[Doc] Adding models/pipelines/features Tutorial (#1196)

Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: dongbo910220 <32610838+dongbo910220@users.noreply.github.com>
2026-02-09 15:53:01 +08:00
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+# Support Sequence Parallel
+
+This section describes how to add Sequence Parallel (SP) to a diffusion transformer model. We use the Qwen-Image transformer and Wan2.2 transformer as reference implementations.
+
+---
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Approach 1: Non-Intrusive `_sp_plan` (Recommended)](#approach-1-non-intrusive-_sp_plan-recommended)
+- [Approach 2: Intrusive Modification (For Complex Cases)](#approach-2-intrusive-modification-for-complex-cases)
+- [Testing](#testing)
+- [Troubleshooting](#troubleshooting)
+- [Reference Implementations](#reference-implementations)
+- [Summary](#summary)
+
+---
+
+## Overview
+
+
+### What is Sequence Parallel?
+
+**Terminology Note:** Our "Sequence Parallelism" (SP) corresponds to "Context Parallelism" (CP) in the [diffusers library](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/_modeling_parallel.py). We use "Sequence Parallelism" to align with vLLM-Omni's terminology.
+
+Diffusion transformers process long sequences of image patches or video frames. For high-resolution generation, these sequences can become very large. Enabling SP allows each GPU to process only a portion of the sequence, with attention mechanisms (Ulysses/Ring) handling cross-GPU communication transparently.
+
+### Architecture
+
+The major APIs for Sequence Parallel:
+
+```python
+from vllm_omni.diffusion.distributed.sp_plan import (
+    SequenceParallelInput,   # For sharding (splitting) tensors
+    SequenceParallelOutput,  # For gathering tensors
+)
+from vllm_omni.diffusion.distributed.sp_sharding import sp_shard, sp_gather
+```
+
+| Method/Class | Purpose | Behavior |
+|--------------|---------|----------|
+| `SequenceParallelInput` | Declare input sharding in `_sp_plan` | Auto-shards tensors at module input |
+| `SequenceParallelOutput` | Declare output gathering in `_sp_plan` | Auto-gathers tensors at module output |
+| `sp_shard()` | Manual tensor sharding | Splits tensor across SP workers |
+| `sp_gather()` | Manual tensor gathering | Gathers sharded tensors from all workers |
+
+---
+
+## Approach 1: Non-Intrusive `_sp_plan` (Recommended)
+
+The `_sp_plan` mechanism allows SP **without modifying `forward()` logic**. The framework automatically registers hooks to shard inputs and gather outputs at module boundaries.
+
+**When to use:**
+- Standard transformer architectures
+- Tensor operations happen at `nn.Module` boundaries
+- Predictable sharding/gathering patterns
+
+**How it works:**
+1. Declare `_sp_plan` dict in your transformer class
+2. Framework automatically applies hooks when `sequence_parallel_size > 1`
+3. Hooks shard/gather tensors at specified module boundaries
+4. Attention layers handle cross-GPU communication internally
+
+```python
+class StandardTransformer(nn.Module):
+    _sp_plan = {
+        # Shard hidden_states at first transformer block input
+        "blocks.0": {
+            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
+        },
+        # Gather at final output projection
+        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
+    }
+```
+
+`StandardTransformer` has a transformer blocks list `self.blocks = nn.ModuleList([...])`, and a projection output layer `self.proj_out`. The `_sp_plan` above defines that when SP is enabled, sharding the input tensor to the first transformer block, and gathering the sharded tensor at the final output projection layer.
+
+**Requirements:**
+- Tensor operations that need sharding/gathering must happen at **`nn.Module` boundaries**
+- Inline Python operations (e.g., `torch.cat`, `pad_sequence`) **cannot be hooked**
+
+**Solution for inline operations:** Extract into a submodule (see Step 2 below).
+
+### Step 1: Understand Module Boundaries
+
+Identify where tensors need to be sharded or gathered in your model's forward pass:
+
+```python
+class MyTransformer(nn.Module):
+    def __init__(self):
+        self.patch_embed = PatchEmbed()      # ← Boundary 1
+        self.pos_embed = RoPE()              # ← Boundary 2
+        self.blocks = nn.ModuleList([...])   # ← Boundary 3
+        self.norm_out = LayerNorm()
+        self.proj_out = Linear()             # ← Boundary 4
+
+    def forward(self, x):
+        x = self.patch_embed(x)              # ← Shard before this?
+        pos = self.pos_embed(x)              # ← Shard RoPE outputs?
+        for block in self.blocks:
+            x = block(x, pos)                # ← Blocks process sharded x
+        x = self.norm_out(x)
+        output = self.proj_out(x)            # ← Gather after this?
+        return output
+```
+
+### Step 2: Handle Inline Operations
+
+If your `forward()` contains inline tensor operations, **extract them into submodules**.
+
+**Example: Z-Image concatenates image + text features inline**
+
+```python
+# ❌ BAD: Inline operation - hooks cannot intercept
+class ZImageTransformer(nn.Module):
+    def forward(self, x, cap_feats):
+        # This concatenation happens inline - _sp_plan can't shard it!
+        unified = torch.cat([x, cap_feats], dim=1)
+
+        for layer in self.layers:
+            unified = layer(unified)
+
+        return unified
+
+# ✅ GOOD: Extract into submodule
+class UnifiedPrepare(nn.Module):
+    """Submodule to concatenate image and text features."""
+    def forward(self, x, cap_feats):
+        return torch.cat([x, cap_feats], dim=1)
+
+class ZImageTransformer(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.unified_prepare = UnifiedPrepare()  # Now a module!
+        self.layers = nn.ModuleList([...])
+
+    def forward(self, x, cap_feats):
+        # Now _sp_plan can shard the output of unified_prepare!
+        unified = self.unified_prepare(x, cap_feats)
+
+        for layer in self.layers:
+            unified = layer(unified)
+
+        return unified
+```
+
+**Other common cases:**
+- `pad_sequence()` → `PadSequenceModule`
+- `torch.cat()` → `ConcatModule`
+- `tensor.reshape()` → `ReshapeModule`
+- Complex preprocessing → `PreprocessModule`
+
+### Step 3: Write `_sp_plan` for Your Model
+
+Create a class-level `_sp_plan` dictionary specifying where to shard/gather tensors.
+
+Typically, there are two patterns for diffusion models:
+
+**Pattern 1: Shard at first block, gather at output projection**
+
+Most common pattern for standard transformers:
+
+```python
+from vllm_omni.diffusion.distributed.sp_plan import (
+    SequenceParallelInput,   # For sharding (splitting) tensors
+    SequenceParallelOutput,  # For gathering tensors
+)
+class StandardTransformer(nn.Module):
+    _sp_plan = {
+        # Shard hidden_states at first transformer block input
+        "blocks.0": {
+            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
+        },
+        # Gather at final output projection
+        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
+    }
+```
+
+**Pattern 2: Shard RoPE embeddings separately**
+
+When RoPE is computed in a separate module:
+
+```python
+from vllm_omni.diffusion.distributed.sp_plan import (
+    SequenceParallelInput,   # For sharding (splitting) tensors
+    SequenceParallelOutput,  # For gathering tensors
+)
+class TransformerWithRoPE(nn.Module):
+    _sp_plan = {
+        # Shard RoPE module OUTPUTS (returns tuple of cos, sin)
+        "rope": {
+            0: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),  # cos
+            1: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),  # sin
+        },
+        # Shard transformer block INPUT
+        "blocks.0": {
+            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
+        },
+        # Gather at output
+        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
+    }
+```
+
+### API Reference
+
+**SequenceParallelInput Parameters:**
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `split_dim` | int | Dimension to split (usually `1` for sequence) |
+| `expected_dims` | int \| None | Expected tensor rank for validation (optional) |
+| `split_output` | bool | `False`: shard **input** params; `True`: shard **output** tensors |
+| `auto_pad` | bool | Auto-pad if sequence not divisible by world_size (default: `False`) |
+
+**SequenceParallelOutput Parameters:**
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `gather_dim` | int | Dimension to gather (usually `1` for sequence) |
+| `expected_dims` | int \| None | Expected tensor rank for validation (optional) |
+
+**Module Naming Conventions:**
+
+| Key | Meaning | Python equivalent |
+|-----|---------|-------------------|
+| `""` | Root model | `model` |
+| `"blocks.0"` | First element of ModuleList | `model.blocks[0]` |
+| `"blocks.*"` | All elements of ModuleList | `for b in model.blocks` |
+| `"rope"` | Named submodule | `model.rope` |
+| `"outputs.main"` | ModuleDict entry | `model.outputs["main"]` |
+
+**Dictionary Value Types:**
+
+| Key type | `split_output` | Description |
+|----------|----------------|-------------|
+| `"param_name"` (str) | `False` | Shard **input parameter** by name |
+| `0`, `1`, ... (int) | `True` | Shard **output tuple** by index |
+
+---
+
+## Approach 2: Intrusive Modification (For Complex Cases)
+
+For models with dynamic sharding logic that cannot be expressed via `_sp_plan`, manually insert shard/gather calls.
+
+**When to use:**
+- Dynamic/conditional sharding logic
+- Complex tensor manipulations that can't be encapsulated
+- Temporary workaround during development
+
+```python
+from vllm_omni.diffusion.distributed.sp_sharding import sp_shard, sp_gather
+
+def forward(self, hidden_states, ...):
+    if self.parallel_config.sequence_parallel_size > 1:
+        hidden_states = sp_shard(hidden_states, dim=1)
+
+    # ... computation ...
+
+    if self.parallel_config.sequence_parallel_size > 1:
+        output = sp_gather(output, dim=1)
+
+    return output
+```
+
+---
+
+## Testing
+
+After implementing Sequence Parallel support, thoroughly test your implementation to ensure correctness and performance across different configurations.
+
+**Test Different `sp_size`:**
+
+Test your model with various sequence parallel world sizes to verify correctness and identify optimal configurations:
+
+```bash
+cd examples/offline_inference/text_to_image
+python text_to_image.py \
+    --model Your-org/your-model \
+    --prompt "a cup of coffee on the table" \
+    --num_inference_steps 50 \
+    --ulysses_degree 2 \
+    --ring_degree 2 \
+    --output sp_test_image_ulysses=2_ring=2.png
+```
+
+**Verify:**
+
+1. **Correctness:** Output should be identical across all `sp_size` values
+2. **Speed:** Throughput should remain stable or improve (especially for large sequences)
+3. **Logs:** Check for any shape mismatch or communication errors
+
+**Test with Tensor Parallel:**
+
+Sequence Parallel can be combined with other parallelism strategies:
+
+```bash
+cd examples/offline_inference/text_to_image
+python text_to_image.py \
+    --model Your-org/your-model \
+    --prompt "a cup of coffee on the table" \
+    --num_inference_steps 50 \
+    --ulysses_degree 2 \
+    --tensor_parallel_size 2 \
+    --output sp_test_image_ulysses=2_tp=2.png
+```
+
+---
+
+## Troubleshooting
+
+### Issue: Shape mismatch errors
+
+**Symptoms:** `RuntimeError: shape mismatch` during forward pass.
+
+**Causes & Solutions:**
+
+- **RoPE dimension mismatch:**
+
+**Problem:** RoPE embeddings not sharded, but hidden_states is sharded.
+
+**Solution:** Shard RoPE outputs in `_sp_plan`:
+```python
+_sp_plan = {
+    "rope": {
+        0: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
+        1: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
+    },
+    ...
+}
+```
+
+- **Sequence Length not divisible by sp_size:**
+
+**Problem:** `SequenceParallelInput(auto_pad=False)` - auto_pad should be True to enable automatic sequence padding.
+
+**Solution:** In `SequenceParallelInput`, set `auto_pad=True`:
+```python
+"blocks.0": {
+    "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3, auto_pad=True)
+}
+```
+
+### Issue: Inline operations not sharded
+
+**Symptoms:** Some tensors remain full-sized, not sharded.
+
+**Causes & Solutions:**
+
+- **Operations happen inline in `forward()`, not at module boundaries:**
+
+**Problem:**
+```python
+def forward(self, x, cap):
+    unified = torch.cat([x, cap], dim=1)  # ← Inline operation!
+    # _sp_plan can't hook this
+```
+
+**Solution:** Extract into submodule:
+```python
+class ConcatModule(nn.Module):
+    def forward(self, x, cap):
+        return torch.cat([x, cap], dim=1)
+
+class MyModel(nn.Module):
+    _sp_plan = {
+        "concat": {
+            0: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
+            1: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
+        },
+        ...
+    }
+    def __init__(self):
+        self.concat = ConcatModule()  # Now hookable!
+
+    def forward(self, x, cap):
+        unified = self.concat(x, cap)  # ← Can be sharded via _sp_plan
+```
+
+---
+
+## Reference Implementations
+
+Complete examples in the codebase:
+
+| Model | Path | Pattern | Notes |
+|-------|------|---------|-------|
+| **Qwen-Image** | `vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py` | Dual-stream + preprocessing | auto_pad, separate RoPE |
+| **Wan2.2** | `vllm_omni/diffusion/models/wan2_2/wan2_2_transformer.py` | Dual-Transformer + RoPE | Video transformer |
+| **Z-Image** | `vllm_omni/diffusion/models/z_image/z_image_transformer.py` | Unified sequence | Concatenated input |
+| **SP Plan Types** | `vllm_omni/diffusion/distributed/sp_plan.py` | Type definitions | SequenceParallelInput/Output |
+| **Hook Implementation** | `vllm_omni/diffusion/hooks/sequence_parallel.py` | Hook mechanics | How hooks work |
+| **Tests** | `tests/diffusion/distributed/test_sp_plan_hooks.py` | Test examples | Validation patterns |
+
+---
+
+## Summary
+
+Adding Sequence Parallel support to a transformer:
+
+1. ✅ **Choose approach** - Use `_sp_plan` for standard cases, intrusive modification for complex cases
+2. ✅ **Identify sharding boundaries** - Where should tensors be split/gathered?
+3. ✅ **Extract inline operations** - Move `torch.cat`, `pad_sequence`, etc. to submodules
+4. ✅ **Define `_sp_plan`** - Declare shard/gather points as class attribute
+5. ✅ **Use `auto_pad` for variable lengths** - Support non-uniform sequences
+6. ✅ **Test** - Verify with different `ulysses_degree` and `ring_degree` combinations