[trainer] Initial Megatron TP + PP Support by erictang000 · Pull Request #223 · NovaSky-AI/SkyRL

erictang000 · 2025-08-29T21:43:39Z

Overview

This PR initializes Megatron support for GRPO with TP + PP (and DP) supported. We use mbridge to translate from huggingface to megatron, and plan to migrate to Megatron-Bridge once the repo is more mature and we can safely upgrade some dependencies.

What's not Included

For a full set of limitations see the code here:

SkyRL/skyrl-train/skyrl_train/utils/utils.py

Line 117 in 1350d7b

def validate_megatron_cfg(cfg: DictConfig):

.

Note: there is a known issue with Qwen2.5-1.5B and Qwen2.5-3B models, which don't specify lm_head.weight in the model.safetensors file, causing a weight loading issue with mbridge - issue here: ISEEKYAN/mbridge#10.

Followups

For a full task list needed for full Megatron support, see #203.

Reproduction runs (gsm8k with Qwen3-0.6B)

TODOs

Port GPU test to be runnable without locally saved training batch input
fix entropy calculation
dependencies
add any needed doc-strings
code-attribution if i missed anything

…tron

gemini-code-assist

Code Review

This pull request introduces initial support for Megatron-LM, enabling Tensor Parallelism (TP) and Pipeline Parallelism (PP) for training. The changes are extensive, adding new strategies, workers, configurations, and utilities for Megatron integration. Key changes include a new MegatronStrategy, MegatronPPOPolicy, and corresponding workers. The PR also includes valuable refactoring, such as moving common distributed functions to the base DistributedStrategy class, and adds validation for Megatron configurations to improve user experience. The addition of comprehensive tests for the new Megatron functionality is commendable.

My review includes a couple of suggestions for improvement. One is to implement the policy entropy calculation, which is currently missing. The other is to make a new utility function more robust.

I also noticed a few limitations with this initial implementation, such as the lack of checkpointing support and support for only the GRPO algorithm. It would be beneficial to document these limitations in the pull request description to set clear expectations for users.

skyrl-train/skyrl_train/workers/megatron/megatron_policy.py

skyrl-train/skyrl_train/utils/utils.py

skyrl-train/skyrl_train/workers/megatron/megatron_policy.py

skyrl-train/tests/gpu/test_megatron_worker.py

erictang000 · 2025-08-29T22:00:28Z

skyrl-train/pyproject.toml


+[dependency-groups]
+
+# This is a default group so that we install these even with bare `uv sync`


todo: figure out how to reconcile deps - flash-rl requires torch 2.7.0, but in order to build transformer-engine-pytorch, i need torch 2.7.1. I think I can get this working by asking the user to build transformer-engine first, but need to test out installation in a fresh env.

FlashRL extra is currently standalone, so for the initial integration we can just say that the two dep groups mcore and flashrl conflict, and that should be enough right?

problem was that i needed to include the build dependency-group as a default which clashes torch versions with flash-rl. but this is maybe just needed to build transformer-engine so can give instructions to build it separately include it as a no-build-isolation dep (btw this takes like more than 10 mins :(. )

will see how that goes tho

skyrl-train/skyrl_train/distributed/megatron/megatron_utils.py

…tron

erictang000 · 2025-08-30T00:37:14Z

skyrl-train/skyrl_train/config/megatron_config/policy.yaml

+expert_tensor_parallel_size: 1
+
+# Allow to override Distributed Data Parallel (DDP) config
+# TODO (erictang000): understand exactly what is going on here


erictang000 · 2025-08-30T00:42:15Z

/gemini review

gemini-code-assist

Code Review

This pull request introduces initial support for Megatron-Core, enabling training with Tensor Parallelism (TP) and Pipeline Parallelism (PP). The integration relies on mbridge for converting Hugging Face models to the Megatron format. The changes include new dependencies, Megatron-specific configurations, distributed strategies, and worker implementations. The implementation correctly handles pipeline parallelism by collecting outputs from the last stage and introduces custom autograd functions for distributed log-probability calculations. Overall, this is a substantial and well-structured addition. My feedback focuses on improving configuration clarity, addressing potential brittleness in the implementation, and noting current limitations for future work.

skyrl-train/skyrl_train/workers/megatron/megatron_policy.py

skyrl-train/examples/training_backends/megatron/run_megatron.sh

skyrl-train/skyrl_train/config/megatron_config/policy.yaml

skyrl-train/skyrl_train/distributed/dispatch.py

skyrl-train/skyrl_train/workers/megatron/megatron_policy.py

skyrl-train/skyrl_train/workers/megatron/megatron_worker.py

SumanthRH

This is great! Leaving some comments after a first pass

SumanthRH · 2025-09-01T06:10:05Z

skyrl-train/skyrl_train/config/megatron_config/policy.yaml

+  average_in_collective: true
+  data_parallel_sharding_strategy: "optim_grads_params"
+
+seed: 42


Nit: is this seed argument needed here? It looks like we pass cfg.trainer.seed to MegatronStrategy at init, so we should not have it in the config here

SumanthRH · 2025-09-01T06:21:55Z

skyrl-train/skyrl_train/distributed/megatron/megatron_utils.py

+    def offload_tensor_to_cpu(tensor):
+        if tensor is None:
+            return
+        tensor.data = tensor.data.to("cpu", non_blocking=True)


Suggested change

tensor.data = tensor.data.to("cpu", non_blocking=True)

tensor = tensor.to("cpu", non_blocking=True)

interestingly using .data here seems to actually impact offloaded memory!

using .data:

not using .data:

maybe we can leave these as is?

SumanthRH · 2025-09-01T06:22:16Z

skyrl-train/skyrl_train/distributed/megatron/megatron_utils.py

+        if tensor is None:
+            return
+        device_id = torch.cuda.current_device()
+        tensor.data = tensor.data.to(device_id, non_blocking=True)


Suggested change

tensor.data = tensor.data.to(device_id, non_blocking=True)

tensor = tensor.to(device_id, non_blocking=True)

SumanthRH · 2025-09-04T01:45:41Z

skyrl-train/skyrl_train/utils/ppo_utils.py

    logger.info("Synced registries to ray actor")


+def _safe_exp_delta(delta: torch.Tensor, clip: float = 20.0, out_dtype=None) -> torch.Tensor:


a small comment on overflow/ underflow here would be nice

SumanthRH · 2025-09-04T05:47:34Z

skyrl-train/skyrl_train/workers/megatron/megatron_worker.py

+        if not torch.distributed.is_initialized():
+            torch.distributed.init_process_group(backend="nccl")
+
+        # override the init_process_group method to use megatron distributed setup to create the mesh


comment doesn't match the code? (or maybe is insufficient)

SumanthRH · 2025-09-04T06:27:11Z

skyrl-train/skyrl_train/workers/megatron/megatron_worker.py

+        self.tokenizer = tokenizer
+
+    def make_megatron_module(self, model_config_kwargs, wrap_with_ddp=True):
+        model = self.bridge.get_model(


Nit: Type hint?

Looks like this is no longer using the model classes in models.py, good to highlight. (is this just an AutoBridge instance, or anything else)

SumanthRH · 2025-09-04T06:28:55Z

skyrl-train/skyrl_train/workers/megatron/megatron_policy.py

+
+            seq_len = sequences.shape[1]
+
+            new_sequences, new_attention_mask, new_position_ids = remove_left_padding(


Btw why is only left padding removed here? Is this related to a quirk for Mbridge models? We currently pad on both sides

yes this is needed for correctness for Megatron models (should be unrelated to the mbridge library). When I tested without it things were wrong

took the util function from verl where they do this: https://github.com/volcengine/verl/blob/e90f18c40aa639cd25092b78a5ff7e2d2508c088/verl/models/mcore/model_forward.py#L66

SumanthRH · 2025-09-04T07:01:30Z

skyrl-train/skyrl_train/workers/megatron/megatron_policy.py

+        self.actor_optimizer = actor_optimizer
+        self.policy_loss_fn = policy_loss_fn
+
+        # NOTE (erictang000): this is a potentially brittle way to disable the finalize_model_grads_func


finalize_model_grads_func is this function : https://github.com/NVIDIA/Megatron-LM/blob/02a1dd02fc72307c1c6327c0667c9e7cecf2eaff/megatron/core/distributed/finalize_model_grads.py#L376

Doing a quick search, it is called at the end of the forward + backward op, ex: https://github.com/NVIDIA/Megatron-LM/blob/02a1dd02fc72307c1c6327c0667c9e7cecf2eaff/megatron/core/pipeline_parallel/schedules.py#L640-L647

Looking at the function, it's performing

grad all reduce across dp replicas

grad all reduce across SP ranks for layernorm layers

grad all reduce for embedding layers for PP

optionally for moe models run an update_expert_token_bias function

scale gradient if num_tokens is provided

1, 2, and 3 should be unaffected if we do one finalize_ call at the end of the mini batch. As far as I understand, there should also not be any effect on memory usage here.

For 4., with a quick glance the function seems to be compatible again with making this finalize_ call at the end of the mini batch, but i haven't looked too closely. the local_token_per_expert attribute for the model is averaged across ranks. This value should acccumulate over micro batches, since I only see it being updated in the forward pass here: https://github.com/NVIDIA/Megatron-LM/blob/cca55ccdf408d56f97348f844a6aeb8f34d07672/megatron/core/transformer/moe/router.py#L448 , apart from the update on finalize_: https://github.com/NVIDIA/Megatron-LM/blob/cca55ccdf408d56f97348f844a6aeb8f34d07672/megatron/core/distributed/finalize_model_grads.py#L294-L295 .

There are actually some subtle ways this change could have an affect, for example, this apply_z_loss function for the router depends on the : https://github.com/NVIDIA/Megatron-LM/blob/cca55ccdf408d56f97348f844a6aeb8f34d07672/megatron/core/transformer/moe/router.py#L333-L366 . This doesn't seem relevant to us AFAIK because we set calculate_per_token_loss=False.

For 5., I again don't think it's relevant since we set calculate_per_token_loss=False

Btw, I see another comment on the utility of this finalize_ call after each micro batch here: https://github.com/NVIDIA/Megatron-LM/blob/cca55ccdf408d56f97348f844a6aeb8f34d07672/megatron/core/models/huggingface/module.py#L28-L34, but I haven't given this much thought.

Wdyt?

thanks for helping dig in more - maybe the conclusion from your last few points is that it might be better to try to refactor this to use the built in megatron forward_backward_func instead of fighting their abstraction to fit into our previous setup? Especially if there's places we are uncertain if it would affect correctness.

I'll try writing a version of the forward_backward_func that does the whole training step and make sure metrics match with the current implementation.

Makes sense to me, thanks!

SumanthRH · 2025-09-04T07:04:57Z

skyrl-train/tests/gpu/test_megatron_worker.py

+# NOTE (erictang000): in the normal training flow, the model weights get downloaded in the HF cache by the inference engine
+# we should figure out a way to download the model weights in the test environment (for now trying to do this by
+# ordering the tests so the inference engine is initialized first). Also will be an issue for disaggregated training


Could you explain the exact issue you faced here?

for the megatron worker it just does AutoConfig(Qwen/Qwen3-0.6B) and inside the mbridge library while loading weights it assumes that the model weights are also there at like /home/ray/.cache/huggingface/hub/models/Qwen/Qwen3-0.6B.

This is true if we are colocated and the inference engine is initialized first (since it'll download the weights and configs to the cache. But not true if we just run a worker test without inference engine init.

Hmm okay.

Ideally we do this download ourselves then before init, it's pretty simple:

from huggingface_hub import snapshot_download if rank == 0: snapshot_download(model_name) # will be no-op if already downloaded torch.distributed.barrier()

hmm does the rank==0 check work multi-node? this won't be an issue colocated but for disagg we might need to download to each machine? can leave a note to handle this later

*local rank == 0

oh sick i forgot we had that

skyrl-train/tests/gpu/test_megatron_worker.py

SumanthRH · 2025-09-04T17:43:23Z

skyrl-train/skyrl_train/workers/megatron/megatron_policy.py

+        output = forward_backward_func(
+            forward_step_func=forward_step,
+            data_iterator=batch_generator,
+            model=self.actor_module,
+            num_microbatches=1,
+            seq_length=seq_len,  # no use when input_shapes was set
+            micro_batch_size=micro_batch_size,  # no use when input_shapes was set
+            forward_only=True,
+        )
+


This seems incorrect, and from what I understand, we will end up with no pipelining for pipeline parallelism (no interleaving). num_microbatches=1

I actually assumed that the "microbatch" for pipelining is the same as the "micro batch" in our terminology. Can we modify the code to do that?

We currently have a synchronization point at the end of each forward_backward_micro_batch where we materialize loss tensors to CPU, so there's no overlap. For pipelining, it would probably be best to just feed a mini batch directly to megatron and use num_microbatches=mini_batch_size_per_gpu/micro_batch_size_per_gpu or so

resolved by above refactoring

SumanthRH · 2025-09-04T18:10:06Z

skyrl-train/pyproject.toml

+    "vllm==0.10.0",
+    "torch==2.7.1",


rebase on main?

…address comments

…tron

erictang000 · 2025-09-04T22:18:07Z

/gemini review

gemini-code-assist

Code Review

This pull request introduces initial support for Megatron-LM, enabling tensor and pipeline parallelism for training. The changes are extensive, adding new strategies, workers, configurations, and utilities. The code is well-structured, and the inclusion of new GPU tests is a great step towards ensuring correctness. My feedback focuses on a few key areas: completing the implementation of features like checkpointing, improving performance in weight synchronization and function compilation, and addressing a couple of correctness and robustness issues. Overall, this is a solid foundation for full Megatron support.

skyrl-train/skyrl_train/distributed/megatron/megatron_strategy.py

skyrl-train/skyrl_train/workers/megatron/megatron_worker.py

skyrl-train/examples/training_backends/megatron/run_megatron.sh

skyrl-train/skyrl_train/distributed/megatron/model_utils.py

skyrl-train/skyrl_train/distributed/megatron/optimizer.py

skyrl-train/skyrl_train/workers/megatron/megatron_worker.py

erictang000 · 2025-09-04T23:50:44Z

note: after refactoring I'm seeing slightly slower convergence with DP=4, and grad norm is almost 2x higher. Tried tuning all the ddp config knobs, but none seem to be relevant. DP=1 runs seem to converge slightly faster and also have lower grad norm.

SumanthRH

Approving, let's disable Megatron DDP for now and fix in a future PR

erictang000 · 2025-09-05T21:44:48Z

/gemini review

gemini-code-assist

Code Review

This pull request introduces initial support for Megatron-LM with Tensor Parallelism (TP) and Pipeline Parallelism (PP), which is a significant and valuable addition. The implementation is comprehensive, including a new distributed strategy, worker implementations, utility functions, configurations, and extensive tests. The code is well-structured and thoughtfully integrated into the existing framework.

My review focuses on correctness and performance opportunities. I've identified a potential correctness issue in a custom autograd function due to an in-place modification of a tensor saved for the backward pass, which could lead to incorrect gradients. Additionally, I've pointed out a performance bottleneck in the weight synchronization logic where collective communication operations are performed inside a loop, and I've suggested a more efficient batching approach.

The rest of the changes, including the refactoring of distributed strategies and configuration updates, look solid. The addition of thorough tests for the new Megatron functionality is particularly commendable and crucial for such a complex feature.

skyrl-train/skyrl_train/distributed/megatron/model_utils.py

skyrl-train/skyrl_train/workers/megatron/megatron_worker.py

Fix for problem described in: #223 (comment). Also adds a basic megatron only torch profiler for dev. Issue was not setting finalize_model_grads (which is [default None](https://github.com/NVIDIA/Megatron-LM/blob/ba97a7e282a8478a02d012bc9b9e45f3a6be216e/megatron/core/model_parallel_config.py#L95) for some reason...) Fixed loss and grad norm: <img width="372" height="653" alt="image" src="https://github.com/user-attachments/assets/cbe82a9c-9528-4c4d-8aa7-84a61a11816e" /> Fixed comparison to tp=4 training: <img width="373" height="655" alt="image" src="https://github.com/user-attachments/assets/c593c6b6-9056-4273-85e6-0dc958714662" />

Fix for problem described in: NovaSky-AI/SkyRL#223 (comment). Also adds a basic megatron only torch profiler for dev. Issue was not setting finalize_model_grads (which is [default None](https://github.com/NVIDIA/Megatron-LM/blob/ba97a7e282a8478a02d012bc9b9e45f3a6be216e/megatron/core/model_parallel_config.py#L95) for some reason...) Fixed loss and grad norm: <img width="372" height="653" alt="image" src="https://github.com/user-attachments/assets/cbe82a9c-9528-4c4d-8aa7-84a61a11816e" /> Fixed comparison to tp=4 training: <img width="373" height="655" alt="image" src="https://github.com/user-attachments/assets/c593c6b6-9056-4273-85e6-0dc958714662" />

# Overview This PR initializes Megatron support for GRPO with TP + PP (and DP) supported. We use [mbridge](https://github.com/ISEEKYAN/mbridge) to translate from huggingface to megatron, and plan to migrate to [Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) once the repo is more mature and we can safely upgrade some dependencies. ## What's not Included For a full set of limitations see the code here: https://github.com/NovaSky-AI/SkyRL/blob/1350d7be6a340587596c91cd1e3854c6900fe3ce/skyrl-train/skyrl_train/utils/utils.py#L117. Note: there is a known issue with `Qwen2.5-1.5B` and `Qwen2.5-3B` models, which don't specify `lm_head.weight` in the model.safetensors file, causing a weight loading issue with `mbridge` - issue here: ISEEKYAN/mbridge#10. ## Followups For a full task list needed for full Megatron support, see #203. ## Reproduction runs (gsm8k with Qwen3-0.6B) <img width="848" height="532" alt="image" src="https://github.com/user-attachments/assets/8e985969-ac4a-4391-a82e-eb77088eb941" /> ## TODOs - [x] Port GPU test to be runnable without locally saved training batch input - [x] fix entropy calculation - [x] dependencies - [x] add any needed doc-strings - [x] code-attribution if i missed anything

Fix for problem described in: NovaSky-AI#223 (comment). Also adds a basic megatron only torch profiler for dev. Issue was not setting finalize_model_grads (which is [default None](https://github.com/NVIDIA/Megatron-LM/blob/ba97a7e282a8478a02d012bc9b9e45f3a6be216e/megatron/core/model_parallel_config.py#L95) for some reason...) Fixed loss and grad norm: <img width="372" height="653" alt="image" src="https://github.com/user-attachments/assets/cbe82a9c-9528-4c4d-8aa7-84a61a11816e" /> Fixed comparison to tp=4 training: <img width="373" height="655" alt="image" src="https://github.com/user-attachments/assets/c593c6b6-9056-4273-85e6-0dc958714662" />

erictang000 added 7 commits August 26, 2025 23:06

tp forward working debugging pp loading

2f273c8

working forward and backward pp + tp!

c316213

Merge branch 'main' of https://github.com/erictang000/SkyRL into mega…

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…tron

weight syncing done debugging convergence

8e8baa6

lint

612f439

training is working! pending cleanup

ae5a0ff

cleanup

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Support Megatron training backend for MoE training #203

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erictang000 added 6 commits August 29, 2025 23:59

fix entropy and tests

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thanks gemini

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skyrl-train/pyproject.toml Outdated

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"vllm==0.10.0",

"torch==2.7.1",

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rebase on main?

refactor to fix pipelining and remove finalize_model_grads hacking + …

1bf09d8

…address comments

erictang000 added 3 commits September 4, 2025 21:44

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disable DP and some cleanup

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erictang000 merged commit ea97943 into NovaSky-AI:main Sep 5, 2025
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erictang000 mentioned this pull request Sep 6, 2025

[trainer][megatron] Fix for DP slower convergence #245

Merged


		[dependency-groups]

		# This is a default group so that we install these even with bare `uv sync`

	tensor.data = tensor.data.to("cpu", non_blocking=True)
	tensor = tensor.to("cpu", non_blocking=True)

	tensor.data = tensor.data.to(device_id, non_blocking=True)
	tensor = tensor.to(device_id, non_blocking=True)

		logger.info("Synced registries to ray actor")


		def _safe_exp_delta(delta: torch.Tensor, clip: float = 20.0, out_dtype=None) -> torch.Tensor:


		seq_len = sequences.shape[1]

		new_sequences, new_attention_mask, new_position_ids = remove_left_padding(

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Overview

What's not Included

Followups

Reproduction runs (gsm8k with Qwen3-0.6B)

TODOs

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