set(fsdp_model.module.transformer.decoder.layers)
for module in modules:
if (
module is fsdp_model
or module in encoder_layers
or module in decoder_layers
):
self.assertTrue(isinstance(module, FSDP))
else:
self.assertFalse(isinstance(module, FSDP))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_custom_policy(self):
"""
Tests ``CustomPolicy`` with both a lambda function that uses uniform
kwargs (so only returns ``False`` or ``True``) and a lambda function
that uses non-uniform kwargs (so returns a dict to override the root
kwargs).
"""
for use_uniform_kwargs in [False, True]:
self._test_custom_policy(use_uniform_kwargs)
def _test_custom_policy(self, use_uniform_kwargs: bool):
print(f"use_uniform_kwargs={use_uniform_kwargs}")
model = TransformerWithSharedParams.init(
self.process_group,
FSDPInitMode.NO_FSDP,
DEVICEInitMode.DEVICE_BEFORE,
{},
)
if use_uniform_kwargs:
def lambda_fn(module: nn.Module):
if module is model.bn:
return True
elif isinstance(
module, (TransformerEncoderLayer, TransformerDecoderLayer)
):
return True
return False
else:
def lambda_fn(module: nn.Module):
if module is model.bn:
return {"sharding_strategy": ShardingStrategy.NO_SHARD}
elif isinstance(module, TransformerEncoderLayer):
return True
elif isinstance(module, TransformerDecoderLayer):
return {
"sharding_strategy": ShardingStrategy.SHARD_GRAD_OP,
"backward_prefetch": BackwardPrefetch.BACKWARD_POST,
}
return False
policy = CustomPolicy(lambda_fn)
# Use a size-2 dummy PG to avoid clamping the sharding strategy to
# `NO_SHARD` as for a size-1 PG
process_group = DummyProcessGroup(rank=0, size=2)
fp16_mp = MixedPrecision(param_dtype=torch.float16)
fp32_mp = MixedPrecision()
model = FSDP(
model,
process_group=process_group,
auto_wrap_policy=policy,
mixed_precision=fp16_mp,
)
encoder_layers = set(model.module.transformer.encoder.layers)
decoder_layers = set(model.module.transformer.decoder.layers)
bn = model.module.bn
bn_strategy = (
ShardingStrategy.FULL_SHARD
if use_uniform_kwargs
else ShardingStrategy.NO_SHARD
)
bn_prefetch = BackwardPrefetch.BACKWARD_PRE
encoder_strategy = root_strategy = ShardingStrategy.FULL_SHARD
encoder_prefetch = root_prefetch = BackwardPrefetch.BACKWARD_PRE
decoder_strategy = (
ShardingStrategy.FULL_SHARD
if use_uniform_kwargs
else ShardingStrategy.SHARD_GRAD_OP
)
decoder_prefetch = (
BackwardPrefetch.BACKWARD_PRE
if use_uniform_kwargs
else BackwardPrefetch.BACKWARD_POST
)
for module in model.modules():
if module is bn:
self.assertTrue(isinstance(module, FSDP))
self.assertEqual(module.sharding_strategy, bn_strategy)
self.assertEqual(module.backward_prefetch, bn_prefetch)
# We currently override batch norm modules to use fp32
self.assertEqual(module.mixed_precision, fp32_mp)
elif module in encoder_layers:
self.assertTrue(isinstance(module, FSDP))
self.assertEqual(module.sharding_strategy, encoder_strategy)
self.assertEqual(module.backward_prefetch, encoder_prefetch)
self.assertEqual(module.mixed_precision, fp16_mp)
elif module in decoder_layers:
self.assertTrue(isinstance(module, FSDP))
self.assertEqual(module.sharding_strategy, decoder_strategy)
self.assertEqual(module.backward_prefetch, decoder_prefetch)
self.assertEqual(module.mixed_precision, fp16_mp)
elif module is model:
self.assertTrue(isinstance(module, FSDP))
self.assertEqual(module.sharding_strategy, root_strategy)
self.assertEqual(module.backward_prefetch, root_prefetch)
self.assertEqual(module.mixed_precision, fp16_mp)
else:
self.assertFalse(isinstance(module, FSDP))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_auto_wrap_api(self):
"""
Test to ensure with auto wrap, we wrap child modules correctly based on the min_num_params.
``nn.Linear(5, 5)`` does not exceed the bucket size, but combined they do.
"""
sequential = TestFSDPWrap.NestedSequentialModel.get_model(cuda=False)
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=40
)
model = FSDP(
sequential,
process_group=self.process_group,
auto_wrap_policy=my_auto_wrap_policy,
)
TestFSDPWrap.NestedSequentialModel.verify_model(self, model)
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_auto_wrap_preset_exclude_wrap(self):
"""
Test to ensure excluded modules are not wrapped, regardless if the total param size is greater than the
min_num_params. the size_based_auto_wrap_policy excludes wrapping for {nn.ModuleList, nn.ModuleDict}
"""
sequential = nn.ModuleList([nn.Linear(5, 5), nn.Linear(5, 5)])
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=40
)
model = FSDP(
sequential,
process_group=self.process_group,
auto_wrap_policy=my_auto_wrap_policy,
)
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model[0], nn.Linear))
self.assertTrue(isinstance(model[1], nn.Linear))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_auto_wrap_preset_exclude_wrap_include_children(self):
"""
Test to ensure excluded modules are not wrapped, but children are if param size is greater than
min_num_params
"""
sequential = nn.ModuleList([nn.Linear(10, 10)])
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=40
)
model = FSDP(
sequential,
process_group=self.process_group,
auto_wrap_policy=my_auto_wrap_policy,
)
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model[0], FSDP))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_auto_wrap_preset_force_leaf(self):
"""
Test to ensure force-leaf modules are not wrapped, and children are not wrapped. The
size_based_auto_wrap_policy forces leaf modules of type {nn.MultiheadAttention} to not be wrapped
"""
sequential = nn.Sequential(nn.Linear(10, 10), nn.MultiheadAttention(100, 1))
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=40
)
model = FSDP(
sequential,
process_group=self.process_group,
auto_wrap_policy=my_auto_wrap_policy,
)
self.assertTrue(isinstance(model.module[0], FSDP))
# Assert children of multihead attention are not wrapped
self.assertTrue(isinstance(model.module[1], nn.MultiheadAttention))
self.assertTrue(isinstance(model.module[1].out_proj, nn.Linear))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_auto_wrap_preset_force_leaf_custom(self):
"""
Test to ensure force-leaf modules are not wrapped.
"""
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy,
min_num_params=40,
force_leaf_modules=size_based_auto_wrap_policy.FORCE_LEAF_MODULES.union(
{nn.Linear}
),
)
sequential = nn.Sequential(
nn.Linear(10, 10), nn.ModuleList([nn.Linear(10, 10)])
)
model = FSDP(
sequential,
process_group=self.process_group,
auto_wrap_policy=my_auto_wrap_policy,
)
# Model was wrapped in FSDP as no inner modules were wrapped.
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model.module[0], nn.Linear))
self.assertTrue(isinstance(model.module[1], nn.ModuleList))
@unittest.skipIf(not TEST_CUDA, "Test Requires CUDA")
@parametrize(
"device_init_mode", [DEVICEInitMode.DEVICE_BEFORE, DEVICEInitMode.DEVICE_AFTER]
)
@parametrize(
"cpu_offload",
[CPUOffload(offload_params=False), CPUOffload(offload_params=True)],
)
@parametrize("use_device_id", [True, False])
def test_auto_wrap_smoke_test(self, device_init_mode, cpu_offload, use_device_id):
# CPU offload and CUDA after don't work together as expected.
if (
cpu_offload.offload_params
and device_init_mode == DEVICEInitMode.DEVICE_AFTER
):
return
device = torch.device("cuda")
torch.cuda.set_device(0)
device_id = (
torch.device("cuda", torch.cuda.current_device()) if use_device_id else None
)
# Random port in case the next test run quickly, same port would cause conflict.
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(find_free_port())
file_name = tempfile.NamedTemporaryFile(delete=False).name
torch.distributed.init_process_group(
backend="nccl",
init_method=f"{FILE_SCHEMA}_{file_name}",
rank=0,
world_size=1,
)
# NOTE: We move model to CUDA after init with FSDP to simulate real use
# cases where full model cannot be loaded onto GPU, but their shards can.
cuda_after_init = device_init_mode == DEVICEInitMode.DEVICE_AFTER
try:
sequential = TestFSDPWrap.NestedSequentialModel.get_model(
cuda=(not cuda_after_init)
)
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=40
)
model = FSDP(
sequential,
cpu_offload=cpu_offload,
auto_wrap_policy=my_auto_wrap_policy,
device_id=device_id,
)
TestFSDPWrap.NestedSequentialModel.verify_model(self, model)
if cuda_after_init:
model = model.cuda()
input = torch.rand((1, 5), dtype=torch.float).to(device)
output = model(input)
loss = F.mse_loss(input, output)
loss.backward()
finally:
torch.distributed.destroy_process_group()
try:
os.remove(file_name)
except FileNotFoundError:
pass
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
@parametrize("wrap_method", [WrapMethod.FSDP_CTOR, WrapMethod.WRAP_API])
def test_always_wrap_with_ignored_modules(self, wrap_method: WrapMethod):
sequential = TestFSDPWrap.NestedSequentialModel.get_model(cuda=False)
ignored_modules = [sequential[1], sequential[2][0]]
fsdp_kwargs = {
"process_group": self.process_group,
"auto_wrap_policy": always_wrap_policy,
"ignored_modules": ignored_modules,
}
if wrap_method == WrapMethod.FSDP_CTOR:
model = FSDP(sequential, **fsdp_kwargs)
elif wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP, **fsdp_kwargs):
model = wrap(sequential)
else:
assert 0, f"Unsupported wrap method: {wrap_method}"
# All non-ignored modules should be wrapped with FSDP
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model.module[0], FSDP))
self.assertTrue(isinstance(model.module[1], nn.Linear))
self.assertTrue(isinstance(model.module[2], FSDP))
self.assertTrue(isinstance(model.module[2].module[0], nn.Linear))
self.assertTrue(isinstance(model.module[2].module[1], FSDP))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
@parametrize("wrap_method", [WrapMethod.FSDP_CTOR, WrapMethod.WRAP_API])
def test_auto_wrap_with_ignored_modules(self, wrap_method: WrapMethod):
sequential = TestFSDPWrap.NestedSequentialModel.get_model(cuda=False)
ignored_modules = [sequential[1], sequential[2][0]]
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy,
min_num_params=40,
)
fsdp_kwargs = {
"process_group": self.process_group,
"auto_wrap_policy": my_auto_wrap_policy,
"ignored_modules": ignored_modules,
}
if wrap_method == WrapMethod.FSDP_CTOR:
model = FSDP(sequential, **fsdp_kwargs)
elif wrap_method == WrapMethod.WRAP_API:
with enable_wrap(wrapper_cls=FSDP, **fsdp_kwargs):
model = wrap(sequential)
else:
assert 0, f"Unsupported wrap method: {wrap_method}"
# Since the 2nd linear (`sequential[1]`) is ignored, the wrapping
# policy does not exceed the parameter threshold before the inner
# sequential (`sequential[2]`) anymore; hence, it flattens
# `sequential[0]` and `sequential[2][0]` into `model` and leaves
# `sequential[1]` and `sequential[2][1]` as-is since they are ignored
self.assertTrue(isinstance(model, FSDP))
self.assertTrue(isinstance(model.module[0], nn.Linear))
self.assertTrue(isinstance(model.module[1], nn.Linear))
self.assertTrue(isinstance(model.module[2], nn.Sequential))
self.assertTrue(isinstance(model.module[2][0], nn.Linear))
self.assertTrue(isinstance(model.module[2][1], nn.Linear))
@unittest.skipIf(not TEST_MULTIGPU, "Requires at least 2 GPUs")
def test_frozen_params(self):
"""
Tests that mixing frozen/non-frozen parameters in an FSDP instance
raises for ``use_orig_params=False`` and warns for ``True``.
"""
module_classes = (LoraAttention, LoraMLP, LoraDecoder)
module_wrap_policy = ModuleWrapPolicy(module_classes)
def lambda_fn_uniform(module: nn.Module):
return isinstance(module, module_classes)
def lambda_fn_nonuniform(module: nn.Module):
if isinstance(module, LoraAttention):
return {"sharding_strategy": ShardingStrategy.SHARD_GRAD_OP}
elif isinstance(module, module_classes):
return True
return False
lambda_wrap_policy_uniform = CustomPolicy(lambda_fn_uniform)
lambda_wrap_policy_nonuniform = CustomPolicy(lambda_fn_nonuniform)
for use_orig_params, policy in itertools.product(
[True, False],
[
module_wrap_policy,
lambda_wrap_policy_uniform,
lambda_wrap_policy_nonuniform,
],
):
self._test_frozen_params(use_orig_params, policy)
def _test_frozen_params(self, use_orig_params: bool, policy: _Policy):
model = LoraModel().cuda()
msg = "layers.0.attn has both parameters with requires_grad=True and False. "
if use_orig_params:
msg += "We do not recommend wrapping such modules"
ctx = self.assertWarnsRegex(UserWarning, msg)
else:
msg += "FSDP does not support wrapping such modules when use_orig_params=False."
ctx = self.assertRaisesRegex(ValueError, msg)
with ctx:
FSDP(
model,
process_group=self.process_group,
auto_wrap_policy=policy,
use_orig_params=use_orig_params,
)
class TestWrapUtils(TestCase):
def test_validate_frozen_params(self):
"""Tests the method ``_validate_frozen_params()``."""
for use_orig_params in [True, False]:
self._test_validate_frozen_params(use_orig_params)
def _test_validate_frozen_params(self, use_orig_params: bool):
model = LoraModel()
# Wrap only LoRA modules
modules_to_wrap = {
module
for module_name, module in model.named_modules()
if "lora_A" in module_name or "lora_B" in module_name
}
_validate_frozen_params(model, modules_to_wrap, set(), use_orig_params)
# Additionally wrap attention
for module in model.modules():
if isinstance(module, LoraAttention):
modules_to_wrap.add(module)
_validate_frozen_params(model, modules_to_wrap, set(), use_orig_params)
# Additionally wrap decoders
for module in model.modules():
if isinstance(module, LoraDecoder):
modules_to_wrap.add(module)
_validate_frozen_params(model, modules_to_wrap, set(), use_orig_params)
# Do not wrap the LoRA-A modules (meaning mixed frozen/non-frozen)
for module_name, module in model.named_modules():
if "lora_A" in module_name:
modules_to_wrap.remove(module)
regex = "layers.0.attn has both parameters with requires_grad=True and False."
if use_orig_params:
# Wrapping the attention manages all parameters except those from
# the LoRA-B module, which is separately wrapped and all nonfrozen
lorab_numel = sum(
p.numel() for p in model.layers[0].attn.lora_B.parameters()
)
attn_frozen_param_numel = sum(
p.numel()
for p in model.layers[0].attn.parameters()
if not p.requires_grad
)
attn_nonfrozen_param_numel = (
sum(
p.numel()
for p in model.layers[0].attn.parameters()
if p.requires_grad
)
- lorab_numel
)
attn_total_param_numel = (
attn_frozen_param_numel + attn_nonfrozen_param_numel
)
regex += (
" We do not recommend wrapping such modules since the "
r"gradient memory usage will be higher than expected \("
f"{attn_total_param_numel} numel instead of {attn_nonfrozen_param_numel} numel "
r"before sharding via reduce-scatter\). "
)
else:
regex += " FSDP does not support wrapping such modules when use_orig_params=False. "
regex += "If possible, wrap the frozen parameters with FSDP separately.\n"
regex += (
"The following parameters have requires_grad=True:\n"
r"\['layers.0.attn.lora_A.weight'\]\n"
"The following parameters have requires_grad=False:\n"
r"\['layers.0.attn.q_proj.weight', 'layers.0.attn.k_proj.weight', "
r"'layers.0.attn.v_proj.weight', 'layers.0.attn.o_proj.weight'\]"
)
if use_orig_params:
ctx = self.assertWarnsRegex(UserWarning, regex)
else:
ctx = self.assertRaisesRegex(ValueError, regex)
with ctx:
_validate_frozen_params(model, modules_to_wrap, set(), use_orig_params)
# Now ignore those LoRA-A modules' parameters
ignored_params = set()
for module_name, module in model.named_modules():
if "lora_A" in module_name:
ignored_params.update(module.parameters())
_validate_frozen_params(model, modules_to_wrap, ignored_params, use_orig_params)
instantiate_parametrized_tests(TestFSDPWrap)
instantiate_parametrized_tests(TestAutoWrap)
if __name__ == "__main__":
run_tests()
|