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|
# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
from typing import Any, Dict
import torch
from torch.distributed._tensor import DeviceMesh
from torch.distributed._tensor.api import distribute_tensor, DTensor
from torch.distributed.tensor.debug import CommDebugMode
from torch.distributed.tensor.parallel import (
ColwiseParallel,
parallelize_module,
RowwiseParallel,
)
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
MLPModule,
MLPStacked,
ModelArgs,
NUM_DEVICES,
skip_unless_torch_gpu,
Transformer,
with_comms,
)
c10d_functional = torch.ops.c10d_functional
class TestCommModeFeatures(DTensorTestBase):
# checks if parameter / sharding info is the same as ground truth
def check_same_set_of_keys(self, dict1, dict2):
"""
Used to ensure the comm_mode parameter/sharding dictionaries contain the same information produced by the
ground truth
"""
dict1_keys = []
dict2_keys = []
for key in dict1:
for nested_key in dict1[key]:
dict1_keys.append((key, nested_key))
for key in dict2:
for nested_key in dict2[key]:
dict2_keys.append((key, nested_key))
self.assertEqual(len(dict1_keys), len(dict2_keys))
for i in range(len(dict1_keys)):
self.assertEqual(dict1_keys[i], dict2_keys[i])
# generates the ground truth parameter and sharding info
def ground_truth(self, model):
"""
Used to generate the ground-truth parameter and sharding info for a given distributed model to
verify comm_mode correctness
"""
module_parameters_dict: Dict[str, Any] = {}
module_sharding_dict: Dict[str, Any] = {}
for name, parameters in model.named_parameters():
# splits name into module name to create FQN and parameter name
module_name = model.__class__.__name__ + "." + name.rsplit(".", 1)[0]
parameter_name = name.rsplit(".", 1)[1]
if module_name not in module_parameters_dict:
module_parameters_dict[module_name] = {}
module_parameters_dict[module_name][parameter_name] = parameters.data
if isinstance(parameters.data, DTensor):
key_name = module_name + "." + parameter_name
module_sharding_dict[key_name] = parameters.data.placements
return module_parameters_dict, module_sharding_dict
@with_comms
def test_MLP_distributed_sharding_display(self):
"""
tests parameters and sharding on a module level
"""
device_mesh = DeviceMesh(
self.device_type,
torch.arange(0, NUM_DEVICES),
)
inp_size = [8, 10]
torch.manual_seed(0)
inp = torch.rand(*inp_size, device=self.device_type)
model = MLPModule(self.device_type)
parallelize_plan = {
"net1": ColwiseParallel(),
"net2": RowwiseParallel(),
}
model = parallelize_module(model, device_mesh, parallelize_plan)
comm_mode = CommDebugMode()
with comm_mode:
output_tp = model(inp)
output_tp.sum().backward()
module_parameters_dict, module_sharding_dict = self.ground_truth(model)
# checks if parameter / sharding info is the same as ground truth
self.check_same_set_of_keys(
module_parameters_dict, comm_mode.get_parameter_info()
)
self.check_same_set_of_keys(module_sharding_dict, comm_mode.get_sharding_info())
@with_comms
def test_MLPStacked_distributed_sharding_display(self):
"""
tests model with nested modules and makes sure comm_mode correctly resets parameter and sharding information
"""
device_mesh = DeviceMesh(
self.device_type,
torch.arange(0, NUM_DEVICES),
)
inp_size = [8, 10]
torch.manual_seed(0)
inp = torch.rand(*inp_size, device=self.device_type)
model = MLPModule(self.device_type)
parallelize_plan = {
"net1": ColwiseParallel(),
"net2": RowwiseParallel(),
}
model = parallelize_module(model, device_mesh, parallelize_plan)
comm_mode = CommDebugMode()
with comm_mode:
output_tp = model(inp)
output_tp.sum().backward()
model2 = MLPStacked(self.device_type)
parallelize_plan = {
"MLPStacked.layers.0.net1": ColwiseParallel(),
"MLPStacked.layers.0.net2": RowwiseParallel(),
"MLPStacked.layers.1.net1": ColwiseParallel(),
"MLPStacked.layers.1.net2": RowwiseParallel(),
}
model2 = parallelize_module(model2, device_mesh, parallelize_plan)
with comm_mode:
# ensures that comm_mode is resetting properly
self.assertEqual(comm_mode.get_parameter_info(), {})
self.assertEqual(comm_mode.get_sharding_info(), {})
output_tp = model2(inp)
module_parameters_dict, module_sharding_dict = self.ground_truth(model2)
self.check_same_set_of_keys(
module_parameters_dict, comm_mode.get_parameter_info()
)
self.check_same_set_of_keys(module_sharding_dict, comm_mode.get_sharding_info())
self.assertEqual(len(comm_mode.get_sharding_info()), 8)
@with_comms
def test_MLP_module_tracing(self):
"""
tests module-level tracing for MLP module
"""
device_mesh = DeviceMesh(
self.device_type,
torch.arange(0, NUM_DEVICES),
)
inp_size = [8, 10]
torch.manual_seed(0)
inp = torch.rand(*inp_size, device=self.device_type)
model = MLPModule(self.device_type)
parallelize_plan = {
"net1": ColwiseParallel(),
"net2": RowwiseParallel(),
}
model = parallelize_module(model, device_mesh, parallelize_plan)
comm_mode = CommDebugMode()
with comm_mode:
output_tp = model(inp)
output_tp.sum().backward()
# checks to see if all sub-modules make it into the module_depth_dictionary
self.assertEqual(len(comm_mode.advanced_module_tracker.module_helper_dict), 5)
# checks to see if all collectives were correctly traced at the module-level
self.assertEqual(
comm_mode.comm_module_counts["Global"]["forward"][
c10d_functional.all_reduce
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["MLPModule"]["forward"][
c10d_functional.all_reduce
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["MLPModule.net2"]["forward"][
c10d_functional.all_reduce
],
1,
)
@skip_unless_torch_gpu
@with_comms
def test_transformer_module_tracing(self, is_seq_parallel=False):
"""
tests module-level tracing for more complicated transformer module and
ensures that comm_module depth and tracing dictionaries correctly reset
"""
device_mesh = DeviceMesh(
self.device_type,
torch.arange(0, NUM_DEVICES),
)
inp_size = [8, 10]
torch.manual_seed(0)
inp = torch.rand(*inp_size, device=self.device_type)
model = MLPModule(self.device_type)
parallelize_plan = {
"net1": ColwiseParallel(),
"net2": RowwiseParallel(),
}
model = parallelize_module(model, device_mesh, parallelize_plan)
comm_mode = CommDebugMode()
with comm_mode:
self.assertEqual(
len(comm_mode.advanced_module_tracker.module_helper_dict), 1
)
self.assertEqual(
comm_mode.comm_module_counts,
{"Global": {"forward": {}, "backward": {}}},
)
output_tp = model(inp)
model_args = ModelArgs(dropout_p=0.0)
model2 = Transformer(model_args).to(device=self.device_type)
model2 = Transformer.parallelize(model2, device_mesh, is_seq_parallel)
inp_size = [8, 8]
inp = torch.randint(model_args.vocab_size, inp_size, device=self.device_type)
inp = distribute_tensor(inp, device_mesh=device_mesh)
comm_mode = CommDebugMode()
with comm_mode:
output = model2(inp)
# checks to see if all collectives were correctly traced at the module-level
self.assertEqual(
comm_mode.comm_module_counts["Global"]["forward"][
c10d_functional.all_reduce
],
6,
)
self.assertEqual(
comm_mode.comm_module_counts["Global"]["forward"][
c10d_functional.all_gather_into_tensor
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer"]["forward"][
c10d_functional.all_reduce
],
6,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer"]["forward"][
c10d_functional.all_gather_into_tensor
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.tok_embeddings"]["forward"][
c10d_functional.all_reduce
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.pos_embeddings"]["forward"][
c10d_functional.all_reduce
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.0"]["forward"][
c10d_functional.all_reduce
],
2,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.0.attention"]["forward"][
c10d_functional.all_reduce
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.0.attention.wo"][
"forward"
][c10d_functional.all_reduce],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.0.feed_forward"][
"forward"
][c10d_functional.all_reduce],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.0.feed_forward.w2"][
"forward"
][c10d_functional.all_reduce],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.1"]["forward"][
c10d_functional.all_reduce
],
2,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.1.attention"]["forward"][
c10d_functional.all_reduce
],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.1.attention.wo"][
"forward"
][c10d_functional.all_reduce],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.1.feed_forward"][
"forward"
][c10d_functional.all_reduce],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.layers.1.feed_forward.w2"][
"forward"
][c10d_functional.all_reduce],
1,
)
self.assertEqual(
comm_mode.comm_module_counts["Transformer.output"]["forward"][
c10d_functional.all_gather_into_tensor
],
1,
)
if __name__ == "__main__":
run_tests()
|
