[cherry-pick][Feature] Support vae tiling parallel encode (#2368)

docs/design/feature/vae_parallel.md

@@ -1,14 +1,15 @@
 # VAE Patch Parallelism
 
 This document describes how to add **VAE Patch Parallelism** support to a diffusion model.
-We use **Qwen-Image** as the reference implementation.
+We use **Qwen-Image** as the reference implementation for decode parallel, and **Wan2.2** for encode parallel.
 
 ---
 
 ## Table of Contents
 
 - [Overview](#overview)
-- [Step-by-Step Implementation](#step-by-step-implementation)
+- [Step-by-Step Implementation (Decode)](#step-by-step-implementation-decode)
+- [Encode Parallel Implementation](#encode-parallel-implementation)
 - [Testing](#testing)
 - [Reference Implementations](#reference-implementations)
 - [Summary](#summary)
@@ -19,13 +20,13 @@ We use **Qwen-Image** as the reference implementation.
 
 ### What is Vae Patch parallel?
 
-**VAE Patch Parallelism** is a decoding acceleration technique. Instead of decoding the entire latent tensor at once, the latent tensor is:
+**VAE Patch Parallelism** is an acceleration technique for both **encoding** and **decoding**. Instead of processing the entire tensor at once, the tensor is:
 
 + Split into multiple spatial tiles
 
 + Distributed across multiple ranks
 
-+ Decoded in parallel
++ Encoded/Decoded in parallel
 
 + Merged to reconstruct the final output
 
@@ -35,10 +36,17 @@ This approach:
 
 + Reduces peak memory usage per device
 
-+ Accelerates decoding latency
++ Accelerates encoding/decoding latency
+
+### When to Use Encode vs Decode Parallel
+
+| Operation | Use Case | Example |
+|-----------|----------|---------|
+| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video |
+| **Encode Parallel** | Image-to-Video (I2V) | Image → Latent (for conditioning) |
 
 ### Architecture
-We introduce **DistributedVaeExecutor** as the core component responsible for distributed VAE decoding.
+We introduce **DistributedVaeExecutor** as the core component responsible for distributed VAE encoding/decoding.
 
 The executor is model-agnostic and accepts three function parameters:
 
@@ -84,7 +92,7 @@ Therefore:
 
 + Merge must perform blending to avoid seams
 
-## Step-by-Step Implementation
+## Step-by-Step Implementation (Decode)
 
 ### Step 1: Implement DistributedAutoencoderKLQwenImage
 `QwenImagePipeline` use `AutoencoderKLQwenImage` for vae, so implement a distributed version:
@@ -205,14 +213,14 @@ def tile_merge(self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid
 We need to override tiled_decode, the main logic is:
 + check distributed is enabled
 + select split/exec/merge
-+ Invoke self.distributed_decoder.execute to decode
++ Invoke self.distributed_executor.execute to decode
 ```
 def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):
     if not self.is_distributed_enabled():
         return super().tiled_decode(z, return_dict=return_dict)
 
     logger.info("Decode run with distributed executor")
-    result = self.distributed_decoder.execute(
+    result = self.distributed_executor.execute(
         z,
         DistributedOperator(split=self.tile_split, exec=self.tile_exec, merge=self.tile_merge),
         broadcast_result=True,
@@ -243,6 +251,166 @@ class YourModelPipeline(nn.Module):
 +       ).to(self.device)
 ```
 
+## Encode Parallel Implementation
+
+For models that require VAE encoding (e.g., Image-to-Video), you can also parallelize the encode operation. We use **Wan2.2** as the reference implementation.
+
+### Step 1: Implement encode_tile_split
+
+Similar to decode, split the input tensor into tiles. Key considerations:
+
++ **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
++ **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)
+
+```python
+def encode_tile_split(self, x: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
+    _, _, num_frames, height, width = x.shape
+    encode_spatial_compression_ratio = self.spatial_compression_ratio
+
+    # Scale tile parameters for patchified coordinate system
+    tile_sample_min_height = self.tile_sample_min_height
+    tile_sample_min_width = self.tile_sample_min_width
+    tile_sample_stride_height = self.tile_sample_stride_height
+    tile_sample_stride_width = self.tile_sample_stride_width
+
+    if self.config.patch_size is not None:
+        # When input is patchified, scale tile parameters accordingly
+        encode_spatial_compression_ratio = self.spatial_compression_ratio // self.config.patch_size
+        tile_sample_min_height = tile_sample_min_height // self.config.patch_size
+        tile_sample_min_width = tile_sample_min_width // self.config.patch_size
+        tile_sample_stride_height = tile_sample_stride_height // self.config.patch_size
+        tile_sample_stride_width = tile_sample_stride_width // self.config.patch_size
+
+    latent_height = height // encode_spatial_compression_ratio
+    latent_width = width // encode_spatial_compression_ratio
+
+    tile_latent_min_height = tile_sample_min_height // encode_spatial_compression_ratio
+    tile_latent_min_width = tile_sample_min_width // encode_spatial_compression_ratio
+    tile_latent_stride_height = tile_sample_stride_height // encode_spatial_compression_ratio
+    tile_latent_stride_width = tile_sample_stride_width // encode_spatial_compression_ratio
+
+    blend_height = tile_latent_min_height - tile_latent_stride_height
+    blend_width = tile_latent_min_width - tile_latent_stride_width
+
+    tiletask_list = []
+    # Use temporal compression ratio from config instead of hardcoding
+    temporal_compression = self.config.scale_factor_temporal
+
+    for i in range(0, height, tile_sample_stride_height):
+        for j in range(0, width, tile_sample_stride_width):
+            time_list = []
+            frame_range = 1 + (num_frames - 1) // temporal_compression
+            for k in range(frame_range):
+                if k == 0:
+                    tile = x[:, :, :1, i : i + tile_sample_min_height, j : j + tile_sample_min_width]
+                else:
+                    tile = x[
+                        :, :,
+                        1 + temporal_compression * (k - 1) : 1 + temporal_compression * k,
+                        i : i + tile_sample_min_height,
+                        j : j + tile_sample_min_width,
+                    ]
+                time_list.append(tile)
+            tiletask_list.append(
+                TileTask(len(tiletask_list), (i // tile_sample_stride_height, j // tile_sample_stride_width),
+                         time_list, workload=time_list[0].shape[3] * time_list[0].shape[4])
+            )
+
+    grid_spec = GridSpec(
+        split_dims=(3, 4),
+        grid_shape=(tiletask_list[-1].grid_coord[0] + 1, tiletask_list[-1].grid_coord[1] + 1),
+        tile_spec={
+            "latent_height": latent_height, "latent_width": latent_width,
+            "blend_height": blend_height, "blend_width": blend_width,
+            "tile_latent_stride_height": tile_latent_stride_height,
+            "tile_latent_stride_width": tile_latent_stride_width,
+        },
+        output_dtype=self.dtype,
+    )
+    return tiletask_list, grid_spec
+```
+
+### Step 2: Implement encode_tile_exec
+
+```python
+def encode_tile_exec(self, task: TileTask) -> torch.Tensor:
+    """Encode a single sample tile into latent space."""
+    self.clear_cache()
+    time = []
+    for k, tile in enumerate(task.tensor):
+        self._enc_conv_idx = [0]
+        encoded = self.encoder(tile, feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
+        encoded = self.quant_conv(encoded)
+        time.append(encoded)
+    result = torch.cat(time, dim=2)
+    self.clear_cache()
+    return result
+```
+
+### Step 3: Implement encode_tile_merge
+
+```python
+def encode_tile_merge(
+    self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid_spec: GridSpec
+) -> torch.Tensor:
+    """Merge encoded tiles into a full latent tensor."""
+    grid_h, grid_w = grid_spec.grid_shape
+    result_rows = []
+    for i in range(grid_h):
+        result_row = []
+        for j in range(grid_w):
+            tile = coord_tensor_map[(i, j)]
+            if i > 0:
+                tile = self.blend_v(coord_tensor_map[(i - 1, j)], tile, grid_spec.tile_spec["blend_height"])
+            if j > 0:
+                tile = self.blend_h(coord_tensor_map[(i, j - 1)], tile, grid_spec.tile_spec["blend_width"])
+            result_row.append(tile[:, :, :,
+                : grid_spec.tile_spec["tile_latent_stride_height"],
+                : grid_spec.tile_spec["tile_latent_stride_width"]])
+        result_rows.append(torch.cat(result_row, dim=-1))
+
+    enc = torch.cat(result_rows, dim=3)[
+        :, :, :, : grid_spec.tile_spec["latent_height"], : grid_spec.tile_spec["latent_width"]
+    ]
+    return enc
+```
+
+### Step 4: Override tiled_encode method
+
+Override `tiled_encode` instead of `encode`. The parent's `_encode()` handles patchify before calling `tiled_encode()`, so input `x` is already patchified.
+
+```python
+def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
+    """
+    Encode using distributed VAE executor.
+
+    Note: x is already patchified by parent's _encode() before calling this method.
+    """
+    if not self.is_distributed_enabled():
+        return super().tiled_encode(x)
+
+    self.clear_cache()
+    result = self.distributed_executor.execute(
+        x,
+        DistributedOperator(
+            split=self.encode_tile_split,
+            exec=self.encode_tile_exec,
+            merge=self.encode_tile_merge,
+        ),
+        broadcast_result=True,  # Latents needed by all ranks for diffusion
+    )
+    self.clear_cache()
+    return result
+```
+
+**Key differences from decode parallel:**
+
+| Aspect | Decode Parallel | Encode Parallel |
+|--------|-----------------|-----------------|
+| `broadcast_result` | Often `False` (only rank 0 needs output) | `True` (all ranks need latents for diffusion) |
+| Patchify | Applied in merge (unpatchify) | Handled by parent `_encode()` before `tiled_encode()` |
+| Temporal chunking | Frame-by-frame | Chunk-based (e.g., 1 + 4n frames) |
+
 ## Testing
 Verify numerical consistency between:
 + vae_patch_parallel_size = 1
@@ -272,18 +440,20 @@ When vae_patch_parallel_size is larger than the DiT world size, it will automati
 
 Complete examples in the codebase:
 
-| Model | Path | Notes |
-|-------|------|-------|
-| **Z-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl.py` | Distributed AutoencoderKL |
-| **Wan2.2** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py` | Distributed AutoencoderKLWan |
-| **Qwen-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py` | Distributed AutoencoderKLQwenImage |
+| Model | Path | Decode Parallel | Encode Parallel |
+|-------|------|-----------------|-----------------|
+| **Z-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl.py` | ✅ | ❌ |
+| **Wan2.2** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py` | ✅ | ✅ |
+| **Qwen-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py` | ✅ | ❌ |
 
 ---
 
 ## Summary
 
-Adding Vae Patch Parallel support to diffusion model:
+Adding VAE Patch Parallel support to diffusion model:
 
-1. **Implement Distributed Vae** - mainly copy from `diffusers` tiled_decode, and refactor into split/exec/merge
-2. **Change vae model in pipeline to Distributed Vae**
-3. **Test** - Verify with `tensor_parallel_size=N` quality
+1. **Implement Distributed VAE** - Inherit from base VAE class and `DistributedVaeMixin`
+2. **Decode Parallel** - Refactor `tiled_decode` into `tile_split`/`tile_exec`/`tile_merge`
+3. **Encode Parallel** (optional) - Implement `encode_tile_split`/`encode_tile_exec`/`encode_tile_merge` for I2V models
+4. **Change VAE model in pipeline** - Use the distributed version
+5. **Test** - Verify numerical consistency with `vae_patch_parallel_size=1` vs `N`