Move FP8 to SGLang (#2370)

Co-authored-by: HaiShaw <hixiao@gmail.com>

python/sglang/srt/layers/quantization/__init__.py

@@ -13,7 +13,6 @@ from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tenso
 from vllm.model_executor.layers.quantization.deepspeedfp import DeepSpeedFPConfig
 from vllm.model_executor.layers.quantization.experts_int8 import ExpertsInt8Config
 from vllm.model_executor.layers.quantization.fbgemm_fp8 import FBGEMMFp8Config
-from vllm.model_executor.layers.quantization.fp8 import Fp8Config, Fp8MoEMethod
 from vllm.model_executor.layers.quantization.gguf import GGUFConfig
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.gptq_marlin import GPTQMarlinConfig
@@ -23,6 +22,7 @@ from vllm.model_executor.layers.quantization.qqq import QQQConfig
 from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
 
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.quantization.fp8 import Fp8Config, Fp8MoEMethod
 
 QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "aqlm": AQLMConfig,
@@ -100,13 +100,13 @@ def fp8_moe_apply(
 def fp8_get_quant_method(self, layer, prefix):
     """Enhanced get_quant_method for FP8 config."""
     from vllm.model_executor.layers.linear import LinearBase
-    from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
     from vllm.model_executor.layers.quantization.utils.quant_utils import (
         is_layer_skipped,
     )
 
     from sglang.srt.layers.fused_moe_triton.layer import FusedMoE
     from sglang.srt.layers.linear import UnquantizedLinearMethod
+    from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
 
     if isinstance(layer, LinearBase):
         if is_layer_skipped(prefix, self.ignored_layers):

python/sglang/srt/layers/quantization/fp8.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py
+
+import logging
+from typing import Any, Callable, Dict, List, Optional
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    apply_fp8_marlin_linear,
+    prepare_fp8_layer_for_marlin,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import is_layer_skipped
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    all_close_1d,
+    apply_fp8_linear,
+    convert_to_channelwise,
+    cutlass_fp8_supported,
+    per_tensor_dequantize,
+    requantize_with_max_scale,
+)
+from vllm.model_executor.parameter import ModelWeightParameter, PerTensorScaleParameter
+
+from sglang.srt.layers.fused_moe_triton import (
+    FusedMoE,
+    FusedMoEMethodBase,
+    FusedMoeWeightScaleSupported,
+)
+from sglang.srt.layers.linear import LinearMethodBase, UnquantizedLinearMethod
+from sglang.srt.layers.quantization.base_config import (
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
+from sglang.srt.utils import (
+    get_bool_env_var,
+    is_hip,
+    print_warning_once,
+    set_weight_attrs,
+)
+
+ACTIVATION_SCHEMES = ["static", "dynamic"]
+
+logger = logging.getLogger(__name__)
+
+
+class Fp8Config(QuantizationConfig):
+    """Config class for FP8."""
+
+    def __init__(
+        self,
+        is_checkpoint_fp8_serialized: bool = False,
+        activation_scheme: str = "dynamic",
+        ignored_layers: Optional[List[str]] = None,
+    ) -> None:
+        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
+        if is_checkpoint_fp8_serialized:
+            logger.warning(
+                "Detected fp8 checkpoint. Please note that the "
+                "format is experimental and subject to change."
+            )
+        if activation_scheme not in ACTIVATION_SCHEMES:
+            raise ValueError(f"Unsupported activation scheme {activation_scheme}")
+        self.activation_scheme = activation_scheme
+        self.ignored_layers = ignored_layers or []
+
+    @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:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
+        quant_method = cls.get_from_keys(config, ["quant_method"])
+        is_checkpoint_fp8_serialized = "fp8" in quant_method
+        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
+        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
+        return cls(
+            is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
+            activation_scheme=activation_scheme,
+            ignored_layers=ignored_layers,
+        )
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+
+        if isinstance(layer, LinearBase):
+            if is_layer_skipped(prefix, self.ignored_layers):
+                return UnquantizedLinearMethod()
+            return Fp8LinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return Fp8MoEMethod(self)
+        elif isinstance(layer, Attention):
+            return Fp8KVCacheMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class Fp8LinearMethod(LinearMethodBase):
+    """Linear method for FP8.
+    Supports loading FP8 checkpoints with static weight scale and
+    dynamic/static activation scale.
+
+    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
+    activation scaling. 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
+        self.cutlass_fp8_supported = cutlass_fp8_supported()
+
+        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
+        # kernel for fast weight-only FP8 quantization
+        self.use_marlin = get_bool_env_var("SGLANG_FORCE_FP8_MARLIN")
+        # Disable marlin for ROCm
+        if is_hip():
+            self.use_marlin = False
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        layer.logical_widths = output_partition_sizes
+
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.orig_dtype = params_dtype
+
+        # WEIGHT
+        weight_dtype = (
+            torch.float8_e4m3fn
+            if self.quant_config.is_checkpoint_fp8_serialized
+            else params_dtype
+        )
+
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition, input_size_per_partition, dtype=weight_dtype
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+
+        # If checkpoint is serialized fp8, load them.
+        # Otherwise, wait until process_weights_after_loading.
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            # WEIGHT SCALE
+            scale = PerTensorScaleParameter(
+                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+                weight_loader=weight_loader,
+            )
+
+            scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("weight_scale", scale)
+
+            # INPUT ACTIVATION SCALE
+            if self.quant_config.activation_scheme == "static":
+                scale = PerTensorScaleParameter(
+                    data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+                    weight_loader=weight_loader,
+                )
+
+                scale[:] = torch.finfo(torch.float32).min
+                layer.register_parameter("input_scale", scale)
+            else:
+                layer.register_parameter("input_scale", None)
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
+        # If checkpoint not serialized fp8, quantize the weights.
+        if not self.quant_config.is_checkpoint_fp8_serialized:
+            qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None)
+
+            # If using marlin (w8a16), kernel uses channelwise weights,
+            # so extend the weight scales to be channelwise.
+            if self.use_marlin:
+                assert weight_scale.numel() == 1
+                weight_scale = convert_to_channelwise(
+                    weight_scale.expand(len(layer.logical_widths)), layer.logical_widths
+                )
+
+            # Update the layer with the new values.
+            layer.weight = Parameter(qweight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+            layer.input_scale = None
+
+        # If checkpoint is fp8, handle that there are N scales for N
+        # shards in a fused module
+        else:
+            layer.weight_scale = torch.nn.Parameter(
+                layer.weight_scale.data, requires_grad=False
+            )
+            if self.quant_config.activation_scheme == "static":
+                layer.input_scale = torch.nn.Parameter(
+                    layer.input_scale.data, requires_grad=False
+                )
+            # If using marlin (w8a16), kernel uses channelwise weights,
+            # so extend the weight scales to be channelwise.
+            if self.use_marlin:
+                weight = layer.weight
+                weight_scale = convert_to_channelwise(
+                    layer.weight_scale, layer.logical_widths
+                )
+
+            # If using w8a8, torch._scaled_mm needs per tensor, so
+            # requantize the logical shards as a single weight.
+            else:
+                # Dequant -> Quant with max scale so we can run per tensor.
+                weight = layer.weight
+                weight_scale = layer.weight_scale
+
+                # If ROCm, normalize the weights and scales to e4m3fnuz
+                if is_hip():
+                    weight, weight_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                        weight=weight,
+                        weight_scale=weight_scale,
+                        input_scale=layer.input_scale,
+                    )
+                    if input_scale is not None:
+                        layer.input_scale = Parameter(input_scale, requires_grad=False)
+
+                weight_scale, weight = requantize_with_max_scale(
+                    weight=weight,
+                    weight_scale=weight_scale,
+                    logical_widths=layer.logical_widths,
+                )
+
+            # Update layer with new values.
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+            if self.quant_config.activation_scheme == "static":
+                layer.input_scale = Parameter(
+                    layer.input_scale.max(), requires_grad=False
+                )
+
+        if self.use_marlin:
+            prepare_fp8_layer_for_marlin(layer)
+            # Activations not quantized for marlin.
+            del layer.input_scale
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        if self.use_marlin:
+            return apply_fp8_marlin_linear(
+                input=x,
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                workspace=layer.workspace,
+                size_n=layer.output_size_per_partition,
+                size_k=layer.input_size_per_partition,
+                bias=bias,
+            )
+
+        return apply_fp8_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            input_scale=layer.input_scale,
+            bias=bias,
+            cutlass_fp8_supported=self.cutlass_fp8_supported,
+            use_per_token_if_dynamic=False,
+        )
+
+
+class Fp8MoEMethod(FusedMoEMethodBase):
+    """MoE method for FP8.
+    Supports loading FP8 checkpoints with static weight scale and
+    dynamic/static activation scale.
+
+    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
+    activation scaling. The weight scaling factor will be initialized after
+    the model weights are loaded.
+
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __init__(self, quant_config: Fp8Config):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            params_dtype = torch.float8_e4m3fn
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts, hidden_size, intermediate_size, dtype=params_dtype
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        # Allocate 2 scales for w1 and w3 respectively.
+        # They will be combined to a single scale after weight loading.
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
+        )
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+
+        w2_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+        )
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        # Add the quantization method used (per tensor/grouped/channel)
+        # to ensure the weight scales are loaded in properly
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
+        )
+        # If loading fp8 checkpoint, pass the weight loaders.
+        # If loading an fp16 checkpoint, do not (we will quantize in
+        #   process_weights_after_loading()
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+            set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        if self.quant_config.activation_scheme == "static":
+            if not self.quant_config.is_checkpoint_fp8_serialized:
+                raise ValueError(
+                    "Found static activation scheme for checkpoint that "
+                    "was not serialized fp8."
+                )
+
+            w13_input_scale = torch.nn.Parameter(
+                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+            )
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+            w2_input_scale = torch.nn.Parameter(
+                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+            )
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+            set_weight_attrs(w2_input_scale, extra_weight_attrs)
+
+        else:
+            layer.w13_input_scale = None
+            layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+
+        # If checkpoint is fp16, quantize in place.
+        if not self.quant_config.is_checkpoint_fp8_serialized:
+            # If ROCm, use float8_e4m3fnuz instead (MI300x HW)
+            fp8_dtype = torch.float8_e4m3fnuz if is_hip() else torch.float8_e4m3fn
+            w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype)
+            w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)
+
+            # Re-initialize w13_scale because we directly quantize
+            # merged w13 weights and generate a single scaling factor.
+            layer.w13_weight_scale = torch.nn.Parameter(
+                torch.ones(
+                    layer.num_experts, dtype=torch.float32, device=w13_weight.device
+                ),
+                requires_grad=False,
+            )
+            for expert in range(layer.num_experts):
+                w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
+                    ops.scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
+                )
+                w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
+                    ops.scaled_fp8_quant(layer.w2_weight.data[expert, :, :])
+                )
+            layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
+            return
+
+        # If checkpoint is fp8, we need to handle that the
+        # MoE kernels require single activation scale and single weight
+        # scale for w13 per expert.
+        else:
+            # Fp8 moe kernels require a single activation scale.
+            # We take the max of all the scales in case they differ.
+            if self.quant_config.activation_scheme == "static":
+                if layer.w13_input_scale is None or layer.w2_input_scale is None:
+                    raise ValueError(
+                        "QuantConfig has static quantization, but found "
+                        "activation scales are None."
+                    )
+                if not all_close_1d(layer.w13_input_scale) or not all_close_1d(
+                    layer.w2_input_scale
+                ):
+                    print_warning_once(
+                        "Found input_scales that are not equal for "
+                        "fp8 MoE layer. Using the maximum across experts "
+                        "for each layer. "
+                    )
+                layer.w13_input_scale = torch.nn.Parameter(
+                    layer.w13_input_scale.max(), requires_grad=False
+                )
+                layer.w2_input_scale = torch.nn.Parameter(
+                    layer.w2_input_scale.max(), requires_grad=False
+                )
+            # If ROCm, normalize the weights and scales to e4m3fnuz
+            if is_hip():
+                # Normalize the weights and scales
+                w13_weight, w13_weight_scale, w13_input_scale = (
+                    normalize_e4m3fn_to_e4m3fnuz(
+                        layer.w13_weight, layer.w13_weight_scale, layer.w13_input_scale
+                    )
+                )
+                w2_weight, w2_weight_scale, w2_input_scale = (
+                    normalize_e4m3fn_to_e4m3fnuz(
+                        layer.w2_weight, layer.w2_weight_scale, layer.w2_input_scale
+                    )
+                )
+                # Reset the parameter
+                layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
+                layer.w13_weight_scale = torch.nn.Parameter(
+                    w13_weight_scale, requires_grad=False
+                )
+                if w13_input_scale is not None:
+                    layer.w13_input_scale = torch.nn.Parameter(
+                        w13_input_scale, requires_grad=False
+                    )
+                layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
+                layer.w2_weight_scale = torch.nn.Parameter(
+                    w2_weight_scale, requires_grad=False
+                )
+                if w2_input_scale is not None:
+                    layer.w2_input_scale = torch.nn.Parameter(
+                        w2_input_scale, requires_grad=False
+                    )
+
+            # Fp8 moe kernel needs single weight scale for w13 per expert.
+            # We take the max then dequant and requant each expert.
+            assert layer.w13_weight_scale is not None
+            shard_size = layer.intermediate_size_per_partition
+            max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+            for expert_id in range(layer.num_experts):
+                start = 0
+                for shard_id in range(2):
+                    dq_weight = per_tensor_dequantize(
+                        layer.w13_weight[expert_id][start : start + shard_size, :],
+                        layer.w13_weight_scale[expert_id][shard_id],
+                    )
+                    layer.w13_weight[expert_id][start : start + shard_size, :], _ = (
+                        ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
+                    )
+                    start += shard_size
+
+            layer.w13_weight_scale = torch.nn.Parameter(
+                max_w13_scales, requires_grad=False
+            )
+            return
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe import fused_experts
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+        )
+
+        return fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            use_fp8_w8a8=True,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+        )
+
+
+class Fp8KVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from FP8 checkpoints.
+    """
+
+    def __init__(self, quant_config: Fp8Config):
+        super().__init__(quant_config)