mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
3e08cd231c
Remove real size allocation Signed-off-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com>
389 lines
17 KiB
Python
389 lines
17 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import platform
|
|
import sys
|
|
from dataclasses import dataclass
|
|
|
|
import pynvml
|
|
import torch
|
|
from cuda import cuda
|
|
|
|
from ._dlpack_utils import pack_strided_memory
|
|
from ._utils import mpi_comm
|
|
from .logger import logger
|
|
from .mapping import Mapping
|
|
|
|
|
|
def _check_cu_result(cu_func_ret):
|
|
if isinstance(cu_func_ret, tuple):
|
|
cu_result, *others = cu_func_ret
|
|
if cu_result != cuda.CUresult.CUDA_SUCCESS:
|
|
raise RuntimeError(cu_result)
|
|
if len(others) == 1:
|
|
return others[0]
|
|
elif len(others) > 1:
|
|
return tuple(others)
|
|
else:
|
|
return None
|
|
else:
|
|
if cu_func_ret != cuda.CUresult.CUDA_SUCCESS:
|
|
raise RuntimeError(cu_func_ret)
|
|
return None
|
|
|
|
|
|
class MnnvlMemory:
|
|
initialized: bool = False
|
|
|
|
current_mem_offset: int = 0
|
|
current_rank_stride: int = 0 # stride for ranks and also address space size.
|
|
current_start_address: int = 0
|
|
|
|
# allocation granularity
|
|
allocation_granularity: int = 0
|
|
|
|
# fabric address page size (512 MB)
|
|
fabric_page_size: int = 1 << 29
|
|
|
|
# MPI communicator
|
|
comm = None
|
|
|
|
dev_id: int = None
|
|
|
|
allocated_map = {}
|
|
address_refcnt = {}
|
|
|
|
def __init__(self, mapping: Mapping, size: int):
|
|
self.mapping = mapping
|
|
self.segment_size = size
|
|
self.ptr, self.rank_stride = MnnvlMemory.open_mnnvl_memory(
|
|
self.mapping, size)
|
|
|
|
def __del__(self):
|
|
if not sys.is_finalizing():
|
|
MnnvlMemory.close_mnnvl_memory(self.ptr)
|
|
|
|
def as_torch_strided_tensor(self, dtype):
|
|
num_segments = MnnvlMemory.comm.Get_size()
|
|
return pack_strided_memory(self.ptr, self.segment_size,
|
|
self.rank_stride, num_segments, dtype,
|
|
MnnvlMemory.dev_id)
|
|
|
|
@staticmethod
|
|
def initialize():
|
|
if not MnnvlMemory.initialized:
|
|
# use a dummy torch CUDA tensor to trigger CUDA context initialization
|
|
_ = torch.empty(1, device='cuda')
|
|
# ensure nvml is initialized.
|
|
try:
|
|
pynvml.nvmlDeviceGetCount()
|
|
except pynvml.NVMLError_Uninitialized:
|
|
pynvml.nvmlInit()
|
|
MnnvlMemory.initialized = True
|
|
|
|
@staticmethod
|
|
def get_comm(mapping: Mapping):
|
|
if MnnvlMemory.comm is not None:
|
|
return MnnvlMemory.comm
|
|
comm = mpi_comm().Split(
|
|
mapping.pp_rank * mapping.cp_size + mapping.cp_rank,
|
|
mapping.tp_rank)
|
|
MnnvlMemory.comm = comm
|
|
return comm
|
|
|
|
@staticmethod
|
|
def get_allocation_prop(dev_id: int):
|
|
location = cuda.CUmemLocation()
|
|
location.type = cuda.CUmemLocationType.CU_MEM_LOCATION_TYPE_DEVICE
|
|
location.id = dev_id
|
|
allocation_prop = cuda.CUmemAllocationProp()
|
|
allocation_prop.type = cuda.CUmemAllocationType.CU_MEM_ALLOCATION_TYPE_PINNED
|
|
allocation_prop.requestedHandleTypes = cuda.CUmemAllocationHandleType.CU_MEM_HANDLE_TYPE_FABRIC
|
|
allocation_prop.location = location
|
|
return allocation_prop
|
|
|
|
@staticmethod
|
|
def get_allocation_granularity(dev_id: int):
|
|
if MnnvlMemory.allocation_granularity != 0:
|
|
return MnnvlMemory.allocation_granularity
|
|
allocation_prop = MnnvlMemory.get_allocation_prop(dev_id)
|
|
option = cuda.CUmemAllocationGranularity_flags(
|
|
cuda.CUmemAllocationGranularity_flags.
|
|
CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)
|
|
granularity = _check_cu_result(
|
|
cuda.cuMemGetAllocationGranularity(prop=allocation_prop,
|
|
option=option))
|
|
MnnvlMemory.allocation_granularity = granularity
|
|
return MnnvlMemory.allocation_granularity
|
|
|
|
@staticmethod
|
|
def new_mnnvl_memory_address(mapping: Mapping, size: int):
|
|
page_count = (size + MnnvlMemory.fabric_page_size -
|
|
1) // MnnvlMemory.fabric_page_size
|
|
current_rank_stride = page_count * MnnvlMemory.fabric_page_size
|
|
logger.info(
|
|
f"[MnnvlMemory] creating address with stride={current_rank_stride}")
|
|
comm = MnnvlMemory.get_comm(mapping)
|
|
comm_size = comm.Get_size()
|
|
address_size = current_rank_stride * comm_size
|
|
ptr = _check_cu_result(
|
|
cuda.cuMemAddressReserve(address_size, MnnvlMemory.fabric_page_size,
|
|
0, 0))
|
|
MnnvlMemory.current_start_address = int(ptr)
|
|
MnnvlMemory.current_rank_stride = current_rank_stride
|
|
MnnvlMemory.current_mem_offset = 0
|
|
|
|
@staticmethod
|
|
def open_mnnvl_memory(mapping: Mapping, size: int):
|
|
dev = _check_cu_result(cuda.cuCtxGetDevice())
|
|
dev_id = int(dev)
|
|
if MnnvlMemory.dev_id is None:
|
|
MnnvlMemory.dev_id = dev_id
|
|
assert dev_id == MnnvlMemory.dev_id,\
|
|
f"Different dev_id found dev_id={dev_id} but MnnvlMemory.dev_id={MnnvlMemory.dev_id}"
|
|
comm = MnnvlMemory.get_comm(mapping)
|
|
comm_rank = comm.Get_rank()
|
|
comm_size = comm.Get_size()
|
|
all_rank_allocate_sizes = comm.allgather(size)
|
|
assert len(all_rank_allocate_sizes) == comm_size
|
|
assert all(
|
|
x == size for x in
|
|
all_rank_allocate_sizes), "Not all rank allocating same size."
|
|
granularity = MnnvlMemory.get_allocation_granularity(dev_id)
|
|
aligned_size = (size + granularity - 1) // granularity * granularity
|
|
|
|
if MnnvlMemory.current_mem_offset + aligned_size > MnnvlMemory.current_rank_stride:
|
|
MnnvlMemory.new_mnnvl_memory_address(mapping, aligned_size)
|
|
|
|
assert MnnvlMemory.current_mem_offset + aligned_size <= MnnvlMemory.current_rank_stride
|
|
|
|
allocation_prop = MnnvlMemory.get_allocation_prop(dev_id)
|
|
allocated_mem_handle = _check_cu_result(
|
|
cuda.cuMemCreate(aligned_size, allocation_prop, flags=0))
|
|
exported_fabric_handle = _check_cu_result(
|
|
cuda.cuMemExportToShareableHandle(
|
|
allocated_mem_handle,
|
|
cuda.CUmemAllocationHandleType.CU_MEM_HANDLE_TYPE_FABRIC, 0))
|
|
all_handles_data = comm.allgather(exported_fabric_handle.data)
|
|
# all_handles_data like b'\x00\x00\x00 \x00\x00\x00\x00\x8f\xec\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\t\x00\x00\x00\x00\x00\x1d\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
|
|
# can use buf = memoryview(data) to import if using plain buffer for data.
|
|
|
|
madesc = cuda.CUmemAccessDesc()
|
|
madesc.location = allocation_prop.location
|
|
madesc.flags = cuda.CUmemAccess_flags.CU_MEM_ACCESS_FLAGS_PROT_READWRITE
|
|
|
|
mem_handles = [None] * comm_size
|
|
|
|
for i, remote_handle_data in enumerate(all_handles_data):
|
|
rank_ptr = MnnvlMemory.current_start_address + MnnvlMemory.current_rank_stride * i + MnnvlMemory.current_mem_offset
|
|
if i == comm_rank:
|
|
# Local memory mapping
|
|
mem_handles[i] = allocated_mem_handle
|
|
_check_cu_result(
|
|
cuda.cuMemMap(rank_ptr, aligned_size, 0,
|
|
allocated_mem_handle, 0))
|
|
else:
|
|
# Fabric memory mapping
|
|
imported_mem_handle = _check_cu_result(
|
|
cuda.cuMemImportFromShareableHandle(
|
|
remote_handle_data, cuda.CUmemAllocationHandleType.
|
|
CU_MEM_HANDLE_TYPE_FABRIC))
|
|
mem_handles[i] = imported_mem_handle
|
|
_check_cu_result(
|
|
cuda.cuMemMap(rank_ptr, aligned_size, 0,
|
|
imported_mem_handle, 0))
|
|
|
|
_check_cu_result(
|
|
cuda.cuMemSetAccess(rank_ptr, aligned_size, [madesc], 1))
|
|
|
|
ptr = MnnvlMemory.current_start_address + MnnvlMemory.current_mem_offset
|
|
stride = MnnvlMemory.current_rank_stride
|
|
MnnvlMemory.allocated_map[ptr] = (mapping, aligned_size, mem_handles,
|
|
MnnvlMemory.current_start_address,
|
|
MnnvlMemory.current_rank_stride,
|
|
MnnvlMemory.current_mem_offset)
|
|
MnnvlMemory.address_refcnt[
|
|
MnnvlMemory.current_start_address] = MnnvlMemory.address_refcnt.get(
|
|
MnnvlMemory.current_start_address, 0) + 1
|
|
|
|
MnnvlMemory.current_mem_offset += aligned_size
|
|
return ptr, stride
|
|
|
|
@staticmethod
|
|
def close_mnnvl_memory(ptr: int):
|
|
mapping, aligned_size, mem_handles, start_address, rank_stride, address_offset = MnnvlMemory.allocated_map.pop(
|
|
ptr)
|
|
comm = MnnvlMemory.get_comm(mapping)
|
|
comm_size = comm.Get_size()
|
|
for i in range(comm_size):
|
|
rank_ptr = start_address + i * rank_stride + address_offset
|
|
_check_cu_result(cuda.cuMemUnmap(rank_ptr, aligned_size))
|
|
_check_cu_result(cuda.cuMemRelease(mem_handles[i]))
|
|
MnnvlMemory.address_refcnt[start_address] -= 1
|
|
|
|
if MnnvlMemory.address_refcnt[start_address] == 0:
|
|
MnnvlMemory.address_refcnt.pop(start_address)
|
|
device_ptr = cuda.CUdeviceptr(start_address)
|
|
_check_cu_result(
|
|
cuda.cuMemAddressFree(device_ptr, comm_size * rank_stride))
|
|
if start_address == MnnvlMemory.current_start_address:
|
|
MnnvlMemory.current_start_address = 0
|
|
MnnvlMemory.current_rank_stride = 0
|
|
MnnvlMemory.current_mem_offset = 0
|
|
|
|
@staticmethod
|
|
def support_nvlink(need_all_up: bool = True):
|
|
dev_id = torch.cuda.current_device()
|
|
handle = pynvml.nvmlDeviceGetHandleByIndex(dev_id)
|
|
link_count = pynvml.NVML_NVLINK_MAX_LINKS
|
|
active_links = 0
|
|
available_links = 0
|
|
for link_idx in range(link_count):
|
|
try:
|
|
if pynvml.nvmlDeviceGetNvLinkCapability(
|
|
handle, link_idx, pynvml.NVML_NVLINK_CAP_P2P_SUPPORTED):
|
|
available_links += 1
|
|
is_active = pynvml.nvmlDeviceGetNvLinkState(
|
|
handle, link_idx)
|
|
if is_active:
|
|
active_links += 1
|
|
except pynvml.NVMLError_NotSupported:
|
|
continue
|
|
return active_links == available_links and available_links > 0 if need_all_up else available_links > 0
|
|
|
|
@staticmethod
|
|
def supports_mnnvl() -> bool:
|
|
# TODO:
|
|
# We check if it is an aarch64 platform and has all NVLink up now.
|
|
# But it is not equivalent to MNNVL support.
|
|
# May need better support check.
|
|
arch = platform.machine().lower()
|
|
is_on_aarch64 = 'aarch64' in arch
|
|
support_nvlink_and_all_up = MnnvlMemory.support_nvlink(True)
|
|
return is_on_aarch64 and support_nvlink_and_all_up
|
|
|
|
|
|
@dataclass
|
|
class MoEAlltoallInfo:
|
|
local_gather_indices: torch.Tensor
|
|
send_rank_count_cumsum: torch.Tensor
|
|
send_rank_local_indices: torch.Tensor
|
|
recv_rank_count_cumsum: torch.Tensor
|
|
recv_rank_local_indices: torch.Tensor
|
|
backward_recv_rank_local_indices: torch.Tensor
|
|
local_token_allocation_count: int
|
|
|
|
|
|
class MnnvlMoe:
|
|
moe_workspace: MnnvlMemory = None
|
|
moe_workspace_tensor: torch.Tensor = None
|
|
moe_mapping: Mapping = None
|
|
|
|
@staticmethod
|
|
def get_moe_workspaces(mapping: Mapping):
|
|
if MnnvlMoe.moe_workspace is not None:
|
|
assert mapping == MnnvlMoe.moe_mapping, "only one moe mapping supported now"
|
|
return MnnvlMoe.moe_workspace_tensor
|
|
|
|
MnnvlMoe.moe_mapping = mapping
|
|
workspace_size_per_rank = torch.ops.trtllm.get_moe_commworkspace_size_per_rank(
|
|
mapping.tp_size)
|
|
MnnvlMoe.moe_workspace = MnnvlMemory(mapping, workspace_size_per_rank)
|
|
MnnvlMoe.moe_workspace_tensor = MnnvlMoe.moe_workspace.as_torch_strided_tensor(
|
|
torch.uint64)
|
|
return MnnvlMoe.moe_workspace_tensor
|
|
|
|
@staticmethod
|
|
def compute_target_rank_id(token_selected_experts: torch.Tensor,
|
|
expert_count: int, ep_size: int):
|
|
assert expert_count % ep_size == 0, "expert_count should be divisible by ep_size"
|
|
expert_per_rank = expert_count // ep_size
|
|
token_target_rank_ids = token_selected_experts // expert_per_rank
|
|
return token_target_rank_ids
|
|
|
|
@staticmethod
|
|
def mnnvl_moe_alltoallv_prepare(gathered_target_rank_ids: torch.Tensor,
|
|
real_rank_token_count_cumsum: torch.Tensor,
|
|
gathered_expert_ids: torch.Tensor,
|
|
gathered_scales: torch.Tensor,
|
|
max_token_count_per_rank: int,
|
|
expert_count: int, top_k: int, ep_rank: int,
|
|
ep_size: int):
|
|
local_gather_indices, send_rank_count_cumsum, send_rank_local_indices, \
|
|
recv_rank_count_cumsum, recv_rank_local_indices, backward_recv_rank_local_indices = \
|
|
torch.ops.trtllm.moe_comm_prepare_indices(gathered_target_rank_ids, real_rank_token_count_cumsum,
|
|
max_token_count_per_rank, expert_count, top_k, ep_rank, ep_size)
|
|
|
|
local_token_allocation_count = max_token_count_per_rank * ep_size
|
|
|
|
local_expert_ids = torch.empty(local_token_allocation_count,
|
|
top_k,
|
|
dtype=torch.int32,
|
|
device=torch.device('cuda'))
|
|
local_scales = torch.empty(local_token_allocation_count,
|
|
top_k,
|
|
dtype=torch.float32,
|
|
device=torch.device('cuda'))
|
|
|
|
torch.ops.trtllm.moe_local_gather(recv_rank_count_cumsum,
|
|
local_gather_indices,
|
|
gathered_expert_ids, gathered_scales,
|
|
local_expert_ids, local_scales,
|
|
max_token_count_per_rank,
|
|
expert_count, top_k, ep_rank, ep_size)
|
|
|
|
alltoall_info = MoEAlltoallInfo(
|
|
local_gather_indices, send_rank_count_cumsum,
|
|
send_rank_local_indices, recv_rank_count_cumsum,
|
|
recv_rank_local_indices, backward_recv_rank_local_indices,
|
|
local_token_allocation_count)
|
|
return alltoall_info, local_expert_ids, local_scales
|
|
|
|
@staticmethod
|
|
def mnnvl_moe_alltoallv(x: torch.Tensor, alltoall_info: MoEAlltoallInfo,
|
|
workspace: torch.Tensor, ep_rank: int,
|
|
ep_size: int):
|
|
assert x.dim() == 2, "only 2D tensor supported, please reshape."
|
|
output_tensor = torch.empty(alltoall_info.local_token_allocation_count,
|
|
x.shape[1],
|
|
dtype=x.dtype,
|
|
device=torch.device('cuda'))
|
|
torch.ops.trtllm.moe_comm(x, alltoall_info.send_rank_count_cumsum,
|
|
alltoall_info.send_rank_local_indices,
|
|
output_tensor,
|
|
alltoall_info.recv_rank_count_cumsum,
|
|
alltoall_info.recv_rank_local_indices,
|
|
workspace, ep_rank, ep_size)
|
|
return output_tensor
|
|
|
|
@staticmethod
|
|
def mnnvl_moe_alltoallv_combine(x: torch.Tensor,
|
|
alltoall_info: MoEAlltoallInfo,
|
|
workspace: torch.Tensor, ep_rank: int,
|
|
ep_size: int, top_k: int, token_count: int):
|
|
assert x.dim() == 2, "2D tensor supported, please reshape."
|
|
output_tensor = torch.zeros(token_count * top_k,
|
|
x.shape[1],
|
|
dtype=x.dtype,
|
|
device=torch.device('cuda'))
|
|
torch.ops.trtllm.moe_comm(
|
|
x, alltoall_info.recv_rank_count_cumsum,
|
|
alltoall_info.recv_rank_local_indices, output_tensor,
|
|
alltoall_info.send_rank_count_cumsum,
|
|
alltoall_info.backward_recv_rank_local_indices, workspace, ep_rank,
|
|
ep_size)
|
|
return torch.sum(output_tensor.reshape(token_count, top_k, x.shape[1]),
|
|
dim=1,
|
|
keepdim=False)
|