Support FP8 Quantization and Inference Run on Intel Gaudi (HPU) using INC...

Support FP8 Quantization and Inference Run on Intel Gaudi (HPU) using INC (Intel Neural Compressor) (#12010)
Signed-off-by: Nir David <ndavid@habana.ai>
Signed-off-by: Uri Livne <ulivne@habana.ai>
Co-authored-by: Uri Livne <ulivne@habana.ai>

docs/features/quantization/README.md

@@ -10,6 +10,7 @@ Contents:
 - [BitBLAS](bitblas.md)
 - [GGUF](gguf.md)
 - [GPTQModel](gptqmodel.md)
+- [INC](inc.md)
 - [INT4 W4A16](int4.md)
 - [INT8 W8A8](int8.md)
 - [FP8 W8A8](fp8.md)

docs/features/quantization/inc.md

+---
+title: FP8 INC
+---
+[](){ #inc }
+
+vLLM supports FP8 (8-bit floating point) weight and activation quantization using Intel® Neural Compressor (INC) on Intel® Gaudi® 2 and Intel® Gaudi® 3 AI accelerators.
+Currently, quantization is validated only in Llama models.
+
+Intel Gaudi supports quantization of various modules and functions, including, but not limited to `Linear`, `KVCache`, `Matmul` and `Softmax`. For more information, please refer to:
+[Supported Modules\\Supported Functions\\Custom Patched Modules](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Quantization/Inference_Using_FP8.html#supported-modules).
+
+!!! note
+    Measurement files are required to run quantized models with vLLM on Gaudi accelerators. The FP8 model calibration procedure is described in the [vllm-hpu-extention](https://github.com/HabanaAI/vllm-hpu-extension/tree/main/calibration/README.md) package.
+
+!!! note
+    `QUANT_CONFIG` is an environment variable that points to the measurement or quantization [JSON config file](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Quantization/Inference_Using_FP8.html#supported-json-config-file-options).
+    The measurement configuration file is used during the calibration procedure to collect measurements for a given model. The quantization configuration is used during inference.
+
+## Run Online Inference Using FP8
+
+Once you've completed the model calibration process and collected the measurements, you can run FP8 inference with vLLM using the following command:
+
+```bash
+export QUANT_CONFIG=/path/to/quant/config/inc/meta-llama-3.1-405b-instruct/maxabs_measure_g3.json
+vllm serve meta-llama/Llama-3.1-405B-Instruct --quantization inc --kv-cache-dtype fp8_inc --tensor_paralel_size 8
+```
+
+!!! tip
+    If you are just prototyping or testing your model with FP8, you can use the `VLLM_SKIP_WARMUP=true` environment variable to disable the warmup stage, which can take a long time. However, we do not recommend disabling this feature in production environments as it causes a significant performance drop.
+
+!!! tip
+    When using FP8 models, you may experience timeouts caused by the long compilation time of FP8 operations. To mitigate this problem, you can use the below environment variables:
+    `VLLM_ENGINE_ITERATION_TIMEOUT_S` - to adjust the vLLM server timeout. You can set the value in seconds, e.g., 600 equals 10 minutes.
+    `VLLM_RPC_TIMEOUT` - to adjust the RPC protocol timeout used by the OpenAI-compatible API. This value is in microseconds, e.g., 600000 equals 10 minutes.
+
+## Run Offline Inference Using FP8
+
+To run offline inference (after completing the model calibration process):
+
+* Set the "QUANT_CONFIG" environment variable to point to a JSON configuration file with QUANTIZE mode.
+* Pass `quantization=inc` and `kv_cache_dtype=fp8_inc` as parameters to the `LLM` object.
+* Call shutdown method of the model_executor at the end of the run.
+
+```python
+from vllm import LLM
+llm = LLM("llama3.1/Meta-Llama-3.1-8B-Instruct", quantization="inc", kv_cache_dtype="fp8_inc")
+...
+# Call llm.generate on the required prompts and sampling params.
+...
+llm.llm_engine.model_executor.shutdown()
+```
+
+## Device for the Model's Weights Uploading
+
+The unquantized weights are first loaded onto the CPU, then quantized and transferred to the target device (HPU) for model execution.
+This reduces the device memory footprint of model weights, as only quantized weights are stored in the device memory.

docs/features/quantization/supported_hardware.md

@@ -2,18 +2,19 @@
 
 The table below shows the compatibility of various quantization implementations with different hardware platforms in vLLM:
 
-| Implementation        | Volta   | Turing   | Ampere   | Ada   | Hopper   | AMD GPU   | Intel GPU   | x86 CPU   | AWS Neuron   | Google TPU   |
-|-----------------------|---------|----------|----------|-------|----------|-----------|-------------|-----------|------------------|--------------|
-| AWQ                   | ❌       | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ✅︎          | ✅︎        | ❌                | ❌            |
-| GPTQ                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ✅︎          | ✅︎        | ❌                | ❌            |
-| Marlin (GPTQ/AWQ/FP8) | ❌       | ❌        | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌           | ❌         | ❌                | ❌            |
-| INT8 (W8A8)           | ❌       | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌           | ✅︎        | ✅︎                | ✅︎           |
-| FP8 (W8A8)            | ❌       | ❌        | ❌        | ✅︎    | ✅︎       | ✅︎        | ❌           | ❌         | ✅︎                | ❌            |
-| BitBLAS (GPTQ)        | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌           | ❌         | ❌                | ❌            |
-| AQLM                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌           | ❌         | ❌                | ❌            |
-| bitsandbytes          | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌           | ❌         | ❌                | ❌            |
-| DeepSpeedFP           | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌           | ❌         | ❌                | ❌            |
-| GGUF                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ✅︎        | ❌           | ❌         | ❌                | ❌            |
+| Implementation        | Volta   | Turing   | Ampere   | Ada   | Hopper   | AMD GPU   | Intel GPU   | Intel Gaudi | x86 CPU   | AWS Neuron   | Google TPU   |
+|-----------------------|---------|----------|----------|-------|----------|-----------|-------------|-------------|-----------|--------------|--------------|
+| AWQ                   | ❌      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌        | ✅︎          | ❌         | ✅︎        | ❌           | ❌           |
+| GPTQ                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎        | ❌        | ✅︎          | ❌         | ✅︎        | ❌           | ❌           |
+| Marlin (GPTQ/AWQ/FP8) | ❌      | ❌      | ✅︎       | ✅︎    | ✅︎       | ❌        | ❌          | ❌         | ❌        | ❌          | ❌           |
+| INT8 (W8A8)           | ❌      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌        | ❌          | ❌         | ✅︎        | ✅︎           | ✅︎            |
+| FP8 (W8A8)            | ❌      | ❌      | ❌       | ✅︎    | ✅︎      | ✅︎         | ❌          | ❌         | ❌        | ✅︎           | ❌           |
+| BitBLAS (GPTQ)        | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
+| AQLM                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
+| bitsandbytes          | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
+| DeepSpeedFP           | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        | ❌          | ❌           |
+| GGUF                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ✅︎         | ❌          | ❌         | ❌         | ❌          | ❌           |
+| INC (W8A8)            | ❌      | ❌      | ❌      | ❌    | ❌      | ❌        | ❌          | ✅︎         | ❌         | ❌           | ❌          |
 
 - Volta refers to SM 7.0, Turing to SM 7.5, Ampere to SM 8.0/8.6, Ada to SM 8.9, and Hopper to SM 9.0.
 - ✅︎ indicates that the quantization method is supported on the specified hardware.

docs/getting_started/installation/intel_gaudi.md

@@ -28,7 +28,7 @@ To verify that the Intel Gaudi software was correctly installed, run:
 hl-smi # verify that hl-smi is in your PATH and each Gaudi accelerator is visible
 apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core, habanalabs-thunk and habanalabs-container-runtime are installed
 pip list | grep habana # verify that habana-torch-plugin, habana-torch-dataloader, habana-pyhlml and habana-media-loader are installed
-pip list | grep neural # verify that neural_compressor is installed
+pip list | grep neural # verify that neural_compressor_pt is installed
 ```
 
 Refer to [Intel Gaudi Software Stack Verification](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html#platform-upgrade)
@@ -120,12 +120,13 @@ docker run \
 - Inference with [HPU Graphs](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html)
   for accelerating low-batch latency and throughput
 - Attention with Linear Biases (ALiBi)
+- INC quantization
 
 ### Unsupported features
 
 - Beam search
 - LoRA adapters
-- Quantization
+- AWQ quantization
 - Prefill chunking (mixed-batch inferencing)
 
 ### Supported configurations

vllm/config.py

@@ -963,7 +963,7 @@ class ModelConfig:
         optimized_quantization_methods = [
             "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
             "awq_marlin", "fbgemm_fp8", "compressed-tensors", "experts_int8",
-            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas"
+            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas", "inc"
         ]
         if self.quantization is not None:
             self.quantization = cast(me_quant.QuantizationMethods,
@@ -1563,7 +1563,7 @@ class ModelConfig:
 
 
 BlockSize = Literal[1, 8, 16, 32, 64, 128]
-CacheDType = Literal["auto", "fp8", "fp8_e4m3", "fp8_e5m2"]
+CacheDType = Literal["auto", "fp8", "fp8_e4m3", "fp8_e5m2", "fp8_inc"]
 PrefixCachingHashAlgo = Literal["builtin", "sha256", "sha256_cbor_64bit"]
 
 
@@ -1593,7 +1593,7 @@ class CacheConfig:
     cache_dtype: CacheDType = "auto"
     """Data type for kv cache storage. If "auto", will use model data type.
     CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ROCm (AMD GPU) supports
-    fp8 (=fp8_e4m3)."""
+    fp8 (=fp8_e4m3). Intel Gaudi (HPU) supports fp8 (using fp8_inc)."""
     is_attention_free: bool = False
     """Whether the model is attention-free. This is primarily set in
     `ModelConfig` and that value should be manually duplicated here."""
@@ -1691,7 +1691,7 @@ class CacheConfig:
                 "Using fp8 data type to store kv cache. It reduces the GPU "
                 "memory footprint and boosts the performance. "
                 "Meanwhile, it may cause accuracy drop without a proper "
-                "scaling factor")
+                "scaling factor.")
         else:
             raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")
 
@@ -1781,6 +1781,9 @@ class LoadConfig:
         default_factory=dict)
     """Extra config for model loader. This will be passed to the model loader
     corresponding to the chosen load_format."""
+    device: Optional[str] = None
+    """Device to which model weights will be loaded, default to
+    device_config.device"""
     ignore_patterns: Optional[Union[list[str], str]] = None
     """The list of patterns to ignore when loading the model. Default to
     "original/**/*" to avoid repeated loading of llama's checkpoints."""
@@ -1907,7 +1910,7 @@ class ParallelConfig:
     or equal to the number of GPUs available, "mp" will be used to
     keep processing on a single host. Otherwise, this will default
     to "ray" if Ray is installed and fail otherwise. Note that tpu
-    and hpu only support Ray for distributed inference."""
+    only support Ray for distributed inference."""
 
     worker_cls: str = "auto"
     """The full name of the worker class to use. If "auto", the worker class

vllm/engine/arg_utils.py

@@ -139,6 +139,10 @@ def get_type_hints(type_hint: TypeHint) -> set[TypeHint]:
     return type_hints
 
 
+def is_online_quantization(quantization: Any) -> bool:
+    return quantization in ["inc"]
+
+
 @functools.lru_cache(maxsize=30)
 def _compute_kwargs(cls: ConfigType) -> dict[str, Any]:
     cls_docs = get_attr_docs(cls)
@@ -960,6 +964,8 @@ class EngineArgs:
         return LoadConfig(
             load_format=self.load_format,
             download_dir=self.download_dir,
+            device="cpu"
+            if is_online_quantization(self.quantization) else None,
             model_loader_extra_config=self.model_loader_extra_config,
             ignore_patterns=self.ignore_patterns,
             use_tqdm_on_load=self.use_tqdm_on_load,
@@ -1359,7 +1365,9 @@ class EngineArgs:
             supported = False
             if current_platform.is_rocm() or (
                     current_platform.is_cuda()
-                    and current_platform.is_device_capability(100)):
+                    and current_platform.is_device_capability(100)) or (
+                        current_platform.device_name
+                        == "hpu"):  # handle hpu also for OOT platform
                 supported = True
             elif fp8_attention and will_use_fa:
                 from vllm.attention.utils.fa_utils import (

vllm/model_executor/layers/quantization/__init__.py

@@ -36,6 +36,7 @@ QuantizationMethods = Literal[
     "torchao",
     "auto-round",
     "rtn",
+    "inc",
 ]
 QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
 
@@ -104,6 +105,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     from .gptq_marlin import GPTQMarlinConfig
     from .gptq_marlin_24 import GPTQMarlin24Config
     from .hqq_marlin import HQQMarlinConfig
+    from .inc import INCConfig
     from .ipex_quant import IPEXConfig
     from .marlin import MarlinConfig
     from .modelopt import ModelOptFp8Config, ModelOptNvFp4Config
@@ -144,7 +146,8 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         "moe_wna16": MoeWNA16Config,
         "torchao": TorchAOConfig,
         "auto-round": AutoRoundConfig,
-        "rtn": RTNConfig
+        "rtn": RTNConfig,
+        "inc": INCConfig,
     }
     # Update the `method_to_config` with customized quantization methods.
     method_to_config.update(_CUSTOMIZED_METHOD_TO_QUANT_CONFIG)

vllm/model_executor/layers/quantization/inc.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Intel Gaudi supports quantization of various modules and functions,
+# including, but not limited to `Linear`, `KVCache`, `Matmul` and `Softmax`.
+# During model loading,
+# INC will patch layers with quantization/dequantization operators.
+# Meanwhile, INC will convert original weight to target datatype
+# and loading to target device.
+# static scaling should be provided through Quant_CONFIG:
+# `QUANT_CONFIG` is an environment variable,
+# that points to the measurement or quantization JSON config file.
+# The measurement configuration file is used during the calibration procedure,
+# to collect measurements for a given model.
+# The quantization configuration is used during inference.
+# For more information, please refer to:
+# https://docs.habana.ai/en/v1.21.1/PyTorch/vLLM_Inference/vLLM_FP8_Inference.html
+
+from typing import Any, Optional
+
+import torch
+
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, UnquantizedFusedMoEMethod)
+from vllm.model_executor.layers.linear import (LinearBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+
+
+class INCConfig(QuantizationConfig):
+    """Config class for FP8 using Intel Neural Compressor."""
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "inc"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "INCConfig":
+        raise AssertionError
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return UnquantizedLinearMethod()
+        elif isinstance(layer, FusedMoE):
+            return UnquantizedFusedMoEMethod(layer.moe_config)
+        return None
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise AssertionError
+
+    @staticmethod
+    def get_config_filenames() -> list[str]:
+        return []

vllm/model_executor/model_loader/base_loader.py

@@ -6,9 +6,12 @@ import torch
 import torch.nn as nn
 
 from vllm.config import LoadConfig, ModelConfig, VllmConfig
+from vllm.logger import init_logger
 from vllm.model_executor.model_loader.utils import (
     initialize_model, process_weights_after_loading, set_default_torch_dtype)
 
+logger = init_logger(__name__)
+
 
 class BaseModelLoader(ABC):
     """Base class for model loaders."""
@@ -32,11 +35,16 @@ class BaseModelLoader(ABC):
                    model_config: ModelConfig) -> nn.Module:
         """Load a model with the given configurations."""
         device_config = vllm_config.device_config
-        target_device = torch.device(device_config.device)
+        load_config = vllm_config.load_config
+        load_device = device_config.device if load_config.device is None else \
+                      load_config.device
+        target_device = torch.device(load_device)
         with set_default_torch_dtype(model_config.dtype):
             with target_device:
                 model = initialize_model(vllm_config=vllm_config,
                                          model_config=model_config)
+
+            logger.debug("Loading weights on %s ...", load_device)
             # Quantization does not happen in `load_weights` but after it
             self.load_weights(model, model_config)
             process_weights_after_loading(model, model_config, target_device)

vllm/model_executor/model_loader/weight_utils.py

@@ -152,8 +152,8 @@ def get_quant_config(model_config: ModelConfig,
     quant_cls = get_quantization_config(model_config.quantization)
 
     # GGUF doesn't have config file
-    if model_config.quantization == "gguf":
-        return quant_cls.from_config({})
+    if model_config.quantization in ("gguf", "inc"):
+        return quant_cls()
 
     # Read the quantization config from the HF model config, if available.
     hf_quant_config = getattr(model_config.hf_config, "quantization_config",

vllm/utils/__init__.py

@@ -179,6 +179,7 @@ STR_DTYPE_TO_TORCH_DTYPE = {
     "fp8_e4m3": torch.uint8,
     "fp8_e5m2": torch.uint8,
     "int8": torch.int8,
+    "fp8_inc": torch.float8_e4m3fn,
 }
 
 TORCH_DTYPE_TO_NUMPY_DTYPE = {