[AutoRound] Add offline quantized W4A16 model support

docs/user_guide/diffusion/quantization/autoround.md

@@ -0,0 +1,91 @@
+# AutoRound Quantization
+
+## Overview
+
+[AutoRound](https://github.com/intel/auto-round) is an advanced quantization toolkit designed for Large Language Models (LLMs), Vision-Language Models (VLMs), and diffusion models. It achieves high accuracy at ultra-low bit widths (2–4 bits) with minimal tuning by leveraging sign-gradient descent, while providing broad hardware compatibility with multi-datatype support.
+
+The quantization config is auto-detected from the checkpoint's `config.json` (`quantization_config.quant_method = "auto-round"`). No extra CLI flags are needed.
+
+### Supported Schemes
+
+| Scheme | Bits | Status |
+|--------|------|--------|
+| W4A16 | 4 | ✅ Supported |
+| W8A16 | 8 | Planned |
+
+W4A16 is the first supported scheme. Additional schemes will be added in future releases.
+
+## Configuration
+
+1. **Python API**: point `model` at a pre-quantized checkpoint. The quantization is detected automatically.
+
+```python
+from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+omni = Omni(model="vllm-project-org/FLUX.1-dev-AutoRound-w4a16")
+
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(num_inference_steps=28),
+)
+outputs[0].save_images("output.png")
+```
+
+2. **CLI**: pass the quantized model path directly.
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model vllm-project-org/FLUX.1-dev-AutoRound-w4a16 \
+  --prompt "A cat sitting on a windowsill" \
+  --num-inference-steps 28 \
+  --output outputs/flux_w4a16.png
+```
+
+No `--quantization` flag is needed — the quantization method is read from the checkpoint.
+
+## How It Works
+
+The checkpoint's `config.json` contains:
+
+```json
+{
+  "quantization_config": {
+    "quant_method": "auto-round",
+    "bits": 4,
+    "group_size": 128,
+    "sym": true,
+    "packing_format": "auto_round:auto_gptq",
+    "block_name_to_quantize": "transformer_blocks,single_transformer_blocks"
+  }
+}
+```
+
+At load time:
+
+1. `TransformerConfig.from_dict()` parses the `quantization_config` section and builds a vLLM `INCConfig` via `build_quant_config("auto-round", ...)`.
+2. `OmniDiffusionConfig.set_tf_model_config()` propagates the detected config to the engine.
+3. The appropriate compute kernel (e.g. GPTQ-Marlin for W4A16) is selected automatically based on the checkpoint's bit-width and packing format.
+
+## Supported Models
+
+| Model | HF Checkpoint | Scheme | Group Size | Backend |
+|-------|--------------|--------|------------|---------|
+| FLUX.1-dev | `vllm-project-org/FLUX.1-dev-AutoRound-w4a16` | W4A16 | 128 | GPTQ-Marlin |
+
+## Creating a Quantized Checkpoint
+
+Use [AutoRound](https://github.com/intel/auto-round) to quantize a BF16 model. The `--scheme` flag selects the quantization scheme:
+
+```bash
+# W4A16 (4-bit weight, 16-bit activation)
+auto-round \
+    --model black-forest-labs/FLUX.1-dev \
+    --scheme W4A16 \
+    --batch_size 1 \
+    --disable_opt_rtn \
+    --dataset coco2014 \
+    --iters 0
+```
+
+The output directory can be used directly as the `model` argument. See the [AutoRound documentation](https://github.com/intel/auto-round) for all available schemes and options.

docs/user_guide/diffusion/quantization/overview.md

@@ -11,6 +11,7 @@ vLLM-Omni provides a unified quantization framework that supports both diffusion
 | FP8 | [FP8](fp8.md) | FP8 W8A8, dynamic or static | Z-Image, Qwen-Image, Flux, Bagel | SM 89 (Ada) |
 | Int8 | [Int8](int8.md) | Int8 W8A8 | Z-Image, Qwen-Image | SM 89 (Ada) / Ascend NPU |
 | GGUF | [GGUF](gguf.md) | GGUF format, dequant+GEMM for N-D tensors | Z-Image, Flux | SM 60 |
+| AutoRound | [AutoRound](autoround.md) | W4A16 (pre-quantized) | Flux | SM 80 (Ampere) |
 
 ### Multi-stage Omni Models (Pre-quantized Checkpoints)
 
@@ -102,6 +103,9 @@ config = build_quant_config("fp8")
 # Dict with parameters
 config = build_quant_config({"method": "fp8", "activation_scheme": "static"})
 
+# AutoRound / INC (auto-detected from checkpoint, or explicit)
+config = build_quant_config("auto-round", bits=4, group_size=128)
+
 # Per-component dict
 config = build_quant_config({
     "transformer": {"method": "fp8"},

tests/diffusion/layers/test_adalayernorm.py

@@ -0,0 +1,237 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for shared AdaLayerNorm layers used by FLUX and other models."""
+
+import os
+
+import pytest
+import torch
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+
+@pytest.fixture(autouse=True)
+def _init_distributed():
+    """Initialize the minimal distributed environment required by
+    ReplicatedLinear (tensor-parallel group must exist)."""
+    from vllm.distributed.parallel_state import (
+        cleanup_dist_env_and_memory,
+        init_distributed_environment,
+        initialize_model_parallel,
+    )
+
+    os.environ.setdefault("MASTER_ADDR", "localhost")
+    os.environ.setdefault("MASTER_PORT", "29501")
+    init_distributed_environment(
+        world_size=1,
+        rank=0,
+        local_rank=0,
+        distributed_init_method="env://",
+    )
+    initialize_model_parallel()
+    yield
+    cleanup_dist_env_and_memory()
+
+
+@pytest.fixture(autouse=True)
+def _force_default_gemm(monkeypatch):
+    """Force CPU-compatible GEMM dispatch for tests using CPU tensors.
+
+    vLLM's dispatch_unquantized_gemm() selects the backend by platform
+    (e.g. rocm_unquantized_gemm on AMD machines), not by tensor device.
+    CPU test tensors crash with NotImplementedError on ROCm.  Monkeypatch
+    the dispatcher to always return the default (torch.nn.functional.linear)
+    implementation which works on any device."""
+    from vllm.model_executor.layers.utils import default_unquantized_gemm
+
+    monkeypatch.setattr(
+        "vllm.model_executor.layers.linear.dispatch_unquantized_gemm",
+        lambda: default_unquantized_gemm,
+    )
+
+
+def test_adalayernorm_import_from_shared_module():
+    """Verify imports work from the shared adalayernorm module."""
+    from vllm_omni.diffusion.layers.adalayernorm import (  # noqa: F401
+        AdaLayerNormContinuous,
+        AdaLayerNormZero,
+        AdaLayerNormZeroSingle,
+    )
+
+
+def test_adalayernorm_zero_forward_shape():
+    """AdaLayerNormZero produces correct output shapes (x, gate, shift, scale, gate)."""
+    from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNormZero
+
+    dim = 64
+    batch = 2
+    seq_len = 4
+    norm = AdaLayerNormZero(dim)
+
+    x = torch.randn(batch, seq_len, dim)
+    emb = torch.randn(batch, dim)
+
+    out_x, gate_msa, shift_mlp, scale_mlp, gate_mlp = norm(x, emb)
+
+    assert out_x.shape == (batch, seq_len, dim)
+    assert gate_msa.shape == (batch, dim)
+    assert shift_mlp.shape == (batch, dim)
+    assert scale_mlp.shape == (batch, dim)
+    assert gate_mlp.shape == (batch, dim)
+
+
+def test_adalayernorm_zero_single_forward_shape():
+    """AdaLayerNormZeroSingle produces (x, gate) with correct shapes."""
+    from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNormZeroSingle
+
+    dim = 64
+    batch = 2
+    seq_len = 4
+    norm = AdaLayerNormZeroSingle(dim)
+
+    x = torch.randn(batch, seq_len, dim)
+    emb = torch.randn(batch, dim)
+
+    out_x, gate = norm(x, emb)
+
+    assert out_x.shape == (batch, seq_len, dim)
+    assert gate.shape == (batch, dim)
+
+
+def test_adalayernorm_continuous_forward_shape():
+    """AdaLayerNormContinuous produces correct output shape."""
+    from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNormContinuous
+
+    dim = 64
+    cond_dim = 64
+    batch = 2
+    seq_len = 4
+    norm = AdaLayerNormContinuous(dim, cond_dim)
+
+    x = torch.randn(batch, seq_len, dim)
+    conditioning = torch.randn(batch, cond_dim)
+
+    out = norm(x, conditioning)
+
+    assert out.shape == (batch, seq_len, dim)
+
+
+def test_adalayernorm_zero_accepts_quant_config():
+    """Constructor accepts quant_config=None and prefix='test' without error."""
+    from vllm_omni.diffusion.layers.adalayernorm import (
+        AdaLayerNormContinuous,
+        AdaLayerNormZero,
+        AdaLayerNormZeroSingle,
+    )
+
+    # Should not raise with quant_config=None and prefix
+    AdaLayerNormZero(64, quant_config=None, prefix="test.norm1")
+    AdaLayerNormZeroSingle(64, quant_config=None, prefix="test.norm")
+    AdaLayerNormContinuous(64, 64, quant_config=None, prefix="test.norm_out")
+
+
+def test_adalayernorm_uses_replicated_linear():
+    """Verify .linear is a ReplicatedLinear instance (not nn.Linear)."""
+    from vllm.model_executor.layers.linear import ReplicatedLinear
+
+    from vllm_omni.diffusion.layers.adalayernorm import (
+        AdaLayerNormContinuous,
+        AdaLayerNormZero,
+        AdaLayerNormZeroSingle,
+    )
+
+    norm_zero = AdaLayerNormZero(64)
+    assert isinstance(norm_zero.linear, ReplicatedLinear)
+
+    norm_zero_single = AdaLayerNormZeroSingle(64)
+    assert isinstance(norm_zero_single.linear, ReplicatedLinear)
+
+    norm_continuous = AdaLayerNormContinuous(64, 64)
+    assert isinstance(norm_continuous.linear, ReplicatedLinear)
+
+
+# ── Numerical equivalence tests against diffusers originals ──
+
+
+def _copy_weights(src_linear, dst_replicated_linear):
+    """Copy weights from nn.Linear to ReplicatedLinear for comparison."""
+    dst_replicated_linear.weight.data.copy_(src_linear.weight.data)
+    if src_linear.bias is not None and dst_replicated_linear.bias is not None:
+        dst_replicated_linear.bias.data.copy_(src_linear.bias.data)
+
+
+def test_adalayernorm_zero_matches_diffusers():
+    """Verify AdaLayerNormZero produces identical output to diffusers original."""
+    from diffusers.models.normalization import (
+        AdaLayerNormZero as DiffusersAdaLayerNormZero,
+    )
+
+    from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNormZero
+
+    dim = 64
+    torch.manual_seed(42)
+    ours = AdaLayerNormZero(dim)
+    ref = DiffusersAdaLayerNormZero(dim)
+
+    # Copy weights: nn.Linear -> ReplicatedLinear
+    _copy_weights(ref.linear, ours.linear)
+
+    x = torch.randn(2, 4, dim)
+    emb = torch.randn(2, dim)
+
+    out_ours = ours(x, emb)
+    out_ref = ref(x, emb=emb)
+
+    for o, r in zip(out_ours, out_ref):
+        torch.testing.assert_close(o, r, atol=1e-5, rtol=1e-5)
+
+
+def test_adalayernorm_zero_single_matches_diffusers():
+    """Verify AdaLayerNormZeroSingle produces identical output to diffusers original."""
+    from diffusers.models.normalization import (
+        AdaLayerNormZeroSingle as DiffusersAdaLayerNormZeroSingle,
+    )
+
+    from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNormZeroSingle
+
+    dim = 64
+    torch.manual_seed(42)
+    ours = AdaLayerNormZeroSingle(dim)
+    ref = DiffusersAdaLayerNormZeroSingle(dim)
+
+    _copy_weights(ref.linear, ours.linear)
+
+    x = torch.randn(2, 4, dim)
+    emb = torch.randn(2, dim)
+
+    out_ours = ours(x, emb)
+    out_ref = ref(x, emb=emb)
+
+    for o, r in zip(out_ours, out_ref):
+        torch.testing.assert_close(o, r, atol=1e-5, rtol=1e-5)
+
+
+def test_adalayernorm_continuous_matches_diffusers():
+    """Verify AdaLayerNormContinuous produces identical output to diffusers original."""
+    from diffusers.models.normalization import (
+        AdaLayerNormContinuous as DiffusersAdaLayerNormContinuous,
+    )
+
+    from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNormContinuous
+
+    dim = 64
+    cond_dim = 64
+    torch.manual_seed(42)
+    # Match constructor args: diffusers defaults elementwise_affine=True, eps=1e-5
+    ours = AdaLayerNormContinuous(dim, cond_dim, elementwise_affine=False, eps=1e-6)
+    ref = DiffusersAdaLayerNormContinuous(dim, cond_dim, elementwise_affine=False, eps=1e-6)
+
+    _copy_weights(ref.linear, ours.linear)
+
+    x = torch.randn(2, 4, dim)
+    cond = torch.randn(2, cond_dim)
+
+    out_ours = ours(x, cond)
+    out_ref = ref(x, cond)
+
+    torch.testing.assert_close(out_ours, out_ref, atol=1e-5, rtol=1e-5)