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[Feat]: Add support for Dynamic Quant 4 bit CPU kleidiai kernels #17112
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135 changes: 135 additions & 0 deletions
135
.../model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a8.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,135 @@ | ||
| # SPDX-FileCopyrightText: Copyright 2025 Arm Limited and/or its affiliate open-source-office@arm.com | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| from typing import Callable, List, Optional, Set | ||
|
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| import torch | ||
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| from vllm.logger import init_logger | ||
| from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( | ||
| CompressedTensorsScheme) | ||
| from vllm.model_executor.layers.quantization.kernels.mixed_precision import ( | ||
| MPLinearLayerConfig, choose_mp_linear_kernel) | ||
| from vllm.model_executor.parameter import (ChannelQuantScaleParameter, | ||
| GroupQuantScaleParameter, | ||
| ModelWeightParameter) | ||
| from vllm.scalar_type import scalar_types | ||
|
|
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| logger = init_logger(__name__) | ||
|
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| __all__ = ["CompressedTensorsW4A8"] | ||
| W4A8_SUPPORTED_TYPES_MAP = { | ||
| 4: scalar_types.int4, | ||
| } | ||
| W4A8_SUPPORTED_BITS = list(W4A8_SUPPORTED_TYPES_MAP.keys()) | ||
|
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| class CompressedTensorsW4A8(CompressedTensorsScheme): | ||
| _kernel_backends_being_used: Set[str] = set() | ||
|
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| def __init__(self, | ||
| strategy: str, | ||
| num_bits: int, | ||
| group_size: Optional[int] = None, | ||
| is_static_input_scheme: bool = False, | ||
| input_symmetric: bool = True): | ||
| self.strategy = strategy | ||
| self.group_size = -1 if group_size is None else group_size | ||
| self.is_static_input_scheme = is_static_input_scheme | ||
| self.input_symmetric = input_symmetric | ||
|
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||
| if num_bits not in W4A8_SUPPORTED_TYPES_MAP: | ||
| raise ValueError( | ||
| f"Unsupported num_bits = {num_bits}." | ||
| f"Supported num_bits = {W4A8_SUPPORTED_TYPES_MAP.keys()}") | ||
| self.quant_type = W4A8_SUPPORTED_TYPES_MAP[num_bits] | ||
|
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||
| @classmethod | ||
| def get_min_capability(cls) -> int: | ||
| return 1 | ||
|
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| def create_weights(self, layer: torch.nn.Module, output_size: int, | ||
| input_size: int, output_partition_sizes: List[int], | ||
| input_size_per_partition: int, | ||
| params_dtype: torch.dtype, weight_loader: Callable, | ||
| **kwargs): | ||
| output_size_per_partition = sum(output_partition_sizes) | ||
| row_parallel = (input_size != input_size_per_partition) | ||
|
|
||
| # Compute effective group_size | ||
| if self.group_size == -1: | ||
| effective_group_size = (input_size_per_partition | ||
| if row_parallel else input_size) | ||
| else: | ||
| effective_group_size = self.group_size | ||
|
|
||
| # Ensure group_size divides input_size_per_partition | ||
| assert input_size_per_partition % effective_group_size == 0, ( | ||
| f"input_size_per_partition {input_size_per_partition}" | ||
| f" not divisible by group_size {effective_group_size}") | ||
|
|
||
| # Determine scale partitioning | ||
| is_channelwise = (self.group_size == -1) | ||
| repeat_scales = (is_channelwise and row_parallel) | ||
| partition_scales = not repeat_scales | ||
|
|
||
| mp_linear_kernel_config = MPLinearLayerConfig( | ||
| full_weight_shape=(input_size, output_size), | ||
| partition_weight_shape=(input_size_per_partition, | ||
| output_size_per_partition), | ||
| weight_type=self.quant_type, | ||
| act_type=params_dtype, | ||
| group_size=effective_group_size, | ||
| zero_points=False, | ||
| has_g_idx=False, | ||
| ) | ||
|
|
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| kernel_type = choose_mp_linear_kernel(mp_linear_kernel_config) | ||
| if kernel_type.__name__ not in self._kernel_backends_being_used: | ||
| logger.info("Using %s for CompressedTensorsW4A8", | ||
| kernel_type.__name__) | ||
| self._kernel_backends_being_used.add(kernel_type.__name__) | ||
|
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| scales_and_zp_size = input_size_per_partition // effective_group_size | ||
|
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| weight = ModelWeightParameter(data=torch.empty( | ||
| output_size_per_partition, | ||
| input_size_per_partition, | ||
| dtype=torch.int8), | ||
| input_dim=1, | ||
| output_dim=0, | ||
| weight_loader=weight_loader) | ||
| layer.register_parameter("weight", weight) | ||
|
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| weight_scale_args = { | ||
| "weight_loader": | ||
| weight_loader, | ||
| "data": | ||
| torch.empty(output_size_per_partition, | ||
| scales_and_zp_size, | ||
| dtype=params_dtype) | ||
| } | ||
|
|
||
| if partition_scales: | ||
| weight_scale = GroupQuantScaleParameter(output_dim=0, | ||
| input_dim=1, | ||
| **weight_scale_args) | ||
| else: | ||
| weight_scale = ChannelQuantScaleParameter(output_dim=0, | ||
| **weight_scale_args) | ||
|
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| layer.register_parameter("weight_packed", weight) | ||
| layer.register_parameter("weight_scale", weight_scale) | ||
|
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| self.kernel = kernel_type(mp_linear_kernel_config, | ||
| w_q_param_name="weight_packed", | ||
| w_s_param_name="weight_scale", | ||
| w_zp_param_name=None, | ||
| w_gidx_param_name=None) | ||
|
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| def process_weights_after_loading(self, layer: torch.nn.Module) -> None: | ||
| self.kernel.process_weights_after_loading(layer) | ||
|
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| def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor, | ||
| bias: Optional[torch.Tensor]) -> torch.Tensor: | ||
| return self.kernel.apply_weights(layer, x, bias) | ||
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92 changes: 92 additions & 0 deletions
92
vllm/model_executor/layers/quantization/kernels/mixed_precision/dynamic_4bit.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,92 @@ | ||
| # SPDX-FileCopyrightText: Copyright 2025 Arm Limited and/or its affiliate open-source-office@arm.com | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| from typing import Optional, Tuple | ||
|
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| import torch | ||
|
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| from vllm.model_executor.layers.quantization.utils import replace_parameter | ||
| from vllm.platforms import CpuArchEnum, current_platform | ||
| from vllm.scalar_type import scalar_types | ||
|
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| from .MPLinearKernel import MPLinearKernel, MPLinearLayerConfig | ||
|
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| class Dynamic4bitLinearKernel(MPLinearKernel): | ||
| SUPPORTED_QUANT_TYPES = [scalar_types.int4] | ||
|
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| @classmethod | ||
| def get_min_capability(cls) -> int: | ||
| return 1 | ||
|
|
||
| @classmethod | ||
| def can_implement(cls, | ||
| c: MPLinearLayerConfig) -> Tuple[bool, Optional[str]]: | ||
| if not current_platform.is_cpu(): | ||
| return False, "Only CPU is supported" | ||
| if c.weight_type not in cls.SUPPORTED_QUANT_TYPES: | ||
| return False, f"Unsupported quant type {c.weight_type}" | ||
| if current_platform.get_cpu_architecture( | ||
| ) == CpuArchEnum.ARM and c.act_type not in [ | ||
| torch.float32, torch.bfloat16 | ||
| ]: | ||
| return False, "Dynamic4bitLinearKernel on Arm requires"\ | ||
| " Float32 or Bfloat16 activations" | ||
| if c.full_weight_shape[0] % c.group_size != 0: | ||
| return False, f"Group size ({c.group_size}) does not evenly divide"\ | ||
| " the number of input features "\ | ||
| f"({c.full_weight_shape[0]})" | ||
| if current_platform.get_cpu_architecture() == CpuArchEnum.ARM: | ||
| try: | ||
| # Attempt to retrieve the operation | ||
| _ = torch.ops.aten._dyn_quant_matmul_4bit | ||
| except AttributeError: | ||
| return False, f"PyTorch {torch.__version__} does not support"\ | ||
| " _dyn_quant_matmul_4bit. Install a newer version" | ||
| return True, None | ||
|
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| def process_weights_after_loading(self, layer: torch.nn.Module): | ||
| c = self.config | ||
| packed_weight = getattr(layer, self.w_q_name) | ||
| packed_weight = packed_weight.add(8) | ||
| uint8_packed = (packed_weight[::, 1::2] << 4 | ||
| | packed_weight[::, ::2]).to(torch.uint8) | ||
|
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| scales = getattr(layer, self.w_s_name) | ||
| block_size = c.group_size | ||
|
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| # Handle scaling factors for partitioned weights | ||
| if block_size == c.partition_weight_shape[0]: | ||
| scales = scales.to( | ||
| torch.float32 | ||
| ) # Float32 & Bfloat16 variants requires float32 scales | ||
| scales = scales.view(-1, 1) # Channel-wise scales | ||
| if layer.bias is not None: | ||
| layer.bias = layer.bias.to( | ||
| torch.float32 | ||
| ) # Float32 & Bfloat16 variants requires float32 bias | ||
| else: | ||
| # KleidiAI kernel requires bfloat16 scales with groupwise scheme | ||
| scales = scales.to(torch.bfloat16) | ||
|
|
||
| # Repack weights as per kernel requirement | ||
| w = torch.ops.aten._dyn_quant_pack_4bit_weight( | ||
| uint8_packed, scales, layer.bias, block_size, | ||
| c.partition_weight_shape[0], c.partition_weight_shape[1]) | ||
| replace_parameter(layer, self.w_q_name, | ||
| torch.nn.Parameter(w, requires_grad=False)) | ||
| setattr(layer, self.w_s_name, None) | ||
|
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| def apply_weights(self, | ||
| layer: torch.nn.Module, | ||
| x: torch.Tensor, | ||
| bias: Optional[torch.Tensor] = None) -> torch.Tensor: | ||
| c = self.config | ||
| x_2d = x.reshape(-1, x.shape[-1]) | ||
| out_shape = x.shape[:-1] + (c.partition_weight_shape[1], ) | ||
|
|
||
| w_q = getattr(layer, self.w_q_name) | ||
| output = torch.ops.aten._dyn_quant_matmul_4bit( | ||
| x_2d, w_q, c.group_size, c.partition_weight_shape[0], | ||
| c.partition_weight_shape[1]) | ||
| return output.reshape(out_shape) |
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These are just int8 values in the in4 range I'm assuming?
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Yes, you are correct