gguf.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from collections.abc import Callable, Mapping
from types import MappingProxyType
from typing import Any, Optional

import gguf
import torch
from gguf import GGMLQuantizationType as WeightType
from torch.nn.parameter import Parameter, UninitializedParameter

from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEConfig,
    FusedMoEQuantConfig,
)
from vllm.model_executor.layers.fused_moe.layer import FusedMoE, FusedMoEMethodBase
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.vocab_parallel_embedding import (
    UnquantizedEmbeddingMethod,
    VocabParallelEmbedding,
)
from vllm.model_executor.models.utils import WeightsMapper
from vllm.model_executor.utils import set_weight_attrs
from vllm.utils.torch_utils import direct_register_custom_op

logger = init_logger(__name__)


class GGUFConfig(QuantizationConfig):
    """Config class for GGUF."""

    def __init__(self, unquantized_modules: list[str] | None = None) -> None:
        super().__init__()
        self.unquantized_modules = unquantized_modules or []

    def __repr__(self) -> str:
        return "GGUFConfig()"

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

    def get_supported_act_dtypes(self) -> list[torch.dtype]:
        return [torch.half, torch.bfloat16, torch.float32]

    @classmethod
    def get_min_capability(cls) -> int:
        return 60

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return []  # no extra configs.

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "GGUFConfig":
        return cls()

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:
        if isinstance(layer, LinearBase):
            if is_layer_skipped_gguf(
                prefix, self.unquantized_modules, self.packed_modules_mapping
            ):
                return UnquantizedLinearMethod()
            return GGUFLinearMethod(self)
        elif isinstance(layer, VocabParallelEmbedding):
            if is_layer_skipped_gguf(
                prefix, self.unquantized_modules, self.packed_modules_mapping
            ):
                return UnquantizedEmbeddingMethod()
            return GGUFEmbeddingMethod(self)
        elif isinstance(layer, FusedMoE):
            return GGUFMoEMethod(self, layer.moe_config)
        return None

    def apply_vllm_mapper(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
        """
        if self.unquantized_modules is not None:
            self.unquantized_modules = hf_to_vllm_mapper.apply_list(
                self.unquantized_modules
            )


def is_layer_skipped_gguf(
    prefix: str,
    unquantized_modules: list[str],
    fused_mapping: Mapping[str, list[str]] = MappingProxyType({}),
):
    # Fused layers like gate_up_proj or qkv_proj will not be fused
    # in the safetensors checkpoint. So, we convert the name
    # from the fused version to unfused + check to make sure that
    # each shard of the fused layer has the same scheme.
    proj_name = prefix.split(".")[-1]
    if proj_name in fused_mapping:
        shard_prefixes = [
            prefix.replace(proj_name, shard_proj_name)
            for shard_proj_name in fused_mapping[proj_name]
        ]

        is_skipped = None
        for shard_prefix in shard_prefixes:
            is_shard_skipped = any(
                shard_prefix in module_name for module_name in unquantized_modules
            )

            if is_skipped is None:
                is_skipped = is_shard_skipped
            elif is_shard_skipped != is_skipped:
                raise ValueError(
                    f"Detected some but not all shards of {prefix} "
                    "are quantized. All shards of fused layers "
                    "to have the same precision."
                )
    else:
        is_skipped = any(module_name in prefix for module_name in unquantized_modules)

    assert is_skipped is not None
    return is_skipped


UNQUANTIZED_TYPES = {WeightType.F32, WeightType.F16, WeightType.BF16}
STANDARD_QUANT_TYPES = {
    WeightType.Q4_0,
    WeightType.Q4_1,
    WeightType.Q5_0,
    WeightType.Q5_1,
    WeightType.Q8_0,
    WeightType.Q8_1,
}
KQUANT_TYPES = {
    WeightType.Q2_K,
    WeightType.Q3_K,
    WeightType.Q4_K,
    WeightType.Q5_K,
    WeightType.Q6_K,
}
IMATRIX_QUANT_TYPES = {
    WeightType.IQ1_M,
    WeightType.IQ1_S,
    WeightType.IQ2_XXS,
    WeightType.IQ2_XS,
    WeightType.IQ2_S,
    WeightType.IQ3_XXS,
    WeightType.IQ3_S,
    WeightType.IQ4_XS,
    WeightType.IQ4_NL,
}
# TODO(Isotr0py): Currently, we don't have MMQ kernel for I-Matrix quantization.
# Consolidate DEQUANT_TYPES, MMVQ_QUANT_TYPES and MMQ_QUANT_TYPES after we add
# MMQ kernel for I-Matrix quantization.
DEQUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
MMVQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
MMQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES


def _fused_mul_mat_gguf(
    x: torch.Tensor, qweight: torch.Tensor, qweight_type: int
) -> torch.Tensor:
    if qweight_type in IMATRIX_QUANT_TYPES:
        mmvq_safe = 8 if qweight.shape[0] > 5120 else 16
    else:
        mmvq_safe = 2 if qweight.shape[0] > 5120 else 6
    # HACK: when doing chunked prefill we don't generate output tokens
    # so input to logits generator is empty which causes invalid parameter
    if x.shape[0] == 0:
        return torch.empty(x.shape[0], qweight.shape[0], dtype=x.dtype, device=x.device)
    # there is no need to call any kernel for fp16/bf16
    if qweight_type in UNQUANTIZED_TYPES:
        return x @ qweight.T
    # enable MMVQ in contiguous batching with batch_size=1
    if x.shape[0] <= mmvq_safe and qweight_type in MMVQ_QUANT_TYPES:
        y = ops.ggml_mul_mat_vec_a8(qweight, x, qweight_type, qweight.shape[0])
    # Use MMQ Kernel if it's available (standard + k-quants)
    elif qweight_type in MMQ_QUANT_TYPES:
        y = ops.ggml_mul_mat_a8(qweight, x, qweight_type, qweight.shape[0])
    # If there is no available MMQ kernel, fallback to dequantize
    elif qweight_type in DEQUANT_TYPES:
        block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
        shape = (qweight.shape[0], qweight.shape[1] // type_size * block_size)
        weight = ops.ggml_dequantize(qweight, qweight_type, *shape, x.dtype)
        y = x @ weight.T
    else:
        # Raise an error if the quantization type is not supported.
        # Might be useful if llama.cpp adds a new quantization type.
        # Wrap to GGMLQuantizationType IntEnum to make sure it's a valid type.
        qweight_type = WeightType(qweight_type)
        raise NotImplementedError(f"Unsupported GGUF quantization type: {qweight_type}")
    return y


def _fused_mul_mat_gguf_fake(
    x: torch.Tensor,
    qweight: torch.Tensor,
    qweight_type: int,
) -> torch.Tensor:
    return torch.empty(x.shape[0], qweight.shape[0], dtype=x.dtype, device=x.device)


try:
    direct_register_custom_op(
        op_name="_fused_mul_mat_gguf",
        op_func=_fused_mul_mat_gguf,
        fake_impl=_fused_mul_mat_gguf_fake,
    )
    fused_mul_mat_gguf = torch.ops.vllm._fused_mul_mat_gguf

except AttributeError as error:
    raise error


def _fused_moe_gguf(
    x: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    qweight_type: int,
    qweight_type2: int,
    activation: str,
) -> torch.Tensor:
    def act(x: torch.Tensor):
        d = x.shape[-1] // 2
        output_shape = x.shape[:-1] + (d,)
        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
        if activation == "silu":
            torch.ops._C.silu_and_mul(out, x)
        elif activation == "gelu":
            torch.ops._C.gelu_and_mul(out, x)
        else:
            raise ValueError(f"Unsupported activation: {activation}")
        return out

    # lazy import to avoid triggering triton import in CPU backend
    from vllm.model_executor.layers.fused_moe.fused_moe import moe_align_block_size

    out_hidden_states = torch.empty_like(x)
    # unless we decent expert reuse we are better off running moe_vec kernel
    if (
        qweight_type2 in MMQ_QUANT_TYPES
        and qweight_type in MMQ_QUANT_TYPES
        and x.shape[0] > 64
    ):
        num_tokens, _ = x.shape
        E, N, _ = w1.shape
        top_k = topk_ids.shape[1]
        BLOCK_SIZE = ops.ggml_moe_get_block_size(qweight_type)

        sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
            topk_ids, BLOCK_SIZE, E
        )
        out = ops.ggml_moe_a8(
            x,
            w1,
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            qweight_type,
            N,
            top_k,
            num_tokens,
        )
        out = act(out)
        out = ops.ggml_moe_a8(
            out,
            w2,
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            qweight_type2,
            w2.shape[1],
            1,
            num_tokens * top_k,
        )
        out = out.reshape(num_tokens, top_k, w2.shape[1]).mul_(
            topk_weights.view(num_tokens, top_k, 1)
        )
        ops.moe_sum(out, out_hidden_states)
    elif qweight_type2 in MMVQ_QUANT_TYPES and qweight_type in MMVQ_QUANT_TYPES:
        num_tokens, _ = x.shape
        E, N, _ = w1.shape
        top_k = topk_ids.shape[1]

        out = ops.ggml_moe_a8_vec(x, w1, topk_ids, top_k, qweight_type, N, num_tokens)
        out = act(out)

        out = ops.ggml_moe_a8_vec(
            out, w2, topk_ids, 1, qweight_type2, w2.shape[1], num_tokens * top_k
        )
        out = out.reshape(num_tokens, top_k, w2.shape[1]).mul_(
            topk_weights.view(num_tokens, top_k, 1)
        )
        ops.moe_sum(out, out_hidden_states)
    else:
        logger.warning_once(
            "There is no support for fast MoE kernel "
            "for current quantization method. "
            "Falling back to slow implementation. "
        )
        for tok, (w, idx) in enumerate(zip(topk_weights, topk_ids)):
            inp = x[tok].reshape((1,) + x.shape[1:])
            current_hidden_state = None
            for ww, ii in zip(w, idx):
                expert_up = w1[ii]

                out = fused_mul_mat_gguf(inp, expert_up, qweight_type)
                out = act(out)

                expert_down = w2[ii]
                current_state = fused_mul_mat_gguf(
                    out, expert_down, qweight_type2
                ).mul_(ww)
                if current_hidden_state is None:
                    current_hidden_state = current_state
                else:
                    current_hidden_state.add_(current_state)
            out_hidden_states[tok] = current_hidden_state
    return out_hidden_states


def _fused_moe_gguf_fake(
    x: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    qweight_type: int,
    qweight_type2: int,
    activation: str,
) -> torch.Tensor:
    return torch.empty_like(x)


try:
    direct_register_custom_op(
        op_name="_fused_moe_gguf",
        op_func=_fused_moe_gguf,
        fake_impl=_fused_moe_gguf_fake,
    )
    fused_moe_gguf = torch.ops.vllm._fused_moe_gguf

except AttributeError as error:
    raise error


def _apply_gguf_embedding(
    x: torch.Tensor,
    qweight: torch.Tensor,
    qweight_type: int,
    hidden_size: int,
    dtype: torch.dtype | None = None,
) -> torch.Tensor:
    if qweight_type in UNQUANTIZED_TYPES:
        return torch.embedding(qweight, x)
    elif qweight_type in DEQUANT_TYPES:
        block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
        x_flat = x.flatten()
        assert hidden_size == qweight.shape[1] // type_size * block_size
        quant = torch.index_select(qweight, dim=0, index=x_flat)
        dequant = ops.ggml_dequantize(
            quant, qweight_type, hidden_size, x_flat.shape[0], dtype
        )
        return dequant.view(*x.shape, hidden_size)
    else:
        qweight_type = WeightType(qweight_type)
        raise NotImplementedError(f"Unsupported GGUF quantization type: {qweight_type}")


def _apply_gguf_embedding_fake(
    x: torch.Tensor,
    qweight: torch.Tensor,
    qweight_type: int,
    hidden_size: int,
    dtype: torch.dtype | None = None,
) -> torch.Tensor:
    return torch.empty(x.shape[0], hidden_size, dtype=dtype, device=x.device)


try:
    direct_register_custom_op(
        op_name="_apply_gguf_embedding",
        op_func=_apply_gguf_embedding,
        fake_impl=_apply_gguf_embedding_fake,
    )
    apply_gguf_embedding = torch.ops.vllm._apply_gguf_embedding

except AttributeError as error:
    raise error


class GGUFLinearMethod(LinearMethodBase):
    """Linear method for GGUF.

    Args:
        quant_config: The GGUF quantization config.
    """

    def __init__(self, quant_config: GGUFConfig):
        self.quant_config = quant_config

    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,
    ):
        self.params_dtype = params_dtype
        output_size_per_partition = sum(output_partition_sizes)

        tensor_shape = (output_size_per_partition, input_size_per_partition)
        qweight = GGUFUninitializedParameter(requires_grad=False)
        set_weight_attrs(
            qweight,
            {
                "input_dim": 1,
                "output_dim": 0,
                "tensor_shape": tensor_shape,
                "is_gguf_weight": True,
                "data_container": [],
                "shard_id": [],
                "shard_id_map": {},
            },
        )
        set_weight_attrs(qweight, extra_weight_attrs)
        layer.register_parameter("qweight", qweight)

        qweight_type = Parameter(
            torch.empty(len(output_partition_sizes), dtype=torch.uint8),
            requires_grad=False,
        )
        set_weight_attrs(
            qweight_type,
            {
                "is_gguf_weight_type": True,
                "weight_type": 0,
                "shard_weight_type": {},
                "ignore_warning": True,
            },
        )
        set_weight_attrs(qweight_type, extra_weight_attrs)
        layer.register_parameter("qweight_type", qweight_type)

    def process_weights_after_loading(self, layer: torch.nn.Module):
        qweight_type = layer.qweight_type.weight_type
        if not (qweight_type in UNQUANTIZED_TYPES or qweight_type in DEQUANT_TYPES):
            qweight_type = WeightType(qweight_type)
            raise ValueError(
                f"Unsupported GGUF quantization type {qweight_type} in layer {layer}."
            )
        # For MergedColumnParallelLinear and QKVParallelLinear, we need to
        # materialize the padded weight parameter for CUDA Graph compatibility.
        self._create_padded_weight_param(layer)

    def _create_padded_weight_param(self, layer: torch.nn.Module):
        """Create padded weight parameter for GGUF MergedLinear layer."""
        qweight = layer.qweight
        shard_id_map = qweight.shard_id_map
        shard_id = qweight.shard_id
        if len(data_container := qweight.data_container) > 1:
            dtype = {data.dtype for data in data_container}
            assert len(dtype) == 1, ValueError(
                f"Data container has mixed dtypes: {dtype}"
            )
            dtype = next(iter(dtype))
            # concat dim0 and pad dim1
            padded_side = max(x.size(1) for x in data_container)
            concat_side = sum(x.size(0) for x in data_container)
            # Pad the quantized weights to dense tensor, and create a map
            # with the location of each shard in the padded tensor.
            padded_data = torch.zeros(
                (concat_side, padded_side), dtype=dtype, device=qweight.device
            )
            # (dim0_start, dim0_end, dim1_size)
            shard_offset_map = dict[str, tuple[int, int, int]]()
            for idx in shard_id:
                id_in_container = shard_id_map[idx]
                start = sum(x.size(0) for x in data_container[:id_in_container])
                end = start + data_container[id_in_container].size(0)
                size = data_container[id_in_container].size(1)
                padded_data[start:end, :size] = data_container[id_in_container]
                shard_offset_map[idx] = (start, end, size)
            qweight.data_container.clear()
            padded_param = Parameter(padded_data, requires_grad=False)
            set_weight_attrs(padded_param, vars(qweight))
            set_weight_attrs(padded_param, {"shard_offset_map": shard_offset_map})
            layer.register_parameter("qweight", padded_param)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        shard_id = layer.qweight.shard_id

        if shard_id:
            # dequantize shard weights respectively
            shard_id = ["q", "k", "v"] if "q" in shard_id else shard_id
            qweight = layer.qweight
            result = []
            for idx in shard_id:
                start, end, offset = layer.qweight.shard_offset_map[idx]
                qweight_type = layer.qweight_type.shard_weight_type[idx]
                result.append(
                    fused_mul_mat_gguf(
                        x, qweight[start:end, :offset].contiguous(), qweight_type
                    )
                )
            out = torch.cat(result, axis=1)
        else:
            qweight = layer.qweight
            qweight_type = layer.qweight_type.weight_type
            out = fused_mul_mat_gguf(x, qweight, qweight_type)
        if bias is not None:
            out.add_(bias)
        return out


class GGUFMoEMethod(FusedMoEMethodBase):
    """MoE method for GGUF.

    Args:
        quant_config: The GGUF quantization config.
    """

    def __init__(
        self,
        quant_config: GGUFConfig,
        moe: FusedMoEConfig,
    ):
        super().__init__(moe)
        self.quant_config = quant_config

    def create_weights(
        self,
        layer: torch.nn.Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size_per_partition: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        tensor_shape = (num_experts, 2 * intermediate_size_per_partition, hidden_size)
        # gate up proj
        w13_qweight = GGUFUninitializedParameter(requires_grad=False)
        set_weight_attrs(
            w13_qweight,
            {
                "input_dim": 1,
                "output_dim": 0,
                "tensor_shape": tensor_shape,
                "is_gguf_weight": True,
                "data_container": [],
            },
        )
        set_weight_attrs(w13_qweight, extra_weight_attrs)
        layer.register_parameter("w13_qweight", w13_qweight)

        w13_qweight_type = Parameter(
            torch.empty(1, dtype=torch.uint8), requires_grad=False
        )
        set_weight_attrs(
            w13_qweight_type,
            {"is_gguf_weight_type": True, "weight_type": 0, "ignore_warning": True},
        )
        set_weight_attrs(w13_qweight_type, extra_weight_attrs)
        layer.register_parameter("w13_qweight_type", w13_qweight_type)

        tensor_shape = (num_experts, intermediate_size_per_partition, hidden_size)
        # gate down proj
        w2_qweight = GGUFUninitializedParameter(requires_grad=False)
        set_weight_attrs(
            w2_qweight,
            {
                "input_dim": 1,
                "output_dim": 0,
                "tensor_shape": tensor_shape,
                "is_gguf_weight": True,
                "data_container": [],
            },
        )
        set_weight_attrs(w2_qweight, extra_weight_attrs)
        layer.register_parameter("w2_qweight", w2_qweight)

        w2_qweight_type = Parameter(
            torch.empty(1, dtype=torch.uint8), requires_grad=False
        )
        set_weight_attrs(
            w2_qweight_type,
            {"is_gguf_weight_type": True, "weight_type": 0, "ignore_warning": True},
        )

        set_weight_attrs(w2_qweight_type, extra_weight_attrs)
        layer.register_parameter("w2_qweight_type", w2_qweight_type)

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> FusedMoEQuantConfig | None:
        return None

    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: int | None = None,
        num_expert_group: int | None = None,
        global_num_experts: int = -1,
        expert_map: torch.Tensor | None = None,
        custom_routing_function: Callable | None = None,
        scoring_func: str = "softmax",
        routed_scaling_factor: float = 1.0,
        e_score_correction_bias: torch.Tensor | None = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: torch.Tensor | None = None,
        logical_to_physical_map: torch.Tensor | None = None,
        logical_replica_count: torch.Tensor | None = None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        if enable_eplb:
            raise NotImplementedError("EPLB not supported for `GGUFMoEMethod` yet.")

        assert activation == "silu", "Only SiLU activation is supported."
        if apply_router_weight_on_input:
            raise NotImplementedError(
                "Apply router weight on input is not supported for"
                "fused GGUF MoE method."
            )

        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,
            scoring_func=scoring_func,
            routed_scaling_factor=routed_scaling_factor,
            e_score_correction_bias=e_score_correction_bias,
            indices_type=self.topk_indices_dtype,
        )
        return fused_moe_gguf(
            x,
            layer.w13_qweight,
            layer.w2_qweight,
            topk_weights,
            topk_ids,
            layer.w13_qweight_type.weight_type,
            layer.w2_qweight_type.weight_type,
            activation,
        )


class GGUFEmbeddingMethod(GGUFLinearMethod):
    """Embedding method for GGUF.

    Args:
        quant_config: The GGUF quantization config.
    """

    def embedding(self, layer: torch.nn.Module, x: torch.Tensor) -> torch.Tensor:
        qweight = layer.qweight
        qweight_type = layer.qweight_type.weight_type
        hidden_size = qweight.tensor_shape[1]

        return apply_gguf_embedding(
            x, qweight, qweight_type, hidden_size, dtype=self.params_dtype
        )


class GGUFUninitializedParameter(UninitializedParameter):
    cls_to_become = Parameter
    data_container: list[torch.Tensor]