[misc] add verbose arg for zero and op builder (#3552)

* [misc] add print verbose

* [gemini] add print verbose

* [zero] add print verbose for low level

* [misc] add print verbose for op builder

colossalai/booster/plugin/gemini_plugin.py

@@ -65,9 +65,9 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
 
 class GeminiModel(ModelWrapper):
 
-    def __init__(self, module: nn.Module, gemini_config: dict) -> None:
+    def __init__(self, module: nn.Module, gemini_config: dict, verbose: bool = False) -> None:
         super().__init__(module)
-        self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config)
+        self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config, verbose=verbose)
 
     def unwrap(self):
         # as save/load state dict is coupled with the GeminiDDP, we only return GeminiDDP model
@@ -76,8 +76,17 @@ class GeminiModel(ModelWrapper):
 
 class GeminiOptimizer(OptimizerWrapper):
 
-    def __init__(self, module: GeminiDDP, optimizer: Optimizer, zero_optim_config: dict, optim_kwargs: dict) -> None:
-        optimizer = zero_optim_wrapper(module, optimizer, optim_config=zero_optim_config, **optim_kwargs)
+    def __init__(self,
+                 module: GeminiDDP,
+                 optimizer: Optimizer,
+                 zero_optim_config: dict,
+                 optim_kwargs: dict,
+                 verbose: bool = False) -> None:
+        optimizer = zero_optim_wrapper(module,
+                                       optimizer,
+                                       optim_config=zero_optim_config,
+                                       **optim_kwargs,
+                                       verbose=verbose)
         super().__init__(optimizer)
 
     def backward(self, loss: Tensor, *args, **kwargs):
@@ -138,6 +147,7 @@ class GeminiPlugin(Plugin):
         max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do
             clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm.
         norm_type (float, optional): norm_type used for `clip_grad_norm`.
+        verbose (bool, optional): verbose mode. Debug info including chunk search result will be printed. Defaults to False.
     """
 
     def __init__(
@@ -161,6 +171,7 @@ class GeminiPlugin(Plugin):
         max_scale: float = 2**32,
         max_norm: float = 0.0,
         norm_type: float = 2.0,
+        verbose: bool = False,
     ) -> None:
 
         assert dist.is_initialized(
@@ -188,6 +199,7 @@ class GeminiPlugin(Plugin):
                                  max_scale=max_scale,
                                  max_norm=max_norm,
                                  norm_type=norm_type)
+        self.verbose = verbose
 
     def support_no_sync(self) -> bool:
         return False
@@ -275,10 +287,11 @@ class GeminiPlugin(Plugin):
             # model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None)
 
             # wrap the model with Gemini
-            model = GeminiModel(model, self.gemini_config)
+            model = GeminiModel(model, self.gemini_config, self.verbose)
 
         if not isinstance(optimizer, OptimizerWrapper):
-            optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs)
+            optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs,
+                                        self.verbose)
 
         return model, optimizer, criterion, dataloader, lr_scheduler
 

colossalai/zero/gemini/chunk/utils.py

@@ -20,6 +20,7 @@ def safe_div(a, b):
 def init_chunk_manager(model: nn.Module,
                        init_device: Optional[torch.device] = None,
                        hidden_dim: Optional[int] = None,
+                       verbose: bool = False,
                        **kwargs) -> ChunkManager:
     if hidden_dim:
         search_interval_byte = hidden_dim
@@ -39,7 +40,7 @@ def init_chunk_manager(model: nn.Module,
     total_size /= mb_size
     wasted_size /= mb_size
 
-    if dist.get_rank() == 0:
+    if verbose and dist.get_rank() == 0:
         print("searching chunk configuration is completed in {:.2f} s.\n".format(span_s),
               "used number: {:.2f} MB, wasted number: {:.2f} MB\n".format(total_size, wasted_size),
               "total wasted percentage is {:.2f}%".format(100 * safe_div(wasted_size, total_size + wasted_size)),

colossalai/zero/gemini/gemini_ddp.py

@@ -567,7 +567,8 @@ class GeminiDDP(ZeroDDP):
                  search_range_mb: int = 32,
                  hidden_dim: Optional[int] = None,
                  min_chunk_size_mb: float = 32,
-                 memstats: Optional[MemStats] = None) -> None:
+                 memstats: Optional[MemStats] = None,
+                 verbose: bool = False) -> None:
         """
         A torch.Module warpper using ZeRO-DP and Genimi.
         ZeRO is for parallel. Gemini is for memory management.
@@ -604,6 +605,7 @@ class GeminiDDP(ZeroDDP):
                                            hidden_dim=hidden_dim,
                                            search_range_mb=search_range_mb,
                                            min_chunk_size_mb=min_chunk_size_mb,
-                                           strict_ddp_flag=strict_ddp_mode)
+                                           strict_ddp_flag=strict_ddp_mode,
+                                           verbose=verbose)
         gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats)
         super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode)

colossalai/zero/gemini/gemini_optimizer.py

@@ -54,6 +54,7 @@ class ZeroOptimizer(ColossalaiOptimizer):
         clipping_norm (float, optional): The norm value used to clip gradient. Defaults to 0.0.
         norm_type (float, optional): The type of norm used for gradient clipping. Currently, only L2-norm (norm_type=2.0)
             is supported in ZeroOptimizer. Defaults to 2.0.
+        verbose (bool, optional): Whether to print verbose information, including grad overflow info. Defaults to False.
     """
 
     def __init__(self,
@@ -69,6 +70,7 @@ class ZeroOptimizer(ColossalaiOptimizer):
                  max_scale: float = 2**32,
                  clipping_norm: float = 0.0,
                  norm_type: float = 2.0,
+                 verbose: bool = False,
                  **defaults: Any):
         super().__init__(optim)
         assert isinstance(module, ZeroDDP)
@@ -83,6 +85,7 @@ class ZeroOptimizer(ColossalaiOptimizer):
         self.chunk16_set: Set[Chunk] = set()
         self.clipping_flag = clipping_norm > 0.0
         self.max_norm = clipping_norm
+        self.verbose = verbose
 
         if self.clipping_flag:
             assert norm_type == 2.0, "ZeroOptimizer only supports L2 norm now"
@@ -221,7 +224,8 @@ class ZeroOptimizer(ColossalaiOptimizer):
         if found_inf:
             self.optim_state = OptimState.UNSCALED    # no need to unscale grad
             self.grad_scaler.update(found_inf)    # update gradient scaler
-            self._logger.info(f'Found overflow. Skip step')
+            if self.verbose:
+                self._logger.info(f'Found overflow. Skip step')
             self._clear_global_norm()    # clear recorded norm
             self.zero_grad()    # reset all gradients
             self._update_fp16_params()

colossalai/zero/low_level/low_level_optim.py

@@ -440,6 +440,8 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
         # update loss scale if overflow occurs
         if found_inf:
             self._grad_store.reset_all_average_gradients()
+            if self._verbose:
+                self._logger.info(f'Found overflow. Skip step')
             self.zero_grad()
             return
 

colossalai/zero/wrapper.py

@@ -7,7 +7,10 @@ import torch.nn as nn
 from .gemini import GeminiDDP
 
 
-def zero_model_wrapper(model: nn.Module, zero_stage: int = 1, gemini_config: Optional[Dict] = None):
+def zero_model_wrapper(model: nn.Module,
+                       zero_stage: int = 1,
+                       gemini_config: Optional[Dict] = None,
+                       verbose: bool = False):
     """This wrapper function is used to wrap your training model for ZeRO DDP.
 
     Example:
@@ -40,7 +43,7 @@ def zero_model_wrapper(model: nn.Module, zero_stage: int = 1, gemini_config: Opt
     if zero_stage in [1, 2]:
         wrapped_model = model
     else:
-        wrapped_model = GeminiDDP(model, **gemini_config)
+        wrapped_model = GeminiDDP(model, **gemini_config, verbose=verbose)
 
     setattr(wrapped_model, "_colo_zero_stage", zero_stage)
 
@@ -58,7 +61,8 @@ def zero_optim_wrapper(model: nn.Module,
                        max_scale: float = 2**32,
                        max_norm: float = 0.0,
                        norm_type: float = 2.0,
-                       optim_config: Optional[Dict] = None):
+                       optim_config: Optional[Dict] = None,
+                       verbose: bool = False):
     """This wrapper function is used to wrap your training optimizer for ZeRO DDP.
 
     Args:
@@ -79,6 +83,7 @@ def zero_optim_wrapper(model: nn.Module,
 
                 >>> zero2_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True)
                 >>> optim = zero_optim_wrapper(model, optim, optim_config=zero2_config)
+        verbose (bool, optional): Whether to print the verbose info.
     """
     assert hasattr(model, "_colo_zero_stage"), "You should use `zero_ddp_wrapper` first"
     zero_stage = getattr(model, "_colo_zero_stage")
@@ -102,8 +107,8 @@ def zero_optim_wrapper(model: nn.Module,
         from colossalai.zero.low_level import LowLevelZeroOptimizer
         config_dict['partition_grad'] = zero_stage == 2
         config_dict['clip_grad_norm'] = max_norm
-        return LowLevelZeroOptimizer(optimizer, **config_dict)
+        return LowLevelZeroOptimizer(optimizer, **config_dict, verbose=verbose)
     else:
         from colossalai.zero.gemini.gemini_optimizer import ZeroOptimizer
         config_dict['clipping_norm'] = max_norm
-        return ZeroOptimizer(optimizer, model, **config_dict)
+        return ZeroOptimizer(optimizer, model, **config_dict, verbose=verbose)

op_builder/builder.py

@@ -7,7 +7,7 @@ import os
 import time
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import List
+from typing import List, Optional
 
 from .utils import check_cuda_availability, check_system_pytorch_cuda_match, print_rank_0
 
@@ -138,7 +138,7 @@ class Builder(ABC):
         # make sure system CUDA and pytorch CUDA match, an error will raised inside the function if not
         check_system_pytorch_cuda_match(CUDA_HOME)
 
-    def load(self, verbose=True):
+    def load(self, verbose: Optional[bool] = None):
         """
         load the kernel during runtime. If the kernel is not built during pip install, it will build the kernel.
         If the kernel is built during runtime, it will be stored in `~/.cache/colossalai/torch_extensions/`. If the
@@ -149,6 +149,8 @@ class Builder(ABC):
         Args:
             verbose (bool, optional): show detailed info. Defaults to True.
         """
+        if verbose is None:
+            verbose = os.environ.get('CAI_KERNEL_VERBOSE', '0') == '1'
         # if the kernel has be compiled and cached, we directly use it
         if self.cached_op_module is not None:
             return self.cached_op_module

op_builder/utils.py

@@ -90,7 +90,6 @@ def check_system_pytorch_cuda_match(cuda_dir):
             'Please make sure you have set the CUDA_HOME correctly and installed the correct PyTorch in https://pytorch.org/get-started/locally/ .'
         )
 
-    print(bare_metal_minor != torch_cuda_minor)
     if bare_metal_minor != torch_cuda_minor:
         warnings.warn(
             f"[extension] The CUDA version on the system ({bare_metal_major}.{bare_metal_minor}) does not match with the version ({torch_cuda_major}.{torch_cuda_minor}) torch was compiled with. "
@@ -156,16 +155,15 @@ def set_cuda_arch_list(cuda_dir):
 
     # we only need to set this when CUDA is not available for cross-compilation
     if not cuda_available:
-        warnings.warn(
-            '\n[extension]  PyTorch did not find available GPUs on this system.\n'
-            'If your intention is to cross-compile, this is not an error.\n'
-            'By default, Colossal-AI will cross-compile for \n'
-            '1. Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
-            '2. Volta (compute capability 7.0)\n'
-            '3. Turing (compute capability 7.5),\n'
-            '4. Ampere (compute capability 8.0, 8.6)if the CUDA version is >= 11.0\n'
-            '\nIf you wish to cross-compile for a single specific architecture,\n'
-            'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
+        warnings.warn('\n[extension]  PyTorch did not find available GPUs on this system.\n'
+                      'If your intention is to cross-compile, this is not an error.\n'
+                      'By default, Colossal-AI will cross-compile for \n'
+                      '1. Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
+                      '2. Volta (compute capability 7.0)\n'
+                      '3. Turing (compute capability 7.5),\n'
+                      '4. Ampere (compute capability 8.0, 8.6)if the CUDA version is >= 11.0\n'
+                      '\nIf you wish to cross-compile for a single specific architecture,\n'
+                      'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
 
         if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
             bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)