[CPU] Refactor CPU WNA16 (#28826)

Signed-off-by: jiang1.li <jiang1.li@intel.com>

vllm/model_executor/layers/quantization/cpu_wna16.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, Optional
+
+import torch
+from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
+
+from vllm._custom_ops import (
+    cpu_gemm_wna16,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import (
+    LinearBase,
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from vllm.model_executor.layers.quantization.utils.gptq_utils import (
+    get_linear_quant_method,
+)
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    marlin_repeat_scales_on_all_ranks,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped,
+    pack_cols,
+    unpack_cols,
+)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.models.utils import WeightsMapper
+from vllm.model_executor.parameter import (
+    ChannelQuantScaleParameter,
+    GroupQuantScaleParameter,
+    PackedColumnParameter,
+    PackedvLLMParameter,
+    RowvLLMParameter,
+)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.transformers_utils.config import get_safetensors_params_metadata
+from vllm.utils.collection_utils import is_list_of
+
+logger = init_logger(__name__)
+
+
+class CPUGPTQConfig(QuantizationConfig):
+    """Config class for CPU GPTQ quant"""
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        desc_act: bool,
+        is_sym: bool,
+        lm_head_quantized: bool,
+        dynamic: dict[str, dict[str, int | bool]],
+        full_config: dict[str, Any],
+        modules_in_block_to_quantize: list[str] | None = None,
+    ) -> None:
+        super().__init__()
+        if desc_act and group_size == -1:
+            # In this case, act_order == True is the same as act_order == False
+            # (since we have only one group per output channel)
+            desc_act = False
+
+        # GPTQModel use `dynamic` config property to allow per module
+        # quantization config so each module can be individually optimized.
+        # Format is dict[str, dict] where key is a regex string that can
+        # perform both positive ("+:" prefixed) or negative ("-:" prefixed)
+        # matching of a module.
+        # Default to positive match, override base quant config mode, if no
+        # prefix is used. Value is in dict format of field key and override
+        # value.
+        # Negative matching will skip quantization init for this module
+        # entirely:
+        # non-quantized inference. More details and quantization examples can be
+        # found at: https://github.com/ModelCloud/GPTQModel
+        # Example:
+        #  # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
+        #  # last 1/4 of the layers 16-21 has 8bit and group_size 64
+        # dynamic = {
+        #  #`.*\.` matches the layers_node prefix
+        #  # positive match layer 10-15
+        #  r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
+        #  # positive match layer 16-21
+        #  r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
+        #  r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
+        # }
+        assert weight_bits == 4
+        self.dynamic = dynamic
+        self.weight_bits = weight_bits
+        self.is_sym = is_sym
+        self.pack_factor = 32 // weight_bits  # packed into int32
+        self.group_size = group_size
+        self.desc_act = desc_act
+        self.lm_head_quantized = lm_head_quantized
+        self.full_config = full_config
+        self.modules_in_block_to_quantize = modules_in_block_to_quantize or []
+
+    def __repr__(self) -> str:
+        return (
+            f"CPUWNA16Config("
+            f"group_size={self.group_size}, "
+            f"desc_act={self.desc_act}, "
+            f"lm_head_quantized={self.lm_head_quantized}, "
+            f"dynamic={self.dynamic}, "
+            f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})"
+        )
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "cpu_gptq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return -1
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "CPUGPTQConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        desc_act = cls.get_from_keys_or(config, ["desc_act"], default=False)
+        dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
+        group_size = cls.get_from_keys(config, ["group_size"])
+        is_sym = cls.get_from_keys(config, ["sym"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
+        modules_in_block_to_quantize = cls.get_from_keys_or(
+            config, ["modules_in_block_to_quantize"], default=None
+        )
+        return cls(
+            weight_bits,
+            group_size,
+            desc_act,
+            is_sym,
+            lm_head_quantized,
+            dynamic,
+            config,
+            modules_in_block_to_quantize,
+        )
+
+    @classmethod
+    def override_quantization_method(
+        cls, hf_quant_cfg, user_quant
+    ) -> QuantizationMethods | None:
+        quant_method = hf_quant_cfg.get("quant_method", "").lower()
+        if current_platform.is_cpu() and (quant_method == "gptq"):
+            return cls.get_name()
+        return None
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional["QuantizeMethodBase"]:
+        return get_linear_quant_method(self, layer, prefix, CPUGPTQLinearMethod)  # type: ignore
+
+    def apply_vllm_mapper(self, hf_to_vllm_mapper):
+        if self.modules_in_block_to_quantize is not None:
+            self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list(
+                self.modules_in_block_to_quantize
+            )
+
+    def maybe_update_config(self, model_name: str, revision: str | None = None):
+        if self.modules_in_block_to_quantize:
+            if is_list_of(self.modules_in_block_to_quantize, list):
+                # original modules_in_block_to_quantize: list[list[str]]
+                # flatten original modules_in_block_to_quantize
+                self.modules_in_block_to_quantize = [
+                    item
+                    for sublist in self.modules_in_block_to_quantize
+                    for item in sublist
+                ]
+            return
+
+        unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+        metadata = get_safetensors_params_metadata(model_name, revision=revision)
+        quant_layers: set[str] = {
+            param_name.rsplit(".", 1)[0]
+            for param_name, info in metadata.items()
+            if (dtype := info.get("dtype", None))
+            and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
+        }
+        self.modules_in_block_to_quantize = list(quant_layers)
+
+
+class CPUGPTQLinearMethod(LinearMethodBase):
+    """Linear method for GPTQ on CPU.
+
+    Args:
+        quant_config: The CPUWNA16 quantization config.
+    """
+
+    def __init__(self, quant_config: CPUGPTQConfig) -> None:
+        self.quant_config = quant_config
+        assert self.quant_config.is_sym, "GPTQ asym quant is not supported on CPU"
+
+    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,
+    ) -> None:
+        output_size_per_partition = sum(output_partition_sizes)
+        assert output_size_per_partition * self.quant_config.weight_bits % 32 == 0
+        assert output_size_per_partition % 32 == 0
+        assert input_size_per_partition % 32 == 0
+
+        is_row_parallel = input_size != input_size_per_partition
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        # Normalize group_size
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+
+        # Determine sharding
+        if marlin_repeat_scales_on_all_ranks(
+            self.quant_config.desc_act, self.quant_config.group_size, is_row_parallel
+        ):
+            # By setting scale_dim == None, weight_loader will
+            # repeat the scales on each rank in TP>1 case.
+            scales_and_zp_input_dim = None
+            scales_and_zp_size = input_size // group_size
+        else:
+            # By setting scale_dim == 0, weight_loader will
+            # shard the scales in TP>1 case.
+            scales_and_zp_input_dim = 0
+            scales_and_zp_size = input_size_per_partition // group_size
+
+        # Quantized weights
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.pack_factor,
+                output_size_per_partition,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=0,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader,
+        )
+
+        # Activation order
+        g_idx = RowvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            weight_loader=weight_loader,
+        )
+        set_weight_attrs(
+            g_idx,
+            {"ignore_warning": True},
+        )
+
+        qzeros_args = {
+            "data": torch.empty(
+                scales_and_zp_size,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            "weight_loader": weight_loader,
+        }
+        weight_scale_args = {
+            "data": torch.empty(
+                scales_and_zp_size,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            "weight_loader": weight_loader,
+        }
+
+        if scales_and_zp_input_dim is None:
+            scales = ChannelQuantScaleParameter(output_dim=1, **weight_scale_args)
+            qzeros = PackedColumnParameter(
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args,
+            )
+
+        else:
+            scales = GroupQuantScaleParameter(
+                output_dim=1, input_dim=0, **weight_scale_args
+            )
+            qzeros = PackedvLLMParameter(
+                input_dim=0,
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args,
+            )
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("g_idx", g_idx)
+        layer.register_parameter("scales", scales)
+        layer.register_parameter("qzeros", qzeros)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        torch.set_printoptions(profile="full", linewidth=5000, sci_mode=False)
+        packed_weight = layer.qweight.data
+        bits = self.quant_config.weight_bits
+        pack_factor = int(self.quant_config.pack_factor)
+        p_w_k, p_w_n = packed_weight.size()
+        input_size = p_w_k * pack_factor
+        output_size = p_w_n
+        isa_hint = _get_isa_hint(layer.scales.dtype)
+        layer.isa_hint = isa_hint
+
+        layer.qzeros = None
+        if not self.quant_config.desc_act:
+            layer.g_idx = None
+
+        # convert input dim packed to output dim packed
+        weight = unpack_cols(packed_weight, bits, p_w_k, p_w_n * pack_factor).view(
+            p_w_k, p_w_n, pack_factor
+        )
+        weight = weight.permute(0, 2, 1).reshape(input_size, output_size).contiguous()
+        weight = pack_cols(weight, bits, input_size, output_size)
+        # make 16 output channel as a block and transpose to the make
+        # the block contigous
+        weight = (
+            weight.view(input_size, -1, 16 // pack_factor)
+            .permute(1, 0, 2)
+            .reshape(-1, input_size * 16 // pack_factor)
+            .contiguous()
+        )
+        layer.qweight.data = weight
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        x = cpu_gemm_wna16(
+            input=x,
+            q_weight=layer.qweight,
+            scales=layer.scales,
+            zeros=layer.qzeros,
+            g_idx=layer.g_idx,
+            bias=bias,
+            pack_factor=8,
+            isa_hint=layer.isa_hint,
+        )
+        return x
+
+
+class CPUAWQConfig(QuantizationConfig):
+    """Config class for CPU AWQ"""
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        zero_point: bool,
+        lm_head_quantized: bool,
+        modules_to_not_convert: list[str] | None,
+        full_config: dict[str, Any],
+    ) -> None:
+        super().__init__()
+        assert weight_bits == 4
+        self.pack_factor = 32 // weight_bits  # packed into int32
+        self.group_size = group_size
+        self.zero_point = zero_point
+        self.lm_head_quantized = lm_head_quantized
+        self.weight_bits = weight_bits
+        self.modules_to_not_convert = modules_to_not_convert or []
+        self.full_config = full_config
+
+    def __repr__(self) -> str:
+        return (
+            f"AWQMarlinConfig("
+            f"group_size={self.group_size}, "
+            f"zero_point={self.zero_point}, "
+            f"lm_head_quantized={self.lm_head_quantized}, "
+            f"modules_to_not_convert={self.modules_to_not_convert})"
+        )
+
+    @classmethod
+    def get_name(cls) -> "QuantizationMethods":
+        return "cpu_awq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return -1
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "CPUAWQConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        zero_point = cls.get_from_keys(config, ["zero_point"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
+        modules_to_not_convert = cls.get_from_keys_or(
+            config, ["modules_to_not_convert"], None
+        )
+        return cls(
+            weight_bits,
+            group_size,
+            zero_point,
+            lm_head_quantized,
+            modules_to_not_convert,
+            config,
+        )
+
+    @classmethod
+    def override_quantization_method(
+        cls, hf_quant_cfg, user_quant
+    ) -> Optional["QuantizationMethods"]:
+        quant_method = hf_quant_cfg.get("quant_method", "").lower()
+        if current_platform.is_cpu() and (quant_method == "awq"):
+            return cls.get_name()
+        return None
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase) or (
+            isinstance(layer, ParallelLMHead) and self.lm_head_quantized
+        ):
+            if is_layer_skipped(
+                prefix,
+                self.modules_to_not_convert,
+                self.packed_modules_mapping,
+                skip_with_substr=True,
+            ):
+                return UnquantizedLinearMethod()
+            return CPUAWQLinearMethod(self)
+        return None
+
+    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
+        if self.modules_to_not_convert:
+            self.modules_to_not_convert = hf_to_vllm_mapper.apply_list(
+                self.modules_to_not_convert
+            )
+
+    def maybe_update_config(self, model_name: str, revision: str | None = None):
+        if self.modules_to_not_convert:
+            return
+
+        unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+        metadata = get_safetensors_params_metadata(model_name, revision=revision)
+        layers = {param_name.rsplit(".", 1)[0] for param_name in metadata}
+        quant_layers: set[str] = {
+            param_name.rsplit(".", 1)[0]
+            for param_name, info in metadata.items()
+            if (dtype := info.get("dtype", None))
+            and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
+        }
+        self.modules_to_not_convert = list(layers - quant_layers)
+
+
+class CPUAWQLinearMethod(LinearMethodBase):
+    """Linear method for CPU AWQ.
+
+    Args:
+        quant_config: The CPU AWQ quantization config.
+    """
+
+    def __init__(self, quant_config: CPUAWQConfig) -> None:
+        self.quant_config = quant_config
+        assert self.quant_config.zero_point
+
+    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,
+    ) -> None:
+        del output_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        # Normalize group_size
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader,
+        )
+
+        num_groups = input_size_per_partition // group_size
+
+        qzeros = PackedvLLMParameter(
+            data=torch.empty(
+                num_groups,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader,
+        )
+
+        scales = GroupQuantScaleParameter(
+            data=torch.empty(
+                num_groups,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            input_dim=0,
+            output_dim=1,
+            weight_loader=weight_loader,
+        )
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("qzeros", qzeros)
+        layer.register_parameter("scales", scales)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        torch.set_printoptions(profile="full", linewidth=5000, sci_mode=False)
+        packed_weight = layer.qweight.data
+        packed_zeros = layer.qzeros.data
+        group_num = packed_zeros.size(0)
+        bits = self.quant_config.weight_bits
+        pack_factor = int(self.quant_config.pack_factor)
+        input_size, packed_output_size = packed_weight.size()
+        output_size = packed_output_size * pack_factor
+        isa_hint = _get_isa_hint(layer.scales.dtype)
+        layer.isa_hint = isa_hint
+
+        interleave_map = (0, 4, 1, 5, 2, 6, 3, 7)
+        weight = unpack_cols(
+            packed_weight,
+            bits,
+            input_size,
+            output_size,
+        )
+        zeros = unpack_cols(
+            packed_zeros,
+            bits,
+            group_num,
+            output_size,
+        )
+        weight = (
+            weight.view(input_size, -1, pack_factor)[:, :, interleave_map]
+            .reshape(input_size, output_size)
+            .contiguous()
+        )
+        zeros = (
+            zeros.view(group_num, -1, pack_factor)[:, :, interleave_map]
+            .reshape(group_num, output_size)
+            .contiguous()
+        )
+
+        zeros = pack_cols(zeros, bits, group_num, output_size).contiguous()
+        # make 16 output channel as a block and transpose to
+        # the make the block contigous
+        weight = pack_cols(weight, bits, input_size, output_size)
+        weight = (
+            weight.view(input_size, -1, 16 // pack_factor)
+            .permute(1, 0, 2)
+            .reshape(-1, input_size * 16 // pack_factor)
+            .contiguous()
+        )
+        layer.qweight.data = weight
+        layer.qzeros.data = zeros
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        x = cpu_gemm_wna16(
+            input=x,
+            q_weight=layer.qweight,
+            scales=layer.scales,
+            zeros=layer.qzeros,
+            g_idx=None,
+            bias=bias,
+            pack_factor=8,
+            isa_hint=layer.isa_hint,
+        )
+        return x
+
+
+def _get_isa_hint(dtype: torch.dtype) -> str:
+    supports_amx = torch._C._cpu._is_amx_tile_supported()
+    if supports_amx and dtype in (torch.bfloat16,):
+        return "amx"
+    else:
+        return "vec"

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -134,7 +134,7 @@ class IPEXConfig(QuantizationConfig):
     def override_quantization_method(
         cls, hf_quant_cfg, user_quant
     ) -> QuantizationMethods | None:
-        if not current_platform.is_cpu() and not current_platform.is_xpu():
+        if not current_platform.is_xpu():
             return None
 
         quant_method = hf_quant_cfg.get("quant_method", "").lower()