Support Custom MaxText model (with vLLM engine) in RL rollouts.

src/MaxText/configs/base.yml

@@ -958,3 +958,9 @@ partial_rotary_factor: 1.0
 # Use tokamax library for gmm kernel implementation
 use_tokamax_gmm: false
 use_tokamax_splash: false
+
+# vLLM Adapter Configurations
+# Path to the HuggingFace-style config directory for the adapter (e.g. src/MaxText/integration/vllm/maxtext_vllm_adapter)
+vllm_hf_config_path: ""
+# JSON string containing additional configuration for the vLLM model (e.g. '{"maxtext_config": {...}}')
+vllm_additional_config: {}

src/MaxText/configs/types.py

@@ -1272,6 +1272,9 @@ class VLLM(BaseModel):
   kv_cache_buffer: int = Field(256, description="Buffer for KV cache.")
   hbm_utilization_vllm: float = Field(0.72, description="Target HBM utilization for vLLM.")
   swap_space_vllm_gb: int = Field(2, description="Swap space in GB for vLLM.")
+  vllm_additional_config: dict[str, Any] = Field(default_factory=dict,
+                                                 description="Additional vLLM config options.")
+  vllm_hf_config_path: str = Field("", description="Path to HuggingFace model config for MaxText model.")
 
 
 class GRPO(BaseModel):

src/MaxText/configs/vllm.yml

@@ -41,6 +41,7 @@ logical_axis_rules: [
                       ['activation_kv_batch_no_exp', ['data']],
                       ['activation_kv_head_dim', ['model']],
                       ['activation_vocab', ['model']],
+                      ['activation_embed', ['model']],
                       ['activation_exp', ['expert']],
                       ['decode_batch', ['data', 'expert']],
                       ['mlp', ['model']],
@@ -56,6 +57,13 @@ logical_axis_rules: [
                       ['cache_heads', ['model']],
                       ['exp', ['expert']],
                       ['paged_kv_heads', ['model']],
+                      ['autoregressive', ['model']],
+                      ['tensor', ['model']],
+                      ['tensor_transpose', ['model']],
+                      ['fsdp', ['data']],
+                      ['fsdp_transpose', ['data']],
+                      ['sequence', ['model']],
+                      ['context', ['model']],
                     ]
 data_sharding: [['data', 'model', 'expert']]
-input_data_sharding_logical_axes: ['activation_embed_and_logits_batch']
+input_data_sharding_logical_axes: ['activation_embed_and_logits_batch']

src/MaxText/integration/tunix/utils.py

@@ -147,7 +147,10 @@ def to_hf_hook_fns(self):
       return STANDALONE_VLLM_WEIGHT_MAPPING[self.model_name].to_hf_hook_fns()
 
     model_family = self.model_name.split("-")[0]
-    return VLLM_HOOK_FNS[model_family]()
+    if model_family in VLLM_HOOK_FNS:
+      return VLLM_HOOK_FNS[model_family]()
+    else:
+      return {}
 
   def lora_to_hf_mappings(self):
     if self.use_standalone_mappings:

src/MaxText/rl/evaluate_rl.py

@@ -121,13 +121,18 @@ def score_responses(tmvp_config, question, responses, answer):
 
     # Check exact correctness
     try:
-      if float(extracted_response.strip()) == float(answer.strip()):
+      # Remove ',' and '$' then convert to float
+      val_extracted = float(extracted_response.replace(',', '').replace('$', '').strip())
+      val_answer = float(answer.replace(',', '').replace('$', '').strip())
+
+      if val_extracted == val_answer:
         is_correct = True
 
       # Check partial correctness (within 10%)
-      ratio = float(extracted_response.strip()) / float(answer.strip())
-      if 0.9 <= ratio <= 1.1:
-        is_partially_correct = True
+      if val_answer != 0.0:
+        ratio = val_extracted / val_answer
+        if 0.9 <= ratio <= 1.1:
+          is_partially_correct = True
     except Exception as e:
       if tmvp_config.debug["rl"]:
         max_logging.log(f"Evaluation Exception: {e}")

src/MaxText/rl/train_rl.py

@@ -54,6 +54,7 @@
 from flax.linen import partitioning as nn_partitioning
 import grain
 from etils import epath
+import json
 
 from vllm.outputs import PoolingRequestOutput  # pylint: disable=unused-import
 import jax
@@ -100,9 +101,16 @@ def get_maxtext_model(config, devices=None):
   # Please ensure that you pass the full path ending in `/0/items` for load_parameters_path to train_rl.py i.e.,
   # load_parameters_path=/path/to/your/output/directory/0/items
   """
-  model, mesh = model_creation_utils.create_nnx_model(config, devices=devices)
+  model, mesh = model_creation_utils.create_nnx_model(config,
+                                                      devices=devices,
+                                                      model_mode="train")
   with mesh:
-    tunix_model = TunixMaxTextAdapter(base_model=model)
+    if "maxtext_config" in config.vllm_additional_config:
+      use_standalone_mappings = False
+    else:
+      use_standalone_mappings = True
+    tunix_model = TunixMaxTextAdapter(base_model=model,
+                                      use_standalone_mappings=use_standalone_mappings)
     tunix_model.config = None
   return tunix_model, mesh
 
@@ -331,6 +339,21 @@ def rl_train(trainer_config, sampler_config, trainer_devices, sampler_devices):
         set_profile_options=False,
     )
 
+  # Parse vllm_additional_config
+  rollout_additional_config = None
+  if trainer_config.vllm_additional_config:
+    if isinstance(trainer_config.vllm_additional_config, dict):
+      # It's already parsed into a dict
+      rollout_additional_config = trainer_config.vllm_additional_config
+    elif isinstance(trainer_config.vllm_additional_config, str):
+      # It's a string, so we need to parse it
+      try:
+        rollout_additional_config = json.loads(trainer_config.vllm_additional_config)
+      except json.JSONDecodeError as e:
+        raise ValueError(f"Failed to parse additional_config JSON: {e}") from e
+
+    max_logging.log(f"Parsed additional config: {rollout_additional_config}")
+
   # RL Cluster config
   # Note that we use vLLM as the rollout engine.
   # and we are using Tensor Parallelism for rollout
@@ -369,6 +392,9 @@ def rl_train(trainer_config, sampler_config, trainer_devices, sampler_devices):
           rollout_vllm_hbm_utilization=trainer_config.hbm_utilization_vllm,
           rollout_vllm_tpu_backend_type="jax",
           rollout_vllm_swap_space_size_gb=trainer_config.swap_space_vllm_gb,
+          rollout_vllm_hf_config_path=trainer_config.vllm_hf_config_path,
+          rollout_vllm_additional_config=rollout_additional_config,
+          rollout_vllm_init_with_random_weights=False
       ),
   )
   grpo_config = GrpoConfig(
@@ -397,14 +423,14 @@ def rl_train(trainer_config, sampler_config, trainer_devices, sampler_devices):
       max_logging.log(
           "enable_tunix_perf_metrics is True but tunix.perf modules are not available, skipping Tunix-managed metrics."
       )
-  with nn_partitioning.axis_rules(trainer_config.logical_axis_rules):
-    rl_cluster = rl_cluster_lib.RLCluster(
-        actor=actor_model,
-        reference=reference_model,
-        tokenizer=model_tokenizer,
-        cluster_config=cluster_config,
-        **rl_cluster_kwargs,
-    )
+
+  rl_cluster = rl_cluster_lib.RLCluster(
+      actor=actor_model,
+      reference=reference_model,
+      tokenizer=model_tokenizer,
+      cluster_config=cluster_config,
+      **rl_cluster_kwargs,
+  )
 
   # Create GRPO trainer
   max_logging.log("Setting up GRPO trainer...")

src/MaxText/utils/ckpt_conversion/utils/param_mapping.py

@@ -1479,5 +1479,5 @@ def transform_query_kernel(arr):
 VLLM_HOOK_FNS = {
     "qwen3": QWEN3_NNX_TO_VLLM_PARAM_HOOK_FN,
     "llama3.1": LLAMA31_NNX_TO_VLLM_PARAM_HOOK_FN,
-    "deepseek3-671b": DEEPSEEK_NNX_TO_VLLM_PARAM_HOOK_FN,
+    "deepseek3": DEEPSEEK_NNX_TO_VLLM_PARAM_HOOK_FN,
 }