[Kernel][FP8] Initial support with dynamic per-tensor scaling (vllm-p…

…roject#4118)

Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726

This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine.

Algorithm:
We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass.

Initial Results:
Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128:

BF16: 1.47s
FP8: 1.66s
I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.

tests/quantization/test_fp8.py

@@ -0,0 +1,24 @@
+"""Tests whether FP8 computation is enabled correctly.
+
+Run `pytest tests/quantization/test_fp8.py --forked`.
+"""
+import pytest
+import torch
+
+from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
+
+capability = torch.cuda.get_device_capability()
+capability = capability[0] * 10 + capability[1]
+
+
+@pytest.mark.skipif(
+    capability < QUANTIZATION_METHODS["fp8"].get_min_capability(),
+    reason="FP8 is not supported on this GPU type.")
+def test_load_fp16_model(vllm_runner) -> None:
+    llm = vllm_runner("facebook/opt-125m", quantization="fp8")
+
+    model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model
+    fc1 = model.model.decoder.layers[0].fc1
+    assert isinstance(fc1.linear_method, Fp8LinearMethod)
+    assert fc1.weight.dtype == torch.float8_e4m3fn

vllm/entrypoints/llm.py

@@ -42,10 +42,11 @@ class LLM:
             However, if the `torch_dtype` in the config is `float32`, we will
             use `float16` instead.
         quantization: The method used to quantize the model weights. Currently,
-            we support "awq", "gptq" and "squeezellm". If None, we first check
-            the `quantization_config` attribute in the model config file. If
-            that is None, we assume the model weights are not quantized and use
-            `dtype` to determine the data type of the weights.
+            we support "awq", "gptq", "squeezellm", and "fp8" (experimental).
+            If None, we first check the `quantization_config` attribute in the
+            model config file. If that is None, we assume the model weights are
+            not quantized and use `dtype` to determine the data type of
+            the weights.
         revision: The specific model version to use. It can be a branch name,
             a tag name, or a commit id.
         tokenizer_revision: The specific tokenizer version to use. It can be a

vllm/model_executor/layers/linear.py

@@ -3,6 +3,7 @@
 
 import torch
 import torch.nn.functional as F
+from torch import nn
 from torch.nn.parameter import Parameter
 
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
@@ -48,6 +49,13 @@ def apply_weights(self,
         Expects create_weights to have been called before on the layer."""
         raise NotImplementedError
 
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        """Process the weight after loading.
+
+        This can be used for example, to transpose weights for computation.
+        """
+        return
+
 
 class UnquantizedLinearMethod(LinearMethodBase):
     """Linear method without quantization.

vllm/model_executor/layers/quantization/__init__.py

@@ -3,12 +3,14 @@
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.quantization.fp8 import FP8Config
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 
 QUANTIZATION_METHODS = {
     "awq": AWQConfig,
+    "fp8": FP8Config,
     "gptq": GPTQConfig,
     "squeezellm": SqueezeLLMConfig,
     "marlin": MarlinConfig,

vllm/model_executor/layers/quantization/fp8.py

@@ -0,0 +1,138 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               set_weight_attrs)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+
+class FP8Config(QuantizationConfig):
+    """Config class for FP8."""
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "fp8"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # TODO: PyTorch 2.3.0+ is required to run FP8 on
+        # SM 89 (e.g. Ada) GPUs. Specifically, this PR has to
+        # be included: https://github.com/pytorch/pytorch/pull/118881
+        return 90
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "FP8Config":
+        return cls()
+
+    def get_linear_method(self) -> "Fp8LinearMethod":
+        return Fp8LinearMethod(self)
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class Fp8LinearMethod(LinearMethodBase):
+    """Linear method for FP8.
+    We now support common FP16/BF16 model checkpoints ONLY. The weight
+    scaling factor will be initialized after the model weights are loaded.
+
+    Limitations:
+    1. Only support per-tensor quantization due to torch._scaled_mm support.
+    2. Only support float8_e4m3fn data type due to the limitation of
+       torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)
+       
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __init__(self, quant_config: FP8Config):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_size_per_partition: int,
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        weight = Parameter(torch.empty(output_size_per_partition,
+                                       input_size_per_partition,
+                                       dtype=params_dtype),
+                           requires_grad=False)
+        layer.register_parameter("weight", weight)
+        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
+        set_weight_attrs(weight, extra_weight_attrs)
+
+        w_scale = Parameter(
+            torch.empty(1, dtype=torch.float32),
+            requires_grad=False,
+        )
+        layer.register_parameter("weight_scaling_factor", w_scale)
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        # Although the linear_method is propagated to all layers,
+        # only linear layers invoke "create_weights". So we check
+        # whether "weight_scaling_facor" is registered to determine
+        # whether the layer is a linear layer that requires quantization.
+        if not hasattr(layer, "weight_scaling_factor"):
+            return
+
+        qweight, weight_scale = per_tensor_quantize(layer.weight)
+        # torch._scaled_mm requires column-major in the second
+        # input (weight), so we transpose the quantized weight.
+        layer.weight = Parameter(qweight.t(), requires_grad=False)
+        layer.weight_scaling_factor.data.copy_(weight_scale)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        qinput, x_scale = per_tensor_quantize(x)
+        output, _ = torch._scaled_mm(
+            qinput,
+            layer.weight,
+            out_dtype=x.dtype,
+            scale_a=x_scale,
+            scale_b=layer.weight_scaling_factor,
+            bias=bias,
+        )
+        return output
+
+
+def per_tensor_quantize(tensor: torch.Tensor) -> tuple[torch.Tensor, float]:
+    """Quantize a tensor using per-tensor static scaling factor.
+
+    Args:
+        tensor: The input tensor.
+    """
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    # Calculate the scale as dtype max divided by absmax.
+    # Since .abs() creates a new tensor, we use aminmax to get
+    # the min and max first and then calculate the absmax.
+    min_val, max_val = tensor.aminmax()
+    amax = min_val.abs().max(max_val.abs())
+    scale = finfo.max / amax.clamp(min=1e-12)
+    # scale and clamp the tensor to bring it to
+    # the representative range of float8 data type
+    # (as default cast is unsaturated)
+    qweight = (tensor * scale).clamp(min=finfo.min, max=finfo.max)
+    # Return both float8 data and the inverse scale (as float),
+    # as both required as inputs to torch._scaled_mm
+    qweight = qweight.to(torch.float8_e4m3fn)
+    scale = scale.float().reciprocal()
+    return qweight, scale

vllm/model_executor/model_loader/loader.py

@@ -228,6 +228,10 @@ def load_model(self, *, model_config: ModelConfig,
                                                model,
                                                "fall_back_to_pt_during_load",
                                                True)), )
+            for _, module in model.named_modules():
+                linear_method = getattr(module, "linear_method", None)
+                if linear_method is not None:
+                    linear_method.process_weights_after_loading(module)
         return model.eval()
 
 

vllm/model_executor/model_loader/weight_utils.py

@@ -134,11 +134,18 @@ def get_quant_config(model_config: ModelConfig,
                                           tqdm_class=DisabledTqdm)
     else:
         hf_folder = model_name_or_path
+
+    possible_config_filenames = quant_cls.get_config_filenames()
+
+    # If the quantization config is not found, use the default config.
+    if not possible_config_filenames:
+        return quant_cls()
+
     config_files = glob.glob(os.path.join(hf_folder, "*.json"))
 
     quant_config_files = [
         f for f in config_files if any(
-            f.endswith(x) for x in quant_cls.get_config_filenames())
+            f.endswith(x) for x in possible_config_filenames)
     ]
     if len(quant_config_files) == 0:
         raise ValueError(