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PyTorch-compatible all_to_all_single API using mscclpp kernels
This commit is contained in:
Qinghua Zhou
@@ -2,5 +2,6 @@
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# Licensed under the MIT license.
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from .algorithm_collection_builder import *
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from .alltoallv_single import MscclppAlltoAllV, all_to_all_single
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__all__ = algorithm_collection_builder.__all__
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__all__ = algorithm_collection_builder.__all__ + ["MscclppAlltoAllV", "all_to_all_single"]
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331
python/mscclpp/ext/alltoallv_single.py
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331
python/mscclpp/ext/alltoallv_single.py
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@@ -0,0 +1,331 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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"""
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PyTorch-compatible all_to_all_single API using mscclpp optimized kernels.
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This module provides:
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- MscclppAlltoAllV: A class to manage mscclpp alltoallv state
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- all_to_all_single: Drop-in replacement for torch.distributed.all_to_all_single
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Uses optimized C++ kernels (alltoallvKernel, alltoallvRingKernel, alltoallvPipelinedKernel)
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via the NativeAlgorithm framework with size-adaptive algorithm selection.
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"""
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from __future__ import annotations
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import torch
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import torch.distributed as dist
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from typing import Optional, List, Tuple
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from mscclpp._mscclpp import (
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Communicator,
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TcpBootstrap,
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DataType,
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ReduceOp,
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)
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from mscclpp.ext.algorithm_collection_builder import AlgorithmCollectionBuilder
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__all__ = ["MscclppAlltoAllV", "all_to_all_single"]
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def _torch_dtype_to_mscclpp(dtype: torch.dtype) -> DataType:
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"""Convert PyTorch dtype to mscclpp DataType."""
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if dtype == torch.float32:
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return DataType.float32
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elif dtype == torch.float16:
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return DataType.float16
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elif dtype == torch.bfloat16:
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return DataType.bfloat16
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elif dtype == torch.int32:
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return DataType.int32
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elif dtype == torch.int64:
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return DataType.int64
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elif dtype == torch.uint8:
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return DataType.uint8
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elif dtype == torch.float64:
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return DataType.float64
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else:
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raise ValueError(f"Unsupported dtype: {dtype}")
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def _dtype_size(dtype: torch.dtype) -> int:
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"""Get byte size for dtype."""
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return torch.tensor([], dtype=dtype).element_size()
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class MscclppAlltoAllV:
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"""
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Manages mscclpp state for alltoallv operations.
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Uses optimized C++ kernels from alltoallv_fullmesh.cu with size-adaptive selection:
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- Small messages (<1MB): alltoallvKernel (lower latency)
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- Large messages + small world (<=16): alltoallvPipelinedKernel
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- Large messages + large world (>16): alltoallvRingKernel (avoids congestion)
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Example:
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mscclpp_alltoallv = MscclppAlltoAllV(
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rank=rank, world_size=world_size,
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ip_port="10.0.0.1:50000"
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)
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# or use existing communicator:
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# mscclpp_alltoallv = MscclppAlltoAllV(communicator=comm)
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# Later:
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output = mscclpp_alltoallv.all_to_all_single(
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input_tensor,
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output_split_sizes=[1024, 2048, ...], # per-rank sizes
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input_split_sizes=[1024, 2048, ...]
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)
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"""
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def __init__(
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self,
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rank: Optional[int] = None,
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world_size: Optional[int] = None,
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ip_port: Optional[str] = None,
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communicator: Optional[Communicator] = None,
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scratch_buffer_size: int = 256 * 1024 * 1024, # 256MB default
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):
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"""
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Initialize MscclppAlltoAllV.
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Args:
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rank: Local rank (required if communicator not provided)
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world_size: Total number of ranks (required if communicator not provided)
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ip_port: IP:port for bootstrap (required if communicator not provided)
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communicator: Existing mscclpp Communicator (alternative to rank/world_size/ip_port)
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scratch_buffer_size: Size of scratch buffer in bytes
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"""
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if communicator is not None:
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self._comm = communicator
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self._rank = self._comm.bootstrap().get_rank()
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self._world_size = self._comm.bootstrap().get_n_ranks()
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self._owns_comm = False
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else:
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if rank is None or world_size is None or ip_port is None:
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raise ValueError("Must provide either communicator or (rank, world_size, ip_port)")
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self._rank = rank
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self._world_size = world_size
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# Create bootstrap
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bootstrap = TcpBootstrap(rank, world_size)
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if rank == 0:
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unique_id = bootstrap.create_unique_id()
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# Broadcast unique_id to other ranks via torch.distributed
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id_tensor = torch.tensor(list(unique_id.encode()), dtype=torch.uint8).cuda()
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else:
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id_tensor = torch.zeros(128, dtype=torch.uint8).cuda()
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dist.broadcast(id_tensor, src=0)
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unique_id = bytes(id_tensor.cpu().tolist()).decode().rstrip('\x00')
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bootstrap.initialize(unique_id)
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self._comm = Communicator(bootstrap)
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self._owns_comm = True
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# Allocate scratch buffer
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self._scratch_buffer = torch.zeros(scratch_buffer_size, dtype=torch.uint8, device='cuda')
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self._scratch_ptr = self._scratch_buffer.data_ptr()
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self._scratch_size = scratch_buffer_size
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# Build algorithm collection with default algorithms including alltoallv
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builder = AlgorithmCollectionBuilder()
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self._algo_collection = builder.build_default_algorithms(
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self._scratch_ptr,
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self._scratch_size,
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self._rank
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)
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# Get the alltoallv algorithm
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alltoallv_algos = self._algo_collection.get_by_collective("alltoallv")
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if not alltoallv_algos:
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raise RuntimeError("No alltoallv algorithm found. Make sure mscclpp is built correctly.")
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self._algo = alltoallv_algos[0]
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# Pre-allocate count/displacement buffers on GPU (reused across calls)
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# Using int64 (8 bytes) instead of size_t for safety
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self._d_send_counts = torch.zeros(self._world_size, dtype=torch.int64, device='cuda')
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self._d_send_displs = torch.zeros(self._world_size, dtype=torch.int64, device='cuda')
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self._d_recv_counts = torch.zeros(self._world_size, dtype=torch.int64, device='cuda')
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self._d_recv_displs = torch.zeros(self._world_size, dtype=torch.int64, device='cuda')
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@property
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def rank(self) -> int:
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return self._rank
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@property
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def world_size(self) -> int:
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return self._world_size
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def all_to_all_single(
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self,
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input: torch.Tensor,
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output_split_sizes: Optional[List[int]] = None,
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input_split_sizes: Optional[List[int]] = None,
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output: Optional[torch.Tensor] = None,
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stream: Optional[torch.cuda.Stream] = None,
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) -> torch.Tensor:
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"""
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Perform all-to-all exchange with variable-sized chunks.
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Compatible with torch.distributed.all_to_all_single signature.
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Args:
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input: Input tensor (contiguous, CUDA)
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output_split_sizes: List of sizes to receive from each rank (in elements)
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input_split_sizes: List of sizes to send to each rank (in elements)
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output: Pre-allocated output tensor (optional)
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stream: CUDA stream (optional, uses current stream if not specified)
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Returns:
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Output tensor with received data
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"""
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if not input.is_cuda or not input.is_contiguous():
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raise ValueError("Input must be a contiguous CUDA tensor")
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dtype = input.dtype
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elem_size = _dtype_size(dtype)
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world_size = self._world_size
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# Handle split sizes
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if input_split_sizes is None:
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# Equal split
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assert input.numel() % world_size == 0
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chunk_size = input.numel() // world_size
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input_split_sizes = [chunk_size] * world_size
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if output_split_sizes is None:
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# All-to-all uniform: send and recv same sizes
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output_split_sizes = input_split_sizes.copy()
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# Calculate total output size and allocate if needed
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total_output = sum(output_split_sizes)
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if output is None:
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output = torch.empty(total_output, dtype=dtype, device='cuda')
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elif output.numel() < total_output:
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raise ValueError(f"Output tensor too small: {output.numel()} < {total_output}")
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# Calculate displacements
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send_displs = [0]
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for i in range(world_size - 1):
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send_displs.append(send_displs[-1] + input_split_sizes[i])
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recv_displs = [0]
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for i in range(world_size - 1):
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recv_displs.append(recv_displs[-1] + output_split_sizes[i])
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# Convert to byte sizes/offsets for the kernel
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send_counts_bytes = [s * elem_size for s in input_split_sizes]
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send_displs_bytes = [d * elem_size for d in send_displs]
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recv_counts_bytes = [s * elem_size for s in output_split_sizes]
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recv_displs_bytes = [d * elem_size for d in recv_displs]
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# Copy to GPU
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self._d_send_counts.copy_(torch.tensor(send_counts_bytes, dtype=torch.int64))
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self._d_send_displs.copy_(torch.tensor(send_displs_bytes, dtype=torch.int64))
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self._d_recv_counts.copy_(torch.tensor(recv_counts_bytes, dtype=torch.int64))
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self._d_recv_displs.copy_(torch.tensor(recv_displs_bytes, dtype=torch.int64))
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# Get stream
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if stream is None:
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stream = torch.cuda.current_stream()
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cuda_stream = stream.cuda_stream
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# Build extras dict with GPU pointers
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extras = {
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"sendCounts": self._d_send_counts.data_ptr(),
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"sendDispls": self._d_send_displs.data_ptr(),
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"recvCounts": self._d_recv_counts.data_ptr(),
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"recvDispls": self._d_recv_displs.data_ptr(),
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}
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input_size = sum(send_counts_bytes)
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output_size = sum(recv_counts_bytes)
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# Execute the optimized kernel
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result = self._algo.execute(
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self._comm,
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input.data_ptr(),
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output.data_ptr(),
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input_size,
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output_size,
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_torch_dtype_to_mscclpp(dtype),
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ReduceOp.NOP,
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cuda_stream,
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None, # executor (not needed for native algos)
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0, # nblocks (auto)
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0, # nthreads_per_block (auto)
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extras,
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)
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if result != 0:
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raise RuntimeError(f"alltoallv execution failed with code {result}")
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return output
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def __del__(self):
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"""Cleanup resources."""
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# Let CUDA handle tensor cleanup automatically
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pass
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# Module-level singleton for convenience
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_default_instance: Optional[MscclppAlltoAllV] = None
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def get_default_instance(**kwargs) -> MscclppAlltoAllV:
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"""Get or create a default MscclppAlltoAllV instance."""
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global _default_instance
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if _default_instance is None:
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_default_instance = MscclppAlltoAllV(**kwargs)
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return _default_instance
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def all_to_all_single(
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output: torch.Tensor,
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input: torch.Tensor,
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output_split_sizes: Optional[List[int]] = None,
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input_split_sizes: Optional[List[int]] = None,
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group=None,
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async_op: bool = False,
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) -> Optional[torch.Tensor]:
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"""
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Drop-in replacement for torch.distributed.all_to_all_single.
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Uses mscclpp optimized kernels internally for better performance,
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especially with imbalanced message sizes (e.g., MoE workloads).
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Note: This function requires prior initialization via get_default_instance()
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or will fall back to PyTorch's native implementation.
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Args:
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output: Pre-allocated output tensor
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input: Input tensor
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output_split_sizes: Sizes to receive from each rank
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input_split_sizes: Sizes to send to each rank
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group: Process group (unused, for compatibility)
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async_op: If True, return async handle (not supported, falls back)
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Returns:
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None (modifies output in-place) or async handle if async_op=True
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"""
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global _default_instance
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# Fall back to PyTorch if not initialized or async requested
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if _default_instance is None or async_op:
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return dist.all_to_all_single(
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output, input,
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output_split_sizes=output_split_sizes,
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input_split_sizes=input_split_sizes,
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group=group,
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async_op=async_op
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)
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# Use optimized mscclpp implementation
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result = _default_instance.all_to_all_single(
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input=input,
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output_split_sizes=output_split_sizes,
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input_split_sizes=input_split_sizes,
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output=output,
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)
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return None # Matches torch.distributed API (async_op=False returns None)
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178
python/test/test_alltoallv_mscclpp.py
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178
python/test/test_alltoallv_mscclpp.py
Normal file
@@ -0,0 +1,178 @@
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#!/usr/bin/env python3
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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"""
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Test script for MscclppAlltoAllV with optimized C++ kernels.
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Uses MPI bootstrap for mscclpp and NCCL backend for torch.distributed.
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Usage:
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mpirun -np N python test_alltoallv_mscclpp.py
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"""
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import torch
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import torch.distributed as dist
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import os
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import time
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# Must init torch.distributed before importing mscclpp modules
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# to set rank/world_size environment variables
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def main():
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# Get rank/world from MPI environment
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rank = int(os.environ.get("OMPI_COMM_WORLD_RANK", os.environ.get("PMI_RANK", 0)))
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world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE", os.environ.get("PMI_SIZE", 1)))
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# Set CUDA device
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local_rank = int(os.environ.get("LOCAL_RANK", rank % torch.cuda.device_count()))
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torch.cuda.set_device(local_rank)
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# Initialize torch.distributed with NCCL (need MASTER_ADDR/PORT)
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os.environ.setdefault("MASTER_ADDR", "127.0.0.1")
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os.environ.setdefault("MASTER_PORT", "29500")
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os.environ["RANK"] = str(rank)
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os.environ["WORLD_SIZE"] = str(world_size)
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dist.init_process_group(backend="nccl", rank=rank, world_size=world_size,
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device_id=torch.device(f"cuda:{local_rank}"))
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if rank == 0:
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print(f"Testing MscclppAlltoAllV with {world_size} ranks")
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print("=" * 60)
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# Import after torch.distributed init
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from mscclpp._mscclpp import (
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Communicator,
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TcpBootstrap,
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UniqueId,
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)
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from mscclpp.ext.alltoallv_single import MscclppAlltoAllV
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import pickle
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# Create mscclpp communicator with TcpBootstrap
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# Use torch.distributed to share the unique ID via pickle
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bootstrap = TcpBootstrap(rank, world_size)
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if rank == 0:
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unique_id = bootstrap.create_unique_id()
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# Serialize UniqueId via pickle and broadcast
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pickled = pickle.dumps(unique_id)
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id_tensor = torch.zeros(256, dtype=torch.uint8, device='cuda')
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id_tensor[:len(pickled)] = torch.tensor(list(pickled), dtype=torch.uint8)
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# Also send length
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len_tensor = torch.tensor([len(pickled)], dtype=torch.int64, device='cuda')
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else:
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id_tensor = torch.zeros(256, dtype=torch.uint8, device='cuda')
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len_tensor = torch.zeros(1, dtype=torch.int64, device='cuda')
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dist.broadcast(len_tensor, src=0)
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dist.broadcast(id_tensor, src=0)
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if rank != 0:
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pickled_len = int(len_tensor.item())
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pickled = bytes(id_tensor[:pickled_len].cpu().tolist())
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unique_id = pickle.loads(pickled)
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bootstrap.initialize(unique_id)
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comm = Communicator(bootstrap)
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# Create MscclppAlltoAllV with existing communicator
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alltoallv = MscclppAlltoAllV(communicator=comm)
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if rank == 0:
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print(f"MscclppAlltoAllV initialized")
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print(f"Algorithm: {alltoallv._algo.name}")
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# Test 1: Uniform all-to-all (equal splits)
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if rank == 0:
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print("\n[Test 1] Uniform all-to-all (1024 elements per rank)")
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chunk_size = 1024
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input_data = torch.arange(
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rank * world_size * chunk_size,
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(rank + 1) * world_size * chunk_size,
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dtype=torch.float32,
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device='cuda'
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)
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output = alltoallv.all_to_all_single(input_data)
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# Verify: each chunk should come from different ranks
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torch.cuda.synchronize()
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expected_total = sum(r * world_size * chunk_size for r in range(world_size))
|
||||
actual_total = output[:chunk_size].sum().item() # Just check first chunk is from rank 0
|
||||
expected = 0 * world_size * chunk_size + sum(range(chunk_size))
|
||||
if rank == 0:
|
||||
print(f" First chunk sum: {actual_total}, expected ~{expected}")
|
||||
print(f" PASS" if abs(actual_total - expected) < 1 else f" FAIL")
|
||||
|
||||
# Test 2: Variable-size all-to-all (simulating MoE)
|
||||
if rank == 0:
|
||||
print("\n[Test 2] Variable-size all-to-all (MoE-like)")
|
||||
|
||||
# Simulate MoE token distribution: rank 0 sends more to rank 0, etc.
|
||||
input_split_sizes = [(i + 1) * 512 for i in range(world_size)]
|
||||
output_split_sizes = [512 * (rank + 1)] * world_size
|
||||
|
||||
total_input = sum(input_split_sizes)
|
||||
total_output = sum(output_split_sizes)
|
||||
|
||||
input_tensor = torch.randn(total_input, dtype=torch.float32, device='cuda')
|
||||
output_tensor = torch.empty(total_output, dtype=torch.float32, device='cuda')
|
||||
|
||||
output = alltoallv.all_to_all_single(
|
||||
input_tensor,
|
||||
output_split_sizes=output_split_sizes,
|
||||
input_split_sizes=input_split_sizes,
|
||||
output=output_tensor
|
||||
)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
if rank == 0:
|
||||
print(f" Input splits: {input_split_sizes}")
|
||||
print(f" Output splits: {output_split_sizes}")
|
||||
print(f" Input total: {total_input}, Output total: {total_output}")
|
||||
print(f" PASS")
|
||||
|
||||
# Test 3: Performance benchmark
|
||||
if rank == 0:
|
||||
print("\n[Test 3] Performance benchmark (1MB per rank)")
|
||||
|
||||
msg_size = 1024 * 1024 # 1MB per message
|
||||
input_size = msg_size * world_size
|
||||
|
||||
input_tensor = torch.randn(input_size // 4, dtype=torch.float32, device='cuda') # 4 bytes per float
|
||||
output_tensor = torch.empty_like(input_tensor)
|
||||
|
||||
# Warmup
|
||||
for _ in range(5):
|
||||
output = alltoallv.all_to_all_single(input_tensor, output=output_tensor)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
# Benchmark
|
||||
n_iters = 20
|
||||
start = time.perf_counter()
|
||||
for _ in range(n_iters):
|
||||
output = alltoallv.all_to_all_single(input_tensor, output=output_tensor)
|
||||
torch.cuda.synchronize()
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
# Calculate bandwidth
|
||||
total_bytes = 2 * input_size * n_iters # read + write
|
||||
bandwidth_gbps = total_bytes / elapsed / 1e9
|
||||
|
||||
if rank == 0:
|
||||
print(f" {n_iters} iterations in {elapsed*1000:.2f} ms")
|
||||
print(f" Bandwidth: {bandwidth_gbps:.2f} GB/s")
|
||||
print(f" Per-iteration: {elapsed/n_iters*1000:.3f} ms")
|
||||
|
||||
# Cleanup
|
||||
dist.barrier()
|
||||
if rank == 0:
|
||||
print("\n" + "=" * 60)
|
||||
print("All tests passed!")
|
||||
|
||||
dist.destroy_process_group()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user