脚本修改

configs/inference/world_model_interaction.yaml

@@ -222,7 +222,7 @@ data:
     test:
       target: unifolm_wma.data.wma_data.WMAData
       params:
-        data_dir: '/mnt/ASC1637/unifolm-world-model-action/examples/world_model_interaction_prompts'
+        data_dir: '/home/qhy/unifolm-world-model-action/examples/world_model_interaction_prompts'
         video_length: ${model.params.wma_config.params.temporal_length}
         frame_stride: 2
         load_raw_resolution: True

scripts/evaluation/world_model_interaction.py

@@ -1,5 +1,7 @@
 import argparse, os, glob
 from contextlib import nullcontext
+import atexit
+from concurrent.futures import ThreadPoolExecutor
 import pandas as pd
 import random
 import torch
@@ -11,13 +13,15 @@ import einops
 import warnings
 import imageio
 
+from typing import Optional, List, Any
+
 from pytorch_lightning import seed_everything
 from omegaconf import OmegaConf
 from tqdm import tqdm
 from einops import rearrange, repeat
 from collections import OrderedDict
 from torch import nn
-from eval_utils import populate_queues, log_to_tensorboard
+from eval_utils import populate_queues
 from collections import deque
 from torch import Tensor
 from torch.utils.tensorboard import SummaryWriter
@@ -28,6 +32,80 @@ from unifolm_wma.utils.utils import instantiate_from_config
 import torch.nn.functional as F
 
 
+# ========== Async I/O utilities ==========
+_io_executor: Optional[ThreadPoolExecutor] = None
+_io_futures: List[Any] = []
+
+
+def _get_io_executor() -> ThreadPoolExecutor:
+    global _io_executor
+    if _io_executor is None:
+        _io_executor = ThreadPoolExecutor(max_workers=2)
+    return _io_executor
+
+
+def _flush_io():
+    """Wait for all pending async I/O to finish."""
+    global _io_futures
+    for fut in _io_futures:
+        try:
+            fut.result()
+        except Exception as e:
+            print(f">>> [async I/O] error: {e}")
+    _io_futures.clear()
+
+
+atexit.register(_flush_io)
+
+
+def _save_results_sync(video_cpu: Tensor, filename: str, fps: int) -> None:
+    """Synchronous save on CPU tensor (runs in background thread)."""
+    video = torch.clamp(video_cpu.float(), -1., 1.)
+    n = video.shape[0]
+    video = video.permute(2, 0, 1, 3, 4)
+    frame_grids = [
+        torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0)
+        for framesheet in video
+    ]
+    grid = torch.stack(frame_grids, dim=0)
+    grid = (grid + 1.0) / 2.0
+    grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
+    torchvision.io.write_video(filename,
+                               grid,
+                               fps=fps,
+                               video_codec='h264',
+                               options={'crf': '10'})
+
+
+def save_results_async(video: Tensor, filename: str, fps: int = 8) -> None:
+    """Submit video saving to background thread pool."""
+    video_cpu = video.detach().cpu()
+    fut = _get_io_executor().submit(_save_results_sync, video_cpu, filename, fps)
+    _io_futures.append(fut)
+
+
+def _log_to_tb_sync(video_cpu: Tensor, writer: SummaryWriter, tag: str, fps: int) -> None:
+    """Synchronous tensorboard logging on CPU tensor (runs in background thread)."""
+    video = video_cpu.float()
+    n = video.shape[0]
+    video = video.permute(2, 0, 1, 3, 4)
+    frame_grids = [
+        torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0)
+        for framesheet in video
+    ]
+    grid = torch.stack(frame_grids, dim=0)
+    grid = (grid + 1.0) / 2.0
+    grid = grid.unsqueeze(dim=0)
+    writer.add_video(tag, grid, fps=fps)
+
+
+def log_to_tensorboard_async(writer: SummaryWriter, video: Tensor, tag: str, fps: int = 10) -> None:
+    """Submit tensorboard logging to background thread pool."""
+    video_cpu = video.detach().cpu()
+    fut = _get_io_executor().submit(_log_to_tb_sync, video_cpu, writer, tag, fps)
+    _io_futures.append(fut)
+
+
 def patch_norm_bypass_autocast():
     """Monkey-patch GroupNorm and LayerNorm to bypass autocast's fp32 policy.
     This eliminates bf16->fp32->bf16 dtype conversions during UNet forward."""
@@ -185,17 +263,18 @@ def get_filelist(data_dir: str, postfixes: list[str]) -> list[str]:
     return file_list
 
 
-def load_model_checkpoint(model: nn.Module, ckpt: str) -> nn.Module:
+def load_model_checkpoint(model: nn.Module, ckpt: str, device: str = "cpu") -> nn.Module:
     """Load model weights from checkpoint file.
 
     Args:
         model (nn.Module): Model instance.
         ckpt (str): Path to the checkpoint file.
+        device (str): Target device for loaded tensors.
 
     Returns:
         nn.Module: Model with loaded weights.
     """
-    state_dict = torch.load(ckpt, map_location="cpu")
+    state_dict = torch.load(ckpt, map_location=device)
     if "state_dict" in list(state_dict.keys()):
         state_dict = state_dict["state_dict"]
         try:
@@ -610,36 +689,63 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
     # Load config
     config = OmegaConf.load(args.config)
-    config['model']['params']['wma_config']['params'][
-        'use_checkpoint'] = False
-    model = instantiate_from_config(config.model)
-    model.perframe_ae = args.perframe_ae
-    assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!"
-    model = load_model_checkpoint(model, args.ckpt_path)
-    model.eval()
-    print(f'>>> Load pre-trained model ...')
 
-    # Apply precision settings before moving to GPU
-    model = apply_precision_settings(model, args)
+    prepared_path = args.ckpt_path + ".prepared.pt"
+    if os.path.exists(prepared_path):
+        # ---- Fast path: load the fully-prepared model ----
+        print(f">>> Loading prepared model from {prepared_path} ...")
+        model = torch.load(prepared_path,
+                           map_location=f"cuda:{gpu_no}",
+                           weights_only=False)
+        model.eval()
 
-    # Compile hot ResBlocks for operator fusion
-    apply_torch_compile(model)
+        # Restore autocast attributes (weights already cast, just need contexts)
+        model.diffusion_autocast_dtype = torch.bfloat16 if args.diffusion_dtype == "bf16" else torch.bfloat16
+        model.projector_autocast_dtype = torch.bfloat16 if args.projector_mode == "autocast" else None
+        model.encoder_autocast_dtype = torch.bfloat16 if args.encoder_mode == "autocast" else None
 
-    # Export precision-converted checkpoint if requested
-    if args.export_precision_ckpt:
-        export_path = args.export_precision_ckpt
-        os.makedirs(os.path.dirname(export_path) or '.', exist_ok=True)
-        torch.save({"state_dict": model.state_dict()}, export_path)
-        print(f">>> Precision-converted checkpoint saved to: {export_path}")
-        return
+        # Compile hot ResBlocks for operator fusion
+        apply_torch_compile(model)
 
-    # Build unnomalizer
+        print(f">>> Prepared model loaded.")
+    else:
+        # ---- Normal path: construct + checkpoint + casting ----
+        config['model']['params']['wma_config']['params'][
+            'use_checkpoint'] = False
+        model = instantiate_from_config(config.model)
+        model.perframe_ae = args.perframe_ae
+        assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!"
+        model = load_model_checkpoint(model, args.ckpt_path,
+                                      device=f"cuda:{gpu_no}")
+        model.eval()
+        print(f'>>> Load pre-trained model ...')
+
+        # Apply precision settings before moving to GPU
+        model = apply_precision_settings(model, args)
+
+        # Export precision-converted checkpoint if requested
+        if args.export_precision_ckpt:
+            export_path = args.export_precision_ckpt
+            os.makedirs(os.path.dirname(export_path) or '.', exist_ok=True)
+            torch.save({"state_dict": model.state_dict()}, export_path)
+            print(f">>> Precision-converted checkpoint saved to: {export_path}")
+            return
+
+        model = model.cuda(gpu_no)
+
+        # Save prepared model for fast loading next time (before torch.compile)
+        print(f">>> Saving prepared model to {prepared_path} ...")
+        torch.save(model, prepared_path)
+        print(f">>> Prepared model saved ({os.path.getsize(prepared_path) / 1024**3:.1f} GB).")
+
+        # Compile hot ResBlocks for operator fusion (after save, compiled objects can't be pickled)
+        apply_torch_compile(model)
+
+    # Build normalizer (always needed, independent of model loading path)
     logging.info("***** Configing Data *****")
     data = instantiate_from_config(config.data)
     data.setup()
     print(">>> Dataset is successfully loaded ...")
-
-    model = model.cuda(gpu_no)
     device = get_device_from_parameters(model)
 
     # Run over data
@@ -817,28 +923,28 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                 # Save the imagen videos for decision-making
                 if pred_videos_0 is not None:
                     sample_tag = f"{args.dataset}-vid{sample['videoid']}-dm-fs-{fs}/itr-{itr}"
-                    log_to_tensorboard(writer,
-                                       pred_videos_0,
-                                       sample_tag,
-                                       fps=args.save_fps)
+                    log_to_tensorboard_async(writer,
+                                             pred_videos_0,
+                                             sample_tag,
+                                             fps=args.save_fps)
                 # Save videos environment changes via world-model interaction
                 sample_tag = f"{args.dataset}-vid{sample['videoid']}-wd-fs-{fs}/itr-{itr}"
-                log_to_tensorboard(writer,
-                                   pred_videos_1,
-                                   sample_tag,
-                                   fps=args.save_fps)
+                log_to_tensorboard_async(writer,
+                                         pred_videos_1,
+                                         sample_tag,
+                                         fps=args.save_fps)
 
                 # Save the imagen videos for decision-making
                 if pred_videos_0 is not None:
                     sample_video_file = f'{video_save_dir}/dm/{fs}/itr-{itr}.mp4'
-                    save_results(pred_videos_0.cpu(),
-                                 sample_video_file,
-                                 fps=args.save_fps)
+                    save_results_async(pred_videos_0,
+                                       sample_video_file,
+                                       fps=args.save_fps)
                 # Save videos environment changes via world-model interaction
                 sample_video_file = f'{video_save_dir}/wm/{fs}/itr-{itr}.mp4'
-                save_results(pred_videos_1.cpu(),
-                             sample_video_file,
-                             fps=args.save_fps)
+                save_results_async(pred_videos_1,
+                                   sample_video_file,
+                                   fps=args.save_fps)
 
                 print('>' * 24)
                 # Collect the result of world-model interactions
@@ -846,12 +952,15 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
             full_video = torch.cat(wm_video, dim=2)
             sample_tag = f"{args.dataset}-vid{sample['videoid']}-wd-fs-{fs}/full"
-            log_to_tensorboard(writer,
-                               full_video,
-                               sample_tag,
-                               fps=args.save_fps)
+            log_to_tensorboard_async(writer,
+                                     full_video,
+                                     sample_tag,
+                                     fps=args.save_fps)
             sample_full_video_file = f"{video_save_dir}/../{sample['videoid']}_full_fs{fs}.mp4"
-            save_results(full_video, sample_full_video_file, fps=args.save_fps)
+            save_results_async(full_video, sample_full_video_file, fps=args.save_fps)
+
+    # Wait for all async I/O to complete
+    _flush_io()
 
 
 def get_parser():

src/unifolm_wma/models/autoencoder.py

@@ -99,6 +99,8 @@ class AutoencoderKL(pl.LightningModule):
         print(f"Restored from {path}")
 
     def encode(self, x, **kwargs):
+        if getattr(self, '_channels_last', False):
+            x = x.to(memory_format=torch.channels_last)
 
         h = self.encoder(x)
         moments = self.quant_conv(h)
@@ -106,6 +108,8 @@ class AutoencoderKL(pl.LightningModule):
         return posterior
 
     def decode(self, z, **kwargs):
+        if getattr(self, '_channels_last', False):
+            z = z.to(memory_format=torch.channels_last)
         z = self.post_quant_conv(z)
         dec = self.decoder(z)
         return dec

src/unifolm_wma/models/ddpms.py

@@ -1074,10 +1074,10 @@ class LatentDiffusion(DDPM):
             encoder_posterior = self.first_stage_model.encode(x)
             results = self.get_first_stage_encoding(encoder_posterior).detach()
         else:  ## Consume less GPU memory but slower
+            bs = getattr(self, 'vae_encode_bs', 1)
             results = []
-            for index in range(x.shape[0]):
-                frame_batch = self.first_stage_model.encode(x[index:index +
-                                                              1, :, :, :])
+            for i in range(0, x.shape[0], bs):
+                frame_batch = self.first_stage_model.encode(x[i:i + bs])
                 frame_result = self.get_first_stage_encoding(
                     frame_batch).detach()
                 results.append(frame_result)
@@ -1109,14 +1109,14 @@ class LatentDiffusion(DDPM):
         vae_dtype = next(self.first_stage_model.parameters()).dtype
         z = z.to(dtype=vae_dtype)
 
+        z = 1. / self.scale_factor * z
         if not self.perframe_ae:
-            z = 1. / self.scale_factor * z
             results = self.first_stage_model.decode(z, **kwargs)
         else:
+            bs = getattr(self, 'vae_decode_bs', 1)
             results = []
-            for index in range(z.shape[0]):
-                frame_z = 1. / self.scale_factor * z[index:index + 1, :, :, :]
-                frame_result = self.first_stage_model.decode(frame_z, **kwargs)
+            for i in range(0, z.shape[0], bs):
+                frame_result = self.first_stage_model.decode(z[i:i + bs], **kwargs)
                 results.append(frame_result)
             results = torch.cat(results, dim=0)
 

src/unifolm_wma/models/samplers/.claude/settings.local.json

@@ -0,0 +1,7 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(python3:*)"
+    ]
+  }
+}

src/unifolm_wma/modules/networks/ae_modules.py

@@ -11,7 +11,7 @@ from unifolm_wma.utils.utils import instantiate_from_config
 
 def nonlinearity(x):
     # swish
-    return x * torch.sigmoid(x)
+    return torch.nn.functional.silu(x)
 
 
 def Normalize(in_channels, num_groups=32):

unitree_z1_dual_arm_cleanup_pencils/case1/run_world_model_interaction.sh

@@ -4,7 +4,7 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
 {
     time TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
         --seed 123 \
-        --ckpt_path ckpts/unifolm_wma_dual_mix_bf16.ckpt \
+        --ckpt_path ckpts/unifolm_wma_dual_mixbf16.ckpt \
         --config configs/inference/world_model_interaction.yaml \
         --savedir "${res_dir}/output" \
         --bs 1 --height 320 --width 512 \
@@ -21,6 +21,9 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
         --perframe_ae \
-        --vae_dtype bf16 \
+        --diffusion_dtype fp32 \
+        --projector_mode fp32 \
+        --encoder_mode fp32 \
+        --vae_dtype fp32 \
         --fast_policy_no_decode
 } 2>&1 | tee "${res_dir}/output.log"