[plugin] add 3d parallel plugin (#4295)

* [amp] add mixed precision optimizer

* [plugin] add 3d parallel plugin

* [booster] support pipeline

* [plugin] 3d parallel plugin support clip grad norm

* [shardformer] fix sharder and add plugin test

* [plugin] rename 3d parallel plugin

* [ci] support testmon core pkg change detection (#4305)

* [hotfix] debug testmon

* [hotfix] fix llama

* [hotfix] fix p2p bugs

* [hotfix] fix requirements

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -0,0 +1,316 @@
+import random
+from contextlib import nullcontext
+from typing import Any, Callable, Iterator, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
+from torch.nn import Module
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+
+from colossalai.amp.naive_amp.mixed_precision_optimizer import MixedPrecisionOptimizer
+from colossalai.checkpoint_io import CheckpointIO
+from colossalai.cluster import ProcessGroupMesh
+from colossalai.interface import ModelWrapper, OptimizerWrapper
+from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer import ShardConfig, ShardFormer
+from colossalai.zero.low_level import LowLevelZeroOptimizer
+
+from .pp_plugin_base import PipelinePluginBase
+
+DP_AXIS, PP_AXIS, TP_AXIS = 0, 1, 2
+
+
+class HybridParallelModule(ModelWrapper):
+
+    def __init__(self, module: Module, precision: str, shard_config: ShardConfig, dp_group: ProcessGroup) -> None:
+        self.stage_manager = shard_config.pipeline_stage_manager
+        self.dp_group = dp_group
+        shardformer = ShardFormer(shard_config)
+        module, self.shared_params = shardformer.optimize(module)
+        # TODO(ver217): add input type cast
+        self.shared_param_process_groups = []
+        for shared_param in self.shared_params:
+            if len(shared_param) > 0:
+                self.stage_manager.init_process_group_by_stages(list(shared_param.keys()))
+        if precision == 'fp16':
+            module = module.half().cuda()
+        elif precision == 'bf16':
+            module = module.to(dtype=torch.bfloat16).cuda()
+        # TODO(ver217): support TP+DP
+        super().__init__(module)
+
+    def sync_shared_params(self):
+        for shared_param, group in zip(self.shared_params, self.shared_param_process_groups):
+            param = shared_param[self.stage_manager.stage]
+            dist.all_reduce(param.grad, group=group)
+
+    def no_sync(self) -> Iterator[None]:
+        # no sync grads across data parallel
+        return nullcontext()
+
+    def sync_grads(self):
+        # sync grad across data parallel
+        if self.dp_group.size() == 1:
+            return
+        for p in self.module.parameters():
+            if p.grad is not None:
+                dist.all_reduce(p.grad, group=self.dp_group)
+
+
+def init_pipeline_optimizer(optim: Optimizer, model: Module):
+    params = set(model.parameters())
+    new_param_groups = []
+    for group in optim.param_groups:
+        params = [p for p in group['params'] if p in params]
+        new_param_groups.append({**group, 'params': params})
+    optim.__setstate__({'param_groups': new_param_groups})
+
+
+class HybridParallelOptimizer(MixedPrecisionOptimizer):
+
+    def __init__(self,
+                 optim: Optimizer,
+                 model: Module,
+                 use_pipeline: bool,
+                 precision: str = 'fp16',
+                 initial_scale: float = 2**16,
+                 min_scale: float = 1,
+                 growth_factor: float = 2,
+                 backoff_factor: float = 0.5,
+                 growth_interval: int = 1000,
+                 hysteresis: int = 2,
+                 max_scale: float = 2**32,
+                 max_norm: float = 0):
+        if use_pipeline:
+            init_pipeline_optimizer(optim, model)
+        super().__init__(optim, precision, initial_scale, min_scale, growth_factor, backoff_factor, growth_interval,
+                         hysteresis, max_scale, max_norm)
+
+
+class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
+
+    def __init__(
+            self,
+            optimizer: Optimizer,
+            model: Module,
+            use_pipeline: bool,
+            initial_scale: int = 2**16,    # grad scaler config
+            min_scale: int = 1,
+            growth_factor: float = 2.,
+            backoff_factor: float = .5,
+            growth_interval: int = 2000,
+            hysteresis: int = 2,
+            max_scale: int = 2**24,
+            clip_grad_norm: float = 0.0,    # grad clipping
+            verbose: bool = False,
+            reduce_bucket_size: int = 1024 * 1024,    # communication
+            communication_dtype: Optional[torch.dtype] = None,
+            overlap_communication: bool = True,
+            partition_grad: bool = False,    # stage 2 flag
+            cpu_offload: bool = False,    # cpu offload
+            dp_process_group: Optional[ProcessGroup] = None,    # the dp pg for comm
+            tp_process_group: Optional[ProcessGroup] = None,    # if using tp
+            forced_dtype: Optional[torch.dtype] = None):
+        if use_pipeline:
+            init_pipeline_optimizer(optimizer, model)
+        super().__init__(optimizer, initial_scale, min_scale, growth_factor, backoff_factor, growth_interval,
+                         hysteresis, max_scale, clip_grad_norm, verbose, reduce_bucket_size, communication_dtype,
+                         overlap_communication, partition_grad, cpu_offload, dp_process_group, tp_process_group,
+                         forced_dtype)
+
+
+class HybridParallelPlugin(PipelinePluginBase):
+
+    def __init__(
+        self,
+        tp_size: int,
+        pp_size: int,
+        precision: str = 'fp16',
+        zero_stage: int = 0,
+        cpu_offload: bool = False,
+        enable_fused_normalization: bool = False,
+        num_microbatches: Optional[int] = None,
+        initial_scale: float = 2**16,
+        min_scale: float = 1,
+        growth_factor: float = 2,
+        backoff_factor: float = 0.5,
+        growth_interval: int = 1000,
+        hysteresis: int = 2,
+        max_scale: float = 2**32,
+        max_norm: float = 0,
+    ) -> None:
+        super().__init__()
+        assert dist.get_world_size() % (
+            tp_size * pp_size
+        ) == 0, f'world size {dist.get_world_size()} is not divisible by tp_size {tp_size} * pp_size {pp_size}'
+        # TODO(ver217): support zero
+        assert zero_stage == 0, 'zero is not support yet'
+        self.tp_size = tp_size
+        self.pp_size = pp_size
+        self.dp_size = dist.get_world_size() // (tp_size * pp_size)
+        self.precision = precision
+        self.zero_stage = zero_stage
+        self.cpu_offload = cpu_offload
+        self.enable_fused_normalization = enable_fused_normalization
+        self.pg_mesh = ProcessGroupMesh(self.dp_size, self.pp_size, self.tp_size)
+        self.stage_manager = None
+        self.schedule = None
+        assert zero_stage in (0, 1, 2)
+        if self.pp_size > 1:
+            assert num_microbatches is not None, 'num_microbatches must be specified when using pipeline parallelism'
+            assert self.zero_stage <= 1, 'zero stage must be 0 or 1 when using pipeline parallelism'
+            self.stage_manager = PipelineStageManager(self.pg_mesh, PP_AXIS)
+            self.schedule = OneForwardOneBackwardSchedule(num_microbatches, self.stage_manager)
+        self.tp_group = self.pg_mesh.get_group_along_axis(TP_AXIS)
+        self.dp_group = self.pg_mesh.get_group_along_axis(DP_AXIS)
+        self.shard_config = ShardConfig(tensor_parallel_process_group=self.tp_group,
+                                        pipeline_stage_manager=self.stage_manager,
+                                        enable_tensor_parallelism=self.tp_size > 1,
+                                        enable_fused_normalization=self.enable_fused_normalization)
+        self.amp_config = dict(
+            initial_scale=initial_scale,
+            growth_factor=growth_factor,
+            backoff_factor=backoff_factor,
+            growth_interval=growth_interval,
+            hysteresis=hysteresis,
+            min_scale=min_scale,
+            max_scale=max_scale,
+        )
+        self.max_norm = max_norm
+
+    @property
+    def enable_pipeline_parallelism(self) -> bool:
+        return self.pp_size > 1
+
+    def supported_devices(self) -> List[str]:
+        return ['cuda']
+
+    def supported_precisions(self) -> List[str]:
+        return ['fp16', 'bf16']
+
+    def control_device(self) -> bool:
+        return True
+
+    def control_precision(self) -> bool:
+        return True
+
+    def support_no_sync(self) -> bool:
+        return False
+
+    def control_checkpoint_io(self) -> bool:
+        return True
+
+    def configure(
+        self,
+        model: Module,
+        optimizer: Optional[Optimizer] = None,
+        criterion: Optional[Callable] = None,
+        dataloader: Optional[DataLoader] = None,
+        lr_scheduler: Optional[LRScheduler] = None,
+    ) -> Tuple[Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
+        if not isinstance(model, ModelWrapper):
+            model = HybridParallelModule(model, self.precision, self.shard_config, self.dp_group)
+        if optimizer is not None and not isinstance(optimizer, OptimizerWrapper):
+            if self.zero_stage == 0:
+                optimizer = HybridParallelOptimizer(optimizer,
+                                                    model,
+                                                    use_pipeline=self.enable_pipeline_parallelism,
+                                                    precision=self.precision,
+                                                    max_norm=self.max_norm,
+                                                    **self.amp_config)
+            else:
+                optimizer = HybridParallelZeroOptimizer(optimizer,
+                                                        model,
+                                                        use_pipeline=self.enable_pipeline_parallelism,
+                                                        partition_grad=(self.zero_stage == 2),
+                                                        cpu_offload=self.cpu_offload,
+                                                        dp_process_group=self.dp_group,
+                                                        tp_process_group=self.tp_group,
+                                                        verbose=True,
+                                                        clip_grad_norm=self.max_norm,
+                                                        **self.amp_config)
+        return model, optimizer, criterion, dataloader, lr_scheduler
+
+    def execute_pipeline(self,
+                         data_iter: Iterator,
+                         model: HybridParallelModule,
+                         criterion: Callable[[Any, Any], torch.Tensor],
+                         optimizer: Union[HybridParallelOptimizer, HybridParallelZeroOptimizer],
+                         return_loss: bool = True,
+                         return_outputs: bool = False) -> dict:
+        assert self.enable_pipeline_parallelism, 'pipeline parallelism is not enabled'
+        # return loss or outputs if needed
+        ctx = optimizer.no_sync() if isinstance(optimizer, HybridParallelZeroOptimizer) else model.no_sync()
+        with ctx:
+            outputs = self.schedule.forward_backward_step(model, optimizer, data_iter, criterion, return_loss,
+                                                          return_outputs)
+        # model.sync_shared_params()
+        if isinstance(optimizer, HybridParallelZeroOptimizer):
+            optimizer.sync_grad()
+        else:
+            model.sync_grads()
+        return outputs
+
+    def prepare_dataloader(self,
+                           dataset,
+                           batch_size,
+                           shuffle=False,
+                           seed=1024,
+                           drop_last=False,
+                           pin_memory=False,
+                           num_workers=0,
+                           **kwargs):
+        r"""
+        Prepare a dataloader for distributed training. The dataloader will be wrapped by
+        `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`.
+
+
+        Args:
+            dataset (`torch.utils.data.Dataset`): The dataset to be loaded.
+            shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False.
+            seed (int, optional): Random worker seed for sampling, defaults to 1024.
+            add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True.
+            drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size
+                is not divisible by the batch size. If False and the size of dataset is not divisible by
+                the batch size, then the last batch will be smaller, defaults to False.
+            pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False.
+            num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0.
+            kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in
+                    `DataLoader <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataloader.html#DataLoader>`_.
+
+        Returns:
+            :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing.
+        """
+        _kwargs = kwargs.copy()
+        sampler = DistributedSampler(dataset,
+                                     num_replicas=self.pg_mesh.size(DP_AXIS),
+                                     rank=self.pg_mesh.coordinate(DP_AXIS),
+                                     shuffle=shuffle)
+
+        # Deterministic dataloader
+        def seed_worker(worker_id):
+            worker_seed = seed
+            np.random.seed(worker_seed)
+            torch.manual_seed(worker_seed)
+            random.seed(worker_seed)
+
+        return DataLoader(dataset,
+                          batch_size=batch_size,
+                          sampler=sampler,
+                          worker_init_fn=seed_worker,
+                          drop_last=drop_last,
+                          pin_memory=pin_memory,
+                          num_workers=num_workers,
+                          **_kwargs)
+
+    def get_checkpoint_io(self) -> CheckpointIO:
+        return None
+
+    def no_sync(self, model: Module) -> Iterator[None]:
+        raise NotImplementedError