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3db1cbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | # Copyright 2026 The HuggingFace Team. All rights reserved.
#
# 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.
"""Neuron backend for tensor parallelism, dispatched from `apply_tensor_parallel` when the TP mesh is on Neuron.
The difference from the generic path is a workaround for a Neuron NRT bug: consecutive `reduce_scatter` collectives for
large weight tensors (≥ 5120×5120) can fail when all layers are distributed in a single `parallelize_module` call. The
fix is to pre-shard each weight locally on CPU via `DTensor.from_local` *before* calling `parallelize_module`; the
latter then sees already-placed DTensors, skips the collective for weights, but still registers the required
input/output hooks for the forward pass.
"""
import torch
import torch.distributed as dist
import torch.nn as nn
def _neuron_styles(relative_plan: dict) -> dict:
"""Map a `{relative_path: style}` plan to no-op-partition styles for Neuron.
Weights (and biases) are pre-sharded in `_pre_shard_and_tp`, so `parallelize_module` runs only to register the
forward hooks; `_partition_linear_fn` must not re-partition. Packed and plain styles share hook behavior, so both
collapse onto the two no-op styles.
"""
from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel
from .tensor_parallel import PackedColwiseParallel, PackedRowwiseParallel
class _NeuronColwise(ColwiseParallel):
def _partition_linear_fn(self, name, module, device_mesh):
pass # weight already Shard(0) via DTensor.from_local; parallelize_module runs only for the hooks
class _NeuronRowwise(RowwiseParallel):
def _partition_linear_fn(self, name, module, device_mesh):
pass # weight already Shard(1) via DTensor.from_local; parallelize_module runs only for the hooks
resolved = {}
for path, style in relative_plan.items():
if style == "colwise" or isinstance(style, PackedColwiseParallel):
resolved[path] = _NeuronColwise()
elif style == "rowwise" or isinstance(style, PackedRowwiseParallel):
resolved[path] = _NeuronRowwise()
else:
raise ValueError(
f"Unsupported tensor-parallel style '{style}' for '{path}'. "
f"Expected 'colwise', 'rowwise', PackedColwiseParallel, or PackedRowwiseParallel."
)
return resolved
def _pre_shard_and_tp(
module: nn.Module,
tp_mesh: "torch.distributed.device_mesh.DeviceMesh",
original_plan: dict,
rank: int,
tp_size: int,
) -> None:
"""Pre-shard Linear weights via `DTensor.from_local`, then call `parallelize_module`.
Workaround for a Neuron NRT bug where consecutive `reduce_scatter` calls for large weight tensors (≥ 5120×5120)
fail when all layers are distributed in a single `parallelize_module` call. Pre-sharding each weight on CPU means
it is already an on-device DTensor when `parallelize_module` runs (via `_neuron_styles`), so the collective is
skipped while the forward hooks are still registered.
"""
from torch.distributed.tensor import DTensor, Replicate, Shard
from torch.distributed.tensor.parallel import parallelize_module
from .tensor_parallel import PackedColwiseParallel, PackedRowwiseParallel, _blocks_to_block_sizes
device = torch.neuron.current_device()
for path, orig_style in original_plan.items():
# Resolve nested attribute path (e.g. "attn.to_q" or "attn.to_out.0")
submod = module
for part in path.split("."):
submod = getattr(submod, part)
if not hasattr(submod, "weight"):
raise ValueError(f"`_tp_plan` entry '{path}' does not resolve to a module with a `weight` parameter.")
w = submod.weight.data # CPU at this point
b = submod.bias.data if submod.bias is not None else None
if isinstance(orig_style, PackedColwiseParallel):
blocks = orig_style.blocks if orig_style.blocks is not None else getattr(submod, "_tp_packed_col_blocks")
block_sizes = _blocks_to_block_sizes(w.shape[0], blocks)
parts, bias_parts, offset = [], [], 0
for bs in block_sizes:
if bs % tp_size != 0:
raise ValueError(
f"Cannot shard packed block of size {bs} across {tp_size} tensor-parallel ranks: "
f"{bs} is not divisible by {tp_size}."
)
chunk = bs // tp_size
sl = slice(offset + rank * chunk, offset + (rank + 1) * chunk)
parts.append(w[sl, :].contiguous())
if b is not None:
bias_parts.append(b[sl].contiguous())
offset += bs
shard = torch.cat(parts, dim=0).to(device)
submod.weight = nn.Parameter(DTensor.from_local(shard, tp_mesh, [Shard(0)]))
if b is not None:
bias_shard = torch.cat(bias_parts, dim=0).to(device)
submod.bias = nn.Parameter(DTensor.from_local(bias_shard, tp_mesh, [Shard(0)]))
elif isinstance(orig_style, PackedRowwiseParallel):
blocks = orig_style.blocks if orig_style.blocks is not None else getattr(submod, "_tp_packed_row_blocks")
block_sizes = _blocks_to_block_sizes(w.shape[1], blocks)
parts, offset = [], 0
for bs in block_sizes:
if bs % tp_size != 0:
raise ValueError(
f"Cannot shard packed block of size {bs} across {tp_size} tensor-parallel ranks: "
f"{bs} is not divisible by {tp_size}."
)
chunk = bs // tp_size
parts.append(w[:, offset + rank * chunk : offset + (rank + 1) * chunk].contiguous())
offset += bs
shard = torch.cat(parts, dim=1).to(device)
submod.weight = nn.Parameter(DTensor.from_local(shard, tp_mesh, [Shard(1)]))
if b is not None: # rowwise bias is added post-reduction → keep it replicated
submod.bias = nn.Parameter(DTensor.from_local(b.to(device), tp_mesh, [Replicate()]))
elif orig_style == "colwise":
if w.shape[0] % tp_size != 0:
raise ValueError(
f"Cannot colwise-shard '{path}' weight rows ({w.shape[0]}) across {tp_size} "
f"tensor-parallel ranks: not divisible by {tp_size}."
)
rows = w.shape[0] // tp_size
sl = slice(rank * rows, (rank + 1) * rows)
submod.weight = nn.Parameter(DTensor.from_local(w[sl, :].contiguous().to(device), tp_mesh, [Shard(0)]))
if b is not None:
submod.bias = nn.Parameter(DTensor.from_local(b[sl].contiguous().to(device), tp_mesh, [Shard(0)]))
elif orig_style == "rowwise":
if w.shape[1] % tp_size != 0:
raise ValueError(
f"Cannot rowwise-shard '{path}' weight columns ({w.shape[1]}) across {tp_size} "
f"tensor-parallel ranks: not divisible by {tp_size}."
)
cols = w.shape[1] // tp_size
shard = w[:, rank * cols : (rank + 1) * cols].contiguous().to(device)
submod.weight = nn.Parameter(DTensor.from_local(shard, tp_mesh, [Shard(1)]))
if b is not None: # rowwise bias is added post-reduction → keep it replicated
submod.bias = nn.Parameter(DTensor.from_local(b.to(device), tp_mesh, [Replicate()]))
# parallelize_module is now a no-op for weight distribution (already DTensors)
# but still registers the input/output hooks required for the forward pass.
parallelize_module(module, tp_mesh, _neuron_styles(original_plan))
def _apply_tp_neuron(
model: nn.Module,
tp_mesh: "torch.distributed.device_mesh.DeviceMesh",
groups: list,
) -> None:
"""Apply tensor parallelism on Neuron from resolved `_tp_plan` groups.
`groups` is produced by `diffusers.hooks.tensor_parallel._resolve_tp_plan` — the same source of truth used by the
generic path, so the two backends shard identical layers. For each `(block, relative_plan)` group this pre-shards
the weights via `DTensor.from_local` (Neuron NRT consecutive-reduce-scatter workaround), then calls
`parallelize_module` to register the forward hooks.
Model weights must be on CPU when this is called.
"""
rank = dist.get_rank()
tp_size = tp_mesh.size()
for block, relative_plan in groups:
_pre_shard_and_tp(block, tp_mesh, relative_plan, rank, tp_size)
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