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[Bugfix] Fix internlm2 tensor parallel inference (#8055)

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Isotr0py 2024-09-02 23:48:56 +08:00 committed by GitHub
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1 changed files with 34 additions and 13 deletions
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vllm/model_executor/models/internlm2.py
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@ -1,4 +1,5 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
from functools import partial
from typing import Any, Dict, Iterable, List, Optional, Tuple from typing import Any, Dict, Iterable, List, Optional, Tuple
import torch import torch
@ -7,7 +8,10 @@ from transformers import PretrainedConfig
from vllm.attention import Attention, AttentionMetadata from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig from vllm.config import CacheConfig
from vllm.distributed import get_tensor_model_parallel_world_size from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
split_tensor_along_last_dim,
tensor_model_parallel_all_gather)
from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@ -70,20 +74,21 @@ class InternLM2Attention(nn.Module):
) -> None: ) -> None:
super().__init__() super().__init__()
self.hidden_size = hidden_size self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size() self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
self.total_num_heads = num_heads self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0 assert self.total_num_heads % self.tp_size == 0
self.num_heads = self.total_num_heads // tp_size self.num_heads = self.total_num_heads // self.tp_size
self.total_num_kv_heads = num_kv_heads self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size: if self.total_num_kv_heads >= self.tp_size:
# Number of KV heads is greater than TP size, so we partition # Number of KV heads is greater than TP size, so we partition
# the KV heads across multiple tensor parallel GPUs. # the KV heads across multiple tensor parallel GPUs.
assert self.total_num_kv_heads % tp_size == 0 assert self.total_num_kv_heads % self.tp_size == 0
else: else:
# Number of KV heads is less than TP size, so we replicate # Number of KV heads is less than TP size, so we replicate
# the KV heads across multiple tensor parallel GPUs. # the KV heads across multiple tensor parallel GPUs.
assert tp_size % self.total_num_kv_heads == 0 assert self.tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
self.head_dim = hidden_size // self.total_num_heads self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim
@ -122,11 +127,27 @@ class InternLM2Attention(nn.Module):
quant_config=quant_config) quant_config=quant_config)
def split_qkv(self, qkv: torch.Tensor): def split_qkv(self, qkv: torch.Tensor):
qkv = qkv.view(-1, self.num_kv_heads, self.key_value_groups + 2, 128) seq_len = qkv.shape[0]
q, k, v = torch.split(qkv, [self.key_value_groups, 1, 1], dim=2) if self.tp_size > 1:
q = q.reshape(-1, self.q_size) qkv_map = [self.q_size, self.kv_size, self.kv_size] * self.tp_size
k = k.reshape(-1, self.kv_size) qkv = tensor_model_parallel_all_gather(qkv)
v = v.reshape(-1, self.kv_size) qkv = torch.split(qkv, qkv_map, dim=-1)
qkv = qkv[::3] + qkv[1::3] + qkv[2::3]
qkv = torch.cat(qkv, dim=-1)
qkv = qkv.view(seq_len, self.total_num_kv_heads,
self.key_value_groups + 2, self.head_dim)
q, k, v = torch.split(qkv, [self.key_value_groups, 1, 1], dim=-2)
q = q.reshape(seq_len, self.q_size * self.tp_size)
k = k.reshape(seq_len, self.kv_size * self.tp_size)
v = v.reshape(seq_len, self.kv_size * self.tp_size)
if self.tp_size > 1:
splitter = partial(split_tensor_along_last_dim,
num_partitions=self.tp_size)
q = splitter(q)[self.tp_rank]
k = splitter(k)[self.tp_rank]
v = splitter(v)[self.tp_rank]
return q, k, v return q, k, v
def forward( def forward(