From ed30c7d0a54db3b679e418e22a867c80c69a9617 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 11 Mar 2025 06:31:34 +0000 Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../common/policies/dexvla/policy_heads/modeling_scaledp.py | 3 +-- .../common/policies/dexvla/qwe2_vla/modeling_qwen2_vla.py | 6 ++++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/lerobot/common/policies/dexvla/policy_heads/modeling_scaledp.py b/lerobot/common/policies/dexvla/policy_heads/modeling_scaledp.py index ba5766fa..b09f5d24 100644 --- a/lerobot/common/policies/dexvla/policy_heads/modeling_scaledp.py +++ b/lerobot/common/policies/dexvla/policy_heads/modeling_scaledp.py @@ -13,6 +13,7 @@ from timm.models.vision_transformer import Mlp, use_fused_attn from torch.jit import Final from transformers import AutoModel from transformers.modeling_utils import PreTrainedModel + from .configuration_scaledp import ScaleDPPolicyConfig _logger = logging.getLogger(__name__) @@ -193,8 +194,6 @@ class FinalLayer(nn.Module): return x - - class ScaleDP(PreTrainedModel): """ Diffusion models with a Transformer backbone. diff --git a/lerobot/common/policies/dexvla/qwe2_vla/modeling_qwen2_vla.py b/lerobot/common/policies/dexvla/qwe2_vla/modeling_qwen2_vla.py index efbc3c3a..164cd4ab 100644 --- a/lerobot/common/policies/dexvla/qwe2_vla/modeling_qwen2_vla.py +++ b/lerobot/common/policies/dexvla/qwe2_vla/modeling_qwen2_vla.py @@ -48,7 +48,7 @@ from transformers.utils import ( replace_return_docstrings, ) -from lerobot.common.policies.dexvla.fusion_modules import ActionProjector,FiLM +from lerobot.common.policies.dexvla.fusion_modules import ActionProjector, FiLM from .configuration_qwen2_vla import Qwen2VLAConfig, Qwen2VLVisionConfig @@ -1376,7 +1376,9 @@ class Qwen2VLModel(Qwen2VLPreTrainedModel): (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) - if config.sliding_window is not None and (not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length): + if config.sliding_window is not None and ( + not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length + ): # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also # the check is needed to verify is current checkpoint was trained with sliding window or not sliding_attend_mask = torch.arange(target_length, device=device) <= (