remove flash-attn requirement && fix bug in inference and fast mode

pyproject.toml

@@ -131,8 +131,7 @@ wallx = [
     "peft==0.17.1",
     "scipy==1.15.3",
     "torchdiffeq==0.2.5",
-    "qwen_vl_utils==0.0.11",
-    "flash-attn==2.7.4.post1"
+    "qwen_vl_utils==0.0.11"
 ]
 pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]

src/lerobot/policies/wall_x/configuration_wall_x.py

@@ -53,13 +53,15 @@ class WallXConfig(PreTrainedConfig):
     # Pretrained model paths
     pretrained_name_or_path: str = "x-square-robot/wall-oss-flow"
 
+    # Tokenizer settings
+    action_tokenizer_path: str | None = "physical-intelligence/fast"
+
     # Action prediction mode: "diffusion" or "fast"
     prediction_mode: str = "diffusion"
 
-    # Tokenizer settings
-    use_fast_tokenizer: bool = False  # True: train FAST, False: train Flow
-    action_tokenizer_path: str | None = None  # Path to action tokenizer (for FAST mode)
-
+    # Attention Implementation, options: "eager", "flash_attention_2", "sdpa"
+    # NOTE: flash-attn==2.7.4.post1 is required for flash_attention_2 implementation
+    attn_implementation: str = "eager"
 
     # ==================== Optimizer Presets ====================
     optimizer_lr: float = 2e-5
@@ -87,11 +89,16 @@ class WallXConfig(PreTrainedConfig):
                 f"prediction_mode must be 'diffusion' or 'fast', got {self.prediction_mode}"
             )
 
-        # Sync prediction_mode with use_fast_tokenizer
-        if self.use_fast_tokenizer:
-            self.prediction_mode = "fast"
+        # Assign use_fast_tokenizer based on prediction_mode
+        if self.prediction_mode == "fast":
+            self.use_fast_tokenizer = True
+        elif self.prediction_mode == "diffusion":
+            self.use_fast_tokenizer = False
+            self.action_tokenizer_path = None # disable action tokenizer for diffusion mode
         else:
-            self.prediction_mode = "diffusion"
+            raise ValueError(
+                f"prediction_mode must be 'diffusion' or 'fast', got {self.prediction_mode}"
+            )
 
     def validate_features(self) -> None:
         """Validate and set up input/output features."""

src/lerobot/policies/wall_x/constant.py

@@ -18,8 +18,6 @@
 Wall-X Constants and Configuration Data.
 """
 
-from lerobot.utils.constants import OBS_STATE, OBS_IMAGES, ACTION
-
 CAMERA_NAME_MAPPING = {
     "face_view": "front view",
     "left_wrist_view": "left wrist view",

src/lerobot/policies/wall_x/modeling_wall_x.py

@@ -63,8 +63,22 @@ from lerobot.policies.utils import populate_queues
 from lerobot.policies.wall_x.configuration_wall_x import WallXConfig
 from lerobot.utils.constants import ACTION, OBS_STATE
 
-from lerobot.policies.wall_x.utils import *
-from lerobot.policies.wall_x.constant import *
+from lerobot.policies.wall_x.utils import (
+    replace_action_token,
+    preprocesser_call,
+    get_wallx_normal_text,
+    process_grounding_points,
+)
+from lerobot.policies.wall_x.constant import (
+    MODEL_TYPE,
+    TOKENIZER_MAX_LENGTH,
+    PRIORITY_ORDER,
+    GENERATE_SUBTASK_RATIO,
+    RESOLUTION,
+    MAX_PIXELS,
+    MIN_PIXELS,
+    IMAGE_FACTOR,
+)
 from lerobot.policies.wall_x.qwen_model.configuration_qwen2_5_vl import Qwen2_5_VLConfig
 from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
     Qwen2_5_VLForConditionalGeneration,
@@ -261,6 +275,7 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
         pretrained_name_or_path,
         config=None,
         action_tokenizer_path=None,
+        attn_implementation: str = 'eager',
         cache_dir: str | PathLike | None = None,
         force_download: bool = False,
         local_files_only: bool = False,
@@ -276,6 +291,7 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
             pretrained_model_path (str): Model directory path containing model.safetensors file
             config_path (str, optional): Configuration file path, if None will look for qwen25_config.json in pretrained_model_path
             action_tokenizer_path (str, optional): Action tokenizer path, if None will load from default config
+            attn_implementation (str, optional): Attention implementation, if None will load from default config
             **kwargs: Additional arguments
 
         Returns:
@@ -292,14 +308,18 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
                 strict=strict,
                 **kwargs,
             )
+        if attn_implementation is not None:
+            config._attn_implementation = attn_implementation
         processor = AutoProcessor.from_pretrained(pretrained_name_or_path, use_fast=True)
         if action_tokenizer_path is not None:
-            processor.action_processor = AutoProcessor.from_pretrained(
+            action_tokenizer = AutoProcessor.from_pretrained(
                 action_tokenizer_path, trust_remote_code=True
             )
-
+            processor.action_processor = action_tokenizer
+        else:
+            action_tokenizer = None
         # Initialize model with configuration and processor
-        model = cls(config, processor=processor, **kwargs)
+        model = cls(config, processor=processor, action_tokenizer=action_tokenizer, **kwargs)
 
         # Resize token embeddings to match processor tokenizer vocabulary size
         model.resize_token_embeddings(len(processor.tokenizer))
@@ -379,6 +399,7 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
         self.flow_loss_weight = flow_loss_weight
         self.use_fast_tokenizer = use_fast_tokenizer
         self.processor = processor
+        self.action_tokenizer = action_tokenizer
 
         # Define action token IDs
         self.define_action_token_id()
@@ -1279,7 +1300,7 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
                 if labels is not None:
                     labels = labels[:, split_pos + 3 :]
             else:
-                raise Warning(
+                raise ValueError(
                     "input_ids does not contain the generation prompt tokens <|im_start|>assistant"
                 )
 
@@ -1826,7 +1847,7 @@ class WallXPolicy(PreTrainedPolicy):
         self.config = config
 
         # Initialize the wall-x model
-        self.model = Qwen2_5_VLMoEForAction.from_pretrained(config.pretrained_name_or_path)
+        self.model = Qwen2_5_VLMoEForAction.from_pretrained(config.pretrained_name_or_path, attn_implementation=config.attn_implementation)
         self.model.to(config.device)
         self.model.to_bfloat16_for_selected_params()
 
@@ -1950,22 +1971,23 @@ class WallXPolicy(PreTrainedPolicy):
             ], dim=-1)
         
         # ==================== PROCESS ACTIONS ====================
-        action = batch[ACTION]  # (batch_size, chunk_size, action_dim)
-        if action.dim() == 2:
-            action = action.unsqueeze(1)
-        dof_mask = (~torch.isnan(action)).float()
-        action = action.nan_to_num(nan=0.0)
+        action = batch.get(ACTION, None)  # (batch_size, chunk_size, action_dim)
+        if action is not None:
+            if action.dim() == 2:
+                action = action.unsqueeze(1)
+            dof_mask = (~torch.isnan(action)).float()
+            action = action.nan_to_num(nan=0.0)
             
-        if action.shape[-1] != 20:
-            pad_size = 20 - action.shape[-1]
-            action = torch.cat([
-                action,
-                torch.zeros(action.shape[0], action.shape[1], pad_size, device=action.device)
-            ], dim=-1)
-            dof_mask = torch.cat([
-                dof_mask,
-                torch.zeros(dof_mask.shape[0], dof_mask.shape[1], pad_size, device=dof_mask.device)
-            ], dim=-1)
+            if action.shape[-1] != 20:
+                pad_size = 20 - action.shape[-1]
+                action = torch.cat([
+                    action,
+                    torch.zeros(action.shape[0], action.shape[1], pad_size, device=action.device)
+                ], dim=-1)
+                dof_mask = torch.cat([
+                    dof_mask,
+                    torch.zeros(dof_mask.shape[0], dof_mask.shape[1], pad_size, device=dof_mask.device)
+                ], dim=-1)
         
         # ==================== ACTION TOKEN REPLACEMENT ====================
         all_texts = replace_action_token(

src/lerobot/policies/wall_x/qwen_model/configuration_qwen2_5_vl.py

@@ -231,11 +231,6 @@ class Qwen2_5_VLConfig(PretrainedConfig):
         self.attention_moe = attention_moe
         self.mlp_moe = mlp_moe
 
-        # Validate the correctness of rotary position embeddings parameters
-        # BC: if there is a 'type' field, move it to 'rope_type'.
-        # and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations
-        # one can set it to "linear"/"dynamic" etc. to have scaled RoPE
-        # TODO: @raushan update config in the hub
         if self.rope_scaling is not None and "type" in self.rope_scaling:
             if self.rope_scaling["type"] == "mrope":
                 self.rope_scaling["type"] = "default"

src/lerobot/policies/wall_x/qwen_model/qwen2_5_vl_moe.py

@@ -1506,7 +1506,7 @@ class Qwen2_5_VLModel(Qwen2_5_VLPreTrainedModel):
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

tests/policies/wall_x/__init__.py

@@ -1,2 +0,0 @@
-# Wall-X policy tests
-

tests/policies/wall_x/test_wallx.py

@@ -31,7 +31,7 @@ from lerobot.policies.factory import make_policy_config  # noqa: E402
 from lerobot.policies.wall_x import (  # noqa: E402
     WallXConfig,
     WallXPolicy,
-    make_wall_x_pre_post_processors,  # noqa: E402
+    make_wall_x_pre_post_processors,
 )
 from lerobot.utils.random_utils import set_seed  # noqa: E402