# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from typing import Any, Optional import torch from packaging import version from torch.nn import Module from vllm._ipex_ops import ipex_ops as ops from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig from vllm.model_executor.layers.linear import ( LinearBase, LinearMethodBase, UnquantizedLinearMethod, ) from vllm.model_executor.layers.quantization import ( QuantizationConfig, QuantizationMethods, ) from vllm.model_executor.layers.quantization.awq import AWQLinearMethod from vllm.model_executor.layers.quantization.fp8 import ( Fp8Config, Fp8LinearMethod, Fp8OnlineMoEMethod, ) from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod from vllm.model_executor.layers.quantization.utils.quant_utils import is_layer_skipped from vllm.model_executor.utils import replace_parameter from vllm.platforms import current_platform MIN_IPEX_VERSION = "2.6.0" 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: list[str] | None = None, desc_act: bool | None = None, lm_head_quantized: bool | None = None, is_sym: bool | None = 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.is_sym = is_sym 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], 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 ) is_sym = not cls.get_from_keys_or(config, ["zero_point"], default=False) return cls( method, weight_bits, group_size, modules_to_not_convert, False, False, is_sym, ) # 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) is_sym = cls.get_from_keys_or(config, ["sym"], default=True) return cls( method, weight_bits, group_size, [], desc_act, lm_head_quantized, is_sym ) @classmethod def override_quantization_method( cls, hf_quant_cfg, user_quant ) -> QuantizationMethods | None: if 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( prefix, self.modules_to_not_convert, self.packed_modules_mapping, skip_with_substr=True, ): 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 assert isinstance(self.quant_config, IPEXConfig) 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"], weight_qscheme="sym" if self.quant_config.is_sym else "asym", ) ) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None = 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 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 assert isinstance(self.quant_config, IPEXConfig) 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 weight_qscheme="sym" if self.quant_config.is_sym else "asym", ) ) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None = 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 getattr(layer, "_already_called_process_weights_after_loading", False): return # 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. replace_parameter(layer, "weight", qweight.data) replace_parameter(layer, "weight_scale", weight_scale.data) layer.input_scale = None def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None = 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(Fp8OnlineMoEMethod): def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module): super().__init__(quant_config, layer) self.quant_config = quant_config def process_weights_after_loading(self, layer: Module) -> None: if getattr(layer, "_already_called_process_weights_after_loading", False): return 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, :, :]) ) replace_parameter(layer, "w13_weight", w13_weight) replace_parameter(layer, "w2_weight", w2_weight) import intel_extension_for_pytorch as ipex ep_rank_start = self.moe.ep_rank * self.moe.num_local_experts 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, experts_start_id=ep_rank_start, ) def get_fused_moe_quant_config( self, layer: torch.nn.Module ) -> FusedMoEQuantConfig | None: return None @property def is_monolithic(self) -> bool: return True def apply_monolithic( self, layer: torch.nn.Module, x: torch.Tensor, router_logits: torch.Tensor, ) -> torch.Tensor: return layer.ipex_fusion( x, layer.use_grouped_topk, layer.top_k, router_logits, layer.renormalize, layer.topk_group, layer.num_expert_group, custom_routing_function=layer.custom_routing_function, )