[NPU]support npu (hpcaitech#6089)

* support npu

* support pretrain

support pretrain

fix

* support lora

fix

fix

* support chatglm

fix

fxi

fix

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fix

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[pre-commit.ci] auto fixes from pre-commit.com hooks

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[pre-commit.ci] auto fixes from pre-commit.com hooks

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fix

fix

fix

* Update train.py

* Update train.py

Author: flybird11111

applications/Colossal-LLaMA/colossal_llama/dataset/conversation.py

@@ -100,7 +100,7 @@ def dict(self):
     messages=[],
     offset=0,
     sep_style=SeparatorStyle.ADD_BOS_EOS_TOKEN,
-    seps=["<|begin_of_text|>", "<|end_of_text|>"],
+    seps=["<|begin_of_text|>", "<|eot_id|>"],
 )
 
 default_conversation = LLaMA3_Conv

applications/Colossal-LLaMA/colossal_llama/dataset/spliced_and_tokenized_dataset.py

@@ -88,7 +88,7 @@ def supervised_tokenize_sft(
 
     assert (
         tokenizer.bos_token == conversation_template.seps[0] and tokenizer.eos_token == conversation_template.seps[1]
-    ), "`bos_token` and `eos_token` should be the same with `conversation_template.seps`."
+    ), f"`bos_token`{tokenizer.bos_token} and `eos_token`{tokenizer.eos_token} should be the same with `conversation_template.seps`{conversation_template.seps}."
 
     if ignore_index is None:
         ignore_index = IGNORE_INDEX

applications/Colossal-LLaMA/colossal_llama/utils/ckpt_io.py

@@ -43,6 +43,7 @@ def save_checkpoint(
     step: int,
     batch_size: int,
     coordinator: DistCoordinator,
+    use_lora: bool = False,
 ) -> None:
     """
     Save model checkpoint, optimizer, LR scheduler and intermedidate running states.
@@ -51,7 +52,10 @@ def save_checkpoint(
     save_dir = os.path.join(save_dir, f"epoch-{epoch}_step-{step}")
     os.makedirs(os.path.join(save_dir, "modeling"), exist_ok=True)
 
-    booster.save_model(model, os.path.join(save_dir, "modeling"), shard=True)
+    if use_lora:
+        booster.save_lora_as_pretrained(model, os.path.join(save_dir, "modeling"))
+    else:
+        booster.save_model(model, os.path.join(save_dir, "modeling"), shard=True)
 
     booster.save_optimizer(optimizer, os.path.join(save_dir, "optimizer"), shard=True)
     booster.save_lr_scheduler(lr_scheduler, os.path.join(save_dir, "lr_scheduler"))

applications/Colossal-LLaMA/train.py

@@ -21,6 +21,7 @@
 from colossal_llama.utils.froze import freeze_non_embeds_parameters
 from colossal_llama.utils.neftune_patch import activate_neftune, deactivate_neftune
 from colossal_llama.utils.utils import all_reduce_mean, format_numel_str, get_model_numel
+from peft import LoraConfig
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -65,7 +66,7 @@ def train(args) -> None:
             initial_scale=2**16,
             max_norm=args.grad_clip,
             enable_gradient_accumulation=(args.accumulation_steps > 1),
-            enable_fused_normalization=torch.cuda.is_available(),
+            enable_fused_normalization=get_accelerator().is_available(),
             enable_flash_attention=args.use_flash_attn,
         )
     elif args.plugin == "gemini_auto":
@@ -75,7 +76,7 @@ def train(args) -> None:
             initial_scale=2**16,
             max_norm=args.grad_clip,
             enable_gradient_accumulation=(args.accumulation_steps > 1),
-            enable_fused_normalization=torch.cuda.is_available(),
+            enable_fused_normalization=get_accelerator().is_available(),
             enable_flash_attention=args.use_flash_attn,
         )
     elif args.plugin == "zero2":
@@ -101,10 +102,9 @@ def train(args) -> None:
             sequence_parallelism_mode=args.sp_mode,
             zero_stage=args.zero_stage,
             enable_flash_attention=args.use_flash_attn,
-            enable_fused_normalization=torch.cuda.is_available(),
+            enable_fused_normalization=get_accelerator().is_available(),
             enable_sequence_parallelism=args.enable_sequence_parallelism,
             cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False,
-            parallel_output=False,
             max_norm=args.grad_clip,
             precision=args.mixed_precision,
             microbatch_size=args.microbatch_size,
@@ -117,11 +117,17 @@ def train(args) -> None:
     # ======================================================
     # Initialize Tokenizer, Dataset, Collator and Dataloader
     # ======================================================
-    tokenizer = AutoTokenizer.from_pretrained(args.pretrained)
+    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)
     if args.pad_token == "eos":
-        tokenizer.pad_token = tokenizer.eos_token
+        try:
+            tokenizer.pad_token = tokenizer.eos_token
+        except AttributeError:
+            coordinator.print_on_master(f"pad_token can't be set")
     elif args.pad_token == "unk":
-        tokenizer.pad_token = tokenizer.unk_token
+        try:
+            tokenizer.pad_token = tokenizer.unk_token
+        except AttributeError:
+            coordinator.print_on_master(f"pad_token can't be set")
     tokenizer.add_bos_token = False
     tokenizer.add_eos_token = False
 
@@ -164,33 +170,31 @@ def train(args) -> None:
     # ======================================================
     # Initialize Model, Objective, Optimizer and LR Scheduler
     # ======================================================
+    # When training the ChatGLM model, LoRA and gradient checkpointing are incompatible.
     init_ctx = (
         LazyInitContext(default_device=get_current_device())
-        if isinstance(plugin, (GeminiPlugin, HybridParallelPlugin))
+        if isinstance(plugin, (GeminiPlugin, HybridParallelPlugin)) and args.lora_rank == 0
         else nullcontext()
     )
     with init_ctx:
-        if args.use_flash_attn:
-            model = AutoModelForCausalLM.from_pretrained(
-                args.pretrained,
-                attn_implementation="flash_attention_2",
-                torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
-                trust_remote_code=True,
-            )
-        else:
-            model = AutoModelForCausalLM.from_pretrained(
-                args.pretrained,
-                torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
-                trust_remote_code=True,
-            )
+        model = AutoModelForCausalLM.from_pretrained(
+            args.pretrained,
+            torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
+            trust_remote_code=True,
+        )
         # Freeze part of parameters.
         if args.freeze_non_embeds_params:
             freeze_non_embeds_parameters(model=model)
+
+    if args.lora_rank > 0:
+        lora_config = LoraConfig(task_type="CAUSAL_LM", r=args.lora_rank, lora_alpha=32, lora_dropout=0.1)
+        model = booster.enable_lora(model, lora_config=lora_config)
+
     # this is essential, otherwise the grad checkpoint will not work.
     model.train()
 
     if args.use_grad_checkpoint:
-        model.gradient_checkpointing_enable()
+        model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
         coordinator.print_on_master(msg="Gradient checkpointing enabled successfully")
 
     model_numel = get_model_numel(model)
@@ -327,6 +331,7 @@ def train(args) -> None:
                         step=step + 1,
                         batch_size=args.batch_size,
                         coordinator=coordinator,
+                        use_lora=(args.lora_rank > 0),
                     )
                     coordinator.print_on_master(
                         f"Saved checkpoint at epoch {epoch} step {step + 1} at folder {args.save_dir}"
@@ -371,44 +376,45 @@ def train(args) -> None:
                     total_loss.fill_(0.0)
                     pbar.update()
 
-            # Save modeling.
-            save_model_condition = (
-                args.save_interval > 0 and (step + 1) % (args.save_interval * args.accumulation_steps) == 0
-            )
+                # Save modeling.
+                save_model_condition = (
+                    args.save_interval > 0 and (step + 1) % (args.save_interval * args.accumulation_steps) == 0
+                )
 
-            if not args.skip_save_each_epoch:
-                save_model_condition = save_model_condition or (step + 1) == len(dataloader)
+                if not args.skip_save_each_epoch:
+                    save_model_condition = save_model_condition or (step + 1) == len(dataloader)
 
-            if save_model_condition and not args.benchmark:
-                coordinator.print_on_master("\nStart saving model checkpoint with running states")
+                if save_model_condition and not args.benchmark:
+                    coordinator.print_on_master("\nStart saving model checkpoint with running states")
 
-                if args.use_neft:
-                    coordinator.print_on_master("Deactivate NEFTune before saving model.")
-                    deactivate_neftune(model, handle)
+                    if args.use_neft:
+                        coordinator.print_on_master("Deactivate NEFTune before saving model.")
+                        deactivate_neftune(model, handle)
 
-                accelerator.empty_cache()
-                save_checkpoint(
-                    save_dir=args.save_dir,
-                    booster=booster,
-                    model=model,
-                    optimizer=optimizer,
-                    lr_scheduler=lr_scheduler,
-                    epoch=epoch,
-                    step=step + 1,
-                    batch_size=args.batch_size,
-                    coordinator=coordinator,
-                )
-                coordinator.print_on_master(
-                    f"Saved checkpoint at epoch {epoch} step {step + 1} at folder {args.save_dir}"
-                )
+                    accelerator.empty_cache()
+                    save_checkpoint(
+                        save_dir=args.save_dir,
+                        booster=booster,
+                        model=model,
+                        optimizer=optimizer,
+                        lr_scheduler=lr_scheduler,
+                        epoch=epoch,
+                        step=step + 1,
+                        batch_size=args.batch_size,
+                        coordinator=coordinator,
+                        use_lora=(args.lora_rank > 0),
+                    )
+                    coordinator.print_on_master(
+                        f"Saved checkpoint at epoch {epoch} step {step + 1} at folder {args.save_dir}"
+                    )
 
-                if args.use_neft:
-                    coordinator.print_on_master("Activate NEFTune.")
-                    model, handle = activate_neftune(model)
+                    if args.use_neft:
+                        coordinator.print_on_master("Activate NEFTune.")
+                        model, handle = activate_neftune(model)
 
-            # Delete cache.
-            # del batch, batch_labels, batch_output, loss
-            accelerator.empty_cache()
+        # Delete cache.
+        # del batch, batch_labels, batch_output, loss
+        accelerator.empty_cache()
 
         # the continue epochs are not resumed, so we need to reset the sampler start index and start step
         dataloader.sampler.set_start_index(start_index=0)
@@ -522,6 +528,7 @@ def train(args) -> None:
     parser.add_argument(
         "--microbatch_size", type=int, default=1, help="Batch size for each process in PP, used for 3d plugin."
     )
+    parser.add_argument("--lora_rank", type=int, default=0, help="lora rank when using lora to train.")
 
     # Additional arguments for benchmark.
     parser.add_argument("--num_samples", type=int, default=500, help="Number of samples for benchmarking.")

colossalai/lazy/lazy_init.py

@@ -509,9 +509,9 @@ def wrap_factory_like_method(orig_target, target):
             # factory_like functions (eg. torch.empty_like())
             def wrapper(*args, **kwargs):
                 orig_t = args[0]
-                return self.tensor_cls(
-                    orig_target, *orig_t.shape, *args[1:], device=orig_t.device, dtype=orig_t.dtype, **kwargs
-                )
+                device = kwargs.pop("device", orig_t.device)
+                dtype = kwargs.pop("dtype", orig_t.dtype)
+                return self.tensor_cls(orig_target, *orig_t.shape, *args[1:], device=device, dtype=dtype, **kwargs)
 
             return wrapper, target
 

colossalai/legacy/communication/p2p.py

@@ -171,7 +171,7 @@ def _communicate(
         for req in reqs:
             req.wait()
     # To protect against race condition when using batch_isend_irecv().
-    torch.cuda.synchronize()
+    get_accelerator().synchronize()
 
     if recv_prev and recv_prev_split:
         if isinstance(tensor_recv_prev, torch.Tensor):

colossalai/pipeline/p2p.py

@@ -14,6 +14,8 @@
 from torch.distributed import distributed_c10d as c10d
 from torch.utils._pytree import tree_flatten, tree_unflatten
 
+from colossalai.accelerator import get_accelerator
+
 from .stage_manager import PipelineStageManager
 
 
@@ -31,7 +33,7 @@ def _cuda_safe_tensor_to_object(tensor: torch.Tensor, tensor_size: torch.Size) -
     buf = tensor.numpy().tobytes()[:tensor_size]
     if b"cuda" in buf:
         buf_array = bytearray(buf)
-        device_index = torch.cuda.current_device()
+        device_index = get_accelerator().current_device()
         # There might be more than one output tensors during forward
         for cuda_str in re.finditer(b"cuda", buf_array):
             pos = cuda_str.start()
@@ -86,7 +88,7 @@ def _broadcast_object_list(
     else:
         current_device = torch.device("cpu")
         if is_nccl_backend:
-            current_device = torch.device("cuda", torch.cuda.current_device())
+            current_device = torch.device("cuda", get_accelerator().current_device())
 
     my_rank = dist.get_rank()
     # Serialize object_list elements to tensors on src rank.
@@ -139,29 +141,29 @@ def _broadcast_object_list(
             # unconsistence in device
             if (
                 isinstance(unpickle_object, torch.Tensor)
-                and unpickle_object.device.index != torch.cuda.current_device()
+                and unpickle_object.device.index != get_accelerator().current_device()
             ):
-                unpickle_object = unpickle_object.cuda()
+                unpickle_object = unpickle_object.to(get_accelerator().current_device())
 
             object_list[i] = unpickle_object
 
 
-def _check_for_nccl_backend(group):
+def _check_for_nccl_hccl_backend(group):
     pg = group or c10d._get_default_group()
     # Gate PG wrapper check on Gloo availability.
     if c10d._GLOO_AVAILABLE:
         # It is not expected for PG to be wrapped many times, but support it just in case
         while isinstance(pg, c10d._ProcessGroupWrapper):
             pg = pg.wrapped_pg
 
-    return c10d.is_nccl_available() and pg.name() == c10d.Backend.NCCL
+    return (c10d.is_nccl_available() or torch.distributed.is_hccl_available()) and pg.name() == c10d.Backend.NCCL
 
 
 def _check_device(group):
-    is_nccl_backend = _check_for_nccl_backend(group)
+    is_nccl_backend = _check_for_nccl_hccl_backend(group)
     current_device = torch.device("cpu")
     if is_nccl_backend:
-        current_device = torch.device("cuda", torch.cuda.current_device())
+        current_device = torch.device(get_accelerator().current_device())
     return current_device, is_nccl_backend
 
 
@@ -348,8 +350,11 @@ def _send_recv_serialization_object(
 
         unpickle_object = _cuda_safe_tensor_to_object(recv_object_tensor, recv_object_size_tensor.item())
 
-        if isinstance(unpickle_object, torch.Tensor) and unpickle_object.device.index != torch.cuda.current_device():
-            unpickle_object = unpickle_object.cuda()
+        if (
+            isinstance(unpickle_object, torch.Tensor)
+            and unpickle_object.device.index != get_accelerator().current_device()
+        ):
+            unpickle_object = unpickle_object.to(get_accelerator().current_device())
 
         return unpickle_object
 
@@ -475,9 +480,11 @@ def _p2p_comm(
     recv_prev_shape = None
 
     if tensor_send_next is not None:
-        send_next_shape = torch.tensor(tensor_send_next.size(), device=torch.cuda.current_device(), dtype=torch.int64)
+        send_next_shape = torch.tensor(
+            tensor_send_next.size(), device=get_accelerator().current_device(), dtype=torch.int64
+        )
     if recv_prev:
-        recv_prev_shape = torch.empty((3), device=torch.cuda.current_device(), dtype=torch.int64)
+        recv_prev_shape = torch.empty((3), device=get_accelerator().current_device(), dtype=torch.int64)
 
     ops = []
     if send_next_shape is not None:
@@ -502,7 +509,7 @@ def _p2p_comm(
     # send and recv data
     tensor_recv_prev = None
     if recv_prev:
-        tensor_recv_prev = torch.empty(recv_prev_shape, device=torch.cuda.current_device(), dtype=comm_dtype)
+        tensor_recv_prev = torch.empty(recv_prev_shape, device=get_accelerator().current_device(), dtype=comm_dtype)
 
     ops = []
     if tensor_send_next is not None:

colossalai/pipeline/schedule/interleaved_pp.py

@@ -2,7 +2,6 @@
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
 
 import torch
-import torch.cuda
 import torch.distributed
 from torch.nn import Module, ModuleList
 from torch.utils._pytree import tree_map
@@ -18,7 +17,7 @@
 from .base import PipelineSchedule
 
 
-def _wait_p2p(wait_handles: List[torch.cuda.Event]) -> None:
+def _wait_p2p(wait_handles: List[get_accelerator().Event]) -> None:
     if wait_handles is not None:
         for req in wait_handles:
             req.wait()

colossalai/pipeline/schedule/one_f_one_b.py

@@ -2,7 +2,6 @@
 from typing import Any, Callable, Dict, Iterable, List, Optional, Union
 
 import torch
-import torch.cuda
 from torch.nn import Module
 from torch.utils._pytree import tree_map