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so101-pi0.5 / code /lerobot.patch

aarontung

Organize RoboColosseum fine-tuned policy

a9dca22 verified 3 days ago

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53.3 kB

diff --git a/pyproject.toml b/pyproject.toml
index cd0ac2f0..1bf2cc05 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -220,7 +220,11 @@ wallx = [
     "torchdiffeq>=0.2.4,<0.3.0",
     "lerobot[qwen-vl-utils-dep]",
 ]
-pi = ["lerobot[transformers-dep]", "lerobot[scipy-dep]"]
+pi = [
+    "lerobot[transformers-dep]",
+    "lerobot[scipy-dep]",
+    "sentencepiece>=0.2.1,<0.3",
+]
 molmoact2 = ["lerobot[transformers-dep]", "lerobot[peft-dep]", "lerobot[scipy-dep]"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "lerobot[accelerate-dep]"]
 multi_task_dit = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]"]
diff --git a/src/lerobot/policies/pi05/configuration_pi05.py b/src/lerobot/policies/pi05/configuration_pi05.py
index 282799a9..9d94cd6c 100644
--- a/src/lerobot/policies/pi05/configuration_pi05.py
+++ b/src/lerobot/policies/pi05/configuration_pi05.py
@@ -97,6 +97,8 @@ class PI05Config(PreTrainedConfig):
 
     # Training settings
     gradient_checkpointing: bool = False  # Enable gradient checkpointing for memory optimization
+    checkpoint_joint_stack: bool = True  # Retain the historical outer checkpoint by default
+    training_attention_backend: str = "eager"  # Optional fused SDPA for joint training attention
     compile_model: bool = False  # Whether to use torch.compile for model optimization
     compile_mode: str = "max-autotune"  # Torch compile mode
     device: str | None = None  # Device to use for the model (None = auto-detect)
diff --git a/src/lerobot/policies/pi05/modeling_pi05.py b/src/lerobot/policies/pi05/modeling_pi05.py
index 20293a1e..f6a02b9f 100644
--- a/src/lerobot/policies/pi05/modeling_pi05.py
+++ b/src/lerobot/policies/pi05/modeling_pi05.py
@@ -175,7 +175,9 @@ def _reduce_training_rtc_loss(
 
 
 # Define the complete layer computation function for gradient checkpointing
-def compute_layer_complete(inputs_embeds, attention_mask, position_ids, adarms_cond, layers, rotary_emb):
+def compute_layer_complete(
+    inputs_embeds, attention_mask, position_ids, adarms_cond, layers, rotary_emb, attention_backend="eager"
+):
     query_states = []
     key_states = []
     value_states = []
@@ -211,14 +213,32 @@ def compute_layer_complete(inputs_embeds, attention_mask, position_ids, adarms_c
     paligemma_layer = layers[0]
     scaling = paligemma_layer.self_attn.scaling
     # Attention computation
-    att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma_layer.self_attn,
-        query_states,
-        key_states,
-        value_states,
-        attention_mask,
-        scaling,
-    )
+    if attention_backend == "sdpa":
+        att_output = (
+            F.scaled_dot_product_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_mask=attention_mask,
+                dropout_p=0.0,
+                is_causal=False,
+                scale=scaling,
+                enable_gqa=query_states.shape[1] != key_states.shape[1],
+            )
+            .transpose(1, 2)
+            .contiguous()
+        )
+    elif attention_backend == "eager":
+        att_output, _ = modeling_gemma.eager_attention_forward(
+            paligemma_layer.self_attn,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            scaling,
+        )
+    else:
+        raise ValueError(f"Unknown training attention backend: {attention_backend}")
     # Get head_dim from the current layer, not from the model
     head_dim = paligemma_layer.self_attn.head_dim
     att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
@@ -476,6 +496,7 @@ class PaliGemmaWithExpertModel(
                         preserve_rng_state=False,
                         layers=layers,
                         rotary_emb=rotary_emb,
+                        attention_backend=getattr(self, "training_attention_backend", "eager"),
                     )
                 else:
                     inputs_embeds = compute_layer_complete(
@@ -485,6 +506,7 @@ class PaliGemmaWithExpertModel(
                         adarms_cond,
                         layers=layers,
                         rotary_emb=rotary_emb,
+                        attention_backend=getattr(self, "training_attention_backend", "eager"),
                     )
 
             # final norm
@@ -557,6 +579,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
 
         # Initialize gradient checkpointing flag
+        self.paligemma_with_expert.training_attention_backend = config.training_attention_backend
         self.gradient_checkpointing_enabled = False
 
         # Compile model if requested
@@ -751,9 +774,14 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             )
             return suffix_out
 
-        suffix_out = self._apply_checkpoint(
-            forward_func, prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond
-        )
+        if self.config.checkpoint_joint_stack:
+            suffix_out = self._apply_checkpoint(
+                forward_func, prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond
+            )
+        else:
+            # Individual decoder layers retain their checkpoints. Avoid checkpointing
+            # the complete stack again, which would repeat its forward a second time.
+            suffix_out = forward_func(prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond)
 
         suffix_out = suffix_out[:, -self.config.chunk_size :]
         suffix_out = suffix_out.to(dtype=torch.float32)
@@ -1228,8 +1256,11 @@ class PI05Policy(PreTrainedPolicy):
             images.append(img)
             bsize = img.shape[0]
             pad_key = f"{key}_is_pad"
-            if is_video and pad_key in batch:
-                mask = ~batch[pad_key].bool()
+            if pad_key in batch:
+                mask = ~batch[pad_key].to(device=device, dtype=torch.bool)
+                expected_shape = img.shape[:2] if is_video else (bsize,)
+                if mask.shape != expected_shape:
+                    raise ValueError(f"{pad_key} has shape {mask.shape}, expected {expected_shape}")
             else:
                 mask_shape = img.shape[:2] if is_video else (bsize,)
                 mask = torch.ones(mask_shape, dtype=torch.bool, device=device)
@@ -1388,6 +1419,12 @@ class PI05Policy(PreTrainedPolicy):
         original_action_dim = self.config.output_features[ACTION].shape[0]
         losses = losses[:, :, :original_action_dim]
 
+        if "action_is_pad" in batch:
+            action_padding = batch["action_is_pad"].to(device=losses.device, dtype=torch.bool)
+            if action_padding.shape != losses.shape[:2]:
+                raise ValueError("action_is_pad must match the batch and action-horizon dimensions")
+            prefix_mask = action_padding if prefix_mask is None else prefix_mask | action_padding
+
         if prefix_mask is None:
             loss_per_dim = losses.mean(dim=(0, 1))
         else:
diff --git a/src/lerobot/policies/pi05/processor_pi05.py b/src/lerobot/policies/pi05/processor_pi05.py
index fefe6428..a93a157b 100644
--- a/src/lerobot/policies/pi05/processor_pi05.py
+++ b/src/lerobot/policies/pi05/processor_pi05.py
@@ -14,8 +14,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import shutil
 from copy import deepcopy
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Any
 
 import numpy as np
@@ -39,6 +41,42 @@ from lerobot.utils.constants import OBS_STATE
 from .configuration_pi05 import PI05Config
 
 
+@ProcessorStepRegistry.register(name="pi05_sentencepiece_tokenizer")
+@dataclass
+class Pi05SentencePieceTokenizerProcessorStep(TokenizerProcessorStep):
+    """OpenPI's native public PaliGemma tokenizer, with serializable local assets."""
+
+    def __post_init__(self):
+        from lerobot.utils.import_utils import require_package
+
+        require_package("sentencepiece", extra="pi")
+        import sentencepiece
+
+        if not self.tokenizer_name or self.padding_side != "right" or self.padding != "max_length":
+            raise ValueError("OpenPI tokenization requires a local model and right max_length padding")
+        self.input_tokenizer = sentencepiece.SentencePieceProcessor(model_file=self.tokenizer_name)
+
+    def _tokenize_text(self, text):
+        texts = [text] if isinstance(text, str) else text
+        ids = torch.zeros(len(texts), self.max_length, dtype=torch.long)
+        mask = torch.zeros_like(ids, dtype=torch.bool)
+        for i, prompt in enumerate(texts):
+            tokens = self.input_tokenizer.encode(prompt, add_bos=True)
+            if len(tokens) > self.max_length and not self.truncation:
+                raise ValueError("Prompt exceeds maximum token length")
+            tokens = tokens[: self.max_length]
+            ids[i, : len(tokens)] = torch.tensor(tokens)
+            mask[i, : len(tokens)] = True
+        return {"input_ids": ids, "attention_mask": mask}
+
+    def save_artifacts(self, save_directory):
+        target = Path(save_directory) / "tokenizer/tokenizer.model"
+        target.parent.mkdir(parents=True, exist_ok=True)
+        if Path(self.tokenizer_name).resolve() != target.resolve():
+            shutil.copy2(self.tokenizer_name, target)
+        return {"tokenizer_name": "tokenizer/tokenizer.model"}
+
+
 @ProcessorStepRegistry.register(name="pi05_prepare_state_tokenizer_processor_step")
 @dataclass
 class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
@@ -150,7 +188,11 @@ def make_pi05_pre_post_processors(
             max_state_dim=config.max_state_dim,
             include_state_in_prompt=not config.use_proprioceptive_memory,
         ),
-        TokenizerProcessorStep(
+        (
+            Pi05SentencePieceTokenizerProcessorStep
+            if config.text_tokenizer_name.endswith(".model")
+            else TokenizerProcessorStep
+        )(
             tokenizer_name=config.text_tokenizer_name,
             max_length=config.tokenizer_max_length,
             padding_side="right",
diff --git a/uv.lock b/uv.lock
index b33bf4b7..ada621ac 100644
--- a/uv.lock
+++ b/uv.lock
@@ -437,10 +437,10 @@ name = "bddl"
 version = "1.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupytext" },
-    { name = "networkx" },
-    { name = "numpy" },
-    { name = "pytest" },
+    { name = "jupytext", marker = "sys_platform == 'linux'" },
+    { name = "networkx", marker = "sys_platform == 'linux'" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "pytest", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5c/37/0211f82891a9f14efcfd2b2096f8d9e4351398ad637fdd1ee59cfc580b0e/bddl-1.0.1.tar.gz", hash = "sha256:1fa4e6e5050b93888ff6fd8455c39bfb29d3864ce06b4c37c0f781f513a2ae26", size = 164809, upload-time = "2022-03-08T01:48:23.564Z" }
 
@@ -1142,7 +1142,7 @@ name = "cuda-bindings"
 version = "12.9.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder" },
+    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/32/45/557d4ed1fa54f0c7db8aee083229f624990d69f7d00f55477eed5c7e169a/cuda_bindings-12.9.7-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0666d3c082ef8f4b2d670950589373550e9f3bf564d635dd883f24a0b40402ff", size = 7071026, upload-time = "2026-05-27T18:44:13.356Z" },
@@ -1175,37 +1175,37 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime-cu12" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "sys_platform == 'linux'" },
 ]
 cufft = [
-    { name = "nvidia-cufft-cu12" },
+    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux'" },
 ]
 cufile = [
-    { name = "nvidia-cufile-cu12" },
+    { name = "nvidia-cufile-cu12", marker = "sys_platform == 'linux'" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti-cu12" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "sys_platform == 'linux'" },
 ]
 curand = [
-    { name = "nvidia-curand-cu12" },
+    { name = "nvidia-curand-cu12", marker = "sys_platform == 'linux'" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver-cu12" },
+    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux'" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse-cu12" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc-cu12" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx-cu12" },
+    { name = "nvidia-nvtx-cu12", marker = "sys_platform == 'linux'" },
 ]
 
 [[package]]
@@ -1268,7 +1268,7 @@ name = "decord"
 version = "0.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", marker = "(platform_machine != 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/11/79/936af42edf90a7bd4e41a6cac89c913d4b47fa48a26b042d5129a9242ee3/decord-0.6.0-py3-none-manylinux2010_x86_64.whl", hash = "sha256:51997f20be8958e23b7c4061ba45d0efcd86bffd5fe81c695d0befee0d442976", size = 13602299, upload-time = "2021-06-14T21:30:55.486Z" },
@@ -1418,10 +1418,10 @@ resolution-markers = [
     "python_full_version == '3.14.*' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py" },
-    { name = "attrs" },
-    { name = "numpy" },
-    { name = "wrapt" },
+    { name = "absl-py", marker = "python_full_version >= '3.14'" },
+    { name = "attrs", marker = "python_full_version >= '3.14'" },
+    { name = "numpy", marker = "python_full_version >= '3.14'" },
+    { name = "wrapt", marker = "python_full_version >= '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a6/83/ce29720ccf934c6cfa9b9c95ebbe96558386e66886626066632b5e44afed/dm_tree-0.1.9.tar.gz", hash = "sha256:a4c7db3d3935a5a2d5e4b383fc26c6b0cd6f78c6d4605d3e7b518800ecd5342b", size = 35623, upload-time = "2025-01-30T20:45:37.13Z" }
 wheels = [
@@ -1459,10 +1459,10 @@ resolution-markers = [
     "python_full_version < '3.13' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py" },
-    { name = "attrs" },
-    { name = "numpy" },
-    { name = "wrapt" },
+    { name = "absl-py", marker = "python_full_version < '3.14'" },
+    { name = "attrs", marker = "python_full_version < '3.14'" },
+    { name = "numpy", marker = "python_full_version < '3.14'" },
+    { name = "wrapt", marker = "python_full_version < '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/66/a3ec619d22b6baffa5ab853e8dc6ec9d0c837127948af59bb15b988d7312/dm_tree-0.1.10.tar.gz", hash = "sha256:22f37b599e01cc3402a17f79c257a802aebd8d326de05b54657650845956208a", size = 35748, upload-time = "2026-03-31T17:35:39.03Z" }
 wheels = [
@@ -2062,7 +2062,7 @@ name = "h5py"
 version = "3.16.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" }
 wheels = [
@@ -2106,23 +2106,23 @@ name = "hf-libero"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "bddl" },
-    { name = "cloudpickle" },
-    { name = "easydict" },
-    { name = "einops" },
-    { name = "future" },
-    { name = "gymnasium" },
-    { name = "hf-egl-probe" },
-    { name = "hydra-core" },
-    { name = "matplotlib" },
-    { name = "mujoco" },
-    { name = "numpy" },
-    { name = "opencv-python" },
-    { name = "robomimic" },
-    { name = "robosuite" },
-    { name = "thop" },
-    { name = "transformers" },
-    { name = "wandb" },
+    { name = "bddl", marker = "sys_platform == 'linux'" },
+    { name = "cloudpickle", marker = "sys_platform == 'linux'" },
+    { name = "easydict", marker = "sys_platform == 'linux'" },
+    { name = "einops", marker = "sys_platform == 'linux'" },
+    { name = "future", marker = "sys_platform == 'linux'" },
+    { name = "gymnasium", marker = "sys_platform == 'linux'" },
+    { name = "hf-egl-probe", marker = "sys_platform == 'linux'" },
+    { name = "hydra-core", marker = "sys_platform == 'linux'" },
+    { name = "matplotlib", marker = "sys_platform == 'linux'" },
+    { name = "mujoco", marker = "sys_platform == 'linux'" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "opencv-python", marker = "sys_platform == 'linux'" },
+    { name = "robomimic", marker = "sys_platform == 'linux'" },
+    { name = "robosuite", marker = "sys_platform == 'linux'" },
+    { name = "thop", marker = "sys_platform == 'linux'" },
+    { name = "transformers", marker = "sys_platform == 'linux'" },
+    { name = "wandb", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/af/aa/4e9eb8715e0bff9cb6553db563a35d253393097d446f82bd53575e8b253d/hf_libero-0.1.4.tar.gz", hash = "sha256:c058d67ad5a2b589529c14d614282ef4cca3a7763dafa134f58a6c9039657e34", size = 2961319, upload-time = "2026-06-10T09:56:13.994Z" }
 wheels = [
@@ -2273,9 +2273,9 @@ name = "hydra-core"
 version = "1.3.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "antlr4-python3-runtime" },
-    { name = "omegaconf" },
-    { name = "packaging" },
+    { name = "antlr4-python3-runtime", marker = "sys_platform == 'linux'" },
+    { name = "omegaconf", marker = "sys_platform == 'linux'" },
+    { name = "packaging", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/78/03/adfa2bf3a9eef568a85f2e7cf18ce1938e7aa0b5257c432e5eb48f89960a/hydra_core-1.3.6.tar.gz", hash = "sha256:8c4c215b6cfba68e9a4e76f565bd7399fc2a86b93cfe8cb7328b046834dfbee5", size = 3272413, upload-time = "2026-08-29T14:36:08.092Z" }
 wheels = [
@@ -2827,11 +2827,11 @@ name = "jupytext"
 version = "1.19.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py" },
-    { name = "mdit-py-plugins" },
-    { name = "nbformat" },
-    { name = "packaging" },
-    { name = "pyyaml" },
+    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
+    { name = "mdit-py-plugins", marker = "sys_platform == 'linux'" },
+    { name = "nbformat", marker = "sys_platform == 'linux'" },
+    { name = "packaging", marker = "sys_platform == 'linux'" },
+    { name = "pyyaml", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/473f8ebb101553fb2ea6ab1d34324d6677844c968947ac050c759d539f2c/jupytext-1.19.5.tar.gz", hash = "sha256:605026446d605aa54fd7f7fc69df6ae51c7a46053d4cebf05afdc64d66de3df0", size = 4600916, upload-time = "2026-07-21T22:00:29.198Z" }
 wheels = [
@@ -3124,6 +3124,7 @@ all = [
     { name = "ruff" },
     { name = "scikit-image" },
     { name = "scipy" },
+    { name = "sentencepiece" },
     { name = "teleop" },
     { name = "timm" },
     { name = "torchcodec", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'arm64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'win32'" },
@@ -3391,6 +3392,7 @@ phone = [
 ]
 pi = [
     { name = "scipy" },
+    { name = "sentencepiece" },
     { name = "transformers" },
 ]
 placo-dep = [
@@ -3731,6 +3733,7 @@ requires-dist = [
     { name = "scikit-image", marker = "extra == 'video-benchmark'", specifier = ">=0.23.2,<0.26.0" },
     { name = "scipy", marker = "extra == 'all'", specifier = ">=1.14.0,<2.0.0" },
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