vllm.model_executor.layers.quantization.quark.quark ¶

class QuarkConfig(QuantizationConfig):
    def __init__(
        self,
        quant_config: dict[str, Any],
        kv_cache_group: list[str] | None = None,
        kv_cache_config: dict[str, Any] | None = None,
        pack_method: str = "reorder",
    ):
        super().__init__()
        if kv_cache_group is None:
            kv_cache_group = []
        self.quant_config = quant_config
        self.kv_cache_group = kv_cache_group
        self.kv_cache_config = kv_cache_config
        self.pack_method = pack_method

    def get_linear_method(self) -> "QuarkLinearMethod":
        return QuarkLinearMethod(self)

    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
        return [torch.float16, torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        return 70

    def get_name(self) -> QuantizationMethods:
        return "quark"

    def apply_vllm_mapper(  # noqa: B027
        self, hf_to_vllm_mapper: "WeightsMapper"
    ):
        """
        Interface for models to update module names referenced in
        quantization configs in order to reflect the vllm model structure

        :param hf_to_vllm_mapper: maps from hf model structure (the assumed
            structure of the qconfig) to vllm model structure
        """
        quant_config_with_hf_to_vllm_mapper = {}

        for k, v in self.quant_config.items():
            if isinstance(v, list):
                quant_config_with_hf_to_vllm_mapper[k] = hf_to_vllm_mapper.apply_list(v)
            elif isinstance(v, dict):
                quant_config_with_hf_to_vllm_mapper[k] = hf_to_vllm_mapper.apply_dict(v)
            else:
                if isinstance(v, str):
                    mapped_v_list = hf_to_vllm_mapper.apply_list([v])
                    if mapped_v_list:
                        quant_config_with_hf_to_vllm_mapper[k] = mapped_v_list[0]
                else:
                    quant_config_with_hf_to_vllm_mapper[k] = v

        self.quant_config = quant_config_with_hf_to_vllm_mapper

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> "QuantizeMethodBase | None":
        # Check if the layer is skipped for quantization.
        exclude_layers = cast(list[str], self.quant_config.get("exclude"))
        if should_ignore_layer(
            prefix, ignore=exclude_layers, fused_mapping=self.packed_modules_mapping
        ):
            return UnquantizedLinearMethod()
        if isinstance(layer, LinearBase):
            scheme = self.get_scheme(layer=layer, layer_name=prefix)
            layer.scheme = scheme
            return QuarkLinearMethod(self)
        if isinstance(layer, Attention):
            return QuarkKVCacheMethod(self)

        if isinstance(layer, FusedMoE):
            return QuarkMoEMethod.get_moe_method(self, module=layer, layer_name=prefix)
        return None

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "QuarkConfig":
        export_config = config.get("export")
        if export_config is None:
            raise ValueError(
                "The export key should be included in "
                "the configurations of Quark quantized model"
            )
        kv_cache_group = cast(list[str], export_config.get("kv_cache_group"))
        pack_method = cast(str, export_config.get("pack_method"))

        # In the export model of quark, the quantization configuration
        # of kv_cache is stored in layer_quant_config. First, it is
        # judged whether kv_cache_group exists, and then it is judged
        # whether layer_quant_config has a quantization configuration
        # that matches kv_cache.
        if len(kv_cache_group) == 0:
            kv_cache_config = None
        else:
            kv_cache_set = set(kv_cache_group)
            layer_quant_config = cast(dict[str, Any], config.get("layer_quant_config"))
            layer_quant_names = list(layer_quant_config.keys())
            layer_quant_set = set(layer_quant_names)

            if not (
                kv_cache_set.issubset(layer_quant_set)
                or any(
                    fnmatch.fnmatchcase(layer_quant, pat)
                    for layer_quant in list(layer_quant_set)
                    for pat in list(kv_cache_set)
                )
            ):
                raise ValueError(
                    "The Quark quantized model has the "
                    "kv_cache_group parameter setting, "
                    "but no kv_cache quantization settings "
                    "were found in the quantization "
                    "configuration."
                )

            q_configs = [
                quant_cfg
                for name, quant_cfg in layer_quant_config.items()
                if any(fnmatch.fnmatchcase(name, pattern) for pattern in kv_cache_group)
            ]

            if not all(
                deep_compare(q_config["output_tensors"], q_configs[0]["output_tensors"])
                for q_config in q_configs
            ):
                raise ValueError(
                    "The quantization method used for kv_cache should "
                    "be the same, but the quantization method for the "
                    "kv_cache layer in the config is different."
                )
            kv_cache_config = q_configs[0].get("output_tensors")
            if kv_cache_config is None:
                raise ValueError("The kv_cache quantization configuration is empty.")

            # Since we have already set kv_cache quantization configurations,
            # we will remove the quantization configuration for the
            # output_tensors corresponding to the kv_cache layer.
            for q_config in q_configs:
                q_config["output_tensors"] = None

            # In case q_proj output is also quantized, remove the configuration
            # to keep qkv consistency.
            q_proj_q_config = cast(dict[str, Any], layer_quant_config.get("*q_proj"))
            if q_proj_q_config is not None:
                q_proj_q_config["output_tensors"] = None

        return cls(
            quant_config=config,
            kv_cache_group=kv_cache_group,
            kv_cache_config=kv_cache_config,
            pack_method=pack_method,
        )

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return []

    def _check_scheme_supported(self, min_capability: int, error: bool = True) -> bool:
        capability_tuple = current_platform.get_device_capability()

        if capability_tuple is not None:
            capability = capability_tuple.to_int()
            supported = capability >= min_capability
            if error and not supported:
                raise RuntimeError(
                    "Quantization scheme is not supported for ",
                    f"the current GPU. Min capability: {min_capability}. ",
                    f"Current capability: {capability}.",
                )
            return supported
        else:
            return False

    def _is_fp8_w4a8(
        self,
        weight_quant: list[dict[str, Any]] | None,
        input_quant: dict[str, Any] | None,
    ) -> bool:
        # Confirm weights and input quantized.
        if weight_quant is None or input_quant is None:
            return False

        if not isinstance(weight_quant, list) or len(weight_quant) != 2:
            return False

        # Confirm weight scheme is supported
        is_w4a8_dtype = (
            weight_quant[0].get("dtype") == "fp8_e4m3"
            and weight_quant[1].get("dtype") == "int4"
            and input_quant.get("dtype") == "fp8_e4m3"
        )
        is_static_weight = not weight_quant[0].get("is_dynamic") and not weight_quant[
            1
        ].get("is_dynamic")
        is_per_tensor_fp8_and_per_channel_int4_weight = (
            weight_quant[0].get("qscheme") == "per_tensor"
            and weight_quant[1].get("qscheme") == "per_channel"
            and weight_quant[1].get("symmetric") is True
            and weight_quant[1].get("ch_axis") == 0
        )

        if not (
            is_w4a8_dtype
            and is_static_weight
            and is_per_tensor_fp8_and_per_channel_int4_weight
        ):
            return False

        # Dynamic quantization is always supported if weights supported.
        if input_quant.get("is_dynamic"):
            return True

        # Confirm activation scheme is supported.
        is_per_tensor_activation = input_quant.get("qscheme") == "per_tensor"
        return is_per_tensor_activation

    def _is_fp8_w8a8(
        self,
        weight_quant: dict[str, Any] | None,
        input_quant: dict[str, Any] | None,
    ) -> bool:
        # Confirm weights and input quantized.
        if weight_quant is None or input_quant is None:
            return False

        # Confirm weight scheme is supported
        is_fp8_dtype = (
            weight_quant.get("dtype") == "fp8_e4m3"
            and input_quant.get("dtype") == "fp8_e4m3"
        )
        is_static_weight = not weight_quant.get("is_dynamic")
        is_per_tensor_or_channel_weight = weight_quant.get("qscheme") in [
            "per_tensor",
            "per_channel",
        ]

        if not (is_fp8_dtype and is_static_weight and is_per_tensor_or_channel_weight):
            return False

        # Dynamic quantization is always supported if weights supported.
        if input_quant.get("is_dynamic"):
            return True

        # Confirm activation scheme is supported.
        is_per_tensor_activation = input_quant.get("qscheme") == "per_tensor"
        return is_per_tensor_activation

    def _is_static_tensor_w8a8(
        self,
        weight_quant: dict[str, Any] | None,
        input_quant: dict[str, Any] | None,
    ) -> bool:
        # Confirm weights and input quantized.
        if weight_quant is None or input_quant is None:
            return False

        is_int8_dtype = (
            weight_quant.get("dtype") == "int8" and input_quant.get("dtype") == "int8"
        )

        is_tensor = (
            weight_quant.get("qscheme") in ["per_tensor", "per_channel"]
            and input_quant.get("qscheme") == "per_tensor"
        )

        is_static = not weight_quant.get("is_dynamic") and not input_quant.get(
            "is_dynamic"
        )

        is_weight_symmetric = weight_quant.get("symmetric") is True

        # Both symmetric and asymmetric input quantization supported.
        # Only symmetric weight quantization supported.
        return is_int8_dtype and is_tensor and is_weight_symmetric and is_static

    def _is_w_ocp_mx_a_x(
        self, weight_quant: dict[str, Any] | None, input_quant: dict[str, Any] | None
    ) -> bool:
        """
        This check returns True only if it is an OCP-MX weight quantization.
        The activation can be any data type (e.g., FP16/BF16, FP8, or OCP-MX format).
        The rationale for checking only the weight type is that
        the model loading concept and process primarily concerns the weights themselves.
        """
        # Confirm weights quantized.
        if weight_quant is None:
            logger.debug(
                "Quark model's weight quantization is incompatible with OCP_MX format: "
                "weight_quant is not set."
            )
            return False

        # Input and weight qscheme needs to be per group.
        if weight_quant.get("qscheme") != "per_group":
            logger.debug(
                "Quark model's weight quantization is incompatible with OCP MX format: "
                "weight is not per_group."
            )
            return False

        # Input and weight group size needs to be 32.
        if weight_quant.get("group_size") != 32:
            logger.debug(
                "Quark model's weight quantization is incompatible with OCP MX format: "
                "group_size of weight is not 32."
            )
            return False

        # Activations and weight scales need to be in e8m0 format.
        if weight_quant.get("scale_format") != "e8m0":
            logger.debug(
                "Quark model's weight quantization is incompatible with OCP MX format: "
                "scale_format of weight is not e8m0."
            )
            return False

        # Input and weight dtypes need to be any of fp4,
        # fp6_e3m2 or fp6_e3m2, possibly mixed.
        if weight_quant.get("dtype") not in {
            "fp4",
            "fp6_e3m2",
            "fp6_e2m3",
        }:
            logger.debug(
                "Quark model's weight quantization is incompatible with OCP MX format: "
                "dtype is not in {fp4, fp6_e3m2, fp6_e2m3}."
            )
            return False

        return True

    def is_mxfp4_quant(self, prefix: str, layer: torch.nn.Module) -> bool:
        """
        For Quark, determine if it's OCP MXFP4 by checking config directly.
        This allows hidden_size rounding to happen before moe_config creation.
        """
        layer_quant_config = self._find_matched_config(prefix, layer)
        weight_config = layer_quant_config.get("weight")
        input_config = layer_quant_config.get("input_tensors")

        return (
            self._is_w_ocp_mx_a_x(weight_config, input_config)
            and weight_config is not None
            and weight_config.get("dtype") == "fp4"
            and getattr(torch, "float4_e2m1fn_x2", None) is not None
        )

    def _find_matched_config(
        self, layer_name: str, module: torch.nn.Module
    ) -> dict[str, Any]:
        proj_name = layer_name.split(".")[-1]
        if proj_name in self.packed_modules_mapping:
            shard_proj_names = self.packed_modules_mapping[proj_name]

            # Convert fused_name --> [shard_names]
            shard_names = [
                layer_name.replace(proj_name, shard_proj_name)
                for shard_proj_name in shard_proj_names
            ]
            shard_configs = [
                self._find_matched_config(shard_name, module)
                for shard_name in shard_names
            ]
            if not all(
                deep_compare(q_config, shard_configs[0]) for q_config in shard_configs
            ):
                raise ValueError(
                    f"Found a different quantization configuration for "
                    f"{shard_proj_names} in {layer_name}. vLLM "
                    "requires all to use the same scheme."
                )
            return shard_configs[0]
        else:
            layer_quant_config = cast(
                dict[str, Any], self.quant_config.get("layer_quant_config")
            )

            def _matches_pattern(layer_name, pattern):
                if "*" not in pattern:
                    return layer_name in pattern
                return fnmatch.fnmatch(layer_name, pattern)

            for name_pattern, config in layer_quant_config.items():
                if _matches_pattern(layer_name, name_pattern):
                    return config

            layer_type = cast(str, type(module))
            layer_type_quant_config = cast(
                dict[str, Any], self.quant_config.get("layer_type_quant_config")
            )
            if layer_type in layer_type_quant_config:
                return layer_type_quant_config[layer_type]

            global_quant_config = cast(
                dict[str, Any], self.quant_config.get("global_quant_config")
            )
            return global_quant_config

    def _get_scheme_from_config(self, config: dict[str, Any]) -> "QuarkScheme":
        if config.get("output_tensors") or config.get("bias"):
            raise NotImplementedError(
                "Currently, Quark models with output_tensors "
                "and bias quantized are not supported"
            )
        weight_config = cast(dict[str, Any], config.get("weight"))
        input_config = cast(dict[str, Any], config.get("input_tensors"))

        if self._is_fp8_w8a8(weight_config, input_config):
            is_fp8_w8a8_supported = self._check_scheme_supported(
                QuarkW8A8Fp8.get_min_capability(), error=False
            )
            if is_fp8_w8a8_supported:
                return QuarkW8A8Fp8(weight_config, input_config)
        elif self._is_static_tensor_w8a8(weight_config, input_config):
            weight_qscheme = cast(str, weight_config.get("qscheme"))
            return QuarkW8A8Int8(
                qscheme=weight_qscheme,
                is_static_input_scheme=True,
                input_symmetric=input_config.get("symmetric"),
            )
        elif self._is_w_ocp_mx_a_x(weight_config, input_config):
            return QuarkOCP_MX(weight_config, input_config)

        raise NotImplementedError(
            "No quark compatible scheme was found. "
            f"Weight config: {weight_config}, "
            f"Input config: {input_config}"
        )

    def get_scheme(self, layer: torch.nn.Module, layer_name: str) -> "QuarkScheme":
        layer_quant_config = self._find_matched_config(layer_name, layer)

        # Find the quant_scheme
        scheme = self._get_scheme_from_config(layer_quant_config)
        # Raise error if device does not support the scheme
        # (e.g. fp8 needs ada lovelace)
        self._check_scheme_supported(scheme.get_min_capability())

        return scheme

    def get_cache_scale(self, name: str) -> str | None:
        """
        Check whether the param name matches the format for k/v cache scales
        in quark. If this is the case, return its equivalent param name
        expected by vLLM

        :param name: param name
        :return: matching param name for KV cache scale in vLLM
        """
        if name.endswith(".output_scale") and ".k_proj" in name:
            return name.replace(".k_proj.output_scale", ".attn.k_scale")
        if name.endswith(".output_scale") and ".v_proj" in name:
            return name.replace(".v_proj.output_scale", ".attn.v_scale")
        if name.endswith(".output_scale") and ".q_proj" in name:
            return name.replace(".q_proj.output_scale", ".attn.q_scale")
        if name.endswith("self_attn.prob_output_scale"):
            return name.replace(".prob_output_scale", ".attn.prob_scale")

        # If no matches, return None
        return None

class QuarkKVCacheMethod(BaseKVCacheMethod):
    """
    Supports loading kv-cache scaling factors from quark checkpoints.
    """

    def __init__(self, quant_config: QuarkConfig):
        self.validate_kv_cache_config(quant_config.kv_cache_config)
        super().__init__(quant_config)

    @staticmethod
    def validate_kv_cache_config(kv_cache_config: dict[str, Any] | None):
        """
        Validator for the kv cache configuration. Useful for controlling the
        kv cache quantization schemes, that are being supported in vLLM
        :param kv_cache_config: the quark kv cache scheme
        """
        if kv_cache_config is None:
            return

        dtype = kv_cache_config.get("dtype")
        if dtype != "fp8_e4m3":
            raise NotImplementedError(
                "Currently supported kv cache quantization is "
                f"dtype=fp8_e4m3, however received {dtype}"
            )

        qscheme = kv_cache_config.get("qscheme")
        if qscheme != "per_tensor":
            raise NotImplementedError(
                "Only support per-tensor scaling factor "
                "for quark KV cache. "
                f"Expected qscheme: per_tensor, found qscheme: {qscheme}"
            )

class QuarkLinearMethod(LinearMethodBase):
    def __init__(self, quantization_config: QuarkConfig):
        self.quantization_config = quantization_config

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        layer.scheme.process_weights_after_loading(layer)

    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,
    ):
        """
        Use the CompressedTensorsScheme associated with each layer to create
        the necessary parameters for the layer. See LinearMethodBase for param
        details
        """
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.scheme.create_weights(
            layer=layer,
            input_size=input_size,
            input_size_per_partition=input_size_per_partition,
            output_partition_sizes=output_partition_sizes,
            output_size=output_size,
            params_dtype=params_dtype,
            weight_loader=weight_loader,
        )

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ):
        """
        Use the output of create_weights and the CompressedTensorsScheme
        associated with the layer to apply the forward pass with the
        layer input.  See LinearMethodBase for param details

        """
        scheme = layer.scheme
        if scheme is None:
            raise ValueError("A scheme must be defined for each layer")

        return scheme.apply_weights(layer, x, bias=bias)