chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""AWQ Quantization"""
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from dataclasses import dataclass, field
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from typing import Any, Callable, Dict, List, Optional # noqa: UP035
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from tvm import DataType, DataTypeCode, te, tirx, topi
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from tvm.relax.frontend import nn
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from tvm.runtime import Tensor
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from mlc_llm.loader import QuantizeMapping
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from .utils import convert_uint_to_float, is_final_fc, is_moe_gate
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def _make_divisible(c, divisor):
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return (c + divisor - 1) // divisor
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def _calculate_zeros_width(in_features, group_size=128, pack_num=8):
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if group_size >= 128:
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size_multiplier = 1
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elif group_size == 64:
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size_multiplier = 2
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elif group_size == 32:
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size_multiplier = 4
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else:
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raise NotImplementedError
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base_width = _make_divisible(in_features // group_size, pack_num)
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base_width = _make_divisible(base_width, size_multiplier) * size_multiplier
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return base_width
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@dataclass
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class AWQQuantize:
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"""Configuration for AWQ quantization"""
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name: str
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kind: str
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group_size: int
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quantize_dtype: str # "int3", "int4", "int8"
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storage_dtype: str # "uint32"
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model_dtype: str # "float16", "float32"
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num_elem_per_storage: int = 0
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num_storage_per_group: int = 0
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max_int_value: int = 0
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prebuilt_quantize_func: Dict[str, Callable[[Tensor], Tensor]] = field( # noqa: UP006
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default_factory=lambda: {}
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)
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def __post_init__(self):
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assert self.kind == "awq"
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quantize_dtype = DataType(self.quantize_dtype)
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storage_dtype = DataType(self.storage_dtype)
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model_dtype = DataType(self.model_dtype)
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assert quantize_dtype.type_code == DataTypeCode.INT
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assert storage_dtype.type_code == DataTypeCode.UINT
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assert model_dtype.type_code == DataTypeCode.FLOAT
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if storage_dtype.bits < quantize_dtype.bits:
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raise ValueError("Storage unit should be greater or equal to quantized element")
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self.num_elem_per_storage = storage_dtype.bits // quantize_dtype.bits
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if self.group_size % self.num_elem_per_storage != 0:
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raise ValueError("Group size should be divisible by numbers of elements per storage")
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self.num_storage_per_group = self.group_size // self.num_elem_per_storage
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self.max_int_value = (2 ** (quantize_dtype.bits - 1)) - 1
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def quantize_model(
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self,
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model: nn.Module,
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quant_map: QuantizeMapping,
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name_prefix: str,
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) -> nn.Module:
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"""
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Quantize model with awq quantization.
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Parameters
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----------
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model : nn.Module
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The non-quantized nn.Module.
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quant_map : QuantizeMapping
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The quantize mapping with name mapping and func mapping.
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name_prefix : str
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The name prefix for visited weight.
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Returns
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-------
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ret : nn.Module
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The quantized nn.Module.
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"""
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class _Mutator(nn.Mutator):
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def __init__(self, config: AWQQuantize, quant_map: QuantizeMapping) -> None:
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super().__init__()
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self.config = config
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self.quant_map = quant_map
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def visit_module(self, name: str, node: nn.Module) -> Any:
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"""
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The visiting method for awq quantization of nn.Module nodes.
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Parameters
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----------
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name : str
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The name of the current node
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node : nn.Module
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The current node of nn.Module to mutate.
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Returns
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-------
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ret_node : Any
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The new node to replace current node.
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"""
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if (
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isinstance(node, nn.Linear)
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and not is_final_fc(name)
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and not is_moe_gate(name, node)
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):
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return AWQQuantizeLinear.from_linear(node, self.config)
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return self.visit(name, node)
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model.to(dtype=self.model_dtype)
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mutator = _Mutator(self, quant_map)
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model = mutator.visit(name_prefix, model)
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return model
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def _dequantize(
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self,
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weight: te.Tensor,
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zeros: te.Tensor,
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scale: te.Tensor,
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out_shape: Optional[List[tirx.Expr]] = None, # noqa: UP006
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):
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float_weight = convert_uint_to_float(
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weight,
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DataType(self.quantize_dtype).bits,
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self.num_elem_per_storage,
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self.storage_dtype,
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self.model_dtype,
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out_shape=[weight.shape[0], weight.shape[1] * self.num_elem_per_storage],
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ft_reorder=True,
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)
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float_zeros = convert_uint_to_float(
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zeros,
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DataType(self.quantize_dtype).bits,
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self.num_elem_per_storage,
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self.storage_dtype,
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self.model_dtype,
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out_shape=[zeros.shape[0], zeros.shape[1] * self.num_elem_per_storage],
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ft_reorder=True,
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)
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float_weight = topi.transpose(float_weight)
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float_zeros = topi.transpose(float_zeros)
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scale = topi.transpose(scale)
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return te.compute(
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shape=(
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[weight.shape[0], weight.shape[1] * self.num_elem_per_storage]
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if out_shape is None
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else out_shape
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),
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fcompute=lambda i, j: tirx.Mul(
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tirx.Sub(float_weight[i, j], float_zeros[i, j // self.group_size]),
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scale[i, j // self.group_size],
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),
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name="dequantize",
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)
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class AWQQuantizeLinear(nn.Module):
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"""An nn.Linear module with AWQ quantization"""
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def __init__(
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self,
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in_features: int,
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out_features: int,
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config: AWQQuantize,
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bias: bool = True,
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out_dtype: Optional[str] = None,
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) -> None:
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.out_dtype = out_dtype
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self.config = config
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self.qweight = nn.Parameter(
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(in_features, out_features // config.num_elem_per_storage),
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config.storage_dtype,
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)
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self.qzeros = nn.Parameter(
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(
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in_features // config.group_size,
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out_features // config.num_elem_per_storage,
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),
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config.storage_dtype,
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)
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self.scales = nn.Parameter(
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(in_features // config.group_size, out_features), config.model_dtype
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)
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if bias:
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self.bias = nn.Parameter(
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(out_features,), config.model_dtype if out_dtype is None else out_dtype
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)
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else:
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self.bias = None
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@staticmethod
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def from_linear(linear: nn.Linear, config: AWQQuantize) -> "AWQQuantizeLinear":
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"""
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Converts a non-quantized nn.Linear to a group quantized AWQQuantizeLinear
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Parameters
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----------
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linear : nn.Linear
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The non-quantized nn.Linear.
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config : AWQQuantize
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The awq quantization config.
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Returns
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-------
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ret : GroupQuantizeLinear
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The awq quantized AWQQuantizeLinear layer.
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"""
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return AWQQuantizeLinear(
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in_features=linear.in_features,
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out_features=linear.out_features,
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config=config,
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bias=getattr(linear, "bias", None) is not None,
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out_dtype=linear.out_dtype,
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)
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def forward(self, x: nn.Tensor) -> nn.Tensor:
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"""
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Forward method for awq quantized linear layer
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Parameters
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----------
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x : nn.Tensor
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The input tensor.
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Returns
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-------
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ret : nn.Tensor
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The output tensor for the group quantized linear layer.
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"""
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w = nn.op.tensor_expr_op(
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lambda weight, zeros, scale: self.config._dequantize(
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weight,
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zeros,
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scale,
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[
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tirx.IntImm("int64", self.out_features),
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tirx.IntImm("int64", self.in_features),
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],
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),
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name_hint="dequantize",
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args=[self.qweight, self.qzeros, self.scales],
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)
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w = nn.op.permute_dims(w)
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x = nn.op.matmul(x, w, out_dtype=self.out_dtype)
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if self.bias is not None:
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x = x + self.bias
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return x
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def to(self, dtype: Optional[str] = None) -> None:
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"""
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Override to() such that we do not convert bias if there is an out_dtype.
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Otherwise, we might run into dtype mismatch when computing x + self.bias.
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"""
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self.qweight.to(dtype=dtype)
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self.qzeros.to(dtype=dtype)
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self.scales.to(dtype=dtype)
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if self.bias is not None and self.out_dtype is None:
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self.bias.to(dtype=dtype)
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if dtype is not None and isinstance(getattr(self, "dtype", None), str):
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self.dtype = dtype
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