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[feat] support meta cache, meta_grad_send, meta_tensor_send; fix runtime too long in Recv Bwd; benchmark for llama + Hybrid(tp+pp);

pull/6083/head
duanjunwen 4 weeks ago
parent
705b18e1e7
commit
2eca112c90
8 changed files with 185 additions and 64 deletions
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  1. 107
      colossalai/pipeline/schedule/zero_bubble_pp.py
  2. 2
      colossalai/pipeline/stage_manager.py
  3. 55
      colossalai/pipeline/weight_grad_store.py
  4. 7
      colossalai/shardformer/modeling/llama.py
  5. 27
      colossalai/shardformer/policies/llama.py
  6. 20
      examples/language/llama/benchmark.py
  7. 15
      examples/language/performance_evaluator.py
  8. 16
      tests/test_pipeline/test_schedule/test_zerobubble_pp.py

107
colossalai/pipeline/schedule/zero_bubble_pp.py
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@ -8,7 +8,7 @@ from torch.utils._pytree import tree_flatten, tree_map
from colossalai.accelerator import get_accelerator
from colossalai.interface import OptimizerWrapper
from colossalai.pipeline.p2p import PipelineP2PCommunication
from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
from colossalai.pipeline.schedule.v_schedule import ScheduledNode
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.pipeline.weight_grad_store import WeightGradStore
@ -62,11 +62,11 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
self.do_post_validation = False
# P2PMeta cache
# self.enable_metadata_cache = enable_metadata_cache
# self.send_tensor_metadata = True
# self.send_grad_metadata = True
# self.tensor_metadata_recv = None
# self.grad_metadata_recv = None
self.enable_metadata_cache = enable_metadata_cache
self.send_tensor_metadata = True
self.send_grad_metadata = True
self.tensor_metadata_recv = None
self.grad_metadata_recv = None
# P2P communication
self.comm = PipelineP2PCommunication(stage_manager, overlap_p2p=overlap_p2p)
@ -105,8 +105,11 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
# dy buffer for local send bwd
self.local_send_backward_buffer = []
# wait pp buffer
self.send_handles = []
def assert_buffer_empty(self):
# assert buuffer is empty at end
# assert buffer is empty at end
assert len(self.input_tensors[0]) == 0
assert len(self.input_tensors[1]) == 0
assert len(self.output_tensors[0]) == 0
@ -125,6 +128,7 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
assert len(self.recv_backward_buffer[1]) == 0
assert len(self.local_send_forward_buffer) == 0
assert len(self.local_send_backward_buffer) == 0
# assert len(self.send_handles) == 0
def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None:
"""Load a batch from data iterator.
@ -221,7 +225,8 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
# do nothing; cause u are chunk 0 in first rank, u have no prev rank;
#################
if self.stage_manager.is_first_stage(ignore_chunk=True):
return None, []
# return None, []
return []
################
# chunk = 0 & not is_first_stage
@ -229,9 +234,14 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
#################
else:
prev_rank = self.stage_manager.get_prev_rank()
input_tensor, wait_handles = self.comm.recv_forward(prev_rank=prev_rank)
input_tensor, wait_handles = self.comm.recv_forward(
prev_rank=prev_rank, metadata_recv=self.tensor_metadata_recv
)
if self.enable_metadata_cache and self.tensor_metadata_recv is None:
self.tensor_metadata_recv = create_send_metadata(input_tensor)
self.recv_forward_buffer[model_chunk_id].append(input_tensor)
return input_tensor, wait_handles
# return input_tensor, wait_handles
return wait_handles
else:
################
@ -239,7 +249,8 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
# do nothing; cause u get y from local_send_forward_buffer in schedule f
################
if self.stage_manager.is_last_stage(ignore_chunk=True):
return None, []
# return None, []
return []
################
# chunk = 1 & not is_last_stage
@ -247,9 +258,14 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
################
else:
next_rank = self.stage_manager.get_next_rank()
input_tensor, wait_handles = self.comm.recv_forward(next_rank)
input_tensor, wait_handles = self.comm.recv_forward(
next_rank, metadata_recv=self.tensor_metadata_recv
)
if self.enable_metadata_cache and self.tensor_metadata_recv is None:
self.tensor_metadata_recv = create_send_metadata(input_tensor)
self.recv_forward_buffer[model_chunk_id].append(input_tensor)
return input_tensor, wait_handles
# return input_tensor, wait_handles
return wait_handles
def recv_backward(self, model_chunk_id: int, next_rank: int = None) -> Tuple[Any, List]:
"""Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage.
@ -271,7 +287,8 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
# do nothing; Already get dy from local_send_backward_buffer in schedule b
################
if self.stage_manager.is_last_stage(ignore_chunk=True):
return None, []
# return None, []
return []
################
# chunk = 0 & not is_last_stage
@ -279,9 +296,14 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
################
else:
next_rank = self.stage_manager.get_next_rank()
output_tensor_grad, wait_handles = self.comm.recv_backward(next_rank)
output_tensor_grad, wait_handles = self.comm.recv_backward(
next_rank, metadata_recv=self.grad_metadata_recv
)
if self.enable_metadata_cache and self.grad_metadata_recv is None:
self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
self.recv_backward_buffer[model_chunk_id].append(output_tensor_grad)
return output_tensor_grad, wait_handles
# return output_tensor_grad, wait_handles
return wait_handles
else:
# bwd chunk1 is left V;
@ -290,7 +312,8 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
# do nothing; get loss from local
################
if self.stage_manager.is_first_stage(ignore_chunk=True):
return None, []
# return None, []
return []
################
# chunk = 1 & not first stage
@ -298,9 +321,14 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
################
else:
prev_rank = self.stage_manager.get_prev_rank()
output_tensor_grad, wait_handles = self.comm.recv_backward(next_rank=prev_rank)
output_tensor_grad, wait_handles = self.comm.recv_backward(
next_rank=prev_rank, metadata_recv=self.grad_metadata_recv
)
if self.enable_metadata_cache and self.grad_metadata_recv is None:
self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
self.recv_backward_buffer[model_chunk_id].append(output_tensor_grad)
return output_tensor_grad, wait_handles
# return output_tensor_grad, wait_handles
return wait_handles
def send_forward(self, model_chunk_id: int, next_rank: int = None) -> List:
"""Sends the input tensor to the next stage in pipeline.
@ -330,7 +358,10 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
else:
next_rank = self.stage_manager.get_next_rank()
output_tensor = self.send_forward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_forward(output_object=output_tensor, next_rank=next_rank)
send_handles = self.comm.send_forward(
output_object=output_tensor, next_rank=next_rank, send_metadata=self.send_tensor_metadata
)
self.send_tensor_metadata = not self.enable_metadata_cache
return send_handles
else:
@ -348,7 +379,10 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
else:
prev_rank = self.stage_manager.get_prev_rank()
output_tensor = self.send_forward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_forward(output_tensor, prev_rank)
send_handles = self.comm.send_forward(
output_tensor, prev_rank, send_metadata=self.send_tensor_metadata
)
self.send_tensor_metadata = not self.enable_metadata_cache
return send_handles
def send_backward(self, model_chunk_id: int, prev_rank: int = None) -> List:
@ -380,7 +414,10 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
else:
prev_rank = self.stage_manager.get_prev_rank()
input_tensor_grad = self.send_backward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_backward(input_tensor_grad, prev_rank)
send_handles = self.comm.send_backward(
input_tensor_grad, prev_rank, send_metadata=self.send_grad_metadata
)
self.send_grad_metadata = not self.enable_metadata_cache
return send_handles
# bwd chunk1 is left V;
@ -399,7 +436,10 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
else:
next_rank = self.stage_manager.get_next_rank()
input_tensor_grad = self.send_backward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_backward(input_tensor_grad, next_rank)
send_handles = self.comm.send_backward(
input_tensor_grad, next_rank, send_metadata=self.send_grad_metadata
)
self.send_grad_metadata = not self.enable_metadata_cache
return send_handles
def forward_step(
@ -479,11 +519,11 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
output_obj_grad_ = []
# For chunk 0 stage 0, use micro_batch as input_obj_; and we don't have to cal microbatch dx.
if model_chunk_id == 0 and self.stage_manager.is_first_stage(ignore_chunk=True):
return None
# if model_chunk_id == 0 and self.stage_manager.is_first_stage(ignore_chunk=True):
# return None
# For loss backward; output_obj is loss; output_obj_grad should be None
elif model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
assert output_obj_grad is None
input_obj_, _ = tree_flatten(input_obj)
output_obj_.append(output_obj) # LOSS
@ -510,7 +550,7 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
tensor=output_obj_,
grad=output_obj_grad_,
# inputs=input_obj_,
# retain_graph=True,
retain_graph=False,
)
# Format output_obj_grad
input_obj_grad = dict()
@ -712,6 +752,12 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
# else:
# # we save output_tensor_grad here
# self.output_tensors_grad_dw[model_chunk_id].append(output_tensor_grad)
# the_output_obj_grad = []
# if isinstance(output_obj, dict):
# for (k, v) in output_obj.items():
# the_output_obj_grad.append(v.requires_grad)
# else:
# the_output_obj_grad.append(output_obj.requires_grad)
input_object_grad = self.backward_b_step(
model_chunk=model_chunk,
@ -844,7 +890,8 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
if scheduled_node.type in AUTO_SCHEDULE_COMMUNICATION_TYPES:
# communication
communication_func = self.communication_map[scheduled_node.type]
communication_func(scheduled_node.chunk)
wait_handle = communication_func(scheduled_node.chunk)
self.send_handles.append(wait_handle)
elif scheduled_node.type == "F":
self.schedule_f(
scheduled_node=scheduled_node,
@ -868,6 +915,9 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
model_chunk_id=scheduled_node.chunk,
optimizer=optimizer,
)
for h in self.send_handles:
for hh in h:
hh.wait()
# return loss & output
if outputs is not None:
@ -907,5 +957,4 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
)
self.assert_buffer_empty()
return result

2
colossalai/pipeline/stage_manager.py
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@ -223,10 +223,10 @@ class PipelineStageManager:
# calculate the num_layers per stage
layers_per_stage = [quotient] * num_stages * num_model_chunks
# deal with the rest layers
if remainder > 0:
start_position = (num_stages * num_model_chunks) // 2 - remainder // 2
for i in range(start_position, start_position + remainder):
layers_per_stage[i] += 1
# print(f"layers_per_stage {layers_per_stage}")
return layers_per_stage

55
colossalai/pipeline/weight_grad_store.py
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@ -1,9 +1,6 @@
import queue
# from megatron import get_args
# from megatron.core import parallel_state
# from megatron.core.distributed.finalize_model_grads import _allreduce_embedding_grads
# from megatron.core.utils import get_model_config, get_attr_wrapped_model
from colossalai.pipeline.stage_manager import PipelineStageManager
class WeightGradStore:
@ -23,6 +20,7 @@ class WeightGradStore:
@classmethod
def pop(cls, chunk=0):
# print(f"chunk id {chunk} queue size {cls.weight_grad_queue[chunk].qsize()}")
if cls.weight_grad_queue[chunk].qsize() > 0:
stored_grads = cls.weight_grad_queue[chunk].get()
for total_input, grad_output, weight, func in stored_grads:
@ -34,3 +32,52 @@ class WeightGradStore:
weight.grad = grad_weight
else:
raise Exception("Pop empty queue.")
@classmethod
def clear(cls, stage_manager: PipelineStageManager, chunk=0):
pass
# print(f"stage {stage_manager.stage} len_chunk_0 {cls.weight_grad_queue[0].qsize()} len_chunk_1 {cls.weight_grad_queue[1].qsize()}")
# while cls.weight_grad_queue[chunk].qsize() > 0:
# stored_grads = cls.weight_grad_queue[chunk].get()
# for total_input, grad_output, weight, func in stored_grads:
# if weight.grad is not None:
# func(total_input, grad_output, weight.grad)
# # for first bwd; weight.grad is None, assign grad_weight to weight.grad
# else:
# grad_weight = func(total_input, grad_output)
# weight.grad = grad_weight
# weight_grad_tasks = []
# while cls.weight_grad_queue[chunk].qsize() > 0:
# stored_grads = cls.weight_grad_queue[chunk].get()
# if len(weight_grad_tasks) == 0:
# for _ in stored_grads:
# weight_grad_tasks.append([])
# else:
# assert len(weight_grad_tasks) == len(stored_grads)
# for i, task in enumerate(stored_grads):
# weight_grad_tasks[i].append(task)
# if stage_manager.is_last_stage(ignore_chunk=True) and chunk == 1:
# assert len(weight_grad_tasks) > 0
# output_layer_grads = weight_grad_tasks[0]
# for j in range(len(output_layer_grads)):
# total_input, grad_output, weight, func = output_layer_grads[j]
# if output_layer_weight is None:
# output_layer_weight = weight
# assert output_layer_weight is weight
# func(total_input, grad_output, weight.grad)
# output_layer_grads[j] = None # release memory
# weight_grad_tasks = weight_grad_tasks[1:]
# for i in range(len(weight_grad_tasks)):
# tasks = weight_grad_tasks[i]
# param = None
# for j in range(len(tasks)):
# total_input, grad_output, weight, func = tasks[j]
# if param is None:
# param = weight
# assert param is weight
# func(total_input, grad_output, weight.grad)
# tasks[j] = None # release memory
# weight_grad_tasks[i] = None # release memory

7
colossalai/shardformer/modeling/llama.py
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@ -32,6 +32,7 @@ from colossalai.shardformer.shard import ShardConfig
from ..layer import ColoAttention, RingAttention, dist_cross_entropy
_SUPPORTED_SP_MODE = ["all_to_all", "split_gather", "ring", "ring_attn"]
_GLOBAL_ORDER_ = 0
class LlamaPipelineForwards:
@ -193,6 +194,10 @@ class LlamaPipelineForwards:
assert num_ckpt_layers <= end_idx - start_idx
for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
# global _GLOBAL_ORDER_
# if torch.distributed.get_rank() == 0:
# print(f"rank {torch.distributed.get_rank()} {stage_manager.stage}; start:{start_idx}, end:{end_idx} hidden_states require grad{hidden_states.requires_grad}")
# # _GLOBAL_ORDER_ += 1
if output_hidden_states:
all_hidden_states += (hidden_states,)
if idx - start_idx < num_ckpt_layers:
@ -216,6 +221,8 @@ class LlamaPipelineForwards:
use_cache=use_cache,
cache_position=cache_position,
)
# if torch.distributed.get_rank() == 0:
# print(f"rank {torch.distributed.get_rank()} {stage_manager.stage}; start:{start_idx}, end:{end_idx} layer_outputs require grad {layer_outputs[0].requires_grad}")
hidden_states = layer_outputs[0]
if use_cache:

27
colossalai/shardformer/policies/llama.py
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@ -96,7 +96,7 @@ class LlamaPolicy(Policy):
target_key=attn_cls,
)
if self.pipeline_stage_manager is None:
if self.pipeline_stage_manager is not None:
self.append_or_create_method_replacement(
description={
"forward": partial(
@ -298,7 +298,6 @@ class LlamaPolicy(Policy):
not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
):
held_layers.append(module.norm)
else:
layers_per_stage = stage_manager.distribute_layers(len(module.layers))
if stage_manager.is_first_stage():
@ -395,8 +394,8 @@ class LlamaForCausalLMPolicy(LlamaPolicy):
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
if self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv:
return []
# if self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv:
# return []
llama_model = self.model.model
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
if (
@ -404,12 +403,20 @@ class LlamaForCausalLMPolicy(LlamaPolicy):
and self.pipeline_stage_manager.num_stages > 1
):
# tie weights
return [
{
0: llama_model.embed_tokens.weight,
self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
}
]
if self.pipeline_stage_manager.use_zbv:
return [
{
0: llama_model.embed_tokens.weight,
0: self.model.lm_head.weight,
}
]
else:
return [
{
0: llama_model.embed_tokens.weight,
self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
}
]
return []

20
examples/language/llama/benchmark.py
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@ -40,6 +40,7 @@ MODEL_CONFIGS = {
),
"5b": LlamaConfig(max_position_embeddings=4096, num_key_value_heads=8),
"7b": LlamaConfig(max_position_embeddings=4096),
# "7b": LlamaConfig(num_hidden_layers=4, max_position_embeddings=4096),
"13b": LlamaConfig(
hidden_size=5120,
intermediate_size=13824,
@ -127,9 +128,12 @@ def main():
{
"gradient_checkpoint_config": PipelineGradientCheckpointConfig(
num_ckpt_layers_per_stage=[19, 19, 19, 13],
# num_ckpt_layers_per_stage=[48, 48, 48, 48],
),
"num_layers_per_stage": [19, 20, 20, 21],
"pp_style": "interleaved",
# "num_layers_per_stage": [48, 48, 48, 48],
# "pp_style": "interleaved",
"pp_style": "1f1b",
}
if args.custom_ckpt
else {}
@ -227,12 +231,14 @@ def main():
b_cost=1000,
w_cost=1000,
c_cost=1,
f_mem=mem_f,
b_mem=mem_b,
w_mem=mem_w,
f_mem=mem_f * 1.5,
b_mem=mem_b * 1.5,
w_mem=mem_w * 1.5,
).get_v_schedule()
else:
scheduler_nodes = None
# print(f"{dist.get_rank()} {scheduler_nodes[]} ")
plugin = HybridParallelPlugin(
tp_size=args.tp,
pp_size=args.pp,
@ -267,7 +273,7 @@ def main():
microbatch_size=args.mbs,
initial_scale=2**8,
precision="bf16",
overlap_p2p=args.overlap,
overlap_p2p=True,
use_fp8=args.use_fp8,
fp8_communication=args.use_fp8_comm,
)
@ -328,7 +334,7 @@ def main():
torch.set_default_dtype(torch.bfloat16)
model, optimizer, _, dataloader, _ = booster.boost(model, optimizer, dataloader=dataloader)
torch.set_default_dtype(torch.float)
# torch.set_default_dtype(torch.float)
coordinator.print_on_master(
f"Booster init max CUDA memory: {get_accelerator().max_memory_allocated()/1024**2:.2f} MB"
)
@ -340,7 +346,7 @@ def main():
args.profile,
args.ignore_steps,
1, # avoid creating massive log files
save_dir=f"profile/{time.strftime('%H:%M', time.localtime())}-{args.plugin}-llama-{args.config}",
save_dir=f"./profile/{time.strftime('%H:%M', time.localtime())}-{args.plugin}-llama-{args.config}",
nsys=args.nsys,
) as prof:
if isinstance(plugin, HybridParallelPlugin) and args.pp > 1:

15
examples/language/performance_evaluator.py
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@ -21,11 +21,16 @@ def divide(x: float, y: float) -> float:
def all_reduce_mean(x: float, world_size: int) -> float:
if world_size == 1:
return x
# Use CPU tensor to avoid OOM/weird NCCl error
gloo_group = dist.new_group(backend="gloo")
tensor = torch.tensor([x], device="cpu")
dist.all_reduce(tensor, group=gloo_group)
# BUG: RuntimeError: Invalid scalar type when use dist.all_reduce(tensor, group=gloo_group)
# # Use CPU tensor to avoid OOM/weird NCCl error
# gloo_group = dist.new_group(backend="gloo")
# tensor = torch.tensor([x], device="cpu")
# dist.all_reduce(tensor, group=gloo_group)
# tensor = tensor / world_size
# return tensor.item()
tensor = torch.tensor([x], device=torch.cuda.current_device(), dtype=torch.float)
dist.all_reduce(tensor)
tensor = tensor / world_size
return tensor.item()

16
tests/test_pipeline/test_schedule/test_zerobubble_pp.py
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@ -758,11 +758,11 @@ def run_with_hybridplugin(test_config):
@parameterize(
"config",
[
(0, 1, 4, 1, 1),
(1, 2, 2, 1, 1),
# (0, 1, 4, 1, 1),
# (1, 2, 2, 1, 1),
(1, 1, 2, 2, 1),
(1, 2, 1, 2, 1),
(1, 2, 1, 1, 2),
# (1, 2, 1, 2, 1),
# (1, 2, 1, 1, 2),
],
)
def run_with_booster_moehybridplugin(config: Tuple[int, ...]):
@ -923,10 +923,10 @@ def run_with_booster_moehybridplugin(config: Tuple[int, ...]):
@parameterize(
"config",
[
(0, 4, 1, 1),
# (0, 4, 1, 1),
(1, 2, 2, 1),
(1, 2, 1, 2),
(1, 1, 2, 2),
# (1, 2, 1, 2),
# (1, 1, 2, 2), # TODO: no pp show gather result err
],
)
def run_with_booster_hybridplugin(config: Tuple[int, ...]):
@ -976,7 +976,7 @@ def run_with_booster_hybridplugin(config: Tuple[int, ...]):
zbv_schedule = graph.get_v_schedule()
# init MoeHybridPlugin
# init HybridParallelPlugin
plugin = HybridParallelPlugin(
pp_size=pp_size,
num_microbatches=pp_size,

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