Datasets:

lupele
/

StatePlay-Reward-2280

# Prompt B: 训练代码修改提示词

你是下一轮本地代码协作 agent。请在 `C:\Users\32297\Downloads\StatePlay` 仓库中实现两阶段训练代码。必须先阅读 Prompt A 和本仓库代码。不要处理 evaluation、probe、CS2、distillation、VLM judge 或指标计算。本轮只处理 tokenizer 训练、latent-state world model 训练、loss、config、checkpoint 和 sanity tests。

## 0. 当前训练路径定位

| 路径 | 当前模块/函数 | 当前行为 | 需要修改 |
|---|---|---|---|
| `training/train.py` | `FlowMatchSFTLossWithStateWeighted` | video flow matching + explicit state SmoothL1；强制要求 `inputs["state"]` | 改成按 `train_mode` 分派 loss；latent modes 不要求 state |
| `training/train.py` | `StatePlayTrainingModule.__init__` | 构造 `StatePlayModel(num_buttons=profile.num_buttons, state_dim=5, **WAN_MODEL_KWARGS)` | 传入 latent-state/tokenizer config，加载 tokenizer checkpoint，控制 requires_grad |
| `training/train.py` | `get_pipeline_inputs` | 强制要求 dataset item 有 `state` | 仅 explicit baseline 强制 state；latent/tokenizer 模式可忽略 state |
| `training/train.py` | `build_parser` | 只有 `state_loss_weight/video_loss_weight` 等旧参数 | 添加 train_mode 和 latent/tokenizer/loss config |
| `training/train.py` | `build_dataset` | 总是包装 `StateAugmentedDataset` | 可以继续读取 state，但 latent/tokenizer 模式不能依赖它；最好允许 state 缺失 |
| `training/runner_with_state.py` | optimizer | `AdamW(model.trainable_modules())` | 最小改动: 通过 requires_grad 控制 Stage 1/2 参数范围 |
| `diffsynth/diffusion/logger.py` | checkpoint | 保存 trainable state dict 到 `step-N.safetensors` | Stage 1 还要保存 `transition_tokenizer.pt` 或 tokenizer-only safetensors |
| `diffsynth/pipelines/stateplay.py` | `WanVideoUnit_InputVideoEmbedder` | training mode 返回 clean `input_latents` | Stage 1/2 从这里取 clean visual tokens |
| `diffsynth/pipelines/stateplay.py` | `WanVideoUnit_ImageEmbedderFused` | 用 first frame VAE latent 覆盖 `latents[:, :, 0:1]`，返回 `first_frame_latents` | Stage 2 继续保持 pinned first frame |

## 1. 新增 train_mode
至少支持:
```text
train_mode = "transition_tokenizer"
train_mode = "latent_state_world_model"
train_mode = "explicit_state_baseline"
train_mode = "latent_state_joint_finetune"  # 仅预留，不作为默认重点
```
含义:
1. `transition_tokenizer`
   - 只训练 `GameTransitionTokenizer` 和 tokenizer reconstruction head。
   - 不训练 latent initializer/transition/predictor/visual generator。
   - 不使用真实 state label。
2. `latent_state_world_model`
   - 冻结 `GameTransitionTokenizer`。
   - 训练 `LatentStateInitializer`、`LatentStateTransition`、`LatentTransitionPredictor`、`LatentStateToVisualAttention`，以及原 visual generator 中需要训练的参数。
   - 不使用真实 state label。
   - 不计算 SmoothL1 state loss。
3. `explicit_state_baseline`
   - 保持原 StatePlay 行为。
   - 当前 `FlowMatchSFTLossWithStateWeighted` 的逻辑基本属于这个模式。
4. `latent_state_joint_finetune`
   - 只预留接口。
   - 若启用: `L_video + 0.1 L_trans + 0.01 L_tok`。
   - 默认不建议启用，因为 tokenizer target 可能漂移。
## 2. Config/argparse 字段
在 `training/train.py::build_parser()` 增加:
```python
p.add_argument("--train_mode", type=str, default="latent_state_world_model",
               choices=("transition_tokenizer", "latent_state_world_model",
                        "explicit_state_baseline", "latent_state_joint_finetune"))

p.add_argument("--use_latent_state", type=_bool_arg, default=True)
p.add_argument("--use_explicit_state", type=_bool_arg, default=False)

p.add_argument("--latent_state_num_tokens", type=int, default=4)
p.add_argument("--latent_state_dim", type=int, default=256)
p.add_argument("--latent_state_initializer_layers", type=int, default=2)
p.add_argument("--latent_state_transition_layers", type=int, default=2)
p.add_argument("--latent_state_num_heads", type=int, default=8)
p.add_argument("--latent_state_mlp_ratio", type=float, default=4.0)
p.add_argument("--latent_state_dropout", type=float, default=0.0)
p.add_argument("--latent_state_max_frames", type=int, default=64)
p.add_argument("--latent_state_bidirectional", type=_bool_arg, default=False)
p.add_argument("--use_transition_tokenizer", type=_bool_arg, default=True)
p.add_argument("--transition_tokenizer_num_tokens", type=int, default=4)
p.add_argument("--transition_tokenizer_dim", type=int, default=256)
p.add_argument("--transition_tokenizer_layers", type=int, default=2)
p.add_argument("--transition_tokenizer_heads", type=int, default=8)
p.add_argument("--transition_tokenizer_mlp_ratio", type=float, default=4.0)
p.add_argument("--transition_tokenizer_dropout", type=float, default=0.0)
p.add_argument("--transition_tokenizer_fsq_levels", type=str, default="8,8,8")
p.add_argument("--transition_vocab_size", type=int, default=512)
p.add_argument("--transition_tokenizer_ckpt", type=str, default=None)

p.add_argument("--freeze_transition_tokenizer", type=_bool_arg, default=False)
p.add_argument("--use_transition_loss", type=_bool_arg, default=True)

p.add_argument("--transition_loss_weight", type=float, default=0.1)
p.add_argument("--tokenizer_loss_weight", type=float, default=1.0)
# Keep existing --video_loss_weight; keep --state_loss_weight for explicit baseline.
# In latent modes force state loss behavior to zero internally even if CLI passes another value.
```
Mode-derived defaults:
- `transition_tokenizer`:
  - `use_latent_state=false`
  - `use_explicit_state=false`
  - `use_transition_tokenizer=true`
  - `freeze_transition_tokenizer=false`
  - `tokenizer_loss_weight=1.0`
  - `state_loss_weight=0.0`
- `latent_state_world_model`:
  - `use_latent_state=true`
  - `use_explicit_state=false`
  - `use_transition_tokenizer=true`
  - `freeze_transition_tokenizer=true`
  - `use_transition_loss=true`
  - `video_loss_weight=1.0`
  - `transition_loss_weight=0.1`
  - `tokenizer_loss_weight=0.0`
  - `state_loss_weight=0.0`
- `explicit_state_baseline`:
  - `use_latent_state=false`
  - `use_explicit_state=true`
  - `use_transition_tokenizer=false`
  - preserve old `state_loss_weight` default and old loss.
If this default would unexpectedly change `scripts/train.sh`, update `scripts/train.sh` to pass `--train_mode explicit_state_baseline` for old baseline runs, and add comments/examples for the two new modes.
## 3. Model construction and parameter freezing
In `StatePlayTrainingModule.__init__`:
1. Store all new config fields on `self`。
2. Instantiate `StatePlayModel` with the flags and latent/tokenizer config from args。
3. Continue using `profile.num_buttons` rather than hardcoding K。
4. Load `resume_from_ckpt` with `strict=False` as before。
5. If `transition_tokenizer_ckpt` is provided:
   - load into `custom_dit.transition_tokenizer` only。
   - accept either raw tokenizer state dict or a checkpoint with key prefix `transition_tokenizer.`。
6. Apply trainability after model construction and checkpoint loading:
```python
def _set_train_mode_requires_grad(self):
    self.pipe.requires_grad_(False)
    if self.train_mode == "transition_tokenizer":
        self.pipe.dit.transition_tokenizer.train()
        self.pipe.dit.transition_tokenizer.requires_grad_(True)
    elif self.train_mode == "latent_state_world_model":
        self.pipe.dit.train()
        # tokenizer frozen
        self.pipe.dit.transition_tokenizer.eval()
        self.pipe.dit.transition_tokenizer.requires_grad_(False)
        # train latent state modules
        self.pipe.dit.latent_state_rollout.requires_grad_(True)
        self.pipe.dit.latent_transition_predictor.requires_grad_(True)
        # train latent-state-to-visual attention params inside blocks
        # train existing visual generator params according to trainable_filter or default policy
    elif self.train_mode == "explicit_state_baseline":
        # preserve old trainable_models/trainable_filter behavior
```
Important:

- The existing runner uses `model.trainable_modules()`; no optimizer group change is required for first version.
- If `trainable_filter` is passed, apply it after mode-specific freezing, but do not accidentally unfreeze tokenizer in Stage 2.
- In Stage 1, VAE/text encoder/base DiT/latent modules must receive no gradients.

## 4. Dataset/input changes

In `get_pipeline_inputs`:

- Always include:
  - `input_video`
  - `keyboard_action`
  - height/width/num_frames
  - scheduler and gradient checkpoint fields
- Only require `data["state"]` when `train_mode == "explicit_state_baseline"`。
- For latent modes:
  - if `state` exists, you may pass it through for logging compatibility, but no loss/model path may use it。
  - if `state` missing, do not raise。

In `build_dataset`:

- Current code always wraps with `StateAugmentedDataset` and therefore requires state columns in the parquet.
- For first version, either:
  1. keep wrapping when state exists, but add `--require_state_labels` defaulting true only for explicit baseline; or
  2. wrap only when `train_mode=="explicit_state_baseline"` or when `--load_state_labels true`。
- The important rule: Stage 1 and Stage 2 must be runnable without state labels.

## 5. Stage 1: train_mode = transition_tokenizer

Objective:

```text
min_phi L_tok
total_loss = tokenizer_loss_weight * L_tok
```

Input path:

```text
video -> VAE encoder -> clean input_latents
input_latents -> frozen DiT patch_embedding -> clean_visual_tokens
clean_visual_tokens -> GameTransitionTokenizer -> z_indices, z_embs, L_tok
```

Implementation plan:

1. Add loss function, e.g. `TransitionTokenizerLoss(pipe, tokenizer_loss_weight, **inputs)`。
2. Use existing pipeline units so `inputs_shared["input_latents"]` is available. This is returned by `WanVideoUnit_InputVideoEmbedder` in training mode.
3. Do not call video denoising model forward.
4. Extract clean visual tokens:

```python
with torch.no_grad():
    clean_visual_tokens, shape_info = pipe.dit.extract_clean_visual_tokens(inputs["input_latents"])
```

5. Call:

```python
tok_out = pipe.dit.transition_tokenizer(
    clean_visual_tokens,
    latent_frames=shape_info["f"],
)
loss_tok = tok_out["loss_tok"]
total_loss = tokenizer_loss_weight * loss_tok
```

6. Return scalar total loss. Also maintain a lightweight loss dict for rank0 logging:

```text
tokenizer_loss, total_loss, f, tokens_per_frame
```

Stage 1 requirements:

- `action` can be read but not used.
- `state` can be read but not used.
- VAE/base video model frozen.
- Only tokenizer parameters have gradients.
- Save tokenizer checkpoint as `transition_tokenizer.pt` under `output_path` at training end and preferably at save steps.

Checkpoint save requirement:

- Implement helper in `StatePlayTrainingModule`:

```python
def save_transition_tokenizer(self, path):
    torch.save(self.pipe.dit.transition_tokenizer.state_dict(), path)
```

- Or implement a small logger branch in `runner_with_state.py` when `train_mode=="transition_tokenizer"` to write:
  - `transition_tokenizer.pt`
  - optional `transition_tokenizer-step-N.pt`

## 6. Stage 2: train_mode = latent_state_world_model

Objective:

```text
total_loss = video_loss_weight * L_video + transition_loss_weight * L_trans
```

Default:

```text
video_loss_weight = 1.0
transition_loss_weight = 0.1
tokenizer_loss_weight = 0.0
state_loss_weight = 0.0
freeze_transition_tokenizer = true
```

Training path:

```text
video -> clean input_latents
frozen GameTransitionTokenizer(clean_visual_tokens) -> z_indices
first_frame_tokens + action_seq -> LatentStateRollout -> G_seq_low, G_seq
G_seq -> visual branch latent-state cross-attn -> pred_video
G_seq_low + action_seq -> LatentTransitionPredictor -> logits
CE(logits, stopgrad(z_indices)) -> L_trans
video flow matching -> L_video
```

Implementation plan:

1. Add loss function, e.g. `FlowMatchSFTLossWithLatentState(...)`。
2. Generate video noising exactly like current `FlowMatchSFTLossWithStateWeighted`:

```python
noise = torch.randn_like(inputs["input_latents"])
inputs["latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
target_video = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
if "first_frame_latents" in inputs:
    inputs["latents"][:, :, 0:1] = inputs["first_frame_latents"]
```

3. Do not compute `clean_state` or `noisy_state`。
4. Extract clean visual tokens from `inputs["input_latents"]`:

```python
with torch.no_grad():
    clean_visual_tokens, shape_info = pipe.dit.extract_clean_visual_tokens(inputs["input_latents"])
    tok_out = pipe.dit.transition_tokenizer(clean_visual_tokens, latent_frames=shape_info["f"])
    z_indices = tok_out["z_indices"].detach()
```

5. Call latent model forward:

```python
outputs = pipe.model_fn(**models, **inputs, timestep=timestep, train_mode="latent_state_world_model")
pred_video = outputs["pred_video"]
transition_logits = outputs["transition_logits"]
```

6. Match old first-frame loss masking:

```python
if "first_frame_latents" in inputs:
    pred_video = pred_video[:, :, 1:]
    target_video = target_video[:, :, 1:]
```

7. Compute:

```python
loss_video = mse_loss(pred_video.float(), target_video.float())
loss_video = loss_video * pipe.scheduler.training_weight(timestep)
loss_trans = F.cross_entropy(
    transition_logits.reshape(-1, transition_vocab_size).float(),
    z_indices.reshape(-1).long(),
)
total_loss = video_loss_weight * loss_video + transition_loss_weight * loss_trans
```

8. Return zero scalar for state loss in logs:

```python
loss_state = loss_video.new_zeros(())
```

Stage 2 requirements:

- `transition_tokenizer_ckpt` should be required unless user explicitly passes a debug flag allowing random tokenizer.
- tokenizer forward under `torch.no_grad()` and tokenizer params `requires_grad=False`。
- `z_indices` must be detached.
- Do not call `state_encoder`/`state_decoder`。
- Do not call `downsample_state_to_latent`。
- dataloader may return `state`, but no latent path uses it。
- If old training loop expects `state_loss`, return/log zero scalar.

## 7. Explicit baseline

Keep old behavior under `train_mode="explicit_state_baseline"`:

- `get_pipeline_inputs` requires state。
- `FlowMatchSFTLossWithStateWeighted` current logic remains available。
- `StatePlayModel(use_explicit_state=True, use_latent_state=False)` uses `StateVideoDiTBlock`, `state_encoder`, `state_decoder`。
- SmoothL1 state loss remains only in this mode。
- Existing `scripts/train.sh` should run as baseline if it passes `--train_mode explicit_state_baseline`, or if you intentionally keep old default for backward compatibility.

## 8. Joint fine-tuning placeholder

Add config acceptance for:

```text
train_mode = "latent_state_joint_finetune"
```

First version can raise `NotImplementedError` with a clear message, or implement:

```text
total_loss = L_video + 0.1 * L_trans + 0.01 * L_tok
```

If implemented:

- tokenizer is trainable.
- note in comments/logs that target drift is possible.
- do not make this default.

## 9. Loss dispatcher

Replace `StatePlayTrainingModule.forward` tail with dispatcher:

```python
if self.train_mode == "explicit_state_baseline":
    return FlowMatchSFTLossWithStateWeighted(...)
if self.train_mode == "transition_tokenizer":
    return TransitionTokenizerLoss(...)
if self.train_mode == "latent_state_world_model":
    return FlowMatchSFTLossWithLatentState(...)
if self.train_mode == "latent_state_joint_finetune":
    return FlowMatchSFTLossWithLatentStateJoint(...)
raise ValueError(...)
```

Keep rank0 component logging:

```text
[loss_components] mode=...
video_loss=...
transition_loss=...
tokenizer_loss=...
state_loss=0.000000
total_loss=...
```

Avoid printing every process/rank.

## 10. Shape logging

Add one-time rank0 shape logging for latent modes. Required fields:

```text
[LatentState shapes]
train_mode=...
input_latents=[B,48,f,H,W]
latents=[B,48,f,H,W]
patchified=[B,C,f,h,w]
clean_visual_tokens=[B,f*h*w,C]
first_frame_tokens=[B,h*w,C]
action_binned=[B,f,K]
G_seq_low=[B,f,M,C_s]
G_seq=[B,f*M,C]
z_indices=[B,f-1,M]
transition_logits=[B,f-1,M,512]
```

This is especially important because 101 frames gives 26 latent slots including the first pinned frame, while many sanity examples use 25 future steps.

## 11. Checkpoints

Stage 1:

- Save tokenizer-only checkpoint:

```text
<output_path>/transition_tokenizer.pt
<output_path>/transition_tokenizer-step-N.pt  # optional
```

Stage 2:

- Load `--transition_tokenizer_ckpt` into `dit.transition_tokenizer`。
- Freeze tokenizer.
- Existing `FullModelLogger` may save trainable latent/world-model params.
- Ensure frozen tokenizer weights are either:
  - not saved in Stage 2 trainable checkpoint, and loaded separately by config; or
  - saved only if explicitly requested.

## 12. Sanity tests

Add CPU tests under `tests/test_latent_state.py` or `training/tests/test_latent_state.py`. The repo currently has no test suite, so add minimal pytest-style tests. These tests should not load the 5B model.

Test 1: `LatentStateInitializer`

```text
input [2, 390, 3072]
output [2, 4, 256]
```

Test 2: `LatentStateTransition`

```text
input G_i [2, 4, 256], action_i [2, 11]
output [2, 4, 256]
```

Test 3: `LatentStateRollout`

```text
input H0 [2, 390, 3072], A [2, 25, 11]
output G_seq_low [2, 25, 4, 256], G_seq [2, 100, 3072]
```

Test 4: `GameTransitionTokenizer`

```text
input clean_visual_tokens [2, L_v, 3072], latent_frames=25
choose L_v = 25 * 390
output z_indices [2, 24, 4], z_embs [2, 24, 4, 256], L_tok scalar
```

Test 5: `LatentTransitionPredictor`

```text
input G_seq_low [2, 25, 4, 256], action_seq [2, 25, 11]
output logits [2, 24, 4, 512]
```

Test 6: Stage 1 backward

```text
total_loss = L_tok
only tokenizer parameters receive gradient
```

Implementation suggestion:

- Instantiate `GameTransitionTokenizer` and a frozen dummy patch/extractor if needed.
- Set all non-tokenizer modules `requires_grad=False`。
- Backward on `loss_tok`。
- Assert tokenizer params have nonzero/nonnull grads; assert frozen dummy params have no grads。

Test 7: Stage 2 backward

```text
total_loss = L_video + 0.1 * L_trans
tokenizer frozen
latent initializer / transition / predictor / state-to-visual attention receive gradient
explicit state encoder / decoder not called
```

Implementation suggestion:

- Use a small fake latent-world wrapper rather than full 5B DiT.
- Include `LatentStateRollout`, `LatentStateToVisualAttention`, `LatentTransitionPredictor`, and a tiny video head to produce a toy `L_video`。
- Freeze tokenizer and create `z_indices` under no_grad。
- For explicit state encoder/decoder, either omit them or monkeypatch modules that raise if called。
- Assert tokenizer grads are None; latent modules grads are present。
## 13. Smoke commands
After implementation, run at least:
```bash
python -m pytest tests/test_latent_state.py
```
If pytest is unavailable, either add it to dev requirements or run tests as a plain Python script with asserts:
```bash
python tests/test_latent_state.py
```
Do not run full training unless the user explicitly asks or a tiny smoke config is already available.
## 14. Final implementation checklist
- `train_mode` parser/config exists.
- Stage 1 can run without state labels.
- Stage 1 loss is only `L_tok`.
- Stage 1 only tokenizer params get gradients.
- Stage 1 saves `transition_tokenizer.pt`.
- Stage 2 requires or loads tokenizer checkpoint.
- Stage 2 freezes tokenizer and detaches `z_indices`.
- Stage 2 total loss is `L_video + 0.1 * L_trans`.
- Stage 2 does not compute SmoothL1 state loss.
- Stage 2 does not call explicit `state_encoder` or `state_decoder`.
- `explicit_state_baseline` still preserves old behavior.
- Shape logging confirms `f`, `tokens_per_frame`, `Lv`, `M`, `C_s`, `K` at runtime.
- Sanity tests 1-7 pass.

	# Prompt B: 训练代码修改提示词

	你是下一轮本地代码协作 agent。请在 `C:\Users\32297\Downloads\StatePlay` 仓库中实现两阶段训练代码。必须先阅读 Prompt A 和本仓库代码。不要处理 evaluation、probe、CS2、distillation、VLM judge 或指标计算。本轮只处理 tokenizer 训练、latent-state world model 训练、loss、config、checkpoint 和 sanity tests。

	## 0. 当前训练路径定位

	\| 路径 \| 当前模块/函数 \| 当前行为 \| 需要修改 \|
	\|---\|---\|---\|---\|
	\| `training/train.py` \| `FlowMatchSFTLossWithStateWeighted` \| video flow matching + explicit state SmoothL1；强制要求 `inputs["state"]` \| 改成按 `train_mode` 分派 loss；latent modes 不要求 state \|
	\| `training/train.py` \| `StatePlayTrainingModule.__init__` \| 构造 `StatePlayModel(num_buttons=profile.num_buttons, state_dim=5, **WAN_MODEL_KWARGS)` \| 传入 latent-state/tokenizer config，加载 tokenizer checkpoint，控制 requires_grad \|
	\| `training/train.py` \| `get_pipeline_inputs` \| 强制要求 dataset item 有 `state` \| 仅 explicit baseline 强制 state；latent/tokenizer 模式可忽略 state \|
	\| `training/train.py` \| `build_parser` \| 只有 `state_loss_weight/video_loss_weight` 等旧参数 \| 添加 train_mode 和 latent/tokenizer/loss config \|
	\| `training/train.py` \| `build_dataset` \| 总是包装 `StateAugmentedDataset` \| 可以继续读取 state，但 latent/tokenizer 模式不能依赖它；最好允许 state 缺失 \|
	\| `training/runner_with_state.py` \| optimizer \| `AdamW(model.trainable_modules())` \| 最小改动: 通过 requires_grad 控制 Stage 1/2 参数范围 \|
	\| `diffsynth/diffusion/logger.py` \| checkpoint \| 保存 trainable state dict 到 `step-N.safetensors` \| Stage 1 还要保存 `transition_tokenizer.pt` 或 tokenizer-only safetensors \|
	\| `diffsynth/pipelines/stateplay.py` \| `WanVideoUnit_InputVideoEmbedder` \| training mode 返回 clean `input_latents` \| Stage 1/2 从这里取 clean visual tokens \|
	\| `diffsynth/pipelines/stateplay.py` \| `WanVideoUnit_ImageEmbedderFused` \| 用 first frame VAE latent 覆盖 `latents[:, :, 0:1]`，返回 `first_frame_latents` \| Stage 2 继续保持 pinned first frame \|

	## 1. 新增 train_mode

	至少支持:

	```text
	train_mode = "transition_tokenizer"
	train_mode = "latent_state_world_model"
	train_mode = "explicit_state_baseline"
	train_mode = "latent_state_joint_finetune" # 仅预留，不作为默认重点
	```

	含义:

	1. `transition_tokenizer`
	- 只训练 `GameTransitionTokenizer` 和 tokenizer reconstruction head。
	- 不训练 latent initializer/transition/predictor/visual generator。
	- 不使用真实 state label。

	2. `latent_state_world_model`
	- 冻结 `GameTransitionTokenizer`。
	- 训练 `LatentStateInitializer`、`LatentStateTransition`、`LatentTransitionPredictor`、`LatentStateToVisualAttention`，以及原 visual generator 中需要训练的参数。
	- 不使用真实 state label。
	- 不计算 SmoothL1 state loss。

	3. `explicit_state_baseline`
	- 保持原 StatePlay 行为。
	- 当前 `FlowMatchSFTLossWithStateWeighted` 的逻辑基本属于这个模式。

	4. `latent_state_joint_finetune`
	- 只预留接口。
	- 若启用: `L_video + 0.1 L_trans + 0.01 L_tok`。
	- 默认不建议启用，因为 tokenizer target 可能漂移。

	## 2. Config/argparse 字段

	在 `training/train.py::build_parser()` 增加:

	```python
	p.add_argument("--train_mode", type=str, default="latent_state_world_model",
	choices=("transition_tokenizer", "latent_state_world_model",
	"explicit_state_baseline", "latent_state_joint_finetune"))

	p.add_argument("--use_latent_state", type=_bool_arg, default=True)
	p.add_argument("--use_explicit_state", type=_bool_arg, default=False)

	p.add_argument("--latent_state_num_tokens", type=int, default=4)
	p.add_argument("--latent_state_dim", type=int, default=256)
	p.add_argument("--latent_state_initializer_layers", type=int, default=2)
	p.add_argument("--latent_state_transition_layers", type=int, default=2)
	p.add_argument("--latent_state_num_heads", type=int, default=8)
	p.add_argument("--latent_state_mlp_ratio", type=float, default=4.0)
	p.add_argument("--latent_state_dropout", type=float, default=0.0)
	p.add_argument("--latent_state_max_frames", type=int, default=64)
	p.add_argument("--latent_state_bidirectional", type=_bool_arg, default=False)

	p.add_argument("--use_transition_tokenizer", type=_bool_arg, default=True)
	p.add_argument("--transition_tokenizer_num_tokens", type=int, default=4)
	p.add_argument("--transition_tokenizer_dim", type=int, default=256)
	p.add_argument("--transition_tokenizer_layers", type=int, default=2)
	p.add_argument("--transition_tokenizer_heads", type=int, default=8)
	p.add_argument("--transition_tokenizer_mlp_ratio", type=float, default=4.0)
	p.add_argument("--transition_tokenizer_dropout", type=float, default=0.0)
	p.add_argument("--transition_tokenizer_fsq_levels", type=str, default="8,8,8")
	p.add_argument("--transition_vocab_size", type=int, default=512)
	p.add_argument("--transition_tokenizer_ckpt", type=str, default=None)

	p.add_argument("--freeze_transition_tokenizer", type=_bool_arg, default=False)
	p.add_argument("--use_transition_loss", type=_bool_arg, default=True)

	p.add_argument("--transition_loss_weight", type=float, default=0.1)
	p.add_argument("--tokenizer_loss_weight", type=float, default=1.0)
	# Keep existing --video_loss_weight; keep --state_loss_weight for explicit baseline.
	# In latent modes force state loss behavior to zero internally even if CLI passes another value.
	```

	Mode-derived defaults:

	- `transition_tokenizer`:
	- `use_latent_state=false`
	- `use_explicit_state=false`
	- `use_transition_tokenizer=true`
	- `freeze_transition_tokenizer=false`
	- `tokenizer_loss_weight=1.0`
	- `state_loss_weight=0.0`

	- `latent_state_world_model`:
	- `use_latent_state=true`
	- `use_explicit_state=false`
	- `use_transition_tokenizer=true`
	- `freeze_transition_tokenizer=true`
	- `use_transition_loss=true`
	- `video_loss_weight=1.0`
	- `transition_loss_weight=0.1`
	- `tokenizer_loss_weight=0.0`
	- `state_loss_weight=0.0`

	- `explicit_state_baseline`:
	- `use_latent_state=false`
	- `use_explicit_state=true`
	- `use_transition_tokenizer=false`
	- preserve old `state_loss_weight` default and old loss.

	If this default would unexpectedly change `scripts/train.sh`, update `scripts/train.sh` to pass `--train_mode explicit_state_baseline` for old baseline runs, and add comments/examples for the two new modes.

	## 3. Model construction and parameter freezing

	In `StatePlayTrainingModule.__init__`:

	1. Store all new config fields on `self`。
	2. Instantiate `StatePlayModel` with the flags and latent/tokenizer config from args。
	3. Continue using `profile.num_buttons` rather than hardcoding K。
	4. Load `resume_from_ckpt` with `strict=False` as before。
	5. If `transition_tokenizer_ckpt` is provided:
	- load into `custom_dit.transition_tokenizer` only。
	- accept either raw tokenizer state dict or a checkpoint with key prefix `transition_tokenizer.`。
	6. Apply trainability after model construction and checkpoint loading:

	```python
	def _set_train_mode_requires_grad(self):
	self.pipe.requires_grad_(False)
	if self.train_mode == "transition_tokenizer":
	self.pipe.dit.transition_tokenizer.train()
	self.pipe.dit.transition_tokenizer.requires_grad_(True)
	elif self.train_mode == "latent_state_world_model":
	self.pipe.dit.train()
	# tokenizer frozen
	self.pipe.dit.transition_tokenizer.eval()
	self.pipe.dit.transition_tokenizer.requires_grad_(False)
	# train latent state modules
	self.pipe.dit.latent_state_rollout.requires_grad_(True)
	self.pipe.dit.latent_transition_predictor.requires_grad_(True)
	# train latent-state-to-visual attention params inside blocks
	# train existing visual generator params according to trainable_filter or default policy
	elif self.train_mode == "explicit_state_baseline":
	# preserve old trainable_models/trainable_filter behavior
	```

	Important:

	- The existing runner uses `model.trainable_modules()`; no optimizer group change is required for first version.
	- If `trainable_filter` is passed, apply it after mode-specific freezing, but do not accidentally unfreeze tokenizer in Stage 2.
	- In Stage 1, VAE/text encoder/base DiT/latent modules must receive no gradients.

	## 4. Dataset/input changes

	In `get_pipeline_inputs`:

	- Always include:
	- `input_video`
	- `keyboard_action`
	- height/width/num_frames
	- scheduler and gradient checkpoint fields
	- Only require `data["state"]` when `train_mode == "explicit_state_baseline"`。
	- For latent modes:
	- if `state` exists, you may pass it through for logging compatibility, but no loss/model path may use it。
	- if `state` missing, do not raise。

	In `build_dataset`:

	- Current code always wraps with `StateAugmentedDataset` and therefore requires state columns in the parquet.
	- For first version, either:
	1. keep wrapping when state exists, but add `--require_state_labels` defaulting true only for explicit baseline; or
	2. wrap only when `train_mode=="explicit_state_baseline"` or when `--load_state_labels true`。
	- The important rule: Stage 1 and Stage 2 must be runnable without state labels.

	## 5. Stage 1: train_mode = transition_tokenizer

	Objective:

	```text
	min_phi L_tok
	total_loss = tokenizer_loss_weight * L_tok
	```

	Input path:

	```text
	video -> VAE encoder -> clean input_latents
	input_latents -> frozen DiT patch_embedding -> clean_visual_tokens
	clean_visual_tokens -> GameTransitionTokenizer -> z_indices, z_embs, L_tok
	```

	Implementation plan:

	1. Add loss function, e.g. `TransitionTokenizerLoss(pipe, tokenizer_loss_weight, **inputs)`。
	2. Use existing pipeline units so `inputs_shared["input_latents"]` is available. This is returned by `WanVideoUnit_InputVideoEmbedder` in training mode.
	3. Do not call video denoising model forward.
	4. Extract clean visual tokens:

	```python
	with torch.no_grad():
	clean_visual_tokens, shape_info = pipe.dit.extract_clean_visual_tokens(inputs["input_latents"])
	```

	5. Call:

	```python
	tok_out = pipe.dit.transition_tokenizer(
	clean_visual_tokens,
	latent_frames=shape_info["f"],
	)
	loss_tok = tok_out["loss_tok"]
	total_loss = tokenizer_loss_weight * loss_tok
	```

	6. Return scalar total loss. Also maintain a lightweight loss dict for rank0 logging:

	```text
	tokenizer_loss, total_loss, f, tokens_per_frame
	```

	Stage 1 requirements:

	- `action` can be read but not used.
	- `state` can be read but not used.
	- VAE/base video model frozen.
	- Only tokenizer parameters have gradients.
	- Save tokenizer checkpoint as `transition_tokenizer.pt` under `output_path` at training end and preferably at save steps.

	Checkpoint save requirement:

	- Implement helper in `StatePlayTrainingModule`:

	```python
	def save_transition_tokenizer(self, path):
	torch.save(self.pipe.dit.transition_tokenizer.state_dict(), path)
	```

	- Or implement a small logger branch in `runner_with_state.py` when `train_mode=="transition_tokenizer"` to write:
	- `transition_tokenizer.pt`
	- optional `transition_tokenizer-step-N.pt`

	## 6. Stage 2: train_mode = latent_state_world_model

	Objective:

	```text
	total_loss = video_loss_weight * L_video + transition_loss_weight * L_trans
	```

	Default:

	```text
	video_loss_weight = 1.0
	transition_loss_weight = 0.1
	tokenizer_loss_weight = 0.0
	state_loss_weight = 0.0
	freeze_transition_tokenizer = true
	```

	Training path:

	```text
	video -> clean input_latents
	frozen GameTransitionTokenizer(clean_visual_tokens) -> z_indices
	first_frame_tokens + action_seq -> LatentStateRollout -> G_seq_low, G_seq
	G_seq -> visual branch latent-state cross-attn -> pred_video
	G_seq_low + action_seq -> LatentTransitionPredictor -> logits
	CE(logits, stopgrad(z_indices)) -> L_trans
	video flow matching -> L_video
	```

	Implementation plan:

	1. Add loss function, e.g. `FlowMatchSFTLossWithLatentState(...)`。
	2. Generate video noising exactly like current `FlowMatchSFTLossWithStateWeighted`:

	```python
	noise = torch.randn_like(inputs["input_latents"])
	inputs["latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
	target_video = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
	if "first_frame_latents" in inputs:
	inputs["latents"][:, :, 0:1] = inputs["first_frame_latents"]
	```

	3. Do not compute `clean_state` or `noisy_state`。
	4. Extract clean visual tokens from `inputs["input_latents"]`:

	```python
	with torch.no_grad():
	clean_visual_tokens, shape_info = pipe.dit.extract_clean_visual_tokens(inputs["input_latents"])
	tok_out = pipe.dit.transition_tokenizer(clean_visual_tokens, latent_frames=shape_info["f"])
	z_indices = tok_out["z_indices"].detach()
	```

	5. Call latent model forward:

	```python
	outputs = pipe.model_fn(models, inputs, timestep=timestep, train_mode="latent_state_world_model")
	pred_video = outputs["pred_video"]
	transition_logits = outputs["transition_logits"]
	```

	6. Match old first-frame loss masking:

	```python
	if "first_frame_latents" in inputs:
	pred_video = pred_video[:, :, 1:]
	target_video = target_video[:, :, 1:]
	```

	7. Compute:

	```python
	loss_video = mse_loss(pred_video.float(), target_video.float())
	loss_video = loss_video * pipe.scheduler.training_weight(timestep)

	loss_trans = F.cross_entropy(
	transition_logits.reshape(-1, transition_vocab_size).float(),
	z_indices.reshape(-1).long(),
	)
	total_loss = video_loss_weight * loss_video + transition_loss_weight * loss_trans
	```

	8. Return zero scalar for state loss in logs:

	```python
	loss_state = loss_video.new_zeros(())
	```

	Stage 2 requirements:

	- `transition_tokenizer_ckpt` should be required unless user explicitly passes a debug flag allowing random tokenizer.
	- tokenizer forward under `torch.no_grad()` and tokenizer params `requires_grad=False`。
	- `z_indices` must be detached.
	- Do not call `state_encoder`/`state_decoder`。
	- Do not call `downsample_state_to_latent`。
	- dataloader may return `state`, but no latent path uses it。
	- If old training loop expects `state_loss`, return/log zero scalar.

	## 7. Explicit baseline

	Keep old behavior under `train_mode="explicit_state_baseline"`:

	- `get_pipeline_inputs` requires state。
	- `FlowMatchSFTLossWithStateWeighted` current logic remains available。
	- `StatePlayModel(use_explicit_state=True, use_latent_state=False)` uses `StateVideoDiTBlock`, `state_encoder`, `state_decoder`。
	- SmoothL1 state loss remains only in this mode。
	- Existing `scripts/train.sh` should run as baseline if it passes `--train_mode explicit_state_baseline`, or if you intentionally keep old default for backward compatibility.

	## 8. Joint fine-tuning placeholder

	Add config acceptance for:

	```text
	train_mode = "latent_state_joint_finetune"
	```

	First version can raise `NotImplementedError` with a clear message, or implement:

	```text
	total_loss = L_video + 0.1 * L_trans + 0.01 * L_tok
	```

	If implemented:

	- tokenizer is trainable.
	- note in comments/logs that target drift is possible.
	- do not make this default.

	## 9. Loss dispatcher

	Replace `StatePlayTrainingModule.forward` tail with dispatcher:

	```python
	if self.train_mode == "explicit_state_baseline":
	return FlowMatchSFTLossWithStateWeighted(...)
	if self.train_mode == "transition_tokenizer":
	return TransitionTokenizerLoss(...)
	if self.train_mode == "latent_state_world_model":
	return FlowMatchSFTLossWithLatentState(...)
	if self.train_mode == "latent_state_joint_finetune":
	return FlowMatchSFTLossWithLatentStateJoint(...)
	raise ValueError(...)
	```

	Keep rank0 component logging:

	```text
	[loss_components] mode=...
	video_loss=...
	transition_loss=...
	tokenizer_loss=...
	state_loss=0.000000
	total_loss=...
	```

	Avoid printing every process/rank.

	## 10. Shape logging

	Add one-time rank0 shape logging for latent modes. Required fields:

	```text
	[LatentState shapes]
	train_mode=...
	input_latents=[B,48,f,H,W]
	latents=[B,48,f,H,W]
	patchified=[B,C,f,h,w]
	clean_visual_tokens=[B,fhw,C]
	first_frame_tokens=[B,h*w,C]
	action_binned=[B,f,K]
	G_seq_low=[B,f,M,C_s]
	G_seq=[B,f*M,C]
	z_indices=[B,f-1,M]
	transition_logits=[B,f-1,M,512]
	```

	This is especially important because 101 frames gives 26 latent slots including the first pinned frame, while many sanity examples use 25 future steps.

	## 11. Checkpoints

	Stage 1:

	- Save tokenizer-only checkpoint:

	```text
	<output_path>/transition_tokenizer.pt
	<output_path>/transition_tokenizer-step-N.pt # optional
	```

	Stage 2:

	- Load `--transition_tokenizer_ckpt` into `dit.transition_tokenizer`。
	- Freeze tokenizer.
	- Existing `FullModelLogger` may save trainable latent/world-model params.
	- Ensure frozen tokenizer weights are either:
	- not saved in Stage 2 trainable checkpoint, and loaded separately by config; or
	- saved only if explicitly requested.

	## 12. Sanity tests

	Add CPU tests under `tests/test_latent_state.py` or `training/tests/test_latent_state.py`. The repo currently has no test suite, so add minimal pytest-style tests. These tests should not load the 5B model.

	Test 1: `LatentStateInitializer`

	```text
	input [2, 390, 3072]
	output [2, 4, 256]
	```

	Test 2: `LatentStateTransition`

	```text
	input G_i [2, 4, 256], action_i [2, 11]
	output [2, 4, 256]
	```

	Test 3: `LatentStateRollout`

	```text
	input H0 [2, 390, 3072], A [2, 25, 11]
	output G_seq_low [2, 25, 4, 256], G_seq [2, 100, 3072]
	```

	Test 4: `GameTransitionTokenizer`

	```text
	input clean_visual_tokens [2, L_v, 3072], latent_frames=25
	choose L_v = 25 * 390
	output z_indices [2, 24, 4], z_embs [2, 24, 4, 256], L_tok scalar
	```

	Test 5: `LatentTransitionPredictor`

	```text
	input G_seq_low [2, 25, 4, 256], action_seq [2, 25, 11]
	output logits [2, 24, 4, 512]
	```

	Test 6: Stage 1 backward

	```text
	total_loss = L_tok
	only tokenizer parameters receive gradient
	```

	Implementation suggestion:

	- Instantiate `GameTransitionTokenizer` and a frozen dummy patch/extractor if needed.
	- Set all non-tokenizer modules `requires_grad=False`。
	- Backward on `loss_tok`。
	- Assert tokenizer params have nonzero/nonnull grads; assert frozen dummy params have no grads。

	Test 7: Stage 2 backward

	```text
	total_loss = L_video + 0.1 * L_trans
	tokenizer frozen
	latent initializer / transition / predictor / state-to-visual attention receive gradient
	explicit state encoder / decoder not called
	```

	Implementation suggestion:

	- Use a small fake latent-world wrapper rather than full 5B DiT.
	- Include `LatentStateRollout`, `LatentStateToVisualAttention`, `LatentTransitionPredictor`, and a tiny video head to produce a toy `L_video`。
	- Freeze tokenizer and create `z_indices` under no_grad。
	- For explicit state encoder/decoder, either omit them or monkeypatch modules that raise if called。
	- Assert tokenizer grads are None; latent modules grads are present。

	## 13. Smoke commands

	After implementation, run at least:

	```bash
	python -m pytest tests/test_latent_state.py
	```

	If pytest is unavailable, either add it to dev requirements or run tests as a plain Python script with asserts:

	```bash
	python tests/test_latent_state.py
	```

	Do not run full training unless the user explicitly asks or a tiny smoke config is already available.

	## 14. Final implementation checklist

	- `train_mode` parser/config exists.
	- Stage 1 can run without state labels.
	- Stage 1 loss is only `L_tok`.
	- Stage 1 only tokenizer params get gradients.
	- Stage 1 saves `transition_tokenizer.pt`.
	- Stage 2 requires or loads tokenizer checkpoint.
	- Stage 2 freezes tokenizer and detaches `z_indices`.
	- Stage 2 total loss is `L_video + 0.1 * L_trans`.
	- Stage 2 does not compute SmoothL1 state loss.
	- Stage 2 does not call explicit `state_encoder` or `state_decoder`.
	- `explicit_state_baseline` still preserves old behavior.
	- Shape logging confirms `f`, `tokens_per_frame`, `Lv`, `M`, `C_s`, `K` at runtime.
	- Sanity tests 1-7 pass.