[pipeline] test pure pipeline process using llama (hpcaitech#4218)

* bloom policy

* llama pipeline forward and tests

* fix the output and attention_mask

* fix name

* bind argument to policy

* Revert "bloom policy"

This reverts commit 8dee68a.

This policy should be revert and copied to feature/bloom

* revert the bloom changes

* cancel unneeded inputs

* gpt

* finish llama

* causal lm and sequence classification

* revision

* add pure pipeline test

* fixed version

* fixed version

* pure pipeline

colossalai/pipeline/p2p.py

@@ -9,6 +9,7 @@
 import torch.distributed as dist
 from torch.distributed import ProcessGroup
 from torch.distributed import distributed_c10d as c10d
+from version_parser.version import Version
 
 from .stage_manager import PipelineStageManager
 
@@ -61,17 +62,6 @@ def _broadcast_object_list(object_list: List[Any],
  c10d._warn_not_in_group("broadcast_object_list")
  return
 
- my_rank = dist.get_rank()
- # Serialize object_list elements to tensors on src rank.
- if my_rank == src:
- if torch.__version__ >= "1.13.0":
- tensor_list, size_list = zip(*[c10d._object_to_tensor(obj, device=device) for obj in object_list])
- else:
- tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list])
- object_sizes_tensor = torch.cat(size_list)
- else:
- object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long)
-
  is_nccl_backend = c10d._check_for_nccl_backend(group)
  current_device = None
 
@@ -83,6 +73,18 @@ def _broadcast_object_list(object_list: List[Any],
  current_device = torch.device("cpu")
  if is_nccl_backend:
  current_device = torch.device("cuda", torch.cuda.current_device())
+
+ my_rank = dist.get_rank()
+ # Serialize object_list elements to tensors on src rank.
+ if my_rank == src:
+ if Version(torch.__version__) >= Version("1.13.0"):
+ tensor_list, size_list = zip(*[c10d._object_to_tensor(obj, device=current_device) for obj in object_list])
+ else:
+ tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list])
+ object_sizes_tensor = torch.cat(size_list)
+ else:
+ object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long)
+
  if is_nccl_backend:
  object_sizes_tensor = object_sizes_tensor.to(current_device)
 

tests/test_shardformer/test_model/test_pure_pipeline.py

@@ -1,3 +1,4 @@
+import copy
 import random
 from contextlib import nullcontext
 from typing import Any, Callable, Iterator, List, Optional, Tuple
@@ -6,7 +7,6 @@
 import pytest
 import torch
 import torch.distributed as dist
-from torch import Tensor
 from torch.nn import Module
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
@@ -94,10 +94,10 @@ def execute_pipeline(
  return outputs
 
 
-class data_iter():
+class data_loader():
 
  def __getitem__(self, x):
- return torch.randint(0, 100, (4, 128)).cuda()
+ return torch.ones((4, 128), dtype=torch.int).cuda() * 10
 
 
 def loss(x, y):
@@ -127,20 +127,30 @@ def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_la
  stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
  sub_model_zoo = model_zoo.get_sub_registry('transformers_llama')
  for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+ if name != 'transformers_llama':
+ continue
  num_microbatches = 2
  org_model = model_fn().cuda()
+ data_iter = iter(data_loader())
+
+ model_copy = copy.deepcopy(org_model)
+ batch = next(data_iter)
+ with torch.no_grad():
+ y = model_copy(batch)
+ org_loss = loss(batch, y)
  optimizer = torch.optim.AdamW(org_model.parameters(), lr=1e-3)
- #dataloader=prepare_dataloader(dataset=dataset['train'],batch_size=4)
  schedule = OneForwardOneBackwardSchedule(num_microbatches, stage_manager)
  shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization,
  enable_tensor_parallelism=enable_tensor_parallelism,
  pipeline_stage_manager=stage_manager)
  pipelined_model = PipelinedModel(org_model, shard_config, stage_manager)
  pp_optimizer = PipelineOptimizer(optimizer, pipelined_model)
- data_it = iter(data_iter())
- results = execute_pipeline(data_it, pipelined_model, loss, pp_optimizer, schedule=schedule)
+ results = execute_pipeline(data_iter, pipelined_model, loss, pp_optimizer, schedule=schedule)
+
  if stage_manager.is_last_stage():
- assert results['loss'] is not None
+ assert results['loss'] == org_loss
+ else:
+ assert results['loss'] is None
  assert results['outputs'] is None
  torch.cuda.empty_cache()