两张 7900 跑 MiniMax-H3 张量并行：每步 18.6 s → 8.3 s，每卡省 10 GiB
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巧了，我也在青岛，测试时候的机器也是远程。
libr4d 就是 gfx1201 专用的， gfx1100 根本没有 FP8 矩阵单元，FP8这条是死路，RDNA3转 int8 是唯一正解。
attention 这块刚巧看到这篇（https://lcz.me/post/28547 ），里面提到 nicejunjie/Strata-rdna3 这个项目带了一套完整的 gfx1100 INT8 WMMA 注意力，可以作为参考，但要是想视频用上得整体评估移植，得喂大肥鱼不少白饭。
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T terry 固定了此主题
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，T terry 引用了 此主题
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，T terry 引用了 此主题
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X xiongjie gao 引用了 此主题
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@懒人烘培
说实话优化空间依然很大，我就是抛砖引玉，后面劳烦大家群策群力TP2 代码全文（tp2-comfy v1.0.0）
直接可跑的最小实现；完整说明/排查清单见
README.md，包见tp2-comfy-v1.0.0.zip。依赖：ComfyUI + ComfyUI-INT8-Fast-ROCM（int8_convrot）+ comfy-kitchen（CK attention）；
模型需为minimax_h3_*_pruned_int8_convrot（qkv 行数 21504、56 头 × 128、ffn 14336）。tp/core.py —— 权重重切 + 块级 all_reduce
"""TP2 共用：int8 权重切分 + 块级 all_reduce 补丁。 所属：tp2-comfy v1.0.0（双卡张量并行，ComfyUI + MiniMax-H3 int8_convrot） 环境变量： COMFY_ROOT ComfyUI 根目录（默认 /home/ly/ComfyUI-ROCm） TP_UNET 仅 build_model() 独立探针用；生产路径从工作流里读模型 """ import os import sys import importlib import torch import torch.distributed as dist APP_VERSION = "1.0.0" TP_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, TP_DIR) import tp_linear ROC = os.environ.get("COMFY_ROOT", "/home/ly/ComfyUI-ROCm") UNET = os.environ.get( "TP_UNET", "/home/ly/ComfyUI Tools/comfyui/models/diffusion_models/" "minimax_h3_fl2va_pruned_int8_convrot.safetensors") def build_model(): """独立探针：不起 ComfyUI，直接加载模型看结构（可选调试入口）。""" os.chdir(ROC) sys.path.insert(0, ROC) sys.path.insert(0, os.path.join(ROC, "custom_nodes")) import comfy.sd import comfy.model_management as mm q = importlib.import_module("ComfyUI-INT8-Fast-ROCM.int8_quant") mp = comfy.sd.load_diffusion_model(UNET, model_options={"custom_operations": q.Int8TensorwiseOps}) mm.load_models_gpu([mp]) return mp.model.diffusion_model def _slice_rows(lin, idx): """沿输出轴（行）取分片；tensorwise scale 不变，per-row scale 同步切。""" w = lin.weight.data lin.weight.data = w[idx].contiguous() lin.out_features = lin.weight.data.shape[0] sc = getattr(lin, "weight_scale", None) if sc is not None and torch.is_tensor(sc) and sc.numel() == w.shape[0]: lin.weight_scale = sc[idx].contiguous() return lin def _slice_cols(lin, idx): w = lin.weight.data lin.weight.data = w[:, idx].contiguous() lin.in_features = lin.weight.data.shape[1] return lin def apply_tp(net, rank, world=2, which="both", slice_weights=True): """按 head 切 attention、按 ffn 切 MLP；返回每层的切分信息供反向检查。 只支持 world=2：heads=56 → 每卡 28；ffn=14336 → 每卡 7168。 """ assert world == 2, "本实现只支持 2 卡（半宽切分）" info = [] for bi, blk in enumerate(net.blocks): attn, mlp = blk.attn, blk.mlp heads, hd = attn.heads, attn.head_dim inner = heads * hd half = heads // world lo, hi = rank * half, (rank + 1) * half hsel = torch.arange(lo * hd, hi * hd) if which in ("both", "attn", "ref"): # qkv：q/k/v 三段各取本 rank 的 head 行 idx = torch.cat([hsel, inner + hsel, 2 * inner + hsel]) attn.heads = half if which != "ref" and slice_weights: _slice_rows(attn.qkv_proj, idx) _slice_cols(attn.out_proj, hsel) tp_linear.install_row_parallel(attn.out_proj) # 行并行：全局行 scale + 由调用方 all_reduce else: attn.heads = heads # MLP：fc1 输出是 [gate(ffn) | up(ffn)]，两半各取 ffn/2 ffn = mlp.fc1.out_features // 2 fh = ffn // world f_lo, f_hi = rank * fh, (rank + 1) * fh if which in ("both", "mlp", "ref"): gidx = torch.cat([torch.arange(f_lo, f_hi), torch.arange(ffn + f_lo, ffn + f_hi)]) if which != "ref" and slice_weights: _slice_rows(mlp.fc1, gidx) _slice_cols(mlp.fc2, torch.arange(f_lo, f_hi)) tp_linear.install_row_parallel(mlp.fc2) # 行并行 info.append((bi, half, (f_lo, f_hi))) return info def patch_blocks(net, which="both"): """在 attn/mlp 输出后插入 all_reduce（Megatron 行并行语义：部分和相加）。""" import comfy.ldm.minimax.model as M def block_forward(self, x, t_emb, mod_segments, rope_freqs, transformer_options={}, attention=None): attention = self.attn if attention is None else attention shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaln_proj(t_emb) h = M._mod_scale_shift(self.norm1(x), shift_msa, scale_msa, mod_segments) a = attention(h, rope_freqs=rope_freqs, transformer_options=transformer_options) if which in ("both", "attn"): dist.all_reduce(a) if a.dtype != x.dtype: a = a.to(x.dtype) x = M._mod_gate(x, gate_msa, a, mod_segments) h = M._mod_scale_shift(self.norm2(x), shift_mlp, scale_mlp, mod_segments) m = self.mlp(h) if which in ("both", "mlp"): dist.all_reduce(m) if m.dtype != x.dtype: m = m.to(x.dtype) return M._mod_gate(x, gate_mlp, m, mod_segments) for b in net.blocks: b.forward = block_forward.__get__(b, type(b)) def _head_rows(attn_heads, head_dim, inner, rank, world=2): half = attn_heads // world lo, hi = rank * half, (rank + 1) * half hsel = torch.arange(lo * head_dim, hi * head_dim) return hsel, torch.cat([hsel, inner + hsel, 2 * inner + hsel]), half def slice_state_dict(sd, rank, world=2): """在模型构造前从 state_dict 切分，避免"先装全量再切"的显存峰值。 H3 配置固定：hidden=5376、56 头 × head_dim 128 → inner=7168、qkv 行数 21504。 形状守卫：qkv 行数不是 21504 就认为已切过（PDD 节点会二次加载，不能切两遍）。 """ HIDDEN, QKV_ROWS, HEAD_DIM, HEADS = 5376, 21504, 128, 56 k0 = "blocks.0.attn.qkv_proj.weight" if k0 not in sd or sd[k0].shape != (QKV_ROWS, HIDDEN): return sd inner = HEADS * HEAD_DIM half = HEADS // world hsel = torch.arange(rank * half * HEAD_DIM, (rank + 1) * half * HEAD_DIM) qkv_idx = torch.cat([hsel, inner + hsel, 2 * inner + hsel]) n = 0 for k in list(sd.keys()): t = sd[k] if k.endswith(".attn.qkv_proj.weight") and t.shape[0] == QKV_ROWS: sd[k] = t[qkv_idx].contiguous(); n += 1 elif k.endswith(".attn.qkv_proj.weight_scale") and t.shape[0] == QKV_ROWS: sd[k] = t[qkv_idx].contiguous(); n += 1 elif k.endswith(".attn.out_proj.weight") and t.shape[1] == inner: sd[k] = t[:, hsel].contiguous(); n += 1 elif k.endswith(".mlp.fc1.weight") and t.shape[1] == HIDDEN: ffn = t.shape[0] // 2 fh = ffn // world gidx = torch.cat([torch.arange(rank * fh, (rank + 1) * fh), torch.arange(ffn + rank * fh, ffn + (rank + 1) * fh)]) sd[k] = t[gidx].contiguous(); n += 1 ks = k[:-len("weight")] + "weight_scale" if ks in sd and sd[ks].shape[0] == 2 * ffn: sd[ks] = sd[ks][gidx].contiguous(); n += 1 elif k.endswith(".mlp.fc2.weight") and t.shape[0] == HIDDEN: ffn = t.shape[1] fh = ffn // world sd[k] = t[:, rank * fh:(rank + 1) * fh].contiguous(); n += 1 print(f"[tp] state_dict 已切分 {n} 个张量（rank{rank}）", flush=True) return sd def finalize_tp(net, rank): """模型已按半宽构造（state_dict 预切）时调用：只装行并行前向、不切权重。""" n = 0 for blk in net.blocks: tp_linear.install_row_parallel(blk.attn.out_proj) tp_linear.install_row_parallel(blk.mlp.fc2) n += 1 b0 = net.blocks[0] print(f"[tp] rank{rank} 半宽模型已确认：heads={b0.attn.heads} qkv_out={b0.attn.qkv_proj.weight.shape[0]} " f"fc1_out={b0.mlp.fc1.out_features} 行并行层={n}", flush=True) return ntp/tp_boot.py —— RCCL 初始化 + 加载钩子
"""TP2 启动引导：在 ComfyUI 外面套一层，挂 RCCL + 模型加载钩子。 教训（照抄会炸）：不能在 ComfyUI 之前 import torch / comfy.sd —— 会破坏 AIMDO 初始化 （报 'NoneType' object has no attribute 'hostbuf_allocate'）。 做法：只提前打开参数解析（comfy.options 很轻），其余工作交给后台线程， 等 main.py 自己把 comfy.sd 导入完之后再 init RCCL 并挂载模型加载钩子。 用法（每个 rank 一个进程，各只暴露一张卡）： TP_RANK=1 ROCR_VISIBLE_DEVICES=<GPU1> python tp_boot.py --port 8191 ... TP_RANK=0 ROCR_VISIBLE_DEVICES=<GPU0> python tp_boot.py --port 8190 ... 环境变量： COMFY_ROOT ComfyUI 根目录（默认 /home/ly/ComfyUI-ROCm） TP_RANK 0 / 1 MASTER_ADDR RCCL 地址（默认 127.0.0.1） MASTER_PORT RCCL 端口（默认 29670） """ import os import sys import threading import time APP_VERSION = "1.0.0" TP_DIR = os.path.dirname(os.path.abspath(__file__)) ROC = os.environ.get("COMFY_ROOT", "/home/ly/ComfyUI-ROCm") sys.path.insert(0, ROC) sys.path.insert(0, TP_DIR) import comfy.options as _co # 只定义开关，不拉 torch/cli_args _co.enable_args_parsing() rank = int(os.environ["TP_RANK"]) os.environ.setdefault("MASTER_ADDR", "127.0.0.1") os.environ.setdefault("MASTER_PORT", "29670") READY = threading.Event() def _on_model(mp): # 注意：PDD 加速节点会重建一次模型（adaln rebase），每次加载都要切，不能只切一次 if mp is None: return mp try: net = getattr(mp.model, "diffusion_model", None) if net is None or not hasattr(net, "blocks"): return mp except Exception: return mp if getattr(net, "_tp_done", False): return mp import core try: rows = int(net.blocks[0].attn.qkv_proj.weight.shape[0]) except Exception: rows = 0 if rows == 10752: # 已经是半宽模型（预切路径） core.finalize_tp(net, rank) elif rows == 21504: # 全宽模型：现场切 core.apply_tp(net, rank) else: raise RuntimeError(f"未预期的 qkv 行数 {rows}（期望 21504 或 10752）") core.patch_blocks(net) net._tp_done = True b0 = net.blocks[0] print(f"[tp] rank{rank} 已切分 DiT：heads={b0.attn.heads} " f"qkv_out={b0.attn.qkv_proj.weight.shape[0]} out_proj_in={b0.attn.out_proj.weight.shape[1]} " f"fc1_out={b0.mlp.fc1.out_features} fc2_in={b0.mlp.fc2.weight.shape[1]}", flush=True) return mp def _boot(): # 等 main.py 把 comfy.sd 导入完，保证 torch 的导入顺序由 ComfyUI 自己决定 for _ in range(12000): if "comfy.sd" in sys.modules: break time.sleep(0.05) import torch import torch.distributed as dist # 每个进程只暴露自己那张卡（ROCR_VISIBLE_DEVICES 单卡）→ 本地 index 恒为 0， # 这样 ComfyUI 主线程怎么设 current device 都不会和别的 rank 撞同一张卡。 torch.cuda.set_device(0) dist.init_process_group("nccl", rank=rank, world_size=2) bus = getattr(torch.cuda.get_device_properties(0), "pci_bus_id", "?") print(f"[tp] rank{rank} 已加入 RCCL，device={torch.cuda.current_device()} bus={bus}", flush=True) READY.set() import comfy.sd as sd orig = sd.load_diffusion_model_state_dict def wrapped(sd=None, *a, **k): READY.wait() import core presliced = False if isinstance(sd, dict): n0 = sd.get("blocks.0.attn.qkv_proj.weight") if n0 is not None and tuple(n0.shape) == (21504, 5376): sd = core.slice_state_dict(sd, rank) # 预切：模型按半宽构造 presliced = True try: import comfy.model_management as _mm _mm.unload_all_models() # 先腾出 26G 文本编码器 import torch as _t _t.cuda.empty_cache() except Exception as e: print(f"[tp] 预卸载失败: {e}", flush=True) mp = orig(sd, *a, **k) if presliced and mp is not None: net = getattr(getattr(mp, "model", None), "diffusion_model", None) if net is not None: net._tp_presliced = True return _on_model(mp) sd.load_diffusion_model_state_dict = wrapped print(f"[tp] rank{rank} 已挂载模型加载钩子", flush=True) print(f"[tp] tp2-comfy v{APP_VERSION} rank{rank} 启动（COMFY_ROOT={ROC}）", flush=True) threading.Thread(target=_boot, daemon=True).start() import runpy runpy.run_path(os.path.join(ROC, "main.py"), run_name="__main__")tp/tp_linear.py —— int8 行并行：共享激活 scale
"""TP 行并行层：只替换激活量化 scale，其余完全走 ComfyUI-INT8 原生路径。 背景：int8 权重按输出行切好（列并行）后，行并行（K 轴切半）的问题在于 原生 per-row 动态量化会给每半各算一个 scale，量化点不同 → 每层 ~1% 误差。 做法：把各行 |x| 最大值跨卡 all_reduce(MAX)，得到整行原本会用的 scale， 注入 triton_quantize_rowwise；其余（int8 GEMM、反量化、convrot、LoRA、 cast_weight 分支）全部走原生，不做任何重写。 """ import torch import torch.distributed as dist _SHARED = {"scale": None} _PATCHED = {"ok": False} def _patch_once(): if _PATCHED["ok"]: return import importlib fk = importlib.import_module("ComfyUI-INT8-Fast-ROCM.int8_fused_kernel") orig = fk.triton_quantize_rowwise def qr(x): y, s = orig(x) sh = _SHARED["scale"] if sh is None: return y, s xf = x.float() y = torch.floor(xf / sh + 0.5).clamp_(-128, 127).to(torch.int8) return y, sh fk.triton_quantize_rowwise = qr _PATCHED["ok"] = True def install_row_parallel(lin): """权重需已按列（K 轴）切好；安装带全局 scale 的原生前向。""" _patch_once() if getattr(lin, "_tp_row_installed", False): return orig_forward = lin.__class__.forward def fwd(x): # 注意：只在"算 scale"时旋转一份副本，传进原生 forward 的仍是未旋转的 x， # 否则原生路径会再旋转一次（H 不是幂等）。 x2d = x.reshape(-1, x.shape[-1]) xr = x2d if getattr(lin, "_use_convrot", False): import importlib cr = importlib.import_module("ComfyUI-INT8-Fast-ROCM.convrot") gs = getattr(lin, "_convrot_groupsize", 256) if x2d.shape[1] % gs == 0: H = cr.build_hadamard(gs, device=x2d.device, dtype=x2d.dtype) xr = cr.rotate_activation(x2d, H, group_size=gs) forced = getattr(lin, "_tp_force_scale", None) if forced is not None: s = forced else: s = (xr.float().abs().amax(dim=1, keepdim=True) / 127.0).clamp_min(1e-30) if dist.is_initialized(): dist.all_reduce(s, op=dist.ReduceOp.MAX) s = s.clamp_min(1e-30) # TP_WIRE=bf16（默认，对齐 Radiance 的 exact 线）| fp32（更精确但通信翻倍） import importlib import os ops = importlib.import_module("ComfyUI-INT8-Fast-ROCM.int8_quant").Int8TensorwiseOps prev = ops.compute_dtype if os.environ.get("TP_WIRE", "bf16") == "bf16": out_dtype_keep = prev _SHARED["scale"] = s if os.environ.get("TP_WIRE", "bf16") != "bf16": ops.compute_dtype = torch.float32 try: out = orig_forward(lin, x) finally: _SHARED["scale"] = None ops.compute_dtype = prev return out lin.forward = fwd lin._tp_row_installed = Truestart_tp2.sh —— 启动器（默认 CK）
#!/usr/bin/env bash # start_tp2.sh —— 起两个 ComfyUI 实例做 H3 张量并行（rank0 / rank1），各只看见一张卡。 # # 必须带 --use-ck-attention：PyTorch SDPA 在长序列上会崩速（见 README「四个必须」）。 # # 可用环境变量覆盖： # COMFY_ROOT ComfyUI 根目录 （默认 /home/ly/ComfyUI-ROCm） # GPU0/GPU1 两张卡的 unique_id （rocm-smi --showuniqueid 查） # PORT0/PORT1 两个实例的 HTTP 端口 （默认 8190 / 8191） # BASE0/BASE1 两个实例的 output/input 目录 # MASTER_PORT RCCL 会合端口 （默认 29670） # ATTN attention 后端参数 （默认 --use-ck-attention） set -euo pipefail COMFY_ROOT=${COMFY_ROOT:-/home/ly/ComfyUI-ROCm} TP_DIR=$(cd "$(dirname "$0")" && pwd)/tp GPU0=${GPU0:-GPU-3c99737f13093cd0} GPU1=${GPU1:-GPU-3ce42265b99a6c53} PORT0=${PORT0:-8190} PORT1=${PORT1:-8191} BASE0=${BASE0:-/home/ly/ComfyUI Tools/comfyui/w7900} BASE1=${BASE1:-/home/ly/ComfyUI Tools/comfyui/w7900-4a} MASTER_PORT=${MASTER_PORT:-29670} ATTN=${ATTN:---use-ck-attention} LOGD=${LOGD:-$COMFY_ROOT/logs} mkdir -p "$BASE0"/{output,input,temp} "$BASE1"/{output,input,temp} "$LOGD" export TRITON_DEFAULT_BACKEND=amd COMFYUI_ENABLE_MIOPEN=1 MIOPEN_FIND_MODE=FAST export MALLOC_MMAP_THRESHOLD_=65536 MALLOC_TRIM_THRESHOLD_=65536 export TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 SAFETENSORS_FAST_GPU=1 PYTHONUTF8=1 export PYTORCH_TUNABLEOP_ENABLED=0 export MASTER_ADDR=127.0.0.1 MASTER_PORT export COMFY_ROOT # rank1（第二张卡）先起：它会 block 在 init_process_group 等 rank0 TP_RANK=1 ROCR_VISIBLE_DEVICES="$GPU1" nohup "$COMFY_ROOT/venv/bin/python" "$TP_DIR/tp_boot.py" \ --listen 127.0.0.1 --port "$PORT1" \ --output-directory "$BASE1/output" --input-directory "$BASE1/input" \ $ATTN --disable-smart-memory --disable-pinned-memory --disable-async-offload --reserve-vram 2 \ >> "$LOGD/tp_rank1_$(date +%Y%m%d).log" 2>&1 & echo "rank1 pid=$! port=$PORT1 gpu=$GPU1" sleep 3 TP_RANK=0 ROCR_VISIBLE_DEVICES="$GPU0" nohup "$COMFY_ROOT/venv/bin/python" "$TP_DIR/tp_boot.py" \ --listen 127.0.0.1 --port "$PORT0" \ --output-directory "$BASE0/output" --input-directory "$BASE0/input" \ $ATTN --disable-smart-memory --disable-pinned-memory --disable-async-offload --reserve-vram 2 \ >> "$LOGD/tp_rank0_$(date +%Y%m%d).log" 2>&1 & echo "rank0 pid=$! port=$PORT0 gpu=$GPU0" echo "已拉起 TP2：$PORT0(rank0) + $PORT1(rank1)，日志 $LOGD/tp_rank{0,1}_$(date +%Y%m%d).log" -
@懒人烘培
说实话优化空间依然很大，我就是抛砖引玉，后面劳烦大家群策群力TP2 代码全文（tp2-comfy v1.0.0）
直接可跑的最小实现；完整说明/排查清单见
README.md，包见tp2-comfy-v1.0.0.zip。依赖：ComfyUI + ComfyUI-INT8-Fast-ROCM（int8_convrot）+ comfy-kitchen（CK attention）；
模型需为minimax_h3_*_pruned_int8_convrot（qkv 行数 21504、56 头 × 128、ffn 14336）。tp/core.py —— 权重重切 + 块级 all_reduce
"""TP2 共用：int8 权重切分 + 块级 all_reduce 补丁。 所属：tp2-comfy v1.0.0（双卡张量并行，ComfyUI + MiniMax-H3 int8_convrot） 环境变量： COMFY_ROOT ComfyUI 根目录（默认 /home/ly/ComfyUI-ROCm） TP_UNET 仅 build_model() 独立探针用；生产路径从工作流里读模型 """ import os import sys import importlib import torch import torch.distributed as dist APP_VERSION = "1.0.0" TP_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, TP_DIR) import tp_linear ROC = os.environ.get("COMFY_ROOT", "/home/ly/ComfyUI-ROCm") UNET = os.environ.get( "TP_UNET", "/home/ly/ComfyUI Tools/comfyui/models/diffusion_models/" "minimax_h3_fl2va_pruned_int8_convrot.safetensors") def build_model(): """独立探针：不起 ComfyUI，直接加载模型看结构（可选调试入口）。""" os.chdir(ROC) sys.path.insert(0, ROC) sys.path.insert(0, os.path.join(ROC, "custom_nodes")) import comfy.sd import comfy.model_management as mm q = importlib.import_module("ComfyUI-INT8-Fast-ROCM.int8_quant") mp = comfy.sd.load_diffusion_model(UNET, model_options={"custom_operations": q.Int8TensorwiseOps}) mm.load_models_gpu([mp]) return mp.model.diffusion_model def _slice_rows(lin, idx): """沿输出轴（行）取分片；tensorwise scale 不变，per-row scale 同步切。""" w = lin.weight.data lin.weight.data = w[idx].contiguous() lin.out_features = lin.weight.data.shape[0] sc = getattr(lin, "weight_scale", None) if sc is not None and torch.is_tensor(sc) and sc.numel() == w.shape[0]: lin.weight_scale = sc[idx].contiguous() return lin def _slice_cols(lin, idx): w = lin.weight.data lin.weight.data = w[:, idx].contiguous() lin.in_features = lin.weight.data.shape[1] return lin def apply_tp(net, rank, world=2, which="both", slice_weights=True): """按 head 切 attention、按 ffn 切 MLP；返回每层的切分信息供反向检查。 只支持 world=2：heads=56 → 每卡 28；ffn=14336 → 每卡 7168。 """ assert world == 2, "本实现只支持 2 卡（半宽切分）" info = [] for bi, blk in enumerate(net.blocks): attn, mlp = blk.attn, blk.mlp heads, hd = attn.heads, attn.head_dim inner = heads * hd half = heads // world lo, hi = rank * half, (rank + 1) * half hsel = torch.arange(lo * hd, hi * hd) if which in ("both", "attn", "ref"): # qkv：q/k/v 三段各取本 rank 的 head 行 idx = torch.cat([hsel, inner + hsel, 2 * inner + hsel]) attn.heads = half if which != "ref" and slice_weights: _slice_rows(attn.qkv_proj, idx) _slice_cols(attn.out_proj, hsel) tp_linear.install_row_parallel(attn.out_proj) # 行并行：全局行 scale + 由调用方 all_reduce else: attn.heads = heads # MLP：fc1 输出是 [gate(ffn) | up(ffn)]，两半各取 ffn/2 ffn = mlp.fc1.out_features // 2 fh = ffn // world f_lo, f_hi = rank * fh, (rank + 1) * fh if which in ("both", "mlp", "ref"): gidx = torch.cat([torch.arange(f_lo, f_hi), torch.arange(ffn + f_lo, ffn + f_hi)]) if which != "ref" and slice_weights: _slice_rows(mlp.fc1, gidx) _slice_cols(mlp.fc2, torch.arange(f_lo, f_hi)) tp_linear.install_row_parallel(mlp.fc2) # 行并行 info.append((bi, half, (f_lo, f_hi))) return info def patch_blocks(net, which="both"): """在 attn/mlp 输出后插入 all_reduce（Megatron 行并行语义：部分和相加）。""" import comfy.ldm.minimax.model as M def block_forward(self, x, t_emb, mod_segments, rope_freqs, transformer_options={}, attention=None): attention = self.attn if attention is None else attention shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaln_proj(t_emb) h = M._mod_scale_shift(self.norm1(x), shift_msa, scale_msa, mod_segments) a = attention(h, rope_freqs=rope_freqs, transformer_options=transformer_options) if which in ("both", "attn"): dist.all_reduce(a) if a.dtype != x.dtype: a = a.to(x.dtype) x = M._mod_gate(x, gate_msa, a, mod_segments) h = M._mod_scale_shift(self.norm2(x), shift_mlp, scale_mlp, mod_segments) m = self.mlp(h) if which in ("both", "mlp"): dist.all_reduce(m) if m.dtype != x.dtype: m = m.to(x.dtype) return M._mod_gate(x, gate_mlp, m, mod_segments) for b in net.blocks: b.forward = block_forward.__get__(b, type(b)) def _head_rows(attn_heads, head_dim, inner, rank, world=2): half = attn_heads // world lo, hi = rank * half, (rank + 1) * half hsel = torch.arange(lo * head_dim, hi * head_dim) return hsel, torch.cat([hsel, inner + hsel, 2 * inner + hsel]), half def slice_state_dict(sd, rank, world=2): """在模型构造前从 state_dict 切分，避免"先装全量再切"的显存峰值。 H3 配置固定：hidden=5376、56 头 × head_dim 128 → inner=7168、qkv 行数 21504。 形状守卫：qkv 行数不是 21504 就认为已切过（PDD 节点会二次加载，不能切两遍）。 """ HIDDEN, QKV_ROWS, HEAD_DIM, HEADS = 5376, 21504, 128, 56 k0 = "blocks.0.attn.qkv_proj.weight" if k0 not in sd or sd[k0].shape != (QKV_ROWS, HIDDEN): return sd inner = HEADS * HEAD_DIM half = HEADS // world hsel = torch.arange(rank * half * HEAD_DIM, (rank + 1) * half * HEAD_DIM) qkv_idx = torch.cat([hsel, inner + hsel, 2 * inner + hsel]) n = 0 for k in list(sd.keys()): t = sd[k] if k.endswith(".attn.qkv_proj.weight") and t.shape[0] == QKV_ROWS: sd[k] = t[qkv_idx].contiguous(); n += 1 elif k.endswith(".attn.qkv_proj.weight_scale") and t.shape[0] == QKV_ROWS: sd[k] = t[qkv_idx].contiguous(); n += 1 elif k.endswith(".attn.out_proj.weight") and t.shape[1] == inner: sd[k] = t[:, hsel].contiguous(); n += 1 elif k.endswith(".mlp.fc1.weight") and t.shape[1] == HIDDEN: ffn = t.shape[0] // 2 fh = ffn // world gidx = torch.cat([torch.arange(rank * fh, (rank + 1) * fh), torch.arange(ffn + rank * fh, ffn + (rank + 1) * fh)]) sd[k] = t[gidx].contiguous(); n += 1 ks = k[:-len("weight")] + "weight_scale" if ks in sd and sd[ks].shape[0] == 2 * ffn: sd[ks] = sd[ks][gidx].contiguous(); n += 1 elif k.endswith(".mlp.fc2.weight") and t.shape[0] == HIDDEN: ffn = t.shape[1] fh = ffn // world sd[k] = t[:, rank * fh:(rank + 1) * fh].contiguous(); n += 1 print(f"[tp] state_dict 已切分 {n} 个张量（rank{rank}）", flush=True) return sd def finalize_tp(net, rank): """模型已按半宽构造（state_dict 预切）时调用：只装行并行前向、不切权重。""" n = 0 for blk in net.blocks: tp_linear.install_row_parallel(blk.attn.out_proj) tp_linear.install_row_parallel(blk.mlp.fc2) n += 1 b0 = net.blocks[0] print(f"[tp] rank{rank} 半宽模型已确认：heads={b0.attn.heads} qkv_out={b0.attn.qkv_proj.weight.shape[0]} " f"fc1_out={b0.mlp.fc1.out_features} 行并行层={n}", flush=True) return ntp/tp_boot.py —— RCCL 初始化 + 加载钩子
"""TP2 启动引导：在 ComfyUI 外面套一层，挂 RCCL + 模型加载钩子。 教训（照抄会炸）：不能在 ComfyUI 之前 import torch / comfy.sd —— 会破坏 AIMDO 初始化 （报 'NoneType' object has no attribute 'hostbuf_allocate'）。 做法：只提前打开参数解析（comfy.options 很轻），其余工作交给后台线程， 等 main.py 自己把 comfy.sd 导入完之后再 init RCCL 并挂载模型加载钩子。 用法（每个 rank 一个进程，各只暴露一张卡）： TP_RANK=1 ROCR_VISIBLE_DEVICES=<GPU1> python tp_boot.py --port 8191 ... TP_RANK=0 ROCR_VISIBLE_DEVICES=<GPU0> python tp_boot.py --port 8190 ... 环境变量： COMFY_ROOT ComfyUI 根目录（默认 /home/ly/ComfyUI-ROCm） TP_RANK 0 / 1 MASTER_ADDR RCCL 地址（默认 127.0.0.1） MASTER_PORT RCCL 端口（默认 29670） """ import os import sys import threading import time APP_VERSION = "1.0.0" TP_DIR = os.path.dirname(os.path.abspath(__file__)) ROC = os.environ.get("COMFY_ROOT", "/home/ly/ComfyUI-ROCm") sys.path.insert(0, ROC) sys.path.insert(0, TP_DIR) import comfy.options as _co # 只定义开关，不拉 torch/cli_args _co.enable_args_parsing() rank = int(os.environ["TP_RANK"]) os.environ.setdefault("MASTER_ADDR", "127.0.0.1") os.environ.setdefault("MASTER_PORT", "29670") READY = threading.Event() def _on_model(mp): # 注意：PDD 加速节点会重建一次模型（adaln rebase），每次加载都要切，不能只切一次 if mp is None: return mp try: net = getattr(mp.model, "diffusion_model", None) if net is None or not hasattr(net, "blocks"): return mp except Exception: return mp if getattr(net, "_tp_done", False): return mp import core try: rows = int(net.blocks[0].attn.qkv_proj.weight.shape[0]) except Exception: rows = 0 if rows == 10752: # 已经是半宽模型（预切路径） core.finalize_tp(net, rank) elif rows == 21504: # 全宽模型：现场切 core.apply_tp(net, rank) else: raise RuntimeError(f"未预期的 qkv 行数 {rows}（期望 21504 或 10752）") core.patch_blocks(net) net._tp_done = True b0 = net.blocks[0] print(f"[tp] rank{rank} 已切分 DiT：heads={b0.attn.heads} " f"qkv_out={b0.attn.qkv_proj.weight.shape[0]} out_proj_in={b0.attn.out_proj.weight.shape[1]} " f"fc1_out={b0.mlp.fc1.out_features} fc2_in={b0.mlp.fc2.weight.shape[1]}", flush=True) return mp def _boot(): # 等 main.py 把 comfy.sd 导入完，保证 torch 的导入顺序由 ComfyUI 自己决定 for _ in range(12000): if "comfy.sd" in sys.modules: break time.sleep(0.05) import torch import torch.distributed as dist # 每个进程只暴露自己那张卡（ROCR_VISIBLE_DEVICES 单卡）→ 本地 index 恒为 0， # 这样 ComfyUI 主线程怎么设 current device 都不会和别的 rank 撞同一张卡。 torch.cuda.set_device(0) dist.init_process_group("nccl", rank=rank, world_size=2) bus = getattr(torch.cuda.get_device_properties(0), "pci_bus_id", "?") print(f"[tp] rank{rank} 已加入 RCCL，device={torch.cuda.current_device()} bus={bus}", flush=True) READY.set() import comfy.sd as sd orig = sd.load_diffusion_model_state_dict def wrapped(sd=None, *a, **k): READY.wait() import core presliced = False if isinstance(sd, dict): n0 = sd.get("blocks.0.attn.qkv_proj.weight") if n0 is not None and tuple(n0.shape) == (21504, 5376): sd = core.slice_state_dict(sd, rank) # 预切：模型按半宽构造 presliced = True try: import comfy.model_management as _mm _mm.unload_all_models() # 先腾出 26G 文本编码器 import torch as _t _t.cuda.empty_cache() except Exception as e: print(f"[tp] 预卸载失败: {e}", flush=True) mp = orig(sd, *a, **k) if presliced and mp is not None: net = getattr(getattr(mp, "model", None), "diffusion_model", None) if net is not None: net._tp_presliced = True return _on_model(mp) sd.load_diffusion_model_state_dict = wrapped print(f"[tp] rank{rank} 已挂载模型加载钩子", flush=True) print(f"[tp] tp2-comfy v{APP_VERSION} rank{rank} 启动（COMFY_ROOT={ROC}）", flush=True) threading.Thread(target=_boot, daemon=True).start() import runpy runpy.run_path(os.path.join(ROC, "main.py"), run_name="__main__")tp/tp_linear.py —— int8 行并行：共享激活 scale
"""TP 行并行层：只替换激活量化 scale，其余完全走 ComfyUI-INT8 原生路径。 背景：int8 权重按输出行切好（列并行）后，行并行（K 轴切半）的问题在于 原生 per-row 动态量化会给每半各算一个 scale，量化点不同 → 每层 ~1% 误差。 做法：把各行 |x| 最大值跨卡 all_reduce(MAX)，得到整行原本会用的 scale， 注入 triton_quantize_rowwise；其余（int8 GEMM、反量化、convrot、LoRA、 cast_weight 分支）全部走原生，不做任何重写。 """ import torch import torch.distributed as dist _SHARED = {"scale": None} _PATCHED = {"ok": False} def _patch_once(): if _PATCHED["ok"]: return import importlib fk = importlib.import_module("ComfyUI-INT8-Fast-ROCM.int8_fused_kernel") orig = fk.triton_quantize_rowwise def qr(x): y, s = orig(x) sh = _SHARED["scale"] if sh is None: return y, s xf = x.float() y = torch.floor(xf / sh + 0.5).clamp_(-128, 127).to(torch.int8) return y, sh fk.triton_quantize_rowwise = qr _PATCHED["ok"] = True def install_row_parallel(lin): """权重需已按列（K 轴）切好；安装带全局 scale 的原生前向。""" _patch_once() if getattr(lin, "_tp_row_installed", False): return orig_forward = lin.__class__.forward def fwd(x): # 注意：只在"算 scale"时旋转一份副本，传进原生 forward 的仍是未旋转的 x， # 否则原生路径会再旋转一次（H 不是幂等）。 x2d = x.reshape(-1, x.shape[-1]) xr = x2d if getattr(lin, "_use_convrot", False): import importlib cr = importlib.import_module("ComfyUI-INT8-Fast-ROCM.convrot") gs = getattr(lin, "_convrot_groupsize", 256) if x2d.shape[1] % gs == 0: H = cr.build_hadamard(gs, device=x2d.device, dtype=x2d.dtype) xr = cr.rotate_activation(x2d, H, group_size=gs) forced = getattr(lin, "_tp_force_scale", None) if forced is not None: s = forced else: s = (xr.float().abs().amax(dim=1, keepdim=True) / 127.0).clamp_min(1e-30) if dist.is_initialized(): dist.all_reduce(s, op=dist.ReduceOp.MAX) s = s.clamp_min(1e-30) # TP_WIRE=bf16（默认，对齐 Radiance 的 exact 线）| fp32（更精确但通信翻倍） import importlib import os ops = importlib.import_module("ComfyUI-INT8-Fast-ROCM.int8_quant").Int8TensorwiseOps prev = ops.compute_dtype if os.environ.get("TP_WIRE", "bf16") == "bf16": out_dtype_keep = prev _SHARED["scale"] = s if os.environ.get("TP_WIRE", "bf16") != "bf16": ops.compute_dtype = torch.float32 try: out = orig_forward(lin, x) finally: _SHARED["scale"] = None ops.compute_dtype = prev return out lin.forward = fwd lin._tp_row_installed = Truestart_tp2.sh —— 启动器（默认 CK）
#!/usr/bin/env bash # start_tp2.sh —— 起两个 ComfyUI 实例做 H3 张量并行（rank0 / rank1），各只看见一张卡。 # # 必须带 --use-ck-attention：PyTorch SDPA 在长序列上会崩速（见 README「四个必须」）。 # # 可用环境变量覆盖： # COMFY_ROOT ComfyUI 根目录 （默认 /home/ly/ComfyUI-ROCm） # GPU0/GPU1 两张卡的 unique_id （rocm-smi --showuniqueid 查） # PORT0/PORT1 两个实例的 HTTP 端口 （默认 8190 / 8191） # BASE0/BASE1 两个实例的 output/input 目录 # MASTER_PORT RCCL 会合端口 （默认 29670） # ATTN attention 后端参数 （默认 --use-ck-attention） set -euo pipefail COMFY_ROOT=${COMFY_ROOT:-/home/ly/ComfyUI-ROCm} TP_DIR=$(cd "$(dirname "$0")" && pwd)/tp GPU0=${GPU0:-GPU-3c99737f13093cd0} GPU1=${GPU1:-GPU-3ce42265b99a6c53} PORT0=${PORT0:-8190} PORT1=${PORT1:-8191} BASE0=${BASE0:-/home/ly/ComfyUI Tools/comfyui/w7900} BASE1=${BASE1:-/home/ly/ComfyUI Tools/comfyui/w7900-4a} MASTER_PORT=${MASTER_PORT:-29670} ATTN=${ATTN:---use-ck-attention} LOGD=${LOGD:-$COMFY_ROOT/logs} mkdir -p "$BASE0"/{output,input,temp} "$BASE1"/{output,input,temp} "$LOGD" export TRITON_DEFAULT_BACKEND=amd COMFYUI_ENABLE_MIOPEN=1 MIOPEN_FIND_MODE=FAST export MALLOC_MMAP_THRESHOLD_=65536 MALLOC_TRIM_THRESHOLD_=65536 export TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 SAFETENSORS_FAST_GPU=1 PYTHONUTF8=1 export PYTORCH_TUNABLEOP_ENABLED=0 export MASTER_ADDR=127.0.0.1 MASTER_PORT export COMFY_ROOT # rank1（第二张卡）先起：它会 block 在 init_process_group 等 rank0 TP_RANK=1 ROCR_VISIBLE_DEVICES="$GPU1" nohup "$COMFY_ROOT/venv/bin/python" "$TP_DIR/tp_boot.py" \ --listen 127.0.0.1 --port "$PORT1" \ --output-directory "$BASE1/output" --input-directory "$BASE1/input" \ $ATTN --disable-smart-memory --disable-pinned-memory --disable-async-offload --reserve-vram 2 \ >> "$LOGD/tp_rank1_$(date +%Y%m%d).log" 2>&1 & echo "rank1 pid=$! port=$PORT1 gpu=$GPU1" sleep 3 TP_RANK=0 ROCR_VISIBLE_DEVICES="$GPU0" nohup "$COMFY_ROOT/venv/bin/python" "$TP_DIR/tp_boot.py" \ --listen 127.0.0.1 --port "$PORT0" \ --output-directory "$BASE0/output" --input-directory "$BASE0/input" \ $ATTN --disable-smart-memory --disable-pinned-memory --disable-async-offload --reserve-vram 2 \ >> "$LOGD/tp_rank0_$(date +%Y%m%d).log" 2>&1 & echo "rank0 pid=$! port=$PORT0 gpu=$GPU0" echo "已拉起 TP2：$PORT0(rank0) + $PORT1(rank1)，日志 $LOGD/tp_rank{0,1}_$(date +%Y%m%d).log" -
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，T terry 引用了 此主题
版主审定：本帖设为精华，作者 +6 积分奖励，希望再接再厉！
，只是给同核心的7900xtx做个参考。
本帖参与讨论人数已达 10+，入选热议，作者 +3 积分奖励！