Skip to content

Commit

Permalink
Merge pull request #6077 from duanjunwen/dev/zero_bubble
Browse files Browse the repository at this point in the history 
[Feature] ZeroBubble support MoeHybridplugin;
  • Loading branch information
@duanjunwen
duanjunwen authored Oct 9, 2024
2 parents 295dd2d + cc500b3 commit 531773f
Show file tree
Hide file tree
Showing 3 changed files with 202 additions and 119 deletions.
11 changes: 8 additions & 3 deletions colossalai/booster/plugin/moe_hybrid_parallel_plugin.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -295,8 +295,11 @@ def __init__(
if self.pp_size > 1:
assert pp_style in ["1f1b", "interleaved", "zbv"], "Unsupported pipeline parallelism style"
assert (
pp_style == "interleaved" or pp_style == "zbv"
) or num_model_chunks == 1, "num_model_chunks must be 1 when using 1f1b"
pp_style in ["interleaved", "zbv"] or num_model_chunks == 1
), "num_model_chunks must be 1 when using 1f1b"
assert (
pp_style in ["1f1b", "interleaved"] or num_model_chunks == 2
), "num_model_chunks must be 2 when using zero bubble pipeline"
assert (
num_microbatches is not None or microbatch_size is not None
), "num_microbatches or microbatch_size must be specified when using pipeline parallelism"
Expand All @@ -309,6 +312,7 @@ def __init__(
enable_interleave=(pp_style == "interleaved" or pp_style == "zbv"),
num_model_chunks=num_model_chunks,
num_layers_per_stage=num_layers_per_stage,
use_zbv=(pp_style == "zbv"),
)

if pp_style == "interleaved":
Expand All @@ -329,7 +333,8 @@ def __init__(
enable_metadata_cache=enable_metadata_cache,
)
elif pp_style == "zbv":
self.schedule = ZeroBubbleVPipeScheduler(
assert num_model_chunks > 1, "number of model chunks must be > 1 when using ZerbubbleV"
self.scheduler = ZeroBubbleVPipeScheduler(
schedule=scheduler_nodes,
stage_manager=self.stage_manager,
num_model_chunks=num_model_chunks,
Expand Down
30 changes: 23 additions & 7 deletions colossalai/shardformer/policies/mixtral.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -258,14 +258,30 @@ def get_held_layers(self) -> List[Module]:
stage_manager = self.pipeline_stage_manager

held_layers = []
layers_per_stage = stage_manager.distribute_layers(len(module.layers))
if stage_manager.is_first_stage():
held_layers.append(module.embed_tokens)
start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
held_layers.extend(module.layers[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.norm)

if stage_manager.is_interleave:
assert stage_manager.num_model_chunks is not None
layers_per_stage = stage_manager.distribute_layers(len(module.layers))
stage_indices = stage_manager.get_stage_index(layers_per_stage)
stage_manager.stage_indices = stage_indices
if stage_manager.is_first_stage(ignore_chunk=True):
held_layers.append(module.embed_tokens)
for start_idx, end_idx in stage_indices:
held_layers.extend(module.layers[start_idx:end_idx])
if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
):
# for zbv, when is_first_stage (last fwd), we append norm
# for interleaved, when is_last_stage (last fwd), we also append norm
held_layers.append(module.norm)
else:
layers_per_stage = stage_manager.distribute_layers(len(module.layers))
if stage_manager.is_first_stage():
held_layers.append(module.embed_tokens)
start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
held_layers.extend(module.layers[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.norm)
return held_layers


Expand Down
280 changes: 171 additions & 109 deletions tests/test_pipeline/test_schedule/test_zerobubble_pp.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -7,17 +7,30 @@
import torch.distributed as dist
import torch.nn as nn
from torch.testing import assert_close
from transformers.models.mixtral.configuration_mixtral import MixtralConfig
from transformers.models.mixtral.modeling_mixtral import MixtralModel

import colossalai
from colossalai.booster.booster import Booster
from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
from colossalai.cluster import ProcessGroupMesh
from colossalai.interface import OptimizerWrapper
from colossalai.logging import disable_existing_loggers
from colossalai.pipeline.schedule.v_schedule import PipelineGraph, ScheduledNode
from colossalai.pipeline.schedule.zero_bubble_pp import ZeroBubbleVPipeScheduler
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo
from colossalai.testing.random import seed_all
from tests.test_moe.moe_utils import assert_loose_close

NUM_BATCH = 8
NUM_TOK_PER_BATCH, NUM_EXPERTS = 4, 4
NUM_LAYERS = 8
HIDDEN_SIZE_PER_HEAD = 4
NUM_HEADS = 4
TOP_K = 1


class MlpModel(nn.Module):
Expand Down Expand Up @@ -730,127 +743,176 @@ def criterion_base(x, *args, **kwargs):
assert_optim_param_groups(optim_base_param_groups, optim_pp_param_groups)


# TODO:4) support Hybrid base 3)
# TODO:3) support booster & Hybrid base 2)
def run_with_hybridplugin(test_config):
pass


# TODO:5) support MoEHybrid base 3)
# TODO:4) support booster & MoEHybrid base 2)
@parameterize(
"test_config",
"config",
[
{
"pp_style": "zbv",
"tp_size": 1,
"ep_size": 1,
"pp_size": 4,
"num_microbatches": 4,
"zero_stage": 1,
"precision": "bf16",
"num_model_chunks": 2,
},
(0, 1, 4, 1, 1),
(1, 2, 2, 1, 1),
(1, 2, 1, 2, 1),
(1, 2, 1, 1, 2),
],
)
def run_with_moehybridplugin(test_config):
sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
# test_config["use_lazy_init"] = False
test_config["initial_scale"] = 2**16
model_list = [
"transformers_bert",
]
clear_layout_converter()

for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
if name in model_list:
# base param
model = model_fn()
data = data_gen_fn()
print(f"data {data}")
criterion = loss_fn
optimizer = torch.optim.SGD(model.parameters(), momentum=0.1, lr=1e-5)

output = model(**data)
loss = criterion(output)
loss.backward()
optimizer.step()
print(f"output {output}")

# # pp param
# model_pp = deepcopy(model)
# data_pp = deepcopy(data)
# optimizer_pp = OptimizerWrapper(torch.optim.SGD(model_pp.parameters(), momentum=0.1, lr=1e-5))

# # init pipeline graph
# h, a, s = model.config.hidden_size, model.config.num_attention_heads, 1024
# mem_f = 34 * h + 5 * a * s
# mem_w = -32 * h
# mem_b = -mem_w - mem_f
# graph = PipelineGraph(
# n_stage=test_config["pp_size"],
# n_micro=test_config["num_microbatches"],
# f_cost=1,
# b_cost=1,
# w_cost=1,
# c_cost=1,
# f_mem=mem_f,
# b_mem=mem_b,
# w_mem=mem_w,
# # max_mem=mem_f * (p * 2 + m_offset),
# )

# zbv_schedule = graph.get_v_schedule()

# test_config["scheduler_nodes"] = zbv_schedule
# plugin = MoeHybridParallelPlugin(
# **test_config
# )
# model_pp, optimizer_pp, criterion, data_pp, _ = plugin.configure(
# model = model_pp,
# optimizer = optimizer_pp,
# criterion = criterion,
# dataloader = data_pp,
# )

# output_pp = plugin.execute_pipeline(
# data_iter=iter(data),
# model=model,
# criterion=criterion,
# optimizer=optimizer,
# return_loss = True,
# return_outputs = True,
# )


# TODO:6) support booster & Hybrid base 4)


# TODO:7) support booster & MoEHybrid base 4)
@parameterize(
"test_config",
[
{
"pp_style": "zbv",
"tp_size": 1,
"ep_size": 1,
"pp_size": 4,
"num_microbatches": 4,
"zero_stage": 1,
"precision": "bf16",
"num_model_chunks": 2,
},
],
)
def run_with_booster_moehybridplugin(test_config):
pass
def run_with_booster_moehybridplugin(config: Tuple[int, ...]):
test_config = config
stage, ep_size, pp_size, tp_size, sp_size = config
num_microbatches = pp_size
dist.get_world_size()
rank = dist.get_rank()
dtype, precision = torch.float16, "fp16"
torch.cuda.set_device(dist.get_rank())

########
# init base model
########
assert pp_size <= NUM_LAYERS, "pp_size should be less than or equal to NUM_LAYERS"
config = MixtralConfig(
hidden_size=HIDDEN_SIZE_PER_HEAD * NUM_HEADS,
intermediate_size=HIDDEN_SIZE_PER_HEAD * NUM_HEADS * 2,
num_hidden_layers=NUM_LAYERS,
num_attention_heads=NUM_HEADS,
num_key_value_heads=NUM_HEADS,
num_local_experts=NUM_EXPERTS,
num_experts_per_tok=TOP_K,
attn_implementation="flash_attention_2",
)

# init model with the same seed
seed_all(10086)

torch_model = MixtralModel(config).to(dtype).cuda()
torch_optimizer = torch.optim.SGD(torch_model.parameters(), lr=1)
# init schedule
h, a, s = config.hidden_size, config.num_attention_heads, 1024
mem_f = 34 * h + 5 * a * s
mem_w = -32 * h
mem_b = -mem_w - mem_f
graph = PipelineGraph(
n_stage=pp_size,
n_micro=num_microbatches,
f_cost=1,
b_cost=1,
w_cost=1,
c_cost=1,
f_mem=mem_f,
b_mem=mem_b,
w_mem=mem_w,
# max_mem=mem_f * (p * 2 + m_offset),
)

zbv_schedule = graph.get_v_schedule()

# init MoeHybridPlugin
plugin = MoeHybridParallelPlugin(
pp_size=pp_size,
num_microbatches=pp_size,
tp_size=tp_size,
sp_size=sp_size,
ep_size=ep_size,
zero_stage=stage,
enable_sequence_parallelism=sp_size > 1,
sequence_parallelism_mode="all_to_all" if sp_size > 1 else None,
overlap_communication=False,
initial_scale=1,
precision=precision,
find_unused_parameters=True,
pp_style="zbv",
scheduler_nodes=zbv_schedule,
num_model_chunks=2,
)

dp_size = plugin.dp_size

booster = Booster(plugin=plugin)

########
# init pp model
########

parallel_model = deepcopy(torch_model)
parallel_optimizer = torch.optim.SGD(parallel_model.parameters(), lr=1)
parallel_model, parallel_optimizer, _, _, _ = booster.boost(parallel_model, parallel_optimizer)
# create different input along dp axis
seed_all(1453 + rank)

torch_model.train()
parallel_model.train()
for _ in range(2):
# gen random input
input_embeddings = torch.rand(
NUM_BATCH, NUM_TOK_PER_BATCH, HIDDEN_SIZE_PER_HEAD * NUM_HEADS, requires_grad=True
).cuda()
dist.all_reduce(
input_embeddings, group=plugin.pp_group
) # pp inputs except the first stage doesn't matter, but need to be replicate for torch model check

dist.all_reduce(input_embeddings, group=plugin.tp_group) # tp group duplicate input
dist.all_reduce(input_embeddings, group=plugin.sp_group) # sp group duplicate input

# run the model with hybrid parallel
if booster.plugin.stage_manager is not None:
# for test with pp
data_iter = iter([{"inputs_embeds": input_embeddings}])
sharded_output = booster.execute_pipeline(
data_iter,
parallel_model,
lambda x, y: x.last_hidden_state.mean(),
parallel_optimizer,
return_loss=True,
return_outputs=True,
)
# stage 0 chunk 0
parallel_output = None
if (
booster.plugin.stage_manager.is_first_stage(ignore_chunk=True)
and rank == dist.get_process_group_ranks(plugin.pp_group)[0]
):
parallel_output = sharded_output["loss"]
else:
parallel_output = torch.tensor(12345.0, device="cuda")
# broadcast along pp axis
dist.broadcast(parallel_output, src=dist.get_process_group_ranks(plugin.pp_group)[0], group=plugin.pp_group)

else:
# for test without pp
parallel_output = parallel_model(inputs_embeds=input_embeddings.to(dtype)).last_hidden_state.mean()
parallel_optimizer.backward(parallel_output)
parallel_optimizer.step()
parallel_optimizer.zero_grad()
dist.all_reduce(parallel_output, group=plugin.dp_group)

# ===================================================================================
# run normal model with all dp(different) inputs
all_inputs = [torch.empty_like(input_embeddings) for _ in range(dp_size)]
dist.all_gather(all_inputs, input_embeddings, group=plugin.dp_group)
torch_output_sum = 0
for input_data_ in all_inputs:
torch_output = torch_model(inputs_embeds=input_data_.to(dtype)).last_hidden_state.mean()
torch_output.backward()
torch_output_sum += torch_output.detach()
# avg dp grads follows zero optimizer
for p in torch_model.parameters():
if p.grad is not None:
p.grad /= dp_size
torch_optimizer.step()
torch_optimizer.zero_grad()
assert_loose_close(parallel_output, torch_output_sum, dtype=dtype)
print(f"rank {dist.get_rank()} config {test_config} test passed")
clear_layout_converter()
Randomizer.reset_index()
torch.cuda.empty_cache()


def run_dist(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
# run_fwd_bwd_iter_input()
run_fwd_bwd_vschedule_with_optim()
# run_with_moehybridplugin()
# run_with_booster_moehybridplugin()
# run_fwd_bwd_vschedule_with_optim()
run_with_booster_moehybridplugin()


@pytest.mark.dist
Expand Down

0 comments on commit 531773f

Please sign in to comment.