8977be1df3
### What this PR does / why we need it?
During the attention quantization process of DeepSeek V3.2, it is
necessary to retrieve the Hadamard matrix from the weights to facilitate
the computation.
### Does this PR introduce _any_ user-facing change?
No. But there will be two new tensor in quant weight.
### How was this patch tested?
- vLLM version: v0.18.0
- vLLM main:
8b6325758c
---------
Signed-off-by: mayumeng <m30059191@china.huawei.com>
Co-authored-by: mayumeng <m30059191@china.huawei.com>
90 lines
4.4 KiB
Python
90 lines
4.4 KiB
Python
import torch
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from vllm.config import get_current_vllm_config
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from vllm.distributed import get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size
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from .registry import register_scheme
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def weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor):
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"""fa_q weight loader."""
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if param.numel() == 1 and loaded_weight.numel() == 1:
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param.data.fill_(loaded_weight.item())
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else:
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tp_rank = get_tensor_model_parallel_rank()
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tp_size = get_tensor_model_parallel_world_size()
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shard_size = loaded_weight.shape[0] // tp_size
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loaded_weight = loaded_weight.narrow(0, shard_size * tp_rank, shard_size)
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assert param.size() == loaded_weight.size(), (
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f"Attempted to load weight ({loaded_weight.size()}) into parameter ({param.size()}) when TP is ({tp_size})"
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)
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param.data.copy_(loaded_weight)
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@register_scheme("FAKQuant", "attention")
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class AscendFAQuantAttentionMethod:
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def __init__(self):
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self.transpose_weight = True
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self.printFlag = False
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vllm_config = get_current_vllm_config()
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config = vllm_config.model_config.hf_config
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self.kv_lora_rank = getattr(config, "kv_lora_rank", 0)
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self.qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
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def create_weights(self, layer: torch.nn.Module) -> None:
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extra_module_names = ["fa_q", "fa_k", "fa_v"]
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for name in extra_module_names:
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setattr(layer, name, torch.nn.Module())
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params_dict = {}
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dtype = torch.get_default_dtype()
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params_dict["fa_q.scale"] = torch.empty((layer.num_heads, 1), dtype=dtype)
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params_dict["fa_k.scale"] = torch.empty((layer.num_kv_heads, 1), dtype=dtype)
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params_dict["fa_v.scale"] = torch.empty((layer.num_kv_heads, 1), dtype=dtype)
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params_dict["fa_q.offset"] = torch.empty((layer.num_heads, 1), dtype=torch.int8)
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params_dict["fa_k.offset"] = torch.empty((layer.num_kv_heads, 1), dtype=torch.int8)
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params_dict["fa_v.offset"] = torch.empty((layer.num_kv_heads, 1), dtype=torch.int8)
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for name, weight in params_dict.items():
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module_name, weight_name = name.rsplit(".", 1)
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module = getattr(layer, module_name)
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weight_param = torch.nn.Parameter(weight, requires_grad=False)
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module.register_parameter(weight_name, weight_param)
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# When loading weights, segment them according to TP
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weight_param.weight_loader = weight_loader
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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fa_k_scale = torch.squeeze(layer.fa_k.scale).unsqueeze(0)
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layer.fak_descale_float = torch.nn.Parameter(fa_k_scale.to(torch.float), requires_grad=False)
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layer.fak_descale = torch.nn.Parameter(fa_k_scale, requires_grad=False)
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layer.fak_descale_reciprocal = 1.0 / torch.nn.Parameter(fa_k_scale, requires_grad=False)
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fa_k_offset = torch.squeeze(layer.fa_k.offset).unsqueeze(0)
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layer.fak_offset = torch.nn.Parameter(fa_k_offset.to(layer.fak_descale.dtype), requires_grad=False)
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repeated_quant_kscale = fa_k_scale.repeat(self.kv_lora_rank)
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layer.quant_kscale = repeated_quant_kscale.view(1, self.kv_lora_rank)
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layer.quant_kscale = 1.0 / torch.nn.Parameter(layer.quant_kscale.to(torch.float), requires_grad=False)
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@register_scheme("INT8_DYNAMIC", "attention")
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class AscendSFAQuantAttentionMethod:
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def __init__(self):
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vllm_config = get_current_vllm_config()
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config = vllm_config.model_config.hf_config
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self.index_head_dim = config.index_head_dim
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def create_weights(self, layer: torch.nn.Module) -> None:
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extra_module_names = ["indexer"]
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for name in extra_module_names:
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setattr(layer, name, torch.nn.Module())
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params_dict = {}
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params_dict["indexer.q_rot"] = torch.empty((self.index_head_dim, self.index_head_dim), dtype=torch.float32)
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params_dict["indexer.k_rot"] = torch.empty((self.index_head_dim, self.index_head_dim), dtype=torch.float32)
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for name, weight in params_dict.items():
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module_name, weight_name = name.split(".")
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module = getattr(layer, module_name)
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weight_param = torch.nn.Parameter(weight, requires_grad=False)
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module.register_parameter(weight_name, weight_param)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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pass
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