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# Owner(s): ["module: unknown"]
import functools
import gc
from typing import Union
import torch
import torch.nn as nn
from torch.distributed._composable import checkpoint
from torch.distributed._tensor import init_device_mesh
from torch.distributed._tools.fsdp2_mem_tracker import FSDPMemTracker
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
apply_activation_checkpointing,
CheckpointWrapper,
)
from torch.distributed.fsdp import (
CPUOffloadPolicy,
fully_shard,
MixedPrecisionPolicy,
OffloadPolicy,
)
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import FSDPTest, MLP
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
ModelArgs,
Transformer,
TransformerBlock,
)
def _init_cublas_workspace(dev: torch.device):
lin = torch.nn.Linear(768, 768, device=dev)
inp = torch.randn(1, 768, device=dev)
lin(inp).sum().backward()
del lin
del inp
def _reset_mem_stats(dev: torch.device):
torch.cuda.empty_cache()
torch.cuda.reset_accumulated_memory_stats(dev)
torch.cuda.reset_peak_memory_stats(dev)
class TestTrackerFullyShard1DTrainingCore(FSDPTest):
@property
def world_size(self) -> int:
return min(4, torch.cuda.device_count())
@skip_if_lt_x_gpu(2)
def test_tracker_multi_group_eager(self):
"""
Tests tracker accuracy when using multiple parameter groups for
communication (for communication and computation overlap plus memory
reduction) and different mixed precision policies.
"""
self.run_subtests(
{
"reshard_after_forward": [True, False],
"offload_policy": [
CPUOffloadPolicy(pin_memory=False),
OffloadPolicy(),
],
"mp_policy": [
MixedPrecisionPolicy(
param_dtype=torch.float16, reduce_dtype=torch.float32
),
],
},
self._test_tracker_multi_group,
)
def _test_tracker_multi_group(
self,
reshard_after_forward: Union[bool, int],
offload_policy: OffloadPolicy,
mp_policy: MixedPrecisionPolicy,
):
debug = False
dev = torch.device(torch.cuda.current_device())
_init_cublas_workspace(dev)
gc.collect()
_reset_mem_stats(dev)
mem_stats = torch.cuda.memory_stats(dev)
pre_cuda_active = mem_stats["active_bytes.all.current"]
torch.manual_seed(42)
lin_dim, bsz = 2048, 8192
with torch.device(dev):
model = nn.Sequential(*[MLP(dim=lin_dim, device=dev) for _ in range(4)])
mesh = init_device_mesh("cuda", (self.world_size,))
fully_shard_fn = functools.partial(
fully_shard,
mesh=mesh,
reshard_after_forward=reshard_after_forward,
offload_policy=offload_policy,
mp_policy=mp_policy,
)
for mlp in model:
fully_shard_fn(mlp)
fully_shard_fn(model)
optim = torch.optim.Adam(model.parameters(), lr=1e-2)
inp = torch.randn((bsz, lin_dim), device=dev)
fmt = FSDPMemTracker(model, optim)
fmt.track_inputs((inp,))
with fmt:
for iter_idx in range(2):
loss = model(inp).sum()
loss.backward()
optim.step()
optim.zero_grad()
if iter_idx == 0:
fmt.reset_mod_stats()
mem_stats = torch.cuda.memory_stats()
tracker_max = fmt.get_tracker_snapshot("peak")[dev]["Total"]
cuda_max = mem_stats["active_bytes.all.peak"] - pre_cuda_active
accuracy = tracker_max / cuda_max
if self.rank == 0 and debug:
print(f"Accuracy: {accuracy} Tracker Max:{tracker_max} CUDA Max:{cuda_max}")
self.assertAlmostEqual(
accuracy,
1.0,
delta=0.1,
msg=f"Tracker Max:{tracker_max} CUDA Max:{cuda_max}",
)
del model
del inp
del optim
@skip_if_lt_x_gpu(2)
def test_tracker_non_root_forward_backward(self):
"""
Tests tracker accracy when running forward/backward through a non-root.
"""
debug = False
dev = torch.device(torch.cuda.current_device())
_init_cublas_workspace(dev)
gc.collect()
_reset_mem_stats(dev)
mem_stats = torch.cuda.memory_stats(dev)
pre_cuda_active = mem_stats["active_bytes.all.current"]
torch.manual_seed(42)
lin_dim, bsz = 2048, 8
model = nn.Sequential(*[MLP(lin_dim, dev) for _ in range(3)])
for mlp in model:
fully_shard(mlp)
fully_shard(model)
optim = torch.optim.Adam(model.parameters(), lr=1e-2, foreach=True)
torch.manual_seed(42 + self.rank)
inp = torch.randn((bsz, lin_dim), device=dev)
fmt = FSDPMemTracker(model, optim)
fmt.track_inputs((inp,))
with fmt:
for iter_idx in range(2):
nonroot_loss = model[0](inp).sum()
nonroot_loss.backward()
optim.step()
optim.zero_grad()
if iter_idx == 0:
fmt.reset_mod_stats()
mem_stats = torch.cuda.memory_stats()
tracker_max = fmt.get_tracker_snapshot("peak")[dev]["Total"]
cuda_max = mem_stats["active_bytes.all.peak"] - pre_cuda_active
accuracy = tracker_max / cuda_max
if self.rank == 0 and debug:
print(f"Accuracy: {accuracy} Tracker Max:{tracker_max} CUDA Max:{cuda_max}")
self.assertAlmostEqual(
accuracy,
1.0,
delta=0.1,
msg=f"Tracker Max:{tracker_max} CUDA Max:{cuda_max}",
)
del inp
del model
del optim
class TestTrackerFullyShard1DTrainingCompose(FSDPTest):
@property
def world_size(self) -> int:
return min(torch.cuda.device_count(), 4)
@skip_if_lt_x_gpu(2)
def test_tracker_with_activation_checkpointing(self):
"""
Tests tracker accuracy when composing with activation checkpointing.
"""
self.run_subtests(
{
"reshard_after_forward": [True, False],
"checkpoint_impl": ["composable", "wrapper"],
},
self._test_tracker_with_activation_checkpointing,
)
def _test_tracker_with_activation_checkpointing(
self, reshard_after_forward: Union[bool, int], checkpoint_impl: str
):
assert checkpoint_impl in ("composable", "wrapper")
debug = False
dev = torch.device(torch.cuda.current_device())
_init_cublas_workspace(dev)
gc.collect()
_reset_mem_stats(dev)
mem_stats = torch.cuda.memory_stats(dev)
pre_cuda_active = mem_stats["active_bytes.all.current"]
torch.manual_seed(42)
vocab_size = 8192
bsz, seq_len = 16, 512
with torch.device(dev):
model_args = ModelArgs(
n_layers=4,
n_heads=4,
vocab_size=vocab_size,
max_seq_len=seq_len,
dropout_p=0.1,
)
model = Transformer(model_args)
foreach = False
fully_shard_fn = functools.partial(
fully_shard,
reshard_after_forward=reshard_after_forward,
)
if checkpoint_impl == "wrapper":
apply_activation_checkpointing(
model, check_fn=lambda m: isinstance(m, TransformerBlock)
)
for module in model.modules():
# Apply to `CheckpointWrapper`, which wraps `TransformerBlock`
if isinstance(module, CheckpointWrapper):
fully_shard_fn(module)
else:
for module in model.modules():
if isinstance(module, TransformerBlock):
if checkpoint_impl == "composable":
checkpoint(module)
fully_shard_fn(module)
fully_shard_fn(model)
optim = torch.optim.Adam(model.parameters(), lr=1e-2, foreach=foreach)
torch.manual_seed(42 + self.rank)
inp = torch.randint(0, vocab_size, (bsz, seq_len), device=dev)
fmt = FSDPMemTracker(model, optim)
fmt.track_inputs((inp,))
with fmt:
for iter_idx in range(2):
loss = model(inp).sum()
loss.backward()
optim.step()
optim.zero_grad()
if iter_idx == 0:
fmt.reset_mod_stats()
mem_stats = torch.cuda.memory_stats()
tracker_max = fmt.get_tracker_snapshot("peak")[dev]["Total"]
cuda_max = mem_stats["active_bytes.all.peak"] - pre_cuda_active
accuracy = tracker_max / cuda_max
if self.rank == 0 and debug:
print(f"Accuracy: {accuracy} Tracker Max:{tracker_max} CUDA Max:{cuda_max}")
self.assertAlmostEqual(
accuracy,
1.0,
delta=0.1,
msg=f"Tracker Max:{tracker_max} CUDA Max:{cuda_max}",
)
del inp
del model
del optim
if __name__ == "__main__":
run_tests()
|
