1100 lines
40 KiB
Python
1100 lines
40 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Callable
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from copy import deepcopy
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from typing import Any, Optional
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import torch
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from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
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import vllm.model_executor.layers.fused_moe # noqa
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from vllm import _custom_ops as ops
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe.config import (
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FusedMoEConfig,
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FusedMoEQuantConfig,
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)
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from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE,
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FusedMoEMethodBase,
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FusedMoeWeightScaleSupported,
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UnquantizedFusedMoEMethod,
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)
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from vllm.model_executor.layers.linear import LinearMethodBase, set_weight_attrs
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from vllm.model_executor.layers.quantization import QuantizationMethods
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
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MPLinearLayerConfig,
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choose_mp_linear_kernel,
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)
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from vllm.model_executor.layers.quantization.utils import replace_parameter
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from vllm.model_executor.layers.quantization.utils.gptq_utils import (
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get_dynamic_override,
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get_linear_quant_method,
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override_config,
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)
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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check_marlin_supported,
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check_moe_marlin_supports_layer,
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marlin_make_workspace_new,
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marlin_moe_permute_scales,
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marlin_permute_bias,
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marlin_repeat_scales_on_all_ranks,
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verify_marlin_supported,
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)
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from vllm.model_executor.parameter import (
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ChannelQuantScaleParameter,
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GroupQuantScaleParameter,
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PackedColumnParameter,
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PackedvLLMParameter,
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RowvLLMParameter,
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)
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from vllm.platforms import current_platform
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from vllm.scalar_type import scalar_types
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from vllm.transformers_utils.config import get_safetensors_params_metadata
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from vllm.utils.collection_utils import is_list_of
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import ixformer.inference.functions as ixfops
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logger = init_logger(__name__)
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#[B,K//8,N] ->[B,K,N]
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# less memmory
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def unpack_k_batch_opt(packed_w: torch.Tensor, num_bits: int = 4, chunk_size: int = 2) -> torch.Tensor:
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"""
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Memory-efficient unpacking for 3D tensor.
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Converts [B, K // pack_factor, N] int32 tensor → [B, K, N] int8 tensor,
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without broadcasting huge intermediate tensors (avoids OOM).
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Args:
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packed_w: torch.int32 tensor of shape [B, K // pack_factor, N].
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num_bits: Number of bits per packed element (e.g., 4 or 2).
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chunk_size: How many bit groups to unpack at once (tradeoff between speed and memory).
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Returns:
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unpacked: torch.int8 tensor of shape [B, K, N].
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"""
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B, k_packed, N = packed_w.shape
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pack_factor = 32 // num_bits
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K = k_packed * pack_factor
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mask = (1 << num_bits) - 1
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# Allocate output tensor once
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unpacked = torch.empty((B, K, N), dtype=torch.int8, device=packed_w.device)
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# Process bit chunks iteratively to save memory
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for i in range(0, pack_factor, chunk_size):
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# Precompute shifts for this chunk
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shift_vals = num_bits * torch.arange(i, min(i + chunk_size, pack_factor), device=packed_w.device)
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# [chunk_size, 1, 1, 1]
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shifts = shift_vals.view(-1, 1, 1, 1)
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# Compute small chunk only
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chunk = ((packed_w.unsqueeze(0) >> shifts) & mask).to(torch.int8)
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# chunk: [chunk_size, B, k_packed, N]
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# write into output
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for j in range(chunk.shape[0]):
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unpacked[:, (i + j)::pack_factor, :] = chunk[j]
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del chunk # release memory early
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return unpacked
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# more memmory
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def unpack_k_batch(packed_w: torch.Tensor, num_bits: int = 4) -> torch.Tensor:
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"""
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Efficient vectorized unpacking for 3D tensor (batch version).
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Converts [B, K // pack_factor, N] int32 tensor → [B, K, N] int8 tensor.
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Args:
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packed_w: torch.int32 tensor of shape [B, K // pack_factor, N].
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num_bits: Number of bits per packed element (e.g., 4).
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Returns:
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unpacked: torch.int8 tensor of shape [B, K, N].
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"""
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B, k_packed, n = packed_w.shape
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pack_factor = 32 // num_bits
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k = k_packed * pack_factor
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mask = (1 << num_bits) - 1
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# [pack_factor, 1, 1, 1]
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shifts = (num_bits * torch.arange(pack_factor, device=packed_w.device)).view(-1, 1, 1, 1)
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# [1, B, k_packed, N]
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packed_expanded = packed_w.unsqueeze(0)
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# Extract each group of num_bits using bitwise ops
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unpacked_groups = ((packed_expanded >> shifts) & mask).to(torch.int8)
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# [pack_factor, B, k_packed, N] → [B, K, N]
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unpacked = unpacked_groups.permute(1, 2, 0, 3).reshape(B, k, n)
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return unpacked
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#[B,K,N] ->[B,K,N//8]
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# less memmory
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def pack_n_batch_opt(x: torch.Tensor, pack_num: int = 8, order_map=None, chunk_size: int = 2) -> torch.Tensor:
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"""
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Memory-efficient batch packing with correct bit order.
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[B, K, N] int4 -> [B, K, N//pack_num] int32.
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"""
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B, K, N = x.shape
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assert N % pack_num == 0, "N must be divisible by pack_num"
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cols = N // pack_num
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unit = 32 // pack_num
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if order_map is None:
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order_map = list(range(pack_num))
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order_map = torch.tensor(order_map, device=x.device)
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shifts = unit * torch.arange(pack_num, device=x.device) # always 0..unit*(pack_num-1)
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packed = torch.zeros((B, K, cols), dtype=torch.int32, device=x.device)
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x_reshape = x.view(B, K, cols, pack_num) & 0xF
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# process in chunks for memory efficiency
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for start in range(0, pack_num, chunk_size):
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end = min(start + chunk_size, pack_num)
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idx_chunk = order_map[start:end]
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shift_chunk = shifts[start:end]
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vals = torch.gather(x_reshape, 3, idx_chunk.view(1,1,1,-1).expand(B,K,cols,-1)).to(torch.int32)
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for j in range(vals.shape[-1]):
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packed.add_(vals[..., j] << shift_chunk[j])
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return packed
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## more memmory
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def pack_n_batch(x: torch.Tensor, pack_num: int = 8, order_map=None) -> torch.Tensor:
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"""
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Efficient vectorized batch packing: [B, K, N] int4 -> [B, K, N//pack_num] int32.
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Args:
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x: torch.int32 tensor of shape [B, K, N], each element 0-15 (int4).
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pack_num: Number of 4-bit elements per packed int32 (default=8).
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order_map: Optional order of elements within each packed int32.
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Returns:
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torch.int32 tensor of shape [B, K, N//pack_num].
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"""
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B, K, N = x.shape
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assert N % pack_num == 0, "N must be divisible by pack_num"
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cols = N // pack_num
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if order_map is None:
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order_map = list(range(pack_num))
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order_map = torch.tensor(order_map, device=x.device)
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unit = 32 // pack_num # number of bits per element
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# reshape to [B, K, cols, pack_num]
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pack_num_int = int(pack_num)
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x_reshape = x.view(B, K, cols, pack_num_int)
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# reorder according to order_map
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x_reorder = torch.gather(
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x_reshape, 3, order_map.view(1, 1, 1, -1).expand(B, K, cols, -1)
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)
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# mask low 4 bits
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x_reorder = x_reorder & 0xF
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# bit shifts [pack_num] -> [1,1,1,pack_num] broadcastable
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shifts = (unit * torch.arange(pack_num_int, device=x.device)).view(1, 1, 1, -1)
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# shift and sum along last dimension to combine bits
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packed = (x_reorder << shifts).sum(dim=-1).to(torch.int32)
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return packed
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def get_moe_quant_method(
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config: "GPTQMarlinConfig",
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layer: torch.nn.Module,
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prefix: str,
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moe_method_cls: type,
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):
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cloned_config = deepcopy(config)
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if isinstance(layer, FusedMoE):
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# False = skip module, None = no override, else = Positive match
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if (
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get_dynamic_override( # noqa: E712
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cloned_config, # noqa: E712
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layer_name=prefix,
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)
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== False
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): # noqa: E712
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return UnquantizedFusedMoEMethod(layer.moe_config)
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if prefix:
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# Dynamic per module/layer rules may override base config
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override_config(cloned_config, prefix=prefix)
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return moe_method_cls(cloned_config, layer.moe_config)
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return None
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class GPTQMarlinConfig(QuantizationConfig):
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"""Config class for GPTQ Marlin"""
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# (num_bits, is_sym) -> quant_type
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TYPE_MAP = {
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(4, True): scalar_types.uint4b8,
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(8, True): scalar_types.uint8b128,
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}
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def __init__(
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self,
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weight_bits: int,
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group_size: int,
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desc_act: bool,
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is_sym: bool,
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lm_head_quantized: bool,
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dynamic: dict[str, dict[str, int | bool]],
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full_config: dict[str, Any],
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modules_in_block_to_quantize: list[str] | None = None,
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) -> None:
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super().__init__()
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if desc_act and group_size == -1:
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# In this case, act_order == True is the same as act_order == False
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# (since we have only one group per output channel)
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desc_act = False
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# GPTQModel use `dynamic` config property to allow per module
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# quantization config so each module can be individually optimized.
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# Format is dict[str, dict] where key is a regex string that can
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# perform both positive ("+:" prefixed) or negative ("-:" prefixed)
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# matching of a module.
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# Default to positive match, override base quant config mode, if no
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# prefix is used. Value is in dict format of field key and override
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# value.
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# Negative matching will skip quantization init for this module
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# entirely:
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# non-quantized inference. More details and quantization examples can be
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# found at: https://github.com/ModelCloud/GPTQModel
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# Example:
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# # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
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# # last 1/4 of the layers 16-21 has 8bit and group_size 64
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# dynamic = {
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# #`.*\.` matches the layers_node prefix
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# # positive match layer 10-15
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# r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
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# # positive match layer 16-21
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# r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
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# r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
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# }
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self.dynamic = dynamic
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self.weight_bits = weight_bits
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self.is_sym = is_sym
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self.pack_factor = 32 // weight_bits # packed into int32
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self.group_size = group_size
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self.desc_act = desc_act
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self.lm_head_quantized = lm_head_quantized
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self.full_config = full_config
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if (weight_bits, is_sym) not in self.TYPE_MAP:
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raise ValueError(
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f"Unsupported quantization config: bits={weight_bits}, sym={is_sym}"
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)
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self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]
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self.modules_in_block_to_quantize = modules_in_block_to_quantize or []
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# used to identify GPTQ model quantized by autoround
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self.autoround_version = full_config.get("autoround_version", "")
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def __repr__(self) -> str:
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return (
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f"GPTQMarlinConfig(quant_type={self.quant_type}, "
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f"group_size={self.group_size}, "
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f"desc_act={self.desc_act}, "
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f"lm_head_quantized={self.lm_head_quantized}, "
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f"dynamic={self.dynamic}, "
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f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})"
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)
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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return "gptq_marlin"
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@classmethod
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def get_supported_act_dtypes(cls) -> list[torch.dtype]:
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return [torch.half, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 80
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@classmethod
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def get_config_filenames(cls) -> list[str]:
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return ["quantize_config.json"]
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> "GPTQMarlinConfig":
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dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
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dynamic = {} if dynamic is None else dynamic
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weight_bits = cls.get_from_keys(config, ["bits"])
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group_size = cls.get_from_keys(config, ["group_size"])
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desc_act = cls.get_from_keys(config, ["desc_act"])
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is_sym = cls.get_from_keys(config, ["sym"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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modules_in_block_to_quantize = cls.get_from_keys_or(
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config, ["modules_in_block_to_quantize"], default=None
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)
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return cls(
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weight_bits,
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group_size,
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desc_act,
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is_sym,
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lm_head_quantized,
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dynamic,
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config,
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modules_in_block_to_quantize,
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)
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@classmethod
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def override_quantization_method(
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cls, hf_quant_cfg, user_quant
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) -> QuantizationMethods | None:
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can_convert = cls.is_gptq_marlin_compatible(hf_quant_cfg)
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is_valid_user_quant = (
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user_quant is None or user_quant == "marlin" or user_quant == "gptq_marlin"
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)
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if can_convert and is_valid_user_quant:
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msg = (
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"The model is convertible to {} during runtime."
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" Using {} kernel.".format(cls.get_name(), cls.get_name())
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)
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logger.info(msg)
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return cls.get_name()
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if can_convert and user_quant == "gptq":
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logger.info(
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"Detected that the model can run with gptq_marlin"
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", however you specified quantization=gptq explicitly,"
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" so forcing gptq. Use quantization=gptq_marlin for"
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" faster inference"
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)
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return None
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional["QuantizeMethodBase"]:
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if isinstance(layer, FusedMoE):
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from vllm.model_executor.layers.quantization.moe_wna16 import MoeWNA16Config
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if not check_moe_marlin_supports_layer(layer, self.group_size):
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logger.warning_once(
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f"Layer '{prefix}' is not supported by GPTQMoeMarlin. "
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"Falling back to Moe WNA16 kernels."
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)
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return MoeWNA16Config.from_config(self.full_config).get_quant_method(
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layer, prefix
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)
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return get_moe_quant_method(self, layer, prefix, GPTQMarlinMoEMethod)
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return get_linear_quant_method(self, layer, prefix, GPTQMarlinLinearMethod)
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@classmethod
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def is_gptq_marlin_compatible(cls, quant_config: dict[str, Any]):
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quant_method = quant_config.get("quant_method", "").lower()
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num_bits = quant_config.get("bits")
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group_size = quant_config.get("group_size")
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sym = quant_config.get("sym")
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desc_act = quant_config.get("desc_act")
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if not current_platform.is_cuda():
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return False
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if quant_method != "gptq":
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return False
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# Marlin conversion is only valid if required properties are found
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if num_bits is None or group_size is None or sym is None or desc_act is None:
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return False
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if (num_bits, sym) not in cls.TYPE_MAP:
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return False
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return check_marlin_supported(
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quant_type=cls.TYPE_MAP[(num_bits, sym)], group_size=group_size
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)
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def apply_vllm_mapper(self, hf_to_vllm_mapper):
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if self.modules_in_block_to_quantize is not None:
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self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list(
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self.modules_in_block_to_quantize
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)
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def maybe_update_config(self, model_name: str, revision: str | None = None):
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if self.modules_in_block_to_quantize:
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if is_list_of(self.modules_in_block_to_quantize, list):
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# original modules_in_block_to_quantize: list[list[str]]
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# flatten original modules_in_block_to_quantize
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self.modules_in_block_to_quantize = [
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item
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for sublist in self.modules_in_block_to_quantize
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for item in sublist
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]
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return
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unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
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metadata = get_safetensors_params_metadata(model_name, revision=revision)
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quant_layers: set[str] = {
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param_name.rsplit(".", 1)[0]
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for param_name, info in metadata.items()
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if (dtype := info.get("dtype", None))
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and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
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}
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self.modules_in_block_to_quantize = list(quant_layers)
|
|
|
|
|
|
class GPTQMarlinLinearMethod(LinearMethodBase):
|
|
"""Linear method for GPTQ Marlin.
|
|
|
|
Args:
|
|
quant_config: The GPTQ Marlin quantization config.
|
|
"""
|
|
|
|
_kernel_backends_being_used: set[str] = set()
|
|
|
|
def __init__(self, quant_config: GPTQMarlinConfig) -> None:
|
|
self.quant_config = quant_config
|
|
|
|
# Verify supported on platform.
|
|
verify_marlin_supported(
|
|
quant_type=self.quant_config.quant_type,
|
|
group_size=self.quant_config.group_size,
|
|
)
|
|
|
|
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)
|
|
is_row_parallel = input_size != input_size_per_partition
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
|
|
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_config.quant_type,
|
|
act_type=params_dtype,
|
|
group_size=self.quant_config.group_size,
|
|
zero_points=False,
|
|
has_g_idx=self.quant_config.desc_act,
|
|
)
|
|
|
|
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 GPTQMarlinLinearMethod", kernel_type.__name__)
|
|
self._kernel_backends_being_used.add(kernel_type.__name__)
|
|
|
|
# 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 GPU 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,
|
|
)
|
|
|
|
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)
|
|
|
|
self.kernel = kernel_type(
|
|
mp_linear_kernel_config,
|
|
w_q_param_name="qweight",
|
|
w_s_param_name="scales",
|
|
w_zp_param_name="qzeros",
|
|
w_gidx_param_name="g_idx",
|
|
)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
self.kernel.process_weights_after_loading(layer)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
return self.kernel.apply_weights(layer, x, bias)
|
|
|
|
|
|
class GPTQMarlinMoEMethod(FusedMoEMethodBase):
|
|
"""MoE Marlin method with quantization."""
|
|
|
|
def __init__(
|
|
self,
|
|
quant_config: GPTQMarlinConfig,
|
|
moe: FusedMoEConfig,
|
|
) -> None:
|
|
super().__init__(moe)
|
|
self.quant_config = quant_config
|
|
if self.quant_config.quant_type.size_bits == 4:
|
|
self.quant_type = scalar_types.uint4b8
|
|
# elif self.quant_config.quant_type.size_bits == 8:
|
|
# self.quant_type = scalar_types.uint8b128
|
|
else:
|
|
raise ValueError("GPTQMarlinMoEMethod only supports int4 and int8 now.")
|
|
self.use_marlin = True
|
|
|
|
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,
|
|
):
|
|
intermediate_size_full = extra_weight_attrs.pop("intermediate_size_full")
|
|
|
|
self.is_k_full = (not self.quant_config.desc_act) or (
|
|
intermediate_size_per_partition == intermediate_size_full
|
|
)
|
|
|
|
if self.quant_config.group_size != -1:
|
|
scales_size13 = hidden_size // self.quant_config.group_size
|
|
w2_scales_size = (
|
|
intermediate_size_full
|
|
if self.quant_config.desc_act
|
|
else intermediate_size_per_partition
|
|
)
|
|
scales_size2 = w2_scales_size // self.quant_config.group_size
|
|
strategy = FusedMoeWeightScaleSupported.GROUP.value
|
|
else:
|
|
scales_size13 = 1
|
|
scales_size2 = 1
|
|
strategy = FusedMoeWeightScaleSupported.CHANNEL.value
|
|
|
|
extra_weight_attrs.update({"quant_method": strategy, "is_transposed": True})
|
|
# Fused gate_up_proj (column parallel)
|
|
w13_qweight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size // self.quant_config.pack_factor,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_qweight", w13_qweight)
|
|
set_weight_attrs(w13_qweight, extra_weight_attrs)
|
|
# down_proj (row parallel)
|
|
w2_qweight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
intermediate_size_per_partition // self.quant_config.pack_factor,
|
|
hidden_size,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_qweight", w2_qweight)
|
|
set_weight_attrs(w2_qweight, extra_weight_attrs)
|
|
# up_proj scales
|
|
w13_scales = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
scales_size13,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_scales", w13_scales)
|
|
set_weight_attrs(w13_scales, extra_weight_attrs)
|
|
# down_proj scales
|
|
w2_scales = torch.nn.Parameter(
|
|
torch.empty(num_experts, scales_size2, hidden_size, dtype=params_dtype),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_scales", w2_scales)
|
|
set_weight_attrs(w2_scales, extra_weight_attrs)
|
|
# don't shard the w2 scales when running act order
|
|
set_weight_attrs(w2_scales, {"load_full_w2": self.quant_config.desc_act})
|
|
# up_proj scales
|
|
w13_qzeros = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
scales_size13,
|
|
2 * intermediate_size_per_partition // self.quant_config.pack_factor,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_qzeros", w13_qzeros)
|
|
set_weight_attrs(w13_qzeros, extra_weight_attrs)
|
|
# down_proj scales
|
|
w2_qzeros = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
scales_size2,
|
|
hidden_size // self.quant_config.pack_factor,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_qzeros", w2_qzeros)
|
|
set_weight_attrs(w2_qzeros, extra_weight_attrs)
|
|
# don't shard the w2 scales when running act order
|
|
set_weight_attrs(w2_qzeros, {"load_full_w2": self.quant_config.desc_act})
|
|
w13_g_idx = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_g_idx", w13_g_idx)
|
|
set_weight_attrs(w13_g_idx, extra_weight_attrs)
|
|
w2_g_idx = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
intermediate_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_g_idx", w2_g_idx)
|
|
set_weight_attrs(w2_g_idx, extra_weight_attrs)
|
|
w13_g_idx_sort_indices = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_g_idx_sort_indices", w13_g_idx_sort_indices)
|
|
set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
|
|
w2_g_idx_sort_indices = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
intermediate_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_g_idx_sort_indices", w2_g_idx_sort_indices)
|
|
set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
|
|
|
|
device = layer.w13_qweight.device
|
|
# layer.workspace = marlin_make_workspace_new(device, 4)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
# Process act_order
|
|
# if self.quant_config.desc_act:
|
|
# Get sorting based on g_idx
|
|
# num_experts = layer.w13_g_idx.shape[0]
|
|
# w13_g_idx_sort_indices = torch.empty_like(layer.w13_g_idx)
|
|
# w2_g_idx_sort_indices = torch.empty_like(layer.w2_g_idx)
|
|
# w13_sorted_g_idx = torch.empty_like(layer.w13_g_idx)
|
|
# w2_sorted_g_idx = torch.empty_like(layer.w2_g_idx)
|
|
# for e in range(num_experts):
|
|
# w13_g_idx_sort_indices[e] = torch.argsort(layer.w13_g_idx[e]).to(
|
|
# torch.int32
|
|
# )
|
|
# w2_g_idx_sort_indices[e] = torch.argsort(layer.w2_g_idx[e]).to(
|
|
# torch.int32
|
|
# )
|
|
# w13_sorted_g_idx[e] = layer.w13_g_idx[e][w13_g_idx_sort_indices[e]]
|
|
# w2_sorted_g_idx[e] = layer.w2_g_idx[e][w2_g_idx_sort_indices[e]]
|
|
# replace_parameter(layer, "w13_g_idx", w13_sorted_g_idx)
|
|
# replace_parameter(layer, "w2_g_idx", w2_sorted_g_idx)
|
|
# replace_parameter(layer, "w13_g_idx_sort_indices", w13_g_idx_sort_indices)
|
|
# replace_parameter(layer, "w2_g_idx_sort_indices", w2_g_idx_sort_indices)
|
|
# else:
|
|
# # Reset g_idx related tensors
|
|
# num_experts = layer.w13_g_idx.shape[0]
|
|
# device = layer.w13_g_idx.device
|
|
# layer.w13_g_idx = torch.nn.Parameter(
|
|
# torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
|
# requires_grad=False,
|
|
# )
|
|
# layer.w2_g_idx = torch.nn.Parameter(
|
|
# torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
|
# requires_grad=False,
|
|
# )
|
|
# layer.w13_g_idx_sort_indices = torch.nn.Parameter(
|
|
# torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
|
# requires_grad=False,
|
|
# )
|
|
# layer.w2_g_idx_sort_indices = torch.nn.Parameter(
|
|
# torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
|
# requires_grad=False,
|
|
# )
|
|
# # Repack weights
|
|
# marlin_w13_qweight = ops.gptq_marlin_moe_repack(
|
|
# layer.w13_qweight,
|
|
# layer.w13_g_idx_sort_indices,
|
|
# layer.w13_qweight.shape[1] * self.quant_config.pack_factor,
|
|
# layer.w13_qweight.shape[2],
|
|
# self.quant_config.quant_type.size_bits,
|
|
# )
|
|
# replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
|
|
# marlin_w2_qweight = ops.gptq_marlin_moe_repack(
|
|
# layer.w2_qweight,
|
|
# layer.w2_g_idx_sort_indices,
|
|
# layer.w2_qweight.shape[1] * self.quant_config.pack_factor,
|
|
# layer.w2_qweight.shape[2],
|
|
# self.quant_config.quant_type.size_bits,
|
|
# )
|
|
# replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
|
|
# # Repack scales
|
|
# marlin_w13_scales = marlin_moe_permute_scales(
|
|
# s=layer.w13_scales,
|
|
# size_k=layer.intermediate_size_per_partition,
|
|
# size_n=layer.w13_scales.shape[2],
|
|
# group_size=self.quant_config.group_size,
|
|
# )
|
|
# replace_parameter(layer, "w13_scales", marlin_w13_scales)
|
|
# marlin_w2_scales = marlin_moe_permute_scales(
|
|
# s=layer.w2_scales,
|
|
# size_k=layer.w2_scales.shape[1]
|
|
# * (
|
|
# self.quant_config.group_size
|
|
# if self.quant_config.group_size != -1
|
|
# else self.quant_config.pack_factor
|
|
# ),
|
|
# size_n=layer.w2_scales.shape[2],
|
|
# group_size=self.quant_config.group_size,
|
|
# )
|
|
# replace_parameter(layer, "w2_scales", marlin_w2_scales)
|
|
|
|
# if hasattr(layer, "w13_bias") and layer.w13_bias is not None:
|
|
# layer.w13_bias.data = marlin_permute_bias(layer.w13_bias)
|
|
|
|
# if hasattr(layer, "w2_bias") and layer.w2_bias is not None:
|
|
# layer.w2_bias.data = marlin_permute_bias(layer.w2_bias)
|
|
if self.quant_config.desc_act:
|
|
raise NotImplementedError(
|
|
"GPTQMarlinMoEMethod now not support desc_act. please fix it")
|
|
w13_qweight_unpacked = unpack_k_batch(layer.w13_qweight)
|
|
w13_qweight_repacked = pack_n_batch(w13_qweight_unpacked,self.quant_config.pack_factor,order_map=[0, 2, 4, 6, 1, 3, 5, 7])
|
|
replace_parameter(layer, "w13_qweight", w13_qweight_repacked)
|
|
|
|
# quant vllm/model_executor/layers/quantization/utils/quant_utils.py#quantize_weights
|
|
# if quant_type.has_bias():
|
|
# w_q += quant_type.bias
|
|
# use quant_type.bias as zp,(ixformer support)
|
|
w13_zp = torch.full_like(layer.w13_scales, self.quant_type.bias, dtype=torch.int32)
|
|
w13_zp_pack = pack_n_batch(w13_zp, self.quant_config.pack_factor, order_map=[0, 2, 4, 6, 1, 3, 5, 7]).contiguous()
|
|
replace_parameter(layer, "w13_qzeros", w13_zp_pack)
|
|
|
|
w2_qweight_unpacked = unpack_k_batch(layer.w2_qweight)
|
|
w2_qweight_repacked = pack_n_batch(w2_qweight_unpacked,self.quant_config.pack_factor,order_map=[0, 2, 4, 6, 1, 3, 5, 7])
|
|
replace_parameter(layer, "w2_qweight", w2_qweight_repacked)
|
|
|
|
w2_zp = torch.full_like(layer.w2_scales, self.quant_type.bias, dtype=torch.int32)
|
|
w2_zp_pack = pack_n_batch(w2_zp, self.quant_config.pack_factor, order_map=[0, 2, 4, 6, 1, 3, 5, 7]).contiguous()
|
|
replace_parameter(layer, "w2_qzeros", w2_zp_pack)
|
|
|
|
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 `GPTQMarlinMoEMethod` yet."
|
|
)
|
|
|
|
assert activation == "silu", "Only SiLU activation is supported."
|
|
use_ep = expert_map is not None
|
|
|
|
if use_ep:
|
|
start_eid = layer.ep_rank * layer.local_num_experts
|
|
end_eid = min((layer.ep_rank + 1) * layer.local_num_experts, global_num_experts)
|
|
|
|
if apply_router_weight_on_input:
|
|
raise NotImplementedError(
|
|
"GPTQMarlinMoEMethod Apply router weight on input is not supported for"
|
|
"fused Marlin MoE method.")
|
|
|
|
if (hasattr(layer, "w13_bias") and layer.w13_bias is not None) or (hasattr(layer, "w2_bias") and layer.w2_bias is not None):
|
|
raise NotImplementedError(
|
|
"GPTQMarlinMoEMethod moe_w4a16_group_gemm not supported bias, please fix this")
|
|
|
|
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)
|
|
|
|
|
|
num_tokens, num_experts = router_logits.shape
|
|
|
|
if use_ep:
|
|
hidden_size = x.shape[1]
|
|
(
|
|
src_to_dst,
|
|
sorted_token_ids,
|
|
expert_sizes_gpu,
|
|
expert_sizes_cpu,
|
|
expand_tokens,
|
|
) = ixfops.moe_compute_token_index_ep(
|
|
topk_ids=topk_ids,
|
|
num_experts=num_experts,
|
|
start_expert_id=start_eid,
|
|
end_expert_id=end_eid,
|
|
)
|
|
if expert_sizes_cpu.sum() == 0:
|
|
return torch.zeros(
|
|
(num_tokens, hidden_size),
|
|
device=x.device,
|
|
dtype=x.dtype,
|
|
)
|
|
else:
|
|
expand_tokens = num_tokens * top_k
|
|
(
|
|
src_to_dst,
|
|
sorted_token_ids,
|
|
expert_sizes_gpu,
|
|
expert_sizes_cpu,
|
|
) = ixfops.moe_compute_token_index(
|
|
topk_ids=topk_ids,
|
|
num_experts=num_experts,
|
|
)
|
|
expert_sizes_cpu = expert_sizes_gpu.cpu()
|
|
|
|
# expand + reorder
|
|
# TODO use kernel
|
|
expand_hidden_states = ixfops.moe_expand_input(
|
|
hidden_states=x,
|
|
dst_to_src=sorted_token_ids,
|
|
dst_tokens=expand_tokens,
|
|
topk=top_k,
|
|
src_to_dst=src_to_dst,
|
|
)
|
|
|
|
# w4a16 group gemm 1
|
|
# pt_output_1: (expand_tokens, 2n) dtype
|
|
pt_output_1 = ixfops.moe_w4a16_group_gemm(
|
|
input=expand_hidden_states,
|
|
weight=layer.w13_qweight,
|
|
w_scales=layer.w13_scales,
|
|
quant_type="awq",
|
|
tokens_per_experts=expert_sizes_cpu,
|
|
w_zeros=layer.w13_qzeros,
|
|
group_size=self.quant_config.group_size,
|
|
dst_to_src=None,
|
|
format="NN",
|
|
tokens_per_experts_gpu=expert_sizes_gpu,
|
|
)
|
|
|
|
# act
|
|
pt_output_2 = ixfops.silu_and_mul(pt_output_1)
|
|
|
|
# w4a16 group gemm 2 + reorder
|
|
# pt_output_3: (expand_tokens, k) dtype
|
|
if use_ep:
|
|
pt_output_3 = torch.empty(
|
|
(num_tokens * top_k, hidden_size),
|
|
device=x.device,
|
|
dtype=x.dtype,
|
|
)
|
|
|
|
ixfops.moe_w4a16_group_gemm(
|
|
input=pt_output_2,
|
|
weight=layer.w2_qweight,
|
|
w_scales=layer.w2_scales,
|
|
quant_type="awq",
|
|
tokens_per_experts=expert_sizes_cpu,
|
|
w_zeros=layer.w2_qzeros,
|
|
group_size=self.quant_config.group_size,
|
|
dst_to_src=sorted_token_ids,
|
|
format="NN",
|
|
output=pt_output_3,
|
|
tokens_per_experts_gpu=expert_sizes_gpu,
|
|
)
|
|
|
|
reduce_mask = src_to_dst == -1
|
|
final_hidden_states = ixfops.moe_output_reduce_sum(
|
|
input=pt_output_3.view(num_tokens, top_k, -1),
|
|
topk_weight=topk_weights,
|
|
scaling_factor=routed_scaling_factor,
|
|
mask=reduce_mask,
|
|
)
|
|
else:
|
|
pt_output_3 = ixfops.moe_w4a16_group_gemm(
|
|
input=pt_output_2,
|
|
weight=layer.w2_qweight,
|
|
w_scales=layer.w2_scales,
|
|
quant_type="awq",
|
|
tokens_per_experts=expert_sizes_cpu,
|
|
w_zeros=layer.w2_qzeros,
|
|
group_size=self.quant_config.group_size,
|
|
dst_to_src=sorted_token_ids,
|
|
format="NN",
|
|
tokens_per_experts_gpu=expert_sizes_gpu,
|
|
)
|
|
|
|
# mul + reduce_sum
|
|
# final_hidden_states: (num_tokens, k)
|
|
final_hidden_states = ixfops.moe_output_reduce_sum(
|
|
input=pt_output_3.view(num_tokens, top_k, -1),
|
|
topk_weight=topk_weights,
|
|
scaling_factor=routed_scaling_factor
|
|
)
|
|
return final_hidden_states
|
|
|
|
|
|
|
|
|
|
|
|
# return torch.ops.vllm.fused_marlin_moe(
|
|
# x,
|
|
# layer.w13_qweight,
|
|
# layer.w2_qweight,
|
|
# getattr(layer, "w13_bias", None),
|
|
# getattr(layer, "w2_bias", None),
|
|
# layer.w13_scales,
|
|
# layer.w2_scales,
|
|
# router_logits,
|
|
# topk_weights,
|
|
# topk_ids,
|
|
# quant_type_id=self.quant_type.id,
|
|
# apply_router_weight_on_input=apply_router_weight_on_input,
|
|
# global_num_experts=global_num_experts,
|
|
# expert_map=expert_map,
|
|
# g_idx1=layer.w13_g_idx,
|
|
# g_idx2=layer.w2_g_idx,
|
|
# sort_indices1=layer.w13_g_idx_sort_indices,
|
|
# sort_indices2=layer.w2_g_idx_sort_indices,
|
|
# workspace=layer.workspace,
|
|
# is_k_full=self.is_k_full)
|