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| 1 | +# SPDX-License-Identifier: Apache-2.0 |
| 2 | +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. |
| 3 | +""" |
| 4 | +Symmetric Memory AllReduce |
| 5 | +
|
| 6 | +This module provides PyTorch Symmetric Memory-based allreduce operations, |
| 7 | +leveraging MULTIMEM hardware instructions. |
| 8 | +""" |
| 9 | + |
| 10 | +from typing import Optional |
| 11 | + |
| 12 | +import torch |
| 13 | +import torch.distributed as dist |
| 14 | +from torch import nn |
| 15 | + |
| 16 | +from tensorrt_llm.logger import logger |
| 17 | +from tensorrt_llm.mapping import Mapping |
| 18 | + |
| 19 | +try: |
| 20 | + import torch.distributed._symmetric_memory as torch_symm_mem |
| 21 | + |
| 22 | + SYMM_MEM_AVAILABLE = True |
| 23 | +except ImportError: |
| 24 | + SYMM_MEM_AVAILABLE = False |
| 25 | + logger.warning( |
| 26 | + "PyTorch symmetric memory not available. Install PyTorch >= 2.8 for MULTIMEM support." |
| 27 | + ) |
| 28 | + |
| 29 | + |
| 30 | +class SymmetricMemoryAllReduce(nn.Module): |
| 31 | + """ |
| 32 | + AllReduce implementation using PyTorch's symmetric memory operations. |
| 33 | + This leverages MULTIMEM hardware instructions for faster allreduce operations. |
| 34 | +
|
| 35 | + Supported configurations (world_size): |
| 36 | + - SM 9.0: 4, 6, 8 GPUs |
| 37 | + - SM 10.0: 6, 8 GPUs |
| 38 | +
|
| 39 | + """ |
| 40 | + |
| 41 | + # World sizes that support MULTIMEM instructions |
| 42 | + _WORLD_SIZES_MULTIMEM = { |
| 43 | + "9.0": [4, 6, 8], |
| 44 | + "10.0": [6, 8], |
| 45 | + } |
| 46 | + |
| 47 | + MiB = 1024 * 1024 |
| 48 | + # Maximum buffer sizes for symmetric memory (bytes) |
| 49 | + _MAX_SIZES = { |
| 50 | + "9.0": { |
| 51 | + 2: 64 * MiB, # 64 MB |
| 52 | + 4: 32 * MiB, # 32 MB |
| 53 | + 6: 64 * MiB, # 64 MB |
| 54 | + 8: 64 * MiB, # 64 MB |
| 55 | + }, |
| 56 | + "10.0": { |
| 57 | + 2: 8 * MiB, # 8 MB |
| 58 | + 4: 32 * MiB, # 32 MB |
| 59 | + 6: 128 * MiB, # 128 MB |
| 60 | + 8: 128 * MiB, # 128 MB |
| 61 | + }, |
| 62 | + } |
| 63 | + |
| 64 | + def __init__( |
| 65 | + self, |
| 66 | + mapping: Mapping, |
| 67 | + dtype: torch.dtype = torch.bfloat16, |
| 68 | + group: Optional[dist.ProcessGroup] = None, |
| 69 | + ): |
| 70 | + super().__init__() |
| 71 | + |
| 72 | + self.disabled = True |
| 73 | + self.mapping = mapping |
| 74 | + self.dtype = dtype |
| 75 | + self.world_size = mapping.tp_size |
| 76 | + |
| 77 | + if not SYMM_MEM_AVAILABLE: |
| 78 | + logger.warning("SymmetricMemoryAllReduce: PyTorch symm_mem not available") |
| 79 | + return |
| 80 | + |
| 81 | + if not torch.cuda.is_available(): |
| 82 | + logger.warning("SymmetricMemoryAllReduce: CUDA not available") |
| 83 | + return |
| 84 | + |
| 85 | + # Get device capability |
| 86 | + device = torch.device(f"cuda:{mapping.tp_rank}") |
| 87 | + capability = torch.cuda.get_device_capability(device) |
| 88 | + self.device_capability = f"{capability[0]}.{capability[1]}" |
| 89 | + |
| 90 | + # Check if this configuration is supported |
| 91 | + if self.device_capability not in self._MAX_SIZES: |
| 92 | + logger.warning( |
| 93 | + f"SymmetricMemoryAllReduce: Device capability {self.device_capability} not supported" |
| 94 | + ) |
| 95 | + return |
| 96 | + |
| 97 | + if self.world_size not in self._MAX_SIZES[self.device_capability]: |
| 98 | + logger.info( |
| 99 | + f"SymmetricMemoryAllReduce: World size {self.world_size} not supported " |
| 100 | + f"for SM {self.device_capability}" |
| 101 | + ) |
| 102 | + return |
| 103 | + |
| 104 | + # Get max buffer size for this configuration |
| 105 | + self.max_size = self._MAX_SIZES[self.device_capability][self.world_size] |
| 106 | + |
| 107 | + # Set up process group |
| 108 | + self.group = group |
| 109 | + if self.group is None: |
| 110 | + # Get or create TP group with correct ranks |
| 111 | + # For TP parallelism, we need ranks [0, 1, 2, ..., tp_size-1] globally |
| 112 | + # NOT starting from tp_rank! |
| 113 | + if not dist.is_initialized(): |
| 114 | + logger.warning("SymmetricMemoryAllReduce: torch.distributed not initialized") |
| 115 | + self.disabled = True |
| 116 | + return |
| 117 | + # Get actual TP group ranks from mapping (tp_group is a property, not a method) |
| 118 | + tp_group_ranks = mapping.tp_group |
| 119 | + self.group = dist.new_group(tp_group_ranks) if len(tp_group_ranks) > 1 else None |
| 120 | + |
| 121 | + # Enable symmetric memory for this group |
| 122 | + try: |
| 123 | + # Get group_name - this may fail if ProcessGroup doesn't have group_name set |
| 124 | + if not hasattr(self.group, "group_name"): |
| 125 | + logger.warning( |
| 126 | + "SymmetricMemoryAllReduce: ProcessGroup does not have group_name attribute" |
| 127 | + ) |
| 128 | + self.disabled = True |
| 129 | + return |
| 130 | + |
| 131 | + group_name_str = str(self.group.group_name) |
| 132 | + torch_symm_mem.enable_symm_mem_for_group(group_name_str) |
| 133 | + logger.debug( |
| 134 | + f"SymmetricMemoryAllReduce: Enabled symmetric memory for group {group_name_str}" |
| 135 | + ) |
| 136 | + except Exception as e: |
| 137 | + logger.warning( |
| 138 | + f"SymmetricMemoryAllReduce: Failed to enable symmetric memory for group: {e}" |
| 139 | + ) |
| 140 | + self.disabled = True |
| 141 | + return |
| 142 | + |
| 143 | + # Allocate symmetric memory buffer |
| 144 | + try: |
| 145 | + self.buffer = torch_symm_mem.empty( |
| 146 | + self.max_size // self.dtype.itemsize, |
| 147 | + device=device, |
| 148 | + dtype=self.dtype, |
| 149 | + ) |
| 150 | + # Pass group name string |
| 151 | + group_name_str = str(self.group.group_name) |
| 152 | + handle = torch_symm_mem.rendezvous(self.buffer, group_name_str) |
| 153 | + |
| 154 | + if handle.multicast_ptr == 0: |
| 155 | + logger.warning( |
| 156 | + "SymmetricMemoryAllReduce: MULTIMEM operations not supported (multicast_ptr is 0)" |
| 157 | + ) |
| 158 | + return |
| 159 | + |
| 160 | + # Only enable if MULTIMEM is supported |
| 161 | + # Otherwise, no benefit over existing TensorRT-LLM strategies |
| 162 | + use_multimem = self.world_size in self._WORLD_SIZES_MULTIMEM.get( |
| 163 | + self.device_capability, [] |
| 164 | + ) |
| 165 | + |
| 166 | + if not use_multimem: |
| 167 | + logger.info( |
| 168 | + f"SymmetricMemoryAllReduce: MULTIMEM not supported for " |
| 169 | + f"world_size={self.world_size}, SM={self.device_capability}. " |
| 170 | + f"Falling back to standard allreduce strategies." |
| 171 | + ) |
| 172 | + return |
| 173 | + |
| 174 | + self.disabled = False |
| 175 | + logger.info( |
| 176 | + f"SymmetricMemoryAllReduce (MULTIMEM) initialized: " |
| 177 | + f"world_size={self.world_size}, " |
| 178 | + f"max_size={self.max_size}, " |
| 179 | + f"SM={self.device_capability}" |
| 180 | + ) |
| 181 | + |
| 182 | + except Exception as e: |
| 183 | + logger.warning(f"SymmetricMemoryAllReduce initialization failed: {e}") |
| 184 | + return |
| 185 | + |
| 186 | + @property |
| 187 | + def process_group(self) -> Optional[dist.ProcessGroup]: |
| 188 | + """Expose the ProcessGroup for use in fallback scenarios.""" |
| 189 | + return self.group if not self.disabled else None |
| 190 | + |
| 191 | + def can_use_symm_mem(self, inp: torch.Tensor) -> bool: |
| 192 | + """Check if symmetric memory can be used for this tensor.""" |
| 193 | + if self.disabled: |
| 194 | + return False |
| 195 | + if inp.dtype != self.dtype: |
| 196 | + return False |
| 197 | + inp_size = inp.numel() * inp.element_size() |
| 198 | + if inp_size % 4 != 0: |
| 199 | + return False |
| 200 | + if inp_size >= self.max_size: |
| 201 | + return False |
| 202 | + return True |
| 203 | + |
| 204 | + def forward( |
| 205 | + self, |
| 206 | + inp: torch.Tensor, |
| 207 | + out: Optional[torch.Tensor] = None, |
| 208 | + ) -> torch.Tensor: |
| 209 | + """ |
| 210 | + Perform allreduce using symmetric memory operations. |
| 211 | +
|
| 212 | + Args: |
| 213 | + inp: Input tensor to reduce |
| 214 | + out: Optional output tensor (if None, will be allocated) |
| 215 | +
|
| 216 | + Returns: |
| 217 | + Reduced tensor |
| 218 | + """ |
| 219 | + if not self.can_use_symm_mem(inp): |
| 220 | + return None # Caller should fall back to other strategy |
| 221 | + |
| 222 | + if out is None: |
| 223 | + out = torch.empty_like(inp) |
| 224 | + |
| 225 | + # Copy input to symmetric memory buffer |
| 226 | + self.buffer[: inp.numel()].copy_(inp.view(-1)) |
| 227 | + |
| 228 | + # Perform MULTIMEM allreduce |
| 229 | + # Pass group name string (matching vLLM's implementation) |
| 230 | + group_name_str = str(self.group.group_name) |
| 231 | + torch.ops.symm_mem.multimem_all_reduce_( |
| 232 | + self.buffer[: inp.numel()], |
| 233 | + "sum", |
| 234 | + group_name_str, |
| 235 | + ) |
| 236 | + |
| 237 | + # Copy result back |
| 238 | + out.copy_(self.buffer[: inp.numel()].view(out.shape)) |
| 239 | + |
| 240 | + return out |
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