mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 21:53:54 +08:00
07e39620ea
### What this PR does / why we need it? Currently, when executing to the Linear layer of models in vLLM-Ascend, the weights format is ND in unquantized case and skipped ascend case. This PR supplements the execution logic for Linear layer. We use a new global variable: VLLM_ASCEND_ENABLE_NZ. When VLLM_ASCEND_ENABLE_NZ=1 and CANN version is 8.3, the weights of the Linear layer will be converted to FRACTAL_NZ, in both unquantized case and skipped ascend case. We also use VLLM_ASCEND_ENABLE_NZ to control the existing NZ conversion, such as w8a8-quantized case. ### Does this PR introduce _any_ user-facing change? Add a new global variable VLLM_ASCEND_ENABLE_NZ. If you want to use NZ format, you should set VLLM_ASCEND_ENABLE_NZ=1. ### How was this patch tested? - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
445 lines
16 KiB
Python
445 lines
16 KiB
Python
# 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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This file extends the functionality of linear operations by encapsulating custom
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communication groups and forward functions into classes (linear ops).
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Current class inheritance structure:
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CustomLinearOp
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├── CustomColumnParallelOp
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│ ├── MLPColumnParallelOp
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│ ├── SequenceColumnParallelOp
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└── CustomRowParallelOp
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│ ├── MLPRowParallelOp
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│ ├── OProjRowParallelOp
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│ ├── MatmulAllreduceRowParallelOp
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│ └── SequenceRowParallelOp
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└── CustomReplicatedOp
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How to extend a new linear op? Taking column parallel op as an example:
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1. Inherit from CustomColumnParallelOp and create a new class MyColumnParallelOp
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2. [Optional] The default communication group is the TP group. If a custom communication group is needed, override the comm_group method
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3. Override the apply method according to requirements, which will replace the original linear.forward
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4. Add selection logic for MyColumnParallelOp in the get_column_parallel_op method, typically based on prefix and configuration judgments
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Row parallel op follows a similar approach - inherit from RowColumnParallelOp and register the new class in get_row_parallel_op.
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"""
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from typing import Optional, Union
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import torch
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import torch.distributed as dist
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import torch_npu
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from torch.distributed import ProcessGroup
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from torch.nn.parameter import Parameter
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from vllm.distributed import split_tensor_along_last_dim
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from vllm.distributed.parallel_state import get_tp_group
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from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group,
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get_otp_group)
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from vllm_ascend.utils import (dense_optim_enable, enable_sp,
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matmul_allreduce_enable, mlp_tp_enable,
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oproj_tp_enable)
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class CustomLinearOp:
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def __init__(self, layer):
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self.layer = layer
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self.bias = None
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self.skip_bias_add = None
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self.return_bias = None
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self.quant_method = None
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# Custom communication group, while determining weight sharding
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@property
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def comm_group(self):
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return get_tp_group()
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@property
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def tp_rank(self):
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return self.comm_group.rank_in_group
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@property
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def tp_size(self):
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return self.comm_group.world_size
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# Update the attributes required by apply(), obtaining them from the layer.
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# Call this after the layer completes its initialization, specifically at the end of layer.init().
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def update_attrs(self):
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if hasattr(self.layer, "bias"):
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self.bias = self.layer.bias
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self.skip_bias_add = self.layer.skip_bias_add
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self.return_bias = self.layer.return_bias
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self.quant_method = self.layer.quant_method
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self.prefix = self.layer.prefix
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def apply_impl(self, input_):
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raise NotImplementedError
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# Replace layer.forward to customize the layer computation process.
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def apply(self, input_):
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output, output_bias = self.apply_impl(input_)
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if not self.return_bias:
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return output
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return output, output_bias
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class CustomColumnParallelOp(CustomLinearOp):
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def __init__(self, layer):
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super().__init__(layer)
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self.gather_output = None
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def update_attrs(self):
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super().update_attrs()
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self.gather_output = self.layer.gather_output
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class CustomRowParallelOp(CustomLinearOp):
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def __init__(self, layer):
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super().__init__(layer)
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self.reduce_results = None
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self.input_is_parallel = None
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self.input_size_per_partition = None
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def update_attrs(self):
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super().update_attrs()
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self.input_is_parallel = self.layer.input_is_parallel
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self.reduce_results = self.layer.reduce_results
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self.input_size_per_partition = self.layer.input_size_per_partition
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def apply(self, input_):
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output, output_bias = self.apply_impl(input_)
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if dense_optim_enable():
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torch.ops.vllm.maybe_prefetch_mlp_gate_up_proj(output, self.prefix)
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if not self.return_bias:
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return output
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return output, output_bias
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class CustomReplicatedOp(CustomLinearOp):
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def apply_impl(self, input_):
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bias = self.bias if not self.skip_bias_add else None
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assert self.quant_method is not None
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output = self.quant_method.apply(self.layer, input_, bias)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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class MLPColumnParallelOp(CustomColumnParallelOp):
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def __init__(self, layer):
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super().__init__(layer)
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@property
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def comm_group(self):
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return get_mlp_tp_group()
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def apply_impl(
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self,
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input_: torch.Tensor,
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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bias = self.bias if not self.skip_bias_add else None
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# Matrix multiply.
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assert self.quant_method is not None
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input_parallel = self.comm_group.all_gather(input_, 0)
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output = self.quant_method.apply(self.layer, input_parallel, bias)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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class MLPRowParallelOp(CustomRowParallelOp):
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def __init__(self, layer):
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super().__init__(layer)
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@property
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def comm_group(self):
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return get_mlp_tp_group()
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def apply_impl(
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self, input_: torch.Tensor
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[self.tp_rank].contiguous()
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assert self.quant_method is not None
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bias_ = None if (self.tp_rank > 0
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or self.skip_bias_add) else self.layer.bias
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output_parallel = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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output = self.comm_group.reduce_scatter(output_parallel, 0)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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class OProjRowParallelOp(CustomRowParallelOp):
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def __init__(self, layer):
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super().__init__(layer)
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@property
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def comm_group(self):
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return get_otp_group()
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def apply_impl(
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self,
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input_: torch.Tensor,
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[self.tp_rank].contiguous()
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# Prepare tensors for all-to-all communication
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local_batch_size = input_parallel.size(0)
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chunk_size = self.input_size_per_partition
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total_batch_size = local_batch_size * self.tp_size
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# Reshape tensor for efficient cross-device transfer:
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# [batch, dim] -> [tp_size, batch, chunk] -> flattened
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send_buf = (input_parallel.reshape(-1,
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self.tp_size, chunk_size).transpose(
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0, 1).contiguous().view(-1))
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# Create receive buffer
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recv_buf = torch.empty(total_batch_size * chunk_size,
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dtype=input_parallel.dtype,
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device=input_parallel.device)
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# Perform all-to-all communication
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dist.all_to_all_single(recv_buf,
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send_buf,
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group=self.comm_group.device_group)
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input_parallel = recv_buf.view(total_batch_size, chunk_size)
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# Only fuse bias add for rank 0 to avoid duplicate bias addition in TP>1
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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assert self.quant_method is not None
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output_parallel = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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# otp-specific: Combine partial results across devices
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output = self.comm_group.reduce_scatter(output_parallel, dim=0)
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output = output.view(input_.shape[0], self.layer.output_size)
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# Handle bias return based on configuration
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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def update_attrs(self):
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super().update_attrs()
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self.input_is_parallel = self.layer.input_is_parallel
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self.input_size_per_partition = self.layer.input_size_per_partition
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class MatmulAllreduceRowParallelOp(CustomRowParallelOp):
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_HCOMM_INFO = None
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def __init__(self, layer):
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super().__init__(layer)
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self.hcomm_info = self.get_hcomm_info(self.comm_group.device_group)
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def apply_impl(
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self, input_: torch.Tensor
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[self.tp_rank].contiguous()
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"""Calculate the output tensor of forward by considering
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fusing communication and computation."""
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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if self.reduce_results and self.tp_size > 1:
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output = torch_npu.npu_mm_all_reduce_base(input_parallel,
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self.weight_t,
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self.hcomm_info,
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bias=bias_)
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else:
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assert self.quant_method is not None
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output = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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@classmethod
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def get_hcomm_info(cls, group: ProcessGroup) -> str:
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"""Get the HCCL communication information for the given group."""
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if cls._HCOMM_INFO is not None:
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return cls._HCOMM_INFO
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rank = torch.distributed.get_rank(group)
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if torch.__version__ > "2.0":
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global_rank = torch.distributed.get_global_rank(group, rank)
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cls._HCOMM_INFO = group._get_backend(
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torch.device("npu")).get_hccl_comm_name(global_rank)
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else:
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cls._HCOMM_INFO = group.get_hccl_comm_name(rank)
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return cls._HCOMM_INFO
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def update_attrs(self):
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super().update_attrs()
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self.weight_t = self.layer.weight.t()
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class SequenceColumnParallelOp(CustomColumnParallelOp):
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def apply_impl(
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self, input_: torch.Tensor
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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"""Linear layer with column parallelism.
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Implemented multiple optimization projects for dense models, such as FlashComm and
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communication-computation fusion.
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"""
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bias = self.bias if not self.skip_bias_add else None
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# Matrix multiply.
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assert self.quant_method is not None
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input_ = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(input_, True)
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output_parallel = self.quant_method.apply(self.layer, input_, bias)
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if self.gather_output:
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# All-gather across the partitions.
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output = self.comm_group.all_gather(output_parallel)
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else:
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output = output_parallel
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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class SequenceRowParallelOp(CustomRowParallelOp):
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def apply_impl(
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self, input_: torch.Tensor
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
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"""Linear layer with column parallelism.
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Implemented multiple optimization projects for dense models, such as FlashComm and
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communication-computation fusion.
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"""
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if self.input_is_parallel:
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input_parallel = input_
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else:
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[self.tp_rank].contiguous()
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assert self.quant_method is not None
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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if self.tp_size == 1 or not self.reduce_results:
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output = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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else:
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output_parallel = self.quant_method.apply(self.layer,
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input_parallel,
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bias=bias_)
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output = torch.ops.vllm.maybe_pad_and_reduce(output_parallel)
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output_bias = self.bias if self.skip_bias_add else None
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return output, output_bias
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def update_attrs(self):
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super().update_attrs()
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self.input_is_parallel = self.layer.input_is_parallel
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self.reduce_results = self.layer.reduce_results
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def _get_column_parallel_op(
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prefix, layer
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) -> Optional[Union[MLPColumnParallelOp, SequenceColumnParallelOp]]:
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if mlp_tp_enable() and "gate_up_proj" in prefix:
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return MLPColumnParallelOp(layer)
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if enable_sp():
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if "shared_expert" in prefix:
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return None
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if "gate_up_proj" in prefix:
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return SequenceColumnParallelOp(layer)
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if "in_proj" in prefix:
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return SequenceColumnParallelOp(layer)
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if "qkv_proj" in prefix or "conv1d" in prefix:
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return SequenceColumnParallelOp(layer)
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return None
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def _get_row_parallel_op(
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prefix, layer
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) -> Optional[Union[MLPRowParallelOp, OProjRowParallelOp,
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MatmulAllreduceRowParallelOp, SequenceRowParallelOp]]:
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if "down_proj" in prefix and mlp_tp_enable():
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return MLPRowParallelOp(layer)
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if "o_proj" in prefix and oproj_tp_enable():
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return OProjRowParallelOp(layer)
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if matmul_allreduce_enable():
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return MatmulAllreduceRowParallelOp(layer)
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if enable_sp():
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if "shared_expert" in prefix:
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return None
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if "o_proj" in prefix or "out_proj" in prefix or "down_proj" in prefix:
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return SequenceRowParallelOp(layer)
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return None
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def get_parallel_op(disable_tp, prefix, layer, direct):
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if disable_tp:
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return None, 0, 1
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custom_op: Optional[Union[MLPColumnParallelOp, SequenceColumnParallelOp,
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MLPRowParallelOp, OProjRowParallelOp,
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MatmulAllreduceRowParallelOp,
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SequenceRowParallelOp]] = None
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if direct == "row":
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custom_op = _get_row_parallel_op(prefix, layer)
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if direct == "column":
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custom_op = _get_column_parallel_op(prefix, layer)
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if custom_op is not None:
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return custom_op, custom_op.tp_rank, custom_op.tp_size
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return None, get_tp_group().rank_in_group, get_tp_group().world_size
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def get_replicated_op(disable_tp, prefix,
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layer) -> Optional[Union[CustomReplicatedOp]]:
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if disable_tp:
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return None
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return CustomReplicatedOp(layer)
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