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https://github.com/kvcache-ai/ktransformers.git
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support npu
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djw
parent
1677e90092
commit
dd0e41b3b8
14 changed files with 1453 additions and 5 deletions
298
ktransformers/operators/ascend/ascend_linear.py
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298
ktransformers/operators/ascend/ascend_linear.py
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from abc import abstractmethod
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import torch
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import torch_npu
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import torch.distributed as dist
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from torch import nn
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from transformers import PretrainedConfig
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from ktransformers.operators.base_operator import BaseInjectedModule
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from ktransformers.operators.linear import KLinearBase, LINEAR_MAP
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from ktransformers.util.ascend.ascend_utils import (
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get_safetensors_cut_weight,
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get_tensor_parallel_size,
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get_tensor_parallel_group
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)
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from ktransformers.util import utils
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from ktransformers.util.custom_gguf import GGUFLoader
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from ktransformers.util.utils import InferenceState
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class KLinearW8A8(KLinearBase):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module = None,
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device: str = "cuda",
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**kwargs,
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):
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super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
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def load_weight(self, override_key: str | None = None, device: str | None = None):
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if override_key is not None:
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keys = override_key
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else:
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keys = [self.key]
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fake_tensor = torch.tensor([1])
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for key in keys:
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if device is None:
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device = utils.CUR_DEVICE
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if key + ".weight" in self.gguf_loader.safetensor_loader.tensor_file_map:
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if key + ".deq_scale" in self.gguf_loader.safetensor_loader.tensor_file_map:
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qweight = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.weight")
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deq_scale = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.deq_scale")
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quant_bias = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.quant_bias")
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input_scale = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.input_scale")
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input_offset = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.input_offset")
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tensors = (qweight, deq_scale, quant_bias, input_scale, input_offset)
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return tensors
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elif key + ".weight_scale" in self.gguf_loader.safetensor_loader.tensor_file_map:
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if key.endswith("ffn_gate_shexp"):
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parts = key.split(".")
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layer = parts[1]
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gate_weight = self.gguf_loader.safetensor_loader.load_tensor(f"blk.{layer}.ffn_gate_shexp.weight")
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gate_weight = get_safetensors_cut_weight(self.key, gate_weight).t()
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up_weight = self.gguf_loader.safetensor_loader.load_tensor(f"blk.{layer}.ffn_up_shexp.weight")
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up_weight = get_safetensors_cut_weight(self.key, up_weight).t()
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gate_scale = self.gguf_loader.safetensor_loader.load_tensor(f"blk.{layer}.ffn_gate_shexp.weight_scale")
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gate_scale = get_safetensors_cut_weight(self.key, gate_scale)
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up_scale = self.gguf_loader.safetensor_loader.load_tensor(f"blk.{layer}.ffn_up_shexp.weight_scale")
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up_scale = get_safetensors_cut_weight(self.key, up_scale)
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gate_up_weight = torch.cat((gate_weight, up_weight), 1)
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gate_up_scale = torch.cat((gate_scale, up_scale), 0)
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gate_offset = self.gguf_loader.safetensor_loader.load_tensor(f"blk.{layer}.ffn_gate_shexp.weight_offset")
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up_offset = self.gguf_loader.safetensor_loader.load_tensor(f"blk.{layer}.ffn_up_shexp.weight_offset")
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gate_up_offset = torch.cat((gate_offset, up_offset), 0)
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tensors = (gate_up_weight, gate_up_scale, gate_up_offset)
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elif key.endswith("ffn_up_shexp"):
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return fake_tensor
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else:
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qweight = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.weight")
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weight_scale = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.weight_scale")
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weight_offset = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.weight_offset")
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tensors = (qweight, weight_scale, weight_offset)
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return tensors
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else:
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weight = self.gguf_loader.safetensor_loader.load_tensor(f"{key}.weight")
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weight = get_safetensors_cut_weight(self.key, weight)
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return weight
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else:
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raise FileNotFoundError(f"Weight file not found for key {key}")
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@abstractmethod
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def load(self, w: dict | nn.Parameter | tuple | None = None, device: str | None = "cuda"):
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pass
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@abstractmethod
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def unload(self):
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pass
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class KLinearTorchW8A8A2(KLinearW8A8):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module = None,
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device: str = "cuda",
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**kwargs,
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):
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super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
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self.has_bias = False
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self.dtype = torch.get_default_dtype()
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self.weight = None
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self.input_scale = None
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self.input_offset = None
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self.quant_bias = None
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self.deq_scale = None
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self.weight_scale = None
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self.weight_offset = None
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def forward(self, x: torch.Tensor, bsz_tensor) -> torch.Tensor:
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tp = get_tensor_parallel_size()
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if tp == 1:
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out = torch.zeros((x.shape[0], x.shape[1], self.weight.shape[-1]), dtype=torch.float16, device=x.device)
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torch_npu._npu_matmul_pp(x, self.weight, out)
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else:
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tp_size = get_tensor_parallel_size()
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tp_group = get_tensor_parallel_group()
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batch_size = x.shape[0]
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seq_length = x.shape[1]
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lm_sep_size = tp_size
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lm_head_group = tp_group
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gathered_list = [torch.empty_like(x) for _ in range(lm_sep_size)]
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dist.all_gather(gathered_list, x, group=lm_head_group)
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input_full = torch.stack(gathered_list, dim=0)
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input_full = input_full.squeeze(dim=1)
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torch_npu.npu_format_cast_(input_full, 2)
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local_logits = torch.zeros((input_full.shape[0], input_full.shape[1], self.weight.shape[-1]),
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dtype=torch.float16, device=input_full.device)
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torch_npu._npu_matmul_pp(input_full, self.weight, local_logits)
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local_logits_transpose = local_logits.transpose(2, 1).reshape(-1, batch_size * seq_length)
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del local_logits
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output_tensor = torch.empty_like(local_logits_transpose)
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dist.all_to_all_single(output_tensor, local_logits_transpose, group=lm_head_group)
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del local_logits_transpose
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output_tensor = output_tensor.transpose(1, 0)
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out = output_tensor.view(batch_size, seq_length, -1)
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del output_tensor
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return out
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def load(self, w: dict | nn.Parameter | tuple | None = None, device: str | None = None):
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if device is None:
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device = self.device
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device = utils.CUR_DEVICE
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if w is None:
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w = self.load_weight()
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if isinstance(w, nn.Parameter):
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try:
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self.weight = w.to(dtype=self.dtype).view(self.out_features, self.in_features).T.contiguous()
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except:
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self.weight = w.to(dtype=self.dtype).T.contiguous()
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self.weight = self.weight.to(device)
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if self.has_bias:
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self.bias = self.bias.to(device)
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elif isinstance(w, tuple):
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w_list = list(w)
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if len(w_list) == 3:
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self.weight = w_list[0]
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self.weight_scale = w_list[1].view(-1)
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self.weight_offset = w_list[2]
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self.weight = self.weight.to(utils.CUR_DEVICE)
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self.weight_scale = self.weight_scale.to(utils.CUR_DEVICE)
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if self.key.endswith("ffn_gate_shexp") is not True:
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self.weight = get_safetensors_cut_weight(self.key, self.weight).t()
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weight_scale = get_safetensors_cut_weight(self.key, self.weight_scale)
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self.weight_scale = weight_scale.clone()
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del weight_scale
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self.weight_offset = self.weight_offset.to(utils.CUR_DEVICE)
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else:
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for i in range(len(w_list)):
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w_list[i] = get_safetensors_cut_weight(self.key, w_list[i])
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w_list[i] = w_list[i].to(utils.CUR_DEVICE)
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self.weight = w_list[0]
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self.deq_scale = w_list[1]
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self.quant_bias = w_list[2]
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if "attn_output" in self.key or "ffn_down" in self.key:
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if torch.distributed.get_rank(get_tensor_parallel_group()) != 0:
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self.quant_bias = torch.zeros_like(self.quant_bias, dtype=self.quant_bias.dtype, device=self.quant_bias.device)
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self.input_scale = w_list[3]
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self.input_offset = w_list[4]
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elif isinstance(w, torch.Tensor):
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self.weight = w.T.contiguous()
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self.weight.to(device)
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if "kv_b" not in self.key:
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self.weight = self.weight.to(device)
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torch_npu.npu_format_cast_(self.weight, 29)
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else:
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raise ValueError(f"Invalid weight type {self.key=} {type(w)=}")
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def unload(self):
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if self.weight is not None:
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self.weight = None
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if self.has_bias:
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self.bias = None
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self.input_scale = None
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self.input_offset = None
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self.quant_bias = None
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self.deq_scale = None
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self.weight_scale = None
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self.weight_offset = None
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LINEAR_MAP["KLinearTorchW8A8A2"] = KLinearTorchW8A8A2
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class KTransformersLinearW8A8A2(BaseInjectedModule, KLinearW8A8):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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generate_device: str = "cuda",
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generate_op: str | None = "KLinearMarlin",
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prefill_device: str = "cuda",
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prefill_op: str | None = "KLinearTorch",
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**kwargs,
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):
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BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs)
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KLinearW8A8.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
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# build all the linear operators
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if prefill_op is not None:
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assert prefill_op in LINEAR_MAP, f"linear_type {prefill_op} not supported"
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self.prefill_linear = LINEAR_MAP[prefill_op](key, gguf_loader, config, orig_module, prefill_device, **kwargs)
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else:
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self.prefill_linear = None
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if generate_op is not None:
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assert generate_op in LINEAR_MAP, f"linear_type {generate_op} not supported"
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self.generate_linear = LINEAR_MAP[generate_op](key, gguf_loader, config, orig_module, generate_device, **kwargs)
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else:
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self.generate_linear = None
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self.mode = InferenceState.UNLOAD
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def forward(self, x, bsz_tensor=None):
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if self.mode == InferenceState.PREFILL:
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assert self.prefill_linear is not None, "cpu linear is not initialized"
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y = self.prefill_linear.forward(x, bsz_tensor)
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else:
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assert self.generate_linear is not None, "gpu linear is not initialized"
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y = self.generate_linear.forward(x, bsz_tensor)
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return y
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def load(self, w: dict | nn.Parameter | tuple | None = None, mode: InferenceState = InferenceState.GENERATE):
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if not mode:
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mode = InferenceState.GENERATE
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# load to device
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if mode == InferenceState.PREFILL:
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self.generate_linear.unload()
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self.prefill_linear.load(w=w)
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self.device = self.prefill_linear.device
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self.weight = self.prefill_linear.weight # modeling_xxx.py may use linear.weight
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self.input_scale = self.prefill_linear.input_scale
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self.input_offset = self.prefill_linear.input_offset
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self.quant_bias = self.prefill_linear.quant_bias
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self.deq_scale = self.prefill_linear.deq_scale
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self.weight_scale = self.prefill_linear.weight_scale
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self.weight_offset = self.prefill_linear.weight_offset
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elif mode == InferenceState.GENERATE:
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self.prefill_linear.unload()
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self.generate_linear.load(w=w)
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self.device = self.generate_linear.device
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self.weight = self.generate_linear.weight # modeling_xxx.py may use linear.weight
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self.input_scale = self.generate_linear.input_scale
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self.input_offset = self.generate_linear.input_offset
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self.quant_bias = self.generate_linear.quant_bias
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self.deq_scale = self.generate_linear.deq_scale
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self.weight_scale = self.generate_linear.weight_scale
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self.weight_offset = self.generate_linear.weight_offset
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elif mode == InferenceState.UNLOAD:
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self.prefill_linear.unload()
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self.generate_linear.unload()
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self.device = "cpu"
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else:
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raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD")
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self.mode = mode
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def unload(self):
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if self.prefill_linear is not None:
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self.prefill_linear.unload()
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if self.generate_linear is not None:
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self.generate_linear.unload()
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self.device = self.generate_linear.device
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def set_inference_mode(self, mode: InferenceState):
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if not mode:
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mode = InferenceState.GENERATE
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if mode == InferenceState.GENERATE:
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self.load(mode=InferenceState.GENERATE)
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elif mode == InferenceState.PREFILL:
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self.load(mode=InferenceState.PREFILL)
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elif mode == InferenceState.UNLOAD:
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self.unload()
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else:
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raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD")
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