[chore] remove unnecessary test & changes

examples/language/gpt/gemini/run_gemini.sh

@@ -6,7 +6,7 @@ export DISTPLAN=${DISTPLAN:-"CAI_Gemini"}
 export GPUNUM=${GPUNUM:-1}
 export BATCH_SIZE=${BATCH_SIZE:-16}
 export MODEL_TYPE=${MODEL_TYPE:-"gpt2_medium"}
-export TRAIN_STEP=${TRAIN_STEP:-2}
+export TRAIN_STEP=${TRAIN_STEP:-10}
 # export PYTHONPATH=$PWD:$PYTHONPATH
 
 

examples/language/gpt/gemini/train_gpt_demo.py

@@ -66,11 +66,11 @@ class GPTLMLoss(nn.Module):
 
 
 def get_cpu_mem():
-    return psutil.Process().memory_info().rss / 1024**2  # MB unit
+    return psutil.Process().memory_info().rss / 1024**2
 
 
 def get_gpu_mem():
-    return torch.cuda.memory_allocated() / 1024**2  # MB unit
+    return torch.cuda.memory_allocated() / 1024**2
 
 
 def get_mem_info(prefix=""):
@@ -78,7 +78,6 @@ def get_mem_info(prefix=""):
 
 
 def get_model_size(model: nn.Module):
-    # get the number of parameter of the model
     total_numel = 0
     for module in model.modules():
         for p in module.parameters(recurse=False):
@@ -130,7 +129,7 @@ def main():
     WARMUP_STEPS = 1
     assert WARMUP_STEPS < NUM_STEPS, "warmup steps should smaller than the total steps"
     assert (NUM_STEPS - WARMUP_STEPS) % 2 == 1, "the number of valid steps should be odd to take the median"
-    PROF_FLAG = True  # The flag of profiling, False by default
+    PROF_FLAG = False  # The flag of profiling, False by default
 
     disable_existing_loggers()
     colossalai.launch_from_torch()
@@ -167,7 +166,7 @@ def main():
                 stage=zero_stage, reduce_bucket_size_in_m=12, overlap_communication=True, verbose=True
             )
         elif args.distplan == "CAI_Gemini":
-            plugin = GeminiPlugin(search_range_m=128, hidden_dim=model.config.n_embd, max_prefetch=1)
+            plugin = GeminiPlugin(search_range_m=128, hidden_dim=model.config.n_embd)
         else:
             raise RuntimeError
 
@@ -249,7 +248,7 @@ def main():
             prof.step()
 
     tflops_list.sort()
-    median_index = min(((NUM_STEPS - WARMUP_STEPS) >> 1) + WARMUP_STEPS, len(tflops_list) - 1)
+    median_index = ((NUM_STEPS - WARMUP_STEPS) >> 1) + WARMUP_STEPS
     logger.info(f"Median TFLOPS is {tflops_list[median_index]:.3f}")
     torch.cuda.synchronize()
 

tests/test_zero/test_gemini/test_fwd_bwd.py

@@ -40,7 +40,7 @@ def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
 @parameterize("model_name", ["transformers_gpt_lm"])
 @parameterize("use_grad_checkpoint", [False, True])
 @parameterize("master_weights", [False, True])
-@parameterize("max_prefetch", [0, 1, 4])
+@parameterize("max_prefetch", [0, 4])
 @parameterize("enable_async_reduce", [False, True])
 def exam_gpt_fwd_bwd(
     placement_config,

tests/test_zero/test_gemini/test_grad_accum.py

@@ -50,7 +50,7 @@ def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
 @parameterize("model_name", ["transformers_gpt_lm"])
 @parameterize("master_weights", [False, True])
 @parameterize("use_grad_checkpoint", [False, True])
-@parameterize("max_prefetch", [0, 1, 4])
+@parameterize("max_prefetch", [0, 4])
 @parameterize("enable_async_reduce", [False, True])
 def exam_gemini_grad_acc(
     placement_config,

tests/test_zero/test_gemini/test_optim.py

@@ -40,7 +40,9 @@ EXAMPLE_MODELS = [
 ]
 
 # bfloat16 cannot represent them exactly
-BF16_IGNORED_KEYS = ["masked_bias"]
+BF16_IGNORED_KEYS = [
+    "masked_bias",
+]
 
 
 def check_param(model: GeminiDDP, torch_model: torch.nn.Module, dtype: torch.dtype):
@@ -71,15 +73,9 @@ def check_param(model: GeminiDDP, torch_model: torch.nn.Module, dtype: torch.dty
 @parameterize("model_name", TEST_MODELS)
 @parameterize("mixed_precision", [torch.half, torch.bfloat16])
 @parameterize("master_weights", [True, False])
-@parameterize("max_prefetch", [0, 1, 4])
 @parameterize("enable_async_reduce", [False, True])
 def exam_model_step(
-    placement_config,
-    model_name: str,
-    mixed_precision: torch.dtype,
-    master_weights: bool,
-    max_prefetch: int,
-    enable_async_reduce=True,
+    placement_config, model_name: str, mixed_precision: torch.dtype, master_weights: bool, enable_async_reduce=True
 ):
     set_seed(42)
     model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
@@ -108,7 +104,6 @@ def exam_model_step(
         **placement_config,
         mixed_precision=mixed_precision,
         master_weights=master_weights,
-        max_prefetch=max_prefetch,
         enable_async_reduce=enable_async_reduce,
     )
 

tests/test_zero/test_gemini/test_zeroddp_state_dict.py

@@ -28,8 +28,7 @@ def ignore_the_first_parameter(model: torch.nn.Module):
 @parameterize("keep_gathered", [True, False])
 @parameterize("model_name", ["transformers_gpt_lm", "transformers_bert_for_sequence_classification"])
 @parameterize("master_weights", [False, True])
-@parameterize("max_prefetch", [0, 1, 4])
-def exam_state_dict(placement_config, keep_gathered, model_name: str, master_weights: bool, max_prefetch: int):
+def exam_state_dict(placement_config, keep_gathered, model_name: str, master_weights: bool):
     set_seed(431)
     model_builder, data_gen_fn, output_transform_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
 
@@ -45,14 +44,7 @@ def exam_state_dict(placement_config, keep_gathered, model_name: str, master_wei
     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
-    model = GeminiDDP(
-        model,
-        config_dict,
-        **placement_config,
-        pin_memory=True,
-        master_weights=master_weights,
-        max_prefetch=max_prefetch,
-    )
+    model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True, master_weights=master_weights)
     model.train()
 
     zero_dict = model.state_dict(only_rank_0=False)

tests/test_zero/test_gemini/test_zerooptim_state_dict.py

@@ -20,8 +20,7 @@ PLACEMENT_CONFIGS = [
 
 @parameterize("placement_config", PLACEMENT_CONFIGS)
 @parameterize("keep_gathered", [True, False])
-@parameterize("max_prefetch", [0, 1, 4])
-def exam_zero_optim_state_dict(placement_config, keep_gathered, max_prefetch):
+def exam_zero_optim_state_dict(placement_config, keep_gathered):
     set_seed(431)
     model_builder, data_gen_fn, output_transform_fn, *_ = next(
         iter(model_zoo.get_sub_registry("transformers_gpt_lm").values())
@@ -36,7 +35,7 @@ def exam_zero_optim_state_dict(placement_config, keep_gathered, max_prefetch):
     config_dict[world_size]["chunk_size"] = 5000
     config_dict[world_size]["keep_gathered"] = keep_gathered
 
-    model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True, max_prefetch=max_prefetch)
+    model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True)
 
     optimizer = HybridAdam(model.parameters())
     optim = GeminiOptimizer(optimizer, model, initial_scale=32)  # initialize the link between chunk16 and chunk32