[gemini] fix argument naming during chunk configuration searching

colossalai/booster/plugin/gemini_plugin.py

@@ -181,11 +181,11 @@ class GeminiPlugin(DPPluginBase):
         pin_memory (bool, optional): use pin memory on CPU. Defaults to False.
         force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False.
         strict_ddp_mode (bool, optional): use strict ddp mode (only use dp without other parallelism). Defaults to False.
-        search_range_mb (int, optional): chunk size searching range in MegaByte. Defaults to 32.
+        search_range_m (int, optional): chunk size searching range divided by 2^20. Defaults to 32.
         hidden_dim (int, optional): the hidden dimension of DNN.
             Users can provide this argument to speed up searching.
             If users do not know this argument before training, it is ok. We will use a default value 1024.
-        min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte.
+        min_chunk_size_m (float, optional): the minimum chunk size divided by 2^20.
             If the aggregate size of parameters is still smaller than the minimum chunk size,
             all parameters will be compacted into one small chunk.
         memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer.
@@ -214,9 +214,9 @@ class GeminiPlugin(DPPluginBase):
         pin_memory: bool = False,
         force_outputs_fp32: bool = False,
         strict_ddp_mode: bool = False,
-        search_range_mb: int = 32,
+        search_range_m: int = 32,
         hidden_dim: Optional[int] = None,
-        min_chunk_size_mb: float = 32,
+        min_chunk_size_m: float = 32,
         memstats: Optional[MemStats] = None,
         gpu_margin_mem_ratio: float = 0.0,
         initial_scale: float = 2**32,
@@ -238,9 +238,9 @@ class GeminiPlugin(DPPluginBase):
             pin_memory=pin_memory,
             force_outputs_fp32=force_outputs_fp32,
             strict_ddp_mode=strict_ddp_mode,
-            search_range_mb=search_range_mb,
+            search_range_m=search_range_m,
             hidden_dim=hidden_dim,
-            min_chunk_size_mb=min_chunk_size_mb,
+            min_chunk_size_m=min_chunk_size_m,
             memstats=memstats,
             mixed_precision=PRECISION_STR_TO_DTYPE[precision],
         )
@@ -295,10 +295,7 @@ class GeminiPlugin(DPPluginBase):
 
         if optimizer is not None and \
                 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/search_utils.py

@@ -114,9 +114,9 @@ def classify_params_by_dp_degree(param_order: OrderedParamGenerator,
 
 def search_chunk_configuration(
         model: nn.Module,
-        search_range_mb: float,
-        search_interval_byte: int,    # hidden size is the best value for the interval
-        min_chunk_size_mb: float = 32,
+        search_range_m: float,
+        search_interval: int,    # hidden size is the best value for the interval
+        min_chunk_size_m: float = 32,
         filter_exlarge_params: bool = True,
         strict_ddp_flag: bool = False,
         memstas: Optional[MemStats] = None) -> Tuple[Dict, int, int]:
@@ -126,9 +126,9 @@ def search_chunk_configuration(
 
     Args:
         model (nn.Module): torch module
-        search_range_mb (float): searching range in mega byte.
-        search_interval_byte (int): searching interval in byte.
-        min_chunk_size_mb (float, optional): the minimum size of a distributed chunk.
+        search_range_m (float): searching range divided by 2^20.
+        search_interval (int): searching interval.
+        min_chunk_size_m (float, optional): the minimum size of a distributed chunk, divided by 2^20..
         filter_exlarge_params (bool, optional): filter extreme large parameters. Defaults to True.
         strict_ddp_flag (bool, optional): whether to enable the strict ddp mode.
             all parameters keep replicated in this mode.
@@ -145,9 +145,9 @@ def search_chunk_configuration(
         for p in model.parameters():
             param_order.append(p)
 
-    search_range_byte = round(search_range_mb * 1024**2)
-    min_chunk_size_byte = round(min_chunk_size_mb * 1024**2)
-    assert search_range_byte >= 0
+    search_range = round(search_range_m * 1024**2)
+    min_chunk_size = round(min_chunk_size_m * 1024**2)
+    assert search_range >= 0
 
     params_dict = classify_params_by_dp_degree(param_order, strict_ddp_flag)
     size_lcm = np.lcm.reduce(list(params_dict.keys()))
@@ -162,7 +162,7 @@ def search_chunk_configuration(
         total_param_size += group_acc_size
 
         # let small parameters keep gathered in CUDA all the time
-        if group_acc_size < min_chunk_size_byte:
+        if group_acc_size < min_chunk_size:
             config_dict[dp_degree] = dict(chunk_size=group_acc_size, keep_gathered=True)
         else:
             size_dict[dp_degree] = size_list
@@ -170,15 +170,15 @@ def search_chunk_configuration(
     if filter_exlarge_params:
         _filter_exlarge_params(model, size_dict)
 
-    max_size = min_chunk_size_byte
+    max_size = min_chunk_size
     for key in size_dict:
         max_size = max(max_size, max(size_dict[key]))
-    start_size = int(math.ceil(max_size / search_interval_byte) * search_interval_byte)
+    start_size = int(math.ceil(max_size / search_interval) * search_interval)
 
     min_chunk_waste = float('+inf')
     best_chunk_size = start_size
 
-    for chunk_size in range(start_size, start_size + search_range_byte + 1, search_interval_byte):
+    for chunk_size in range(start_size, start_size + search_range + 1, search_interval):
         temp_waste = 0
         for key in size_dict:
             temp_waste += _get_unused_byte(size_dict[key], chunk_size)

colossalai/zero/gemini/chunk/utils.py

@@ -23,10 +23,10 @@ def init_chunk_manager(model: nn.Module,
                        verbose: bool = False,
                        **kwargs) -> ChunkManager:
     if hidden_dim:
-        search_interval_byte = hidden_dim
+        search_interval = hidden_dim
     else:
-        search_interval_byte = 1024    # defaults to 1kb
-    kwargs["search_interval_byte"] = search_interval_byte
+        search_interval = 1024    # defaults to 1024
+    kwargs["search_interval"] = search_interval
 
     dist.barrier()
     begin = time()
@@ -36,13 +36,13 @@ def init_chunk_manager(model: nn.Module,
     dist.barrier()
     end = time()
     span_s = end - begin
-    mb_size = 1024**2
-    total_size /= mb_size
-    wasted_size /= mb_size
+    mega_unit = 1024**2
+    total_size /= mega_unit
+    wasted_size /= mega_unit
 
     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),
+              "used number: {:.2f} * 2^20, wasted number: {:.2f} * 2^20\n".format(total_size, wasted_size),
               "total wasted percentage is {:.2f}%".format(100 * safe_div(wasted_size, total_size + wasted_size)),
               sep='',
               flush=True)

colossalai/zero/gemini/gemini_ddp.py

@@ -739,9 +739,9 @@ class GeminiDDP(ZeroDDP):
                  force_outputs_fp32: bool = False,
                  strict_ddp_mode: bool = False,
                  scatter_after_inference: bool = True,
-                 search_range_mb: int = 32,
+                 search_range_m: int = 32,
                  hidden_dim: Optional[int] = None,
-                 min_chunk_size_mb: float = 32,
+                 min_chunk_size_m: float = 32,
                  memstats: Optional[MemStats] = None,
                  mixed_precision: torch.dtype = torch.float16,
                  verbose: bool = False) -> None:
@@ -763,24 +763,24 @@ class GeminiDDP(ZeroDDP):
             placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu".
             pin_memory (bool, optional): use pin memory on CPU. Defaults to False.
             force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False.
-            search_range_mb (int, optional): chunk size searching range in MegaByte. Defaults to 32.
+            search_range_m (int, optional): chunk size searching range divided by 2^20. Defaults to 32.
             hidden_dim (int, optional): the hidden dimension of DNN.
                 Users can provide this argument to speed up searching.
                 If users do not know this argument before training, it is ok. We will use a default value 1024.
-            min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte.
+            min_chunk_size_m (float, optional): the minimum chunk size divided by 2^20.
                 If the aggregate size of parameters is still smaller than the minimum chunk size,
                 all parameters will be compacted into one small chunk.
             memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer.
         """
         # some ugly hotfix for the compatibility with Lightning
-        if search_range_mb is None:
-            search_range_mb = 32
+        if search_range_m is None:
+            search_range_m = 32
 
         chunk_manager = init_chunk_manager(model=module,
                                            init_device=device,
                                            hidden_dim=hidden_dim,
-                                           search_range_mb=search_range_mb,
-                                           min_chunk_size_mb=min_chunk_size_mb,
+                                           search_range_m=search_range_m,
+                                           min_chunk_size_m=min_chunk_size_m,
                                            strict_ddp_flag=strict_ddp_mode,
                                            verbose=verbose)
         gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats)

tests/test_auto_parallel/test_offload/test_perf.py

@@ -60,7 +60,7 @@ def exam_fwd_bwd(model_name: str, memory_budget: float, solver_name: str):
                          placement_policy='cpu',
                          pin_memory=True,
                          hidden_dim=8192,
-                         search_range_mb=128)
+                         search_range_m=128)
     gemini_model = zero_model_wrapper(gemini_model, 3, gemini_config)
     optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True)
     gemini_optim = zero_optim_wrapper(gemini_model, hybrid_optimizer, optim_config=optim_config)

tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py

@@ -75,7 +75,7 @@ def check_auto_parallel_with_gemini(rank, world_size, port):
                          device=get_current_device(),
                          placement_policy='cpu',
                          pin_memory=True,
-                         search_range_mb=128)
+                         search_range_m=128)
 
     post_process_colo_init_ctx(gm, device=get_current_device(), default_pg=dp_process_group)
     gm = zero_model_wrapper(gm, zero_stage=3, gemini_config=gemini_config)

tests/test_checkpoint_io/test_gemini_checkpoint_io.py

@@ -30,7 +30,7 @@ def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: b
         bert_model.config.save_pretrained(save_directory=pretrained_path)
 
         # TODO(ver217): use boost api
-        config_dict, *_ = search_chunk_configuration(bert_model, search_range_mb=1, search_interval_byte=100)
+        config_dict, *_ = search_chunk_configuration(bert_model, search_range_m=1, search_interval=100)
         chunk_manager = ChunkManager(config_dict)
         gemini_manager = GeminiManager(placement_policy, chunk_manager)
         bert_model = ZeroDDP(bert_model, gemini_manager)

tests/test_tensor/test_tp_with_zero.py

@@ -79,7 +79,7 @@ def run_gpt(placement_policy, tp_init_spec_func=None):
         tp_init_spec_func(model, pg)
 
     dp_world_size = pg.dp_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[dp_world_size]['chunk_size'] = 5000
     config_dict[dp_world_size]['keep_gathered'] = False
     if placement_policy != 'cuda':

tests/test_zero/test_gemini/test_fwd_bwd.py

@@ -52,7 +52,7 @@ def exam_gpt_fwd_bwd(
         torch_p.data.copy_(p.data)
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = keep_gather
     chunk_manager = ChunkManager(config_dict)
@@ -113,7 +113,7 @@ def exam_gpt_inference(
         torch_p.data.copy_(p.data)
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = keep_gather
     chunk_manager = ChunkManager(config_dict)

tests/test_zero/test_gemini/test_gemini_use_rmt.py

@@ -56,7 +56,7 @@ def run_gemini_use_rmt(placement_policy, keep_gather, model_name: str, use_grad_
                 assert len(step_list) == 4
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = keep_gather
     chunk_manager = ChunkManager(config_dict)

tests/test_zero/test_gemini/test_grad_clip.py

@@ -51,7 +51,7 @@ def exam_grad_clipping(placement_policy, model_name: str):
         p.data.copy_(torch_p.data)
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = False
     if placement_policy != 'cuda':

tests/test_zero/test_gemini/test_inference.py

@@ -34,7 +34,7 @@ def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
 
 def multi_chunk_init(model: torch.nn.Module, placement_policy: str):
     world_size = dist.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = False
     if placement_policy != 'cuda':

tests/test_zero/test_gemini/test_optim.py

@@ -73,7 +73,7 @@ def exam_model_step(placement_policy, model_name: str, mixed_precision: torch.dt
         p.data.copy_(torch_p.data)
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = False
     if placement_policy != 'cuda':
@@ -130,7 +130,7 @@ def exam_tiny_example(placement_policy, model_name: str, mixed_precision: torch.
     for torch_p, p in zip(torch_model.parameters(), model.parameters()):
         p.data.copy_(torch_p.data)
 
-    chunk_manager = init_chunk_manager(model=model, init_device=get_current_device(), search_range_mb=1)
+    chunk_manager = init_chunk_manager(model=model, init_device=get_current_device(), search_range_m=1)
     gemini_manager = GeminiManager(placement_policy, chunk_manager)
     model = ZeroDDP(model, gemini_manager, pin_memory=True, mixed_precision=mixed_precision)
     optimizer = HybridAdam(model.parameters(), lr=1e-3)

tests/test_zero/test_gemini/test_search.py

@@ -30,9 +30,9 @@ def exam_search_chunk_size():
         model = model_builder()
     init_1d_row_spec(model, pg_tp)
     config_dict, *_ = search_chunk_configuration(model,
-                                                 search_range_mb=1,
-                                                 search_interval_byte=16,
-                                                 min_chunk_size_mb=0,
+                                                 search_range_m=1,
+                                                 search_interval=16,
+                                                 min_chunk_size_m=0,
                                                  filter_exlarge_params=True)
 
     for key in config_dict:
@@ -54,9 +54,9 @@ def exam_search_strict_ddp():
     with ColoInitContext(device=get_current_device()):
         ddp_model = model_builder()
     re_dict, re_total, re_wasted = search_chunk_configuration(ddp_model,
-                                                              search_range_mb=1,
-                                                              search_interval_byte=16,
-                                                              min_chunk_size_mb=0,
+                                                              search_range_m=1,
+                                                              search_interval=16,
+                                                              min_chunk_size_m=0,
                                                               filter_exlarge_params=True,
                                                               strict_ddp_flag=False)
     # get the chunk configuration over sharded ddp models
@@ -64,9 +64,9 @@ def exam_search_strict_ddp():
                          default_dist_spec=default_shard_spec):
         sharded_ddp_model = model_builder()
     sh_dict, sh_total, sh_wasted = search_chunk_configuration(sharded_ddp_model,
-                                                              search_range_mb=1,
-                                                              search_interval_byte=16,
-                                                              min_chunk_size_mb=0,
+                                                              search_range_m=1,
+                                                              search_interval=16,
+                                                              min_chunk_size_m=0,
                                                               filter_exlarge_params=True,
                                                               strict_ddp_flag=True)
     assert re_dict == sh_dict
@@ -91,8 +91,8 @@ def exam_chunk_manager():
     chunk_manager = init_chunk_manager(sharded_ddp_model,
                                        get_current_device(),
                                        hidden_dim=16,
-                                       search_range_mb=1,
-                                       min_chunk_size_mb=0,
+                                       search_range_m=1,
+                                       min_chunk_size_m=0,
                                        filter_exlarge_params=True,
                                        strict_ddp_flag=True)
     config_dict = chunk_manager.dp_degree_chunk_size_dict

tests/test_zero/test_gemini/test_zeroddp_state_dict.py

@@ -35,7 +35,7 @@ def exam_state_dict(placement_policy, keep_gathered, model_name: str):
         torch_p.data.copy_(p.data)
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = keep_gathered
     chunk_manager = ChunkManager(config_dict)
@@ -67,7 +67,7 @@ def exam_load_state_dict(placement_policy, keep_gathered, model_name: str):
     torch_model = model_builder()    # get a different model
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = keep_gathered
 

tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py

@@ -22,7 +22,7 @@ def exam_state_dict(placement_policy, model_name: str):
 
     model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2
 
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     chunk_manager = ChunkManager(config_dict)
     gemini_manager = GeminiManager(placement_policy, chunk_manager)
     model = ZeroDDP(model, gemini_manager)
@@ -38,6 +38,7 @@ def exam_state_dict(placement_policy, model_name: str):
             assert key in zero_dict, f"{key} not in ZeRO dictionary."
             assert torch.equal(value, zero_dict[key]), f"{key} not equal."
 
+
 def run_dist(rank, world_size, port):
     config = {}
     colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')

tests/test_zero/test_gemini/test_zerooptim_state_dict.py

@@ -27,7 +27,7 @@ def exam_zero_optim_state_dict(placement_policy, keep_gathered):
     torch_model = model_builder()    # get a different model
 
     world_size = torch.distributed.get_world_size()
-    config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
     config_dict[world_size]['chunk_size'] = 5000
     config_dict[world_size]['keep_gathered'] = keep_gathered