ca6f631cba
### What this PR does / why we need it?
Remove Pangu Related Code
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: weichen <calvin_zhu0210@outlook.com>
204 lines
8.0 KiB
Python
204 lines
8.0 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Any, Dict, Optional
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import torch
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import torch_npu
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from vllm.forward_context import get_forward_context
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ,
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COMPRESSED_TENSORS_METHOD, AscendDeviceType,
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get_ascend_device_type, is_enable_nz)
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def quant_per_tensor(in_tensor: torch.Tensor,
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input_scale: torch.Tensor,
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input_offset: torch.Tensor,
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function=False):
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return torch_npu.npu_quantize(in_tensor, input_scale, input_offset,
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torch.qint8, -1, function)
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class AscendW8A8LinearMethod:
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"""Linear method for Ascend W8A8.
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Args:
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w_sym: whether the linear weight is symmetrically quantized.
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"""
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def __init__(self) -> None:
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# aclnn quant matmul requires to transpose matrix B, set to true by default.
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self.transpose_weight = get_ascend_device_type(
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) != AscendDeviceType._310P
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@staticmethod
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def get_weight(
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype = torch.bfloat16,
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) -> Dict[str, Any]:
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params_dict = {
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"weight": torch.empty(output_size, input_size, dtype=torch.int8)
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}
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return params_dict
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@staticmethod
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def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {}
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params_dict["input_scale"] = torch.empty(1, dtype=params_dtype)
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params_dict["input_offset"] = torch.empty(1, dtype=torch.int8)
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return params_dict
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@staticmethod
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def get_perchannel_param(
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output_size: int,
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params_dtype: torch.dtype,
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) -> Dict[str, Any]:
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params_dict = {}
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params_dict["quant_bias"] = torch.empty(output_size, dtype=torch.int32)
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if params_dtype == torch.bfloat16:
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params_dict["deq_scale"] = torch.empty(output_size,
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dtype=torch.float32)
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elif params_dtype == torch.float16:
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params_dict["deq_scale"] = torch.empty(output_size,
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dtype=torch.int64)
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params_dict["weight_scale"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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params_dict["weight_offset"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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return params_dict
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def get_pergroup_param(self,
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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layer_type: Optional[str] = None) -> Dict[str, Any]:
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return {}
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@staticmethod
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def apply(
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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tp_rank: Optional[int] = 0,
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) -> torch.Tensor:
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if x.dtype != torch.int8:
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layer_cls_name = layer.__class__.__name__
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try:
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weight_prefetch_method = get_forward_context(
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).weight_prefetch_method
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except AssertionError:
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weight_prefetch_method = None
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# prefetch qkvo_proj.weight preprocess
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if weight_prefetch_method:
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weight_prefetch_method.maybe_prefetch_attn_weight_preprocess(
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layer_cls_name=layer_cls_name,
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weight=layer.weight,
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start_flag=x,
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)
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try:
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quant_comm_config = getattr(layer, "_quant_comm_config")
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except AttributeError:
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quant_comm_config = {}
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comm_fn = quant_comm_config.get("communication_fn")
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enable_flashcomm2_quant_comm = comm_fn is not None and (
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"o_proj" in layer.prefix or "out_proj" in layer.prefix)
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if enable_flashcomm2_quant_comm:
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quant_input_x = x.contiguous().view(
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-1, layer.aclnn_input_scale_reciprocal.size(0))
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quant_x = torch.ops.vllm.quantize(
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quant_input_x,
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layer.aclnn_input_scale,
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layer.aclnn_input_scale_reciprocal,
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layer.aclnn_input_offset,
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)
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comm_input = quant_x.view(x.size(0), -1)
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assert comm_fn is not None
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x = comm_fn(comm_input)
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else:
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# quant
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x = torch.ops.vllm.quantize(
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x,
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layer.aclnn_input_scale,
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layer.aclnn_input_scale_reciprocal,
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layer.aclnn_input_offset,
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)
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# prefetch qkvo_proj.weight postprocess
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if weight_prefetch_method:
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weight_prefetch_method.maybe_prefetch_attn_weight_postprocess(
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layer_cls_name=layer_cls_name,
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stop_flag=x,
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)
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quant_bias = layer.quant_bias if tp_rank == 0 else None
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try:
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ascend_quant_method = getattr(layer, "ascend_quant_method")
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except AttributeError:
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ascend_quant_method = ""
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if ascend_quant_method == COMPRESSED_TENSORS_METHOD:
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quant_bias = bias
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if get_ascend_device_type() == AscendDeviceType._310P:
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# On 300I Duo platform, we need transpose again if
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# using nz. This transpose can be skipped in torchair.
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output = torch_npu.npu_quant_matmul(
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x,
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layer.weight.data.transpose(1, 0),
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layer.deq_scale,
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bias=quant_bias,
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output_dtype=layer.params_dtype,
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)
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else:
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output = torch_npu.npu_quant_matmul(
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x,
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layer.weight,
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layer.deq_scale,
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bias=quant_bias,
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output_dtype=layer.params_dtype,
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)
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return output
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def process_weights_after_loading(self, layer):
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expanding_factor = layer.weight.data.shape[1]
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layer.aclnn_input_scale = torch.nn.Parameter(
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layer.input_scale.data.repeat(expanding_factor),
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requires_grad=False)
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layer.aclnn_input_scale_reciprocal = 1 / torch.nn.Parameter(
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layer.input_scale.data.repeat(expanding_factor),
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requires_grad=False)
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layer.aclnn_input_offset = torch.nn.Parameter(
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layer.input_offset.data.repeat(expanding_factor),
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requires_grad=False).to(layer.aclnn_input_scale.dtype)
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if self.transpose_weight:
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layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
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if is_enable_nz():
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layer.weight.data = torch_npu.npu_format_cast(
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layer.weight.data, ACL_FORMAT_FRACTAL_NZ)
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layer.weight_scale.data = torch.flatten(layer.weight_scale.data)
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layer.weight_offset.data = torch.flatten(layer.weight_offset.data)
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ascend_quant_method = getattr(layer, "ascend_quant_method", "")
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if ascend_quant_method == COMPRESSED_TENSORS_METHOD:
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deq_scale = layer.input_scale.data * layer.weight_scale.data
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layer.deq_scale = torch.nn.Parameter(deq_scale,
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requires_grad=False)
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