chore: bump mcore and mbridge (#1902)

Signed-off-by: Yi-Fu Wu <yifu.wu@gmail.com>
Co-authored-by: Shanmugam Ramasamy <shanmugamr@cw-dfw-cs-001-login-01.cm.cluster>

Author: yfw

.gitmodules

@@ -1,7 +1,7 @@
 [submodule "3rdparty/Megatron-LM"]
 	path = 3rdparty/Megatron-LM-workspace/Megatron-LM
-	url = https://github.com/terrykong/Megatron-LM.git
-	branch = yuya/nemo-rl-use-dev
+	url = https://github.com/yaoyu-33/Megatron-LM.git
+	branch = main
 	shallow = true
 [submodule "3rdparty/Megatron-Bridge"]
 	path = 3rdparty/Megatron-Bridge-workspace/Megatron-Bridge

3rdparty/Megatron-Bridge-workspace/Megatron-Bridge

@@ -26,8 +26,9 @@
 bridge_package_name = "megatron.bridge"
 
 CACHED_DEPENDENCIES = [
-    "transformers>=4.57.1",
+    "transformers<5.0.0",
     "datasets",
+    "accelerate",
     "omegaconf>=2.3.0",
     "tensorboard>=2.19.0",
     "typing-extensions",
@@ -40,7 +41,7 @@
     "hydra-core>1.3,<=1.3.2",
     "megatron-core[dev,mlm]>=0.15.0a0,<0.17.0",
     "qwen-vl-utils",
-    "transformer-engine[pytorch]>=2.9.0a0,<2.10.0",
+    "transformer-engine[pytorch]>=2.10.0a0,<2.12.0",
     "mamba-ssm",
     "nvidia-resiliency-ext",
     "causal-conv1d",

3rdparty/Megatron-Bridge-workspace/setup.py

@@ -44,30 +44,31 @@
 CACHED_DEPENDENCIES = [
     # Default dependencies from pyproject.toml
     "torch",
-    "numpy<2.0.0",
+    "numpy",
     "packaging>=24.2",
     # Dev dependencies from pyproject.toml
-    "nvidia-modelopt[torch]>=0.33.0a0,<0.34.0; sys_platform != 'darwin'",
-    "transformer-engine[pytorch]>=2.9.0a0,<2.10.0",
-    "nvidia-resiliency-ext>=0.4.0a0,<0.5.0",
+    "nvidia-modelopt[torch]; sys_platform != 'darwin'",
+    "transformer-engine[pytorch,core_cu13]>=2.9.0a0,<2.12.0",
+    "nvidia-resiliency-ext",
     "tqdm",
     "einops~=0.8",
     "tensorstore~=0.1,!=0.1.46,!=0.1.72",
     "nvtx~=0.2",
     "multi-storage-client~=0.27",
     "opentelemetry-api~=1.33.1",
-    "setuptools<80.0.0",
     "mamba-ssm~=2.2",
     "causal-conv1d~=1.5",
+    "flash-linear-attention~=0.3.2",
     "nv-grouped-gemm~=1.1",
     "megatron-energon[av_decode]~=6.0",
-    "av<16.0.0",
-    "flashinfer-python",
+    "av",
+    "flashinfer-python~=0.5.0",
     "wget",
     "onnxscript",
-    "flash-linear-attention~=0.3.2",
     # VCS dependency - must match pyproject.toml [tool.uv.sources]
     "emerging_optimizers @ git+https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git@v0.1.0",
+    "datasets",
+    "fastapi~=0.50",
 ]
 
 

3rdparty/Megatron-LM-workspace/Megatron-LM

@@ -148,11 +148,10 @@ policy:
     top_k: null
     mcore_generation_config:
       buffer_size_gb: 20  # Total GPU memory (in GB) allocated for KV cache buffers
-      buffer_guaranteed_fraction: 0.1  # Fraction of buffer reserved for guaranteed active requests
       num_cuda_graphs: 16  # Number of CUDA graphs to pre-compile for different batch sizes
       block_size_tokens: 256  # Size of each KV cache block in tokens (affects memory granularity)
       use_cuda_graphs_for_non_decode_steps: true  # Enable CUDA graphs for prefill/context processing
-      enable_chunked_prefill: true  # Split long prefills into chunks for better memory management
+      enable_chunked_prefill: false  # Split long prefills into chunks for better memory management
       unified_memory_level: 0  # Unified memory usage level (0=disabled, higher values enable more aggressive paging)
       max_tokens: 16384 # Maximum number of tokens to use in a single step. Analogous to vllm's max_num_batched_tokens
 

3rdparty/Megatron-LM-workspace/setup.py

@@ -25,8 +25,6 @@
 class MegatronGenerationConfig(TypedDict):
     # Total GPU memory (in GB) allocated for KV cache buffers
     buffer_size_gb: int
-    # Fraction of buffer reserved for guaranteed active requests
-    buffer_guaranteed_fraction: float
     # Number of CUDA graphs to pre-compile for different batch sizes
     num_cuda_graphs: int
     # Size of each KV cache block in tokens (affects memory granularity)

examples/configs/grpo_math_1B_megatron.yaml

@@ -51,6 +51,7 @@
 from megatron.bridge.utils.instantiate_utils import InstantiationMode
 from megatron.bridge.utils.vocab_utils import calculate_padded_vocab_size
 from megatron.core import parallel_state
+from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer import MegatronModule
 from megatron.core.transformer.module import Float16Module
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -663,12 +664,15 @@ def setup_model_and_optimizer(
     checkpointing_context = init_checkpointing_context(megatron_cfg.checkpoint)
 
     # Tokenizer
+    if megatron_cfg.tokenizer.hf_tokenizer_kwargs is None:
+        megatron_cfg.tokenizer.hf_tokenizer_kwargs = {}
+    megatron_cfg.tokenizer.hf_tokenizer_kwargs["trust_remote_code"] = True
+    megatron_cfg.tokenizer.hf_tokenizer_kwargs["use_fast"] = True
     build_tokenizer(
         megatron_cfg.tokenizer,
         make_vocab_size_divisible_by=megatron_cfg.model.make_vocab_size_divisible_by
         // megatron_cfg.model.tensor_model_parallel_size,
         tensor_model_parallel_size=megatron_cfg.model.tensor_model_parallel_size,
-        trust_remote_code=True,
     )
     assert megatron_cfg.model.vocab_size, "vocab size must be specified in model config"
 
@@ -731,6 +735,8 @@ def composed_peft_hook(model: list[MegatronModule]) -> list[MegatronModule]:
         pre_wrap_hook.extend([composed_peft_hook])
 
     # Model, optimizer, and learning rate.
+    pg_collection = ProcessGroupCollection.use_mpu_process_groups()
+    setattr(megatron_cfg.model, "_pg_collection", pg_collection)
     model = get_model(
         megatron_cfg.model,
         megatron_cfg.ddp,
@@ -739,6 +745,7 @@ def composed_peft_hook(model: list[MegatronModule]) -> list[MegatronModule]:
         data_parallel_random_init=megatron_cfg.rng.data_parallel_random_init,
         pre_wrap_hook=pre_wrap_hook,
         mixed_precision_wrapper=mixed_precision_wrapper,
+        pg_collection=pg_collection,
     )
     if load_optimizer:
         optimizer, scheduler = setup_optimizer(
@@ -872,6 +879,7 @@ def setup_reference_model_state(
         overlap_param_gather_with_optimizer_step=megatron_cfg.optimizer.overlap_param_gather_with_optimizer_step,
         pre_wrap_hook=megatron_cfg.rng.data_parallel_random_init,
         mixed_precision_wrapper=ref_mixed_precision_wrapper,
+        pg_collection=ProcessGroupCollection.use_mpu_process_groups(),
     )
 
     print("Loading the Reference Model")
@@ -925,19 +933,23 @@ def finalize_megatron_setup(
         megatron_cfg.ddp,
         optimizer,
         align_grad_reduce=megatron_cfg.dist.align_grad_reduce,
+        pg_collection=ProcessGroupCollection.use_mpu_process_groups(),
     )
 
     tokenizer_config = TokenizerConfig(
         tokenizer_type="HuggingFaceTokenizer",
         tokenizer_model=hf_model_name,
+        hf_tokenizer_kwargs={
+            "trust_remote_code": True,
+            "use_fast": True,
+        },
     )
 
     megatron_tokenizer = build_tokenizer(
         tokenizer_config,
         make_vocab_size_divisible_by=megatron_cfg.model.make_vocab_size_divisible_by
         // config["megatron_cfg"]["tensor_model_parallel_size"],
         tensor_model_parallel_size=config["megatron_cfg"]["tensor_model_parallel_size"],
-        trust_remote_code=True,
     )
 
     dp_size = worker_sharding_annotations.get_axis_size("data_parallel")

nemo_rl/models/megatron/config.py

@@ -27,6 +27,7 @@
     maybe_finalize_async_save,
     save_checkpoint,
 )
+from megatron.bridge.training.utils.pg_utils import get_pg_collection
 from megatron.bridge.training.utils.train_utils import (
     logical_and_across_model_parallel_group,
     reduce_max_stat_across_model_parallel_group,
@@ -415,18 +416,20 @@ def train(
                 else:
                     update_successful, grad_norm, num_zeros_in_grad = (True, 0.0, 0.0)
 
+                pg_collection = get_pg_collection(self.model)
+
                 # when freezing sub-models we may have a mixture of successful and unsucessful ranks,
                 # so we must gather across mp ranks
                 update_successful = logical_and_across_model_parallel_group(
-                    update_successful
+                    update_successful, mp_group=pg_collection.mp
                 )
                 # grad_norm and num_zeros_in_grad will be None on ranks without trainable params,
                 # so we must gather across mp ranks
                 grad_norm: float = reduce_max_stat_across_model_parallel_group(
-                    grad_norm
+                    grad_norm, mp_group=pg_collection.mp
                 )
                 num_zeros_in_grad: float = reduce_max_stat_across_model_parallel_group(
-                    num_zeros_in_grad
+                    num_zeros_in_grad, mp_group=pg_collection.mp
                 )
 
                 if update_successful:
@@ -1036,9 +1039,6 @@ def generate(
         ]
         enable_chunked_prefill = mcore_generation_config["enable_chunked_prefill"]
         unified_memory_level = mcore_generation_config["unified_memory_level"]
-        buffer_guaranteed_fraction = mcore_generation_config[
-            "buffer_guaranteed_fraction"
-        ]
         max_tokens = mcore_generation_config["max_tokens"]
 
         model_config = self.model.config
@@ -1050,7 +1050,6 @@ def generate(
             kv_channels=model_config.kv_channels,
             num_attention_heads=model_config.num_query_groups,
             max_sequence_length=self.cfg["generation"]["max_new_tokens"],
-            buffer_guaranteed_fraction=buffer_guaranteed_fraction,
             buffer_size_gb=buffer_size_gb,
             materialize_only_last_token_logits=False,
             num_cuda_graphs=num_cuda_graphs,
@@ -1061,7 +1060,7 @@ def generate(
             use_cuda_graphs_for_non_decode_steps=use_cuda_graphs_for_non_decode_steps,
             use_flashinfer_fused_rope=False,
             unified_memory_level=unified_memory_level,
-            max_tokens_override=max_tokens,
+            max_tokens=max_tokens,
         )
         inference_wrapped_model = GPTInferenceWrapper(
             self.model, inference_wrapper_config, dynamic_context
@@ -1134,23 +1133,27 @@ def generate(
 
         result = []
         while dynamic_engine.has_unfinished_requests():
-            result_step = dynamic_engine.step_modern(verbose=False)
-            finished_requests = result_step.get("finished_requests", [])
-            for finished_request in finished_requests:
-                result.append(finished_request)
+            result_step = dynamic_engine.step_modern()
+            result.extend(result_step["finished_request_records"])
 
         # Sort results by request_id to maintain original batch order
         result.sort(key=lambda x: x.request_id)
 
         out = {
-            "tokens": [x.prompt_tokens.tolist() + x.generated_tokens for x in result],
-            "logprobs": [x.prompt_log_probs + x.generated_log_probs for x in result],
+            "tokens": [
+                x.requests[0].prompt_tokens.tolist() + x.requests[0].generated_tokens
+                for x in result
+            ],
+            "logprobs": [
+                x.requests[0].prompt_log_probs + x.requests[0].generated_log_probs
+                for x in result
+            ],
         }
 
         input_lengths = data["input_lengths"]
         # pad the out "tokens" and "logprobs" and make them into tensors from lists
         batch_size = data["input_ids"].size(0)
-        max_gen_seq_len = max([len(x.generated_tokens) for x in result])
+        max_gen_seq_len = max([len(x.requests[0].generated_tokens) for x in result])
         padded_input_length = input_ids.size(1)
 
         max_seq_len = padded_input_length + max_gen_seq_len

nemo_rl/models/megatron/setup.py

@@ -920,6 +920,7 @@ def test_reinitialize_parallel_state_after_import(
 class TestSetupModelAndOptimizer:
     """Tests for setup_model_and_optimizer function."""
 
+    @patch("nemo_rl.models.megatron.setup.ProcessGroupCollection")
     @patch("nemo_rl.models.megatron.setup.GlobalState")
     @patch("nemo_rl.models.megatron.setup.initialize_megatron")
     @patch("nemo_rl.models.megatron.setup.set_jit_fusion_options")
@@ -946,6 +947,7 @@ def test_setup_with_param_sync_and_frozen_moe_router(
         mock_set_jit,
         mock_init_megatron,
         mock_global_state,
+        mock_pg_collection,
     ):
         """Test setup_model_and_optimizer with MoE router freezing."""
         from nemo_rl.models.megatron.setup import setup_model_and_optimizer
@@ -1006,6 +1008,7 @@ def test_setup_with_param_sync_and_frozen_moe_router(
 class TestSetupReferenceModelState:
     """Tests for setup_reference_model_state function."""
 
+    @patch("nemo_rl.models.megatron.setup.ProcessGroupCollection")
     @patch("nemo_rl.models.megatron.setup.init_checkpointing_context")
     @patch("nemo_rl.models.megatron.setup.GlobalState")
     @patch("nemo_rl.models.megatron.setup.get_model")
@@ -1019,6 +1022,7 @@ def test_setup_reference_model(
         mock_get_model,
         mock_global_state,
         mock_init_ckpt_context,
+        mock_pg_collection,
         capsys,
     ):
         """Test setup_reference_model_state when checkpoint exists."""
@@ -1075,6 +1079,7 @@ def test_setup_reference_model(
 class TestFinalizeMegatronSetup:
     """Tests for finalize_megatron_setup function."""
 
+    @patch("nemo_rl.models.megatron.setup.ProcessGroupCollection")
     @patch("nemo_rl.models.megatron.setup._update_model_config_funcs")
     @patch("nemo_rl.models.megatron.setup.build_tokenizer")
     @patch("nemo_rl.models.megatron.setup.AutoBridge")
@@ -1083,6 +1088,7 @@ def test_basic_finalize_setup(
         mock_auto_bridge,
         mock_build_tokenizer,
         mock_update_model_config,
+        mock_pg_collection,
     ):
         """Test basic finalize_megatron_setup."""
         from nemo_rl.models.megatron.setup import finalize_megatron_setup