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# Owner(s): ["oncall: distributed"]
import itertools
import sys
from typing import Union
import torch
import torch.nn as nn
from torch import distributed as dist
from torch.distributed.fsdp import ShardingStrategy
from torch.distributed.fsdp.fully_sharded_data_parallel import (
CPUOffload,
FullyShardedDataParallel as FSDP,
MixedPrecision,
)
from torch.distributed.fsdp.wrap import ModuleWrapPolicy
from torch.nn import TransformerDecoderLayer, TransformerEncoderLayer
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.testing._internal.common_device_type import instantiate_device_type_tests
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import (
DEVICEInitMode,
FSDPInitMode,
FSDPTest,
get_devtype,
NestedWrappedModule,
TransformerWithSharedParams,
)
from torch.testing._internal.common_utils import run_tests, TEST_WITH_DEV_DBG_ASAN
device_type = torch.device(get_devtype())
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
class TestClipGradNorm(FSDPTest):
"""Tests :meth:`FullyShardedDataParallel.clip_grad_norm_`."""
@skip_if_lt_x_gpu(2)
def test_non_root(self, device):
"""
Tests that calling ``clip_grad_norm_()`` on a non-root FSDP instance
raises an error.
"""
class Model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.lin1 = nn.Linear(5, 5)
self.lin2 = nn.Linear(5, 5)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.lin2(self.lin1(x))
model = Model().to(device_type.type)
model.lin2 = FSDP(model.lin2)
fsdp_model = FSDP(model)
# fsdp_model(torch.randn((2, 5), device=torch.device(self.device_type))).sum().backward()
fsdp_model(torch.randn((2, 5), device=device_type)).sum().backward()
error_regex = "should only be called on the root FSDP instance"
with self.assertRaisesRegex(RuntimeError, error_regex):
fsdp_model.lin2.clip_grad_norm_(max_norm=2)
@skip_if_lt_x_gpu(2)
def test_ddp_parity(self, device):
"""
Tests FSDP with ``FullyShardedDataParallel.clip_grad_norm_()` against
DDP with ``torch.nn.utils.clip_grad_norm_()` when using full precision.
"""
self.run_subtests(
{
"device": [device],
"max_norm": [1, 2.5],
"norm_type": [1, 2, float("inf")],
"sharding_strategy": [
ShardingStrategy.FULL_SHARD,
ShardingStrategy.NO_SHARD,
"mixed_strategy",
],
"use_orig_params": [False, True],
"offload_params": [False, True],
},
self._test_ddp_parity,
)
def _test_ddp_parity(
self,
device,
max_norm: Union[float, int],
norm_type: Union[float, int],
sharding_strategy: Union[ShardingStrategy, str],
use_orig_params: bool,
offload_params: bool,
):
local_model = TransformerWithSharedParams.init(
self.process_group,
FSDPInitMode.NO_FSDP,
DEVICEInitMode.DEVICE_BEFORE,
deterministic=True,
)
ddp_model = DDP(local_model, device_ids=[device_type])
fsdp_kwargs = {
"cpu_offload": CPUOffload(offload_params=offload_params),
"use_orig_params": use_orig_params,
"device_id": device_type.type,
}
if sharding_strategy == "mixed_strategy":
fsdp_model = TransformerWithSharedParams.init(
self.process_group,
FSDPInitMode.NO_FSDP,
DEVICEInitMode.DEVICE_BEFORE,
deterministic=True,
)
# Apply `NO_SHARD` to the encoder
fsdp_model.transformer.encoder = FSDP(
fsdp_model.transformer.encoder,
sharding_strategy=ShardingStrategy.NO_SHARD,
**fsdp_kwargs,
)
# Apply `FULL_SHARD` to the decoder
fsdp_model.transformer.decoder = FSDP(
fsdp_model.transformer.decoder,
sharding_strategy=ShardingStrategy.FULL_SHARD,
**fsdp_kwargs,
)
# TODO: FSDP's `clip_grad_norm_()` is not a static method, so we
# must make the root module an FSDP instance
fsdp_model = FSDP(
fsdp_model, sharding_strategy=ShardingStrategy.FULL_SHARD, **fsdp_kwargs
)
else:
fsdp_kwargs.update(
{
"sharding_strategy": sharding_strategy,
"auto_wrap_policy": ModuleWrapPolicy(
{
TransformerEncoderLayer,
TransformerDecoderLayer,
}
),
}
)
fsdp_model = TransformerWithSharedParams.init(
self.process_group,
FSDPInitMode.RECURSIVE,
DEVICEInitMode.DEVICE_BEFORE,
deterministic=True,
fsdp_kwargs=fsdp_kwargs,
)
LR = 1e-2
ddp_optim = torch.optim.Adam(ddp_model.parameters(), lr=LR)
fsdp_optim = torch.optim.Adam(fsdp_model.parameters(), lr=LR)
device = torch.device(self.device_type)
LARGE_FACTOR = 100
inp = ddp_model.module.get_input(device)
for model in (ddp_model, fsdp_model):
out = model(*inp)
if isinstance(model, (DDP, FSDP)):
loss = model.module.get_loss(inp, out)
else:
loss = model.get_loss(inp, out)
loss.backward()
# Multiply gradients by a large factor to ensure that gradients will
# actually be clipped
for param in itertools.chain(ddp_model.parameters(), fsdp_model.parameters()):
if (
param.grad is not None
): # gradients may be `None` for `use_orig_params=True`
param.grad *= LARGE_FACTOR
orig_ddp_grads = [
param.grad.detach().clone() for param in ddp_model.parameters()
]
orig_fsdp_grads = [
param.grad.detach().clone() if param.grad is not None else None
for param in fsdp_model.parameters()
]
ddp_total_norm = torch.nn.utils.clip_grad_norm_(
ddp_model.parameters(),
max_norm=max_norm,
norm_type=norm_type,
)
fsdp_total_norm = fsdp_model.clip_grad_norm_(
max_norm=max_norm, norm_type=norm_type
)
self.assertEqual(ddp_total_norm, fsdp_total_norm)
# Check that the gradients were modified by `clip_grad_norm_()`
for param, orig_grad in zip(ddp_model.parameters(), orig_ddp_grads):
assert not torch.equal(param.grad, orig_grad)
for param, orig_grad in zip(fsdp_model.parameters(), orig_fsdp_grads):
if param.grad is None:
self.assertEqual(param.grad, orig_grad) # `None`
else:
assert not torch.equal(param.grad, orig_grad)
# Run an optimizer step to ensure gradients matched after clipping
ddp_optim.step()
fsdp_optim.step()
with FSDP.summon_full_params(fsdp_model):
for (n1, p1), (n2, p2) in zip(
ddp_model.module.named_parameters(),
fsdp_model.named_parameters(),
):
self.assertEqual(n1, n2)
self.assertEqual(p1, p2)
if offload_params:
# TODO: Gradient computation on CPU and GPU differ slightly causing
# drift unrelated to `clip_grad_norm_()`.
# https://github.com/pytorch/pytorch/issues/89133
return
# Run a few more iterations
# TODO: We cannot run too many iterations, or else there is drift:
# https://github.com/pytorch/pytorch/issues/89136
for i in range(3):
set_to_none = i % 2 == 0 # exercise both
ddp_optim.zero_grad(set_to_none=set_to_none)
fsdp_optim.zero_grad(set_to_none=set_to_none)
inp = ddp_model.module.get_input(device)
for model in (ddp_model, fsdp_model):
out = model(*inp)
out.sum().backward()
ddp_total_norm = torch.nn.utils.clip_grad_norm_(
ddp_model.parameters(),
max_norm=max_norm,
norm_type=norm_type,
)
fsdp_total_norm = fsdp_model.clip_grad_norm_(
max_norm=max_norm, norm_type=norm_type
)
self.assertEqual(ddp_total_norm, fsdp_total_norm)
ddp_optim.step()
fsdp_optim.step()
@skip_if_lt_x_gpu(2)
def test_low_precision_grads(self, device):
"""Tests ``clip_grad_norm_()`` when using low precision gradients."""
self.run_subtests(
{
"device": [device],
"max_norm": [1, 2.5],
"norm_type": [1, 2, float("inf")],
"sharding_strategy": [
ShardingStrategy.FULL_SHARD,
ShardingStrategy.NO_SHARD,
],
"use_orig_params": [False, True],
},
self._test_low_precision_grads,
)
def _test_low_precision_grads(
self,
device,
max_norm: Union[float, int],
norm_type: Union[float, int],
sharding_strategy: ShardingStrategy,
use_orig_params: bool,
):
fsdp_kwargs = {
"sharding_strategy": sharding_strategy,
"use_orig_params": use_orig_params,
"mixed_precision": MixedPrecision(
param_dtype=torch.float16,
reduce_dtype=torch.float16,
keep_low_precision_grads=True,
),
"device_id": device_type.type,
}
fsdp_model = FSDP(
NestedWrappedModule.init(
self.process_group,
FSDPInitMode.RECURSIVE,
DEVICEInitMode.DEVICE_BEFORE,
deterministic=True,
fsdp_kwargs=fsdp_kwargs,
),
**fsdp_kwargs,
)
inp = fsdp_model.module.get_input(torch.device(self.device_type))
out = fsdp_model(*inp)
out.sum().backward()
for param in fsdp_model.parameters():
if param.grad is not None:
self.assertEqual(param.grad.dtype, torch.float16)
total_norm = fsdp_model.clip_grad_norm_(max_norm=max_norm, norm_type=norm_type)
# Check that the total norm is in FP16 to match the gradient dtype
self.assertEqual(total_norm.dtype, torch.float16)
# As a best effort, check that each gradient has norm at most the max
# norm (since DDP does not support mixed precision natively, we cannot
# directly compare for parity)
for param in fsdp_model.parameters():
if param.grad is not None:
self.assertTrue(
torch.linalg.vector_norm(param.grad, norm_type).item() <= max_norm,
)
@skip_if_lt_x_gpu(2)
def test_no_gradients(self, device):
"""
Tests that calling ``clip_grad_norm_()`` when the FDSP module has no
gradients simply returns a scalar zero tensor in FP32 without erroring.
"""
self.run_subtests(
{"device": [device], "use_orig_params": [False, True]},
self._test_no_gradients,
)
def _test_no_gradients(self, device, use_orig_params: bool):
lin_module = nn.Linear(24, 24)
mixed_precision_config = MixedPrecision(
param_dtype=torch.float16,
reduce_dtype=torch.float32,
buffer_dtype=torch.float32,
)
fsdp_module = FSDP(
lin_module,
sharding_strategy=ShardingStrategy.SHARD_GRAD_OP,
mixed_precision=mixed_precision_config,
device_id=device_type.type,
use_orig_params=use_orig_params,
)
inp = torch.randn(32, 24, device=self.device_type)
fsdp_module(inp)
with self.assertWarnsRegex(
expected_warning=UserWarning,
expected_regex="on rank "
rf"{self.rank} with no gradients -- returning the total "
"norm in the default dtype torch.float32",
):
total_norm = fsdp_module.clip_grad_norm_(1)
self.assertEqual(total_norm.dtype, torch.float32)
self.assertEqual(total_norm, torch.tensor(0.0, device=self.device_type))
devices = ("cuda", "hpu")
instantiate_device_type_tests(TestClipGradNorm, globals(), only_for=devices)
if __name__ == "__main__":
run_tests()
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