vllm.model_executor.layers.quantization.ipex_quant

class IPEXAWQLinearMethod(AWQLinearMethod):
    """AWQ linear method using IPEX for the CPU/XPU backend.
    """

    def __init__(self, quant_config: IPEXConfig):
        self.quant_config = quant_config  # type: ignore

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

        bias = layer.bias if not layer.skip_bias_add else None

        try:
            import intel_extension_for_pytorch as ipex
            if version.parse(
                    ipex.__version__) < version.parse(MIN_IPEX_VERSION):
                raise ImportError(
                    "intel_extension_for_pytorch version is "
                    "wrong. Please install "
                    f"intel_extension_for_pytorch>={MIN_IPEX_VERSION}.")
        except ImportError as err:
            raise ImportError(
                "Please install "
                f"intel_extension_for_pytorch>={MIN_IPEX_VERSION} via "
                f"`pip install intel_extension_for_pytorch>={MIN_IPEX_VERSION}`"
                " to use IPEX-AWQ linear method.") from err

        # Using the compute dtype (lowp_mode) as INT8 to leverage instructions
        # with better performance.
        lowp_mode = ipex.quantization.WoqLowpMode.INT8
        # The weight will be de-packed from INT4 to INT8.
        weight_dtype = ipex.quantization.WoqWeightDtype.INT4
        # The float activation will be quantized (dynamic, per-token) to INT8.
        act_quant_mode = ipex.quantization.WoqActQuantMode.PER_BATCH

        qconfig = ipex.quantization.get_weight_only_quant_qconfig_mapping(
            weight_dtype=weight_dtype,
            lowp_mode=lowp_mode,
            act_quant_mode=act_quant_mode,
            group_size=self.quant_config.group_size,
        )

        layer.ipex_output_size = layer.qweight.size(
            1) * self.quant_config.pack_factor
        layer.ipex_qlinear = ipex.llm.quantization.woq_linear. \
            IPEXWeightOnlyQuantizedLinear.from_weight(
            layer.qweight,
            layer.scales,
            layer.qzeros,
            layer.qweight.size(0),
            layer.ipex_output_size,
            qconfig=qconfig,
            bias=bias,
            group_size=self.quant_config.group_size,
            quant_method=IPEXConfig.IPEX_QUANT_METHOD_MAP["awq"]  # type: ignore
        )

    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        reshaped_x = x.reshape(-1, x.shape[-1])
        out = layer.ipex_qlinear(reshaped_x)
        return out.reshape(x.shape[:-1] + (layer.ipex_output_size, ))

class IPEXConfig(QuantizationConfig):
    """INT8 quantization config class using IPEX for the CPU/XPU backend,
    including AWQ, GPTQ.
    """

    IPEX_QUANT_METHOD_MAP = {
        "awq": 1,
        "gptq": 0,
    }

    def __init__(
        self,
        method: str,
        weight_bits: int,
        group_size: int,
        modules_to_not_convert: Optional[list[str]] = None,
        desc_act: Optional[bool] = None,
        lm_head_quantized: Optional[bool] = None,
    ) -> None:
        super().__init__()
        self.method = method
        self.weight_bits = weight_bits
        self.group_size = group_size
        self.modules_to_not_convert = modules_to_not_convert or []
        self.desc_act = desc_act
        self.lm_head_quantized = lm_head_quantized
        self.pack_factor = 32 // self.weight_bits

        if self.weight_bits not in [4]:
            raise ValueError(f"IPEX quantization supports weight bits [4], "
                             f"but got {self.weight_bits}.")

        if self.method not in ["awq", "gptq"]:
            raise ValueError(f"IPEX quantization supports [awq, gptq], "
                             f"but got {self.method}.")

    def __repr__(self) -> str:
        return (f"IPEXConfig(method={self.method},"
                f"weight_bits={self.weight_bits}, "
                f"group_size={self.group_size})")

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "ipex"

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

    @classmethod
    def get_min_capability(cls) -> int:
        return -1

    @staticmethod
    def get_config_filenames() -> list[str]:
        return [
            "quant_config.json",
            "quantize_config.json",
        ]

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "IPEXConfig":
        method = cls.get_from_keys(config, ["quant_method"]).lower()
        if method == "awq":
            weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
            group_size = cls.get_from_keys(config,
                                           ["q_group_size", "group_size"])
            modules_to_not_convert = cls.get_from_keys_or(
                config, ["modules_to_not_convert"], None)
            return cls(method, weight_bits, group_size, modules_to_not_convert,
                       False, False)
        # otherwise for gptq
        weight_bits = cls.get_from_keys(config, ["bits"])
        group_size = cls.get_from_keys(config, ["group_size"])
        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
                                                 default=False)
        desc_act = cls.get_from_keys_or(config, ["desc_act"], default=False)
        return cls(method, weight_bits, group_size, [], desc_act,
                   lm_head_quantized)

    @classmethod
    def override_quantization_method(
            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
        if not current_platform.is_cpu() and not current_platform.is_xpu():
            return None

        quant_method = hf_quant_cfg.get("quant_method", "").lower()

        if quant_method in ["awq", "gptq"]:
            return cls.get_name()

        return None

    def get_quant_method(self, layer: torch.nn.Module,
                         prefix: str) -> Optional["LinearMethodBase"]:
        if isinstance(layer, LinearBase):
            if self.method == "awq":
                if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
                    return UnquantizedLinearMethod()
                return IPEXAWQLinearMethod(self)
            if self.method == "gptq":
                return IPEXGPTQLinearMethod(self)
        return None

class IPEXGPTQLinearMethod(GPTQLinearMethod):
    """GPTQ linear method using IPEX for the CPU/XPU backend.
    """

    def __init__(self, quant_config: IPEXConfig):
        self.quant_config = quant_config  # type: ignore

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        bias = layer.bias if not layer.skip_bias_add else None

        try:
            import intel_extension_for_pytorch as ipex
            if version.parse(
                    ipex.__version__) < version.parse(MIN_IPEX_VERSION):
                raise ImportError(
                    "intel_extension_for_pytorch version is "
                    "wrong. Please install "
                    f"intel_extension_for_pytorch>={MIN_IPEX_VERSION}.")
        except ImportError as err:
            raise ImportError(
                "Please install "
                f"intel_extension_for_pytorch>={MIN_IPEX_VERSION} via "
                f"`pip install intel_extension_for_pytorch>={MIN_IPEX_VERSION}`"
                " to use IPEX-AWQ linear method.") from err
        # Using the compute dtype (lowp_mode) as INT8 to leverage instructions
        # with better performance.
        lowp_mode = ipex.quantization.WoqLowpMode.INT8
        # The weight will be de-packed from INT4 to INT8.
        weight_dtype = ipex.quantization.WoqWeightDtype.INT4
        # The float activation will be quantized (dynamic, per-token) to INT8.
        act_quant_mode = ipex.quantization.WoqActQuantMode.PER_BATCH_IC_BLOCK

        qconfig = ipex.quantization.get_weight_only_quant_qconfig_mapping(
            weight_dtype=weight_dtype,
            lowp_mode=lowp_mode,
            act_quant_mode=act_quant_mode,
            group_size=self.quant_config.group_size,
        )
        layer.ipex_output_size = layer.qweight.shape[-1]
        g_idx = layer.g_idx if self.quant_config.desc_act else None
        layer.ipex_qlinear = ipex.llm.quantization.woq_linear. \
            IPEXWeightOnlyQuantizedLinear.from_weight(
            layer.qweight,
            layer.scales,
            layer.qzeros,
            layer.qweight.size(0),
            layer.ipex_output_size,
            qconfig=qconfig,
            g_idx=g_idx,
            bias=bias,
            group_size=self.quant_config.group_size,
            quant_method=IPEXConfig.IPEX_QUANT_METHOD_MAP["gptq"]
        )

    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        reshaped_x = x.reshape(-1, x.shape[-1])
        out = layer.ipex_qlinear(reshaped_x)
        return out.reshape(x.shape[:-1] + (layer.ipex_output_size, ))

class XPUFp8LinearMethod(Fp8LinearMethod):

    def __init__(self, quant_config: Fp8Config):
        super().__init__(quant_config)

    def process_weights_after_loading(self, layer: Module) -> None:
        # 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)
            # Update the layer with the new values.
            layer.weight = Parameter(qweight, requires_grad=False)
            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
            layer.input_scale = None

    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        weight = layer.weight.data
        weight_scale = layer.weight_scale.data
        output = torch.ops.torch_ipex.fp8_gemm_w8a16(x, weight, True,
                                                     weight_scale, bias)
        return output

class XPUFp8MoEMethod(FusedMoEMethodBase):

    def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module):
        super().__init__(layer.moe_config)
        self.quant_config = quant_config

    def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
                       intermediate_size_per_partition: int,
                       params_dtype: torch.dtype, **extra_weight_attrs):

        layer.intermediate_size_per_partition = intermediate_size_per_partition
        layer.hidden_size = hidden_size
        layer.num_experts = num_experts
        layer.orig_dtype = params_dtype
        layer.weight_block_size = None
        # WEIGHTS
        w13_weight = torch.nn.Parameter(torch.empty(
            num_experts,
            2 * intermediate_size_per_partition,
            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_per_partition,
            dtype=params_dtype),
                                       requires_grad=False)
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

        # 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)
        w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
                                                        dtype=torch.float32),
                                             requires_grad=False)
        layer.register_parameter("w13_weight_scale", w13_weight_scale)
        layer.register_parameter("w2_weight_scale", w2_weight_scale)

        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
        # INPUT_SCALES
        layer.w13_input_scale = None
        layer.w2_input_scale = None

    def process_weights_after_loading(self, layer: Module) -> None:
        if not self.quant_config.is_checkpoint_fp8_serialized:
            fp8_dtype = current_platform.fp8_dtype()
            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.local_num_experts,
                dtype=torch.float32,
                device=w13_weight.device),
                                                        requires_grad=False)
            for expert in range(layer.local_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)
        import intel_extension_for_pytorch as ipex
        layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
            layer.w13_weight,
            layer.w2_weight,
            w1_scale_inv=layer.w13_weight_scale,
            w2_scale_inv=layer.w2_weight_scale,
            a1_scale_inv=layer.w13_input_scale,
            a2_scale_inv=layer.w2_input_scale,
            use_prepack=True,
        )

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        routed_scaling_factor: float = 1.0,
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        return layer.ipex_fusion(
            x,
            use_grouped_topk,
            top_k,
            router_logits,
            renormalize,
            topk_group,
            num_expert_group,
            custom_routing_function=custom_routing_function,
        )