FearL0rd
04907c683f
Feature (Pipeline Parallelism): implemented logic to handle batch_size=1 by splitting transformer blocks across GPUs. Introduced ParallelBlock wrapper to manage execution across devices. Data now flows in a pipeline sequence (e.g., GPU0 → GPU1 → ...) and automatically returns to the Lead Device for final processing. Maintains original Data Parallel (batch splitting) behavior for batch_size > 1. Fix (Device Mismatch): Resolved RuntimeError: Expected all tensors to be on the same device. Updated ParallelBlock to recursively identify and move all input arguments (tensors, nested lists/tuples, dicts, and dataclasses) to the target device, rather than just the first argument. Ensured auxiliary inputs (like timesteps and context) are correctly transferred alongside the main hidden states. Fix (Syntax): Fixed a SyntaxError in clone_dataclass_or_object where a try block was missing its corresponding except. Refactor: Improved tensor movement logic to be "deep" and recursive, preventing crashes when models use complex input structures.
1363 lines
59 KiB
Python
1363 lines
59 KiB
Python
import torch
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import torch.nn as nn
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import types
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import copy
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import gc
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import weakref
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import threading
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from types import SimpleNamespace
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from dataclasses import dataclass, fields, is_dataclass
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import comfy.model_management
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# ----------------------------------------------------------------------------
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# Thread-local state for pipeline mode
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# ----------------------------------------------------------------------------
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_pipeline_state = threading.local()
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def get_pipeline_mode():
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return getattr(_pipeline_state, 'active', False)
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def set_pipeline_mode(active):
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_pipeline_state.active = active
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class ParallelBlock(nn.Module):
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"""
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A wrapper that handles dual-mode execution:
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1. Data Parallel (Batch > 1): Runs the local block immediately.
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2. Pipeline Parallel (Batch = 1): Moves data to assigned device/replica.
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"""
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def __init__(self, local_block, block_idx, owner_device, peers, is_last_block, lead_device):
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super().__init__()
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self.local_block = local_block
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self.block_idx = block_idx
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self.owner_device = owner_device
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self.peers = peers
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self.is_last_block = is_last_block
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self.lead_device = lead_device
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self.owner_block = self.peers.get(str(owner_device), local_block)
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def _move_tensor(self, x, device):
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"""Recursively move tensors, lists, tuples, dicts, and dataclasses to target device."""
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if isinstance(x, torch.Tensor):
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if x.device != device:
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return x.to(device, non_blocking=True)
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return x
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elif isinstance(x, (list, tuple)):
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return type(x)(self._move_tensor(item, device) for item in x)
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elif isinstance(x, dict):
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return {k: self._move_tensor(v, device) for k, v in x.items()}
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elif is_dataclass(x):
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# Shallow copy dataclass and move its tensor fields
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new_obj = copy.copy(x)
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for field in fields(x):
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val = getattr(x, field.name)
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setattr(new_obj, field.name, self._move_tensor(val, device))
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return new_obj
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return x
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def _move_args(self, args, device):
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return tuple(self._move_tensor(a, device) for a in args)
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def _move_kwargs(self, kwargs, device):
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return {k: self._move_tensor(v, device) for k, v in kwargs.items()}
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def forward(self, *args, **kwargs):
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if not get_pipeline_mode():
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# Standard execution for Data Parallel (Batch > 1)
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return self.local_block(*args, **kwargs)
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target_device = self.owner_device
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# --- CRITICAL FIX ---
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# Do NOT check args[0].device to skip moving.
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# Main input might be on target_device (from prev block),
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# but auxiliary inputs (timesteps, context) might still be on Lead Device.
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# We must ensure ALL inputs are on target_device.
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args_moved = self._move_args(args, target_device)
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kwargs_moved = self._move_kwargs(kwargs, target_device)
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# Execute on target replica
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out = self.owner_block(*args_moved, **kwargs_moved)
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if self.is_last_block:
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# Return data to Lead Device for final layers/output processing
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out = self._move_tensor(out, self.lead_device)
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return out
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# ----------------------------------------------------------------------------
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# Utilities
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# ----------------------------------------------------------------------------
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def is_float8_dtype(dtype):
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if dtype is None:
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return False
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dtype_name = str(dtype)
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fp8_types = ['float8_e4m3fn', 'float8_e5m2', 'float8_e4m3fnuz', 'float8_e5m2fnuz', 'float8']
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return any(t in dtype_name for t in fp8_types)
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def check_sm80_support(device_name):
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if not device_name.startswith("cuda"):
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return True
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try:
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idx = int(device_name.split(":")[-1])
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capability = torch.cuda.get_device_capability(idx)
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major, _ = capability
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return major >= 8
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except Exception as e:
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print(f"[ParallelAnything] Warning: Could not check compute capability for {device_name}: {e}")
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return False
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def device_supports_float8(device):
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if isinstance(device, str):
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if not device.startswith("cuda"):
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return False
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device = torch.device(device)
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if device.type != 'cuda':
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return False
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try:
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cap = torch.cuda.get_device_capability(device)
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major, minor = cap
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return (major, minor) >= (9, 0)
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except:
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return False
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def disable_flash_xformers(model):
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disable_configs = [
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('set_use_memory_efficient_attention_xformers', False),
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('set_use_flash_attention_2', False),
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('disable_xformers_memory_efficient_attention', None),
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('use_xformers', False),
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('use_flash_attention', False),
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('use_flash_attention_2', False),
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('_use_memory_efficient_attention', False),
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('_flash_attention_enabled', False),
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]
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for attr_or_method, value in disable_configs:
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if hasattr(model, attr_or_method):
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try:
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method = getattr(model, attr_or_method)
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if callable(method) and value is None:
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method()
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elif callable(method):
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method(value)
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else:
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setattr(model, attr_or_method, value)
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except Exception:
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pass
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for name, module in model.named_modules():
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if any(x in name.lower() for x in ['attn', 'attention', 'transformer']):
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for attr in ['use_xformers', 'use_flash_attention', 'use_flash_attention_2',
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'_use_memory_efficient_attention', 'enable_flash', 'enable_xformers']:
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if hasattr(module, attr):
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try:
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setattr(module, attr, False)
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except AttributeError:
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pass
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if hasattr(module, 'set_processor'):
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try:
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from diffusers.models.attention_processor import Attention
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module.set_processor(Attention())
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except Exception:
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pass
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def clear_flux_caches(model):
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cache_attrs = [
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'img_ids', 'txt_ids', '_img_ids', '_txt_ids', 'cached_img_ids', 'cached_txt_ids',
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'pos_emb', '_pos_emb', 'pos_embed', '_pos_embed', 'cached_pos_emb',
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'rope', '_rope', 'freqs_cis', '_freqs_cis', 'freqs', '_freqs',
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'cache', '_cache', 'kv_cache', '_kv_cache', 'attn_bias', '_attn_bias',
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'rope_cache', '_rope_cache', 'freqs_cis_cache', '_freqs_cis_cache',
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'temporal_ids', 'frame_ids', 'video_ids', 'temp_pos_emb',
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]
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cleared_count = 0
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def clear_attrs(obj, name_prefix=""):
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nonlocal cleared_count
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for attr in cache_attrs:
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if hasattr(obj, attr):
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try:
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val = getattr(obj, attr)
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if val is not None:
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setattr(obj, attr, None)
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cleared_count += 1
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except (AttributeError, TypeError):
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pass
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clear_attrs(model)
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for name, module in model.named_modules():
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clear_attrs(module, name)
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if cleared_count > 0:
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print(f"[ParallelAnything] Cleared {cleared_count} cached tensors")
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def aggressive_cleanup():
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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torch.cuda.empty_cache()
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for i in range(torch.cuda.device_count()):
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try:
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with torch.cuda.device(f'cuda:{i}'):
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torch.cuda.synchronize()
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torch.cuda.empty_cache()
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except:
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pass
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comfy.model_management.soft_empty_cache()
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def cleanup_parallel_model(model_ref):
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model = model_ref() if isinstance(model_ref, weakref.ref) else model_ref
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if model is None:
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return
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if not getattr(model, '_true_parallel_active', False):
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return
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print("[ParallelAnything] Cleaning up parallel model...")
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should_purge_cache = getattr(model, '_parallel_purge_cache', True)
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should_purge_models = getattr(model, '_parallel_purge_models', False)
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if hasattr(model, '_original_forward'):
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try:
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model.forward = model._original_forward
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delattr(model, '_original_forward')
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except Exception:
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pass
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if hasattr(model, '_parallel_replicas'):
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replicas = model._parallel_replicas
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for dev_name, replica in list(replicas.items()):
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try:
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if hasattr(replica, 'cpu'):
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replica.cpu()
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clear_flux_caches(replica)
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if hasattr(replica, 'gradient_checkpointing'):
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replica.gradient_checkpointing = False
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if hasattr(replica, '_gradient_checkpointing_func'):
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replica._gradient_checkpointing_func = None
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except Exception as e:
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print(f"[ParallelAnything] Warning: Error cleaning up replica on {dev_name}: {e}")
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try:
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delattr(model, '_parallel_replicas')
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except Exception:
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pass
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for attr in ['_true_parallel_active', '_parallel_devices', '_parallel_streams',
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'_parallel_weights', '_auto_vram_balance', '_parallel_purge_cache',
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'_parallel_purge_models']:
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if hasattr(model, attr):
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try:
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delattr(model, attr)
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except Exception:
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pass
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if should_purge_models:
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try:
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print("[ParallelAnything] Purging models from VRAM...")
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comfy.model_management.unload_all_models()
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except Exception as e:
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print(f"[ParallelAnything] Warning: Could not unload models: {e}")
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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if should_purge_cache:
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try:
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comfy.model_management.soft_empty_cache()
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except:
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pass
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if torch.cuda.is_available():
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for i in range(torch.cuda.device_count()):
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try:
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with torch.cuda.device(f'cuda:{i}'):
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torch.cuda.empty_cache()
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except:
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pass
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def extract_model_config(model):
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config = {}
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possible_attrs = [
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'in_channels', 'out_channels', 'vec_in_dim', 'context_in_dim', 'hidden_size',
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'mlp_ratio', 'num_heads', 'depth', 'depth_single_blocks', 'depth_single',
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'axes_dim', 'theta', 'patch_size', 'qkv_bias', 'guidance_embed',
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'txt_ids_dim', 'img_ids_dim', 'num_res_blocks', 'attention_resolutions',
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'dropout', 'channel_mult', 'num_classes', 'use_checkpoint',
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'num_heads_upsample', 'use_scale_shift_norm', 'resblock_updown',
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'use_new_attention_order', 'adm_in_channels', 'num_noises', 'context_dim',
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'n_heads', 'd_head', 'transformer_depth', 'model_channels', 'max_depth',
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'num_frames', 'temporal_compression', 'temporal_dim', 'video_length',
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]
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for attr in possible_attrs:
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if hasattr(model, attr):
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try:
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val = getattr(model, attr)
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if not callable(val) and not isinstance(val, (nn.Module, torch.nn.Parameter, torch.Tensor)):
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config[attr] = val
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except Exception:
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pass
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if hasattr(model, 'params'):
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try:
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params = model.params
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if isinstance(params, dict):
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config.update(params)
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elif is_dataclass(params):
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for field_info in fields(params):
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try:
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val = getattr(params, field_info.name)
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if not isinstance(val, (torch.Tensor, nn.Module)):
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config[field_info.name] = val
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except Exception:
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pass
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else:
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params_dict = vars(params)
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if params_dict:
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config.update({k: v for k, v in params_dict.items()
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if not isinstance(v, (torch.Tensor, nn.Module))})
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except Exception:
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pass
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if hasattr(model, 'config'):
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try:
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cfg = model.config
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if isinstance(cfg, dict):
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config.update({k: v for k, v in cfg.items()
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if not isinstance(v, (torch.Tensor, nn.Module))})
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else:
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cfg_dict = {k: v for k, v in vars(cfg).items()
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if not k.startswith('_') and not callable(v)
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and not isinstance(v, (torch.Tensor, nn.Module))}
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config.update(cfg_dict)
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except Exception:
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pass
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if hasattr(model, 'unet_config') and isinstance(model.unet_config, dict):
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config.update({k: v for k, v in model.unet_config.items()
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if not isinstance(v, (torch.Tensor, nn.Module))})
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clean_config = {}
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for k, v in config.items():
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if v is not None and not isinstance(v, (torch.Tensor, nn.Module)):
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try:
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copy.deepcopy(v)
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clean_config[k] = v
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except Exception:
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pass
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return clean_config
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def clone_dataclass_or_object(obj, target_device=None):
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if is_dataclass(obj):
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try:
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field_values = {}
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for field_info in fields(obj):
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try:
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val = getattr(obj, field_info.name)
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if isinstance(val, torch.Tensor):
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field_values[field_info.name] = val.clone().detach()
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elif is_dataclass(val):
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field_values[field_info.name] = clone_dataclass_or_object(val)
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elif isinstance(val, (list, tuple)):
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new_val = []
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for v in val:
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if is_dataclass(v):
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new_val.append(clone_dataclass_or_object(v))
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elif isinstance(v, torch.Tensor):
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new_val.append(v.clone())
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else:
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new_val.append(copy.deepcopy(v))
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field_values[field_info.name] = type(val)(new_val)
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else:
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field_values[field_info.name] = copy.deepcopy(val)
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except Exception:
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# Fallback to direct reference if deep copy fails
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field_values[field_info.name] = getattr(obj, field_info.name)
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return obj.__class__(**field_values)
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except Exception:
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pass
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try:
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return copy.deepcopy(obj)
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except Exception:
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return obj
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def safe_getattr(obj, attr, default=None):
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return getattr(obj, attr, default)
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|
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def clone_module_simple(module, target_device):
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if module is None:
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return None
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module_class = module.__class__
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if isinstance(module, (nn.Linear, nn.Conv2d, nn.Conv1d)):
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try:
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weight = safe_getattr(module, 'weight', None)
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weight_dtype = weight.dtype if weight is not None else torch.float32
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has_bias = safe_getattr(module, 'bias', None) is not None
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supports_fp8 = device_supports_float8(target_device)
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if not supports_fp8 and is_float8_dtype(weight_dtype):
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weight_dtype = torch.float16
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|
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if isinstance(module, nn.Linear):
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new_mod = nn.Linear(safe_getattr(module, 'in_features'), safe_getattr(module, 'out_features'),
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bias=has_bias, device=target_device, dtype=weight_dtype)
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elif isinstance(module, nn.Conv2d):
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new_mod = nn.Conv2d(safe_getattr(module, 'in_channels'), safe_getattr(module, 'out_channels'),
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safe_getattr(module, 'kernel_size', 1), stride=safe_getattr(module, 'stride', 1),
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padding=safe_getattr(module, 'padding', 0), dilation=safe_getattr(module, 'dilation', 1),
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groups=safe_getattr(module, 'groups', 1), bias=has_bias,
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padding_mode=safe_getattr(module, 'padding_mode', 'zeros'),
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device=target_device, dtype=weight_dtype)
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elif isinstance(module, nn.Conv1d):
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new_mod = nn.Conv1d(safe_getattr(module, 'in_channels'), safe_getattr(module, 'out_channels'),
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safe_getattr(module, 'kernel_size', 1), stride=safe_getattr(module, 'stride', 1),
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padding=safe_getattr(module, 'padding', 0), dilation=safe_getattr(module, 'dilation', 1),
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groups=safe_getattr(module, 'groups', 1), bias=has_bias,
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padding_mode=safe_getattr(module, 'padding_mode', 'zeros'),
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device=target_device, dtype=weight_dtype)
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|
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with torch.no_grad():
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if weight is not None:
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if not supports_fp8 and is_float8_dtype(weight.dtype):
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weight = weight.half()
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new_mod.weight.copy_(weight)
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if has_bias and safe_getattr(module, 'bias') is not None:
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new_mod.bias.copy_(module.bias)
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return new_mod
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except Exception as e:
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print(f"[ParallelAnything] Warning: Failed to reconstruct {module_class}: {e}")
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|
|
|
if isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
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try:
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weight = safe_getattr(module, 'weight', None)
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weight_dtype = weight.dtype if weight is not None else torch.float32
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|
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supports_fp8 = device_supports_float8(target_device)
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if not supports_fp8 and is_float8_dtype(weight_dtype):
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weight_dtype = torch.float16
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|
|
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if isinstance(module, nn.LayerNorm):
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new_mod = nn.LayerNorm(safe_getattr(module, 'normalized_shape'),
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eps=safe_getattr(module, 'eps', 1e-5),
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elementwise_affine=safe_getattr(module, 'elementwise_affine', True),
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device=target_device, dtype=weight_dtype)
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elif isinstance(module, nn.BatchNorm2d):
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new_mod = nn.BatchNorm2d(safe_getattr(module, 'num_features'),
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eps=safe_getattr(module, 'eps', 1e-5),
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momentum=safe_getattr(module, 'momentum', 0.1),
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affine=safe_getattr(module, 'affine', True),
|
|
track_running_stats=safe_getattr(module, 'track_running_stats', True),
|
|
device=target_device, dtype=weight_dtype)
|
|
elif isinstance(module, nn.GroupNorm):
|
|
new_mod = nn.GroupNorm(safe_getattr(module, 'num_groups'), safe_getattr(module, 'num_channels'),
|
|
eps=safe_getattr(module, 'eps', 1e-5), affine=safe_getattr(module, 'affine', True),
|
|
device=target_device, dtype=weight_dtype)
|
|
|
|
with torch.no_grad():
|
|
if weight is not None:
|
|
if not supports_fp8 and is_float8_dtype(weight.dtype):
|
|
weight = weight.half()
|
|
new_mod.weight.copy_(weight)
|
|
if safe_getattr(module, 'bias') is not None:
|
|
new_mod.bias.copy_(module.bias)
|
|
if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
|
|
if safe_getattr(module, 'running_mean') is not None:
|
|
new_mod.running_mean.copy_(module.running_mean)
|
|
if safe_getattr(module, 'running_var') is not None:
|
|
new_mod.running_var.copy_(module.running_var)
|
|
return new_mod
|
|
except Exception as e:
|
|
print(f"[ParallelAnything] Warning: Failed to reconstruct norm layer {module_class}: {e}")
|
|
|
|
try:
|
|
new_mod = module_class.__new__(module_class)
|
|
nn.Module.__init__(new_mod)
|
|
supports_fp8 = device_supports_float8(target_device)
|
|
|
|
for name, param in module.named_parameters(recurse=False):
|
|
if param is not None:
|
|
new_param_data = param.clone().detach()
|
|
if not supports_fp8 and is_float8_dtype(new_param_data.dtype):
|
|
new_param_data = new_param_data.half()
|
|
new_param = nn.Parameter(new_param_data.to(device=target_device), requires_grad=False)
|
|
new_mod.register_parameter(name, new_param)
|
|
else:
|
|
new_mod.register_parameter(name, None)
|
|
|
|
for name, buffer in module.named_buffers(recurse=False):
|
|
if buffer is not None:
|
|
new_buffer_data = buffer.clone().detach()
|
|
if not supports_fp8 and is_float8_dtype(new_buffer_data.dtype):
|
|
new_buffer_data = new_buffer_data.half()
|
|
new_buffer = new_buffer_data.to(device=target_device)
|
|
new_mod.register_buffer(name, new_buffer)
|
|
else:
|
|
new_mod.register_buffer(name, None)
|
|
|
|
for name, child in module.named_children():
|
|
if child is not None:
|
|
cloned_child = clone_module_simple(child, target_device)
|
|
new_mod.add_module(name, cloned_child)
|
|
|
|
excluded_attrs = {
|
|
'_parameters', '_buffers', '_modules', '_non_persistent_buffers_set',
|
|
'_backward_pre_hooks', '_backward_hooks', '_forward_pre_hooks',
|
|
'_forward_hooks', '_state_dict_hooks', '_load_state_dict_pre_hooks',
|
|
'_extra_state', '_modules_to_load'
|
|
}
|
|
cache_attrs = {
|
|
'img_ids', 'txt_ids', '_img_ids', '_txt_ids', 'cached_img_ids',
|
|
'cached_txt_ids', 'pos_emb', '_pos_emb', 'pos_embed', '_pos_embed',
|
|
'freqs_cis', '_freqs_cis', 'freqs', '_freqs', 'cache', '_cache',
|
|
'kv_cache', '_kv_cache', 'attn_bias', '_attn_bias'
|
|
}
|
|
|
|
for key, value in module.__dict__.items():
|
|
if key in excluded_attrs:
|
|
continue
|
|
try:
|
|
if is_dataclass(value):
|
|
setattr(new_mod, key, clone_dataclass_or_object(value))
|
|
elif isinstance(value, torch.Tensor):
|
|
if key in cache_attrs:
|
|
setattr(new_mod, key, None)
|
|
else:
|
|
tensor_val = value.clone().detach()
|
|
if not supports_fp8 and is_float8_dtype(tensor_val.dtype):
|
|
tensor_val = tensor_val.half()
|
|
setattr(new_mod, key, tensor_val.to(target_device))
|
|
elif isinstance(value, (list, tuple)) and len(value) > 0 and isinstance(value[0], torch.Tensor):
|
|
if key in cache_attrs:
|
|
setattr(new_mod, key, None)
|
|
else:
|
|
converted = []
|
|
for t in value:
|
|
t_conv = t.clone().detach()
|
|
if not supports_fp8 and is_float8_dtype(t_conv.dtype):
|
|
t_conv = t_conv.half()
|
|
converted.append(t_conv.to(target_device))
|
|
setattr(new_mod, key, type(value)(converted))
|
|
else:
|
|
setattr(new_mod, key, copy.deepcopy(value))
|
|
except Exception:
|
|
pass
|
|
|
|
return new_mod
|
|
except Exception as e:
|
|
raise RuntimeError(f"Failed to clone module {module_class}: {e}")
|
|
|
|
def safe_model_clone(source_model, target_device, disable_flash=False):
|
|
clear_flux_caches(source_model)
|
|
|
|
try:
|
|
src_device = next(source_model.parameters()).device
|
|
except StopIteration:
|
|
src_device = torch.device('cpu')
|
|
|
|
if str(src_device) == str(target_device):
|
|
print(f"[ParallelAnything] Model already on {target_device}, using reference...")
|
|
clear_flux_caches(source_model)
|
|
return source_model
|
|
|
|
if src_device.type != 'cpu':
|
|
model_cpu = source_model.cpu()
|
|
if torch.cuda.is_available():
|
|
torch.cuda.synchronize()
|
|
torch.cuda.empty_cache()
|
|
comfy.model_management.soft_empty_cache()
|
|
else:
|
|
model_cpu = source_model
|
|
|
|
model_class = model_cpu.__class__
|
|
replica = None
|
|
method_used = "unknown"
|
|
|
|
try:
|
|
print(f"[ParallelAnything] Attempting efficient clone to {target_device}...")
|
|
config = extract_model_config(model_cpu)
|
|
state_dict = model_cpu.state_dict()
|
|
|
|
if not config:
|
|
raise RuntimeError("Could not extract model config")
|
|
|
|
try:
|
|
replica = model_class(**config)
|
|
replica = replica.to(target_device, non_blocking=False)
|
|
except TypeError as e:
|
|
if "missing" in str(e) and "required" in str(e):
|
|
try:
|
|
replica = model_class(config)
|
|
replica = replica.to(target_device, non_blocking=False)
|
|
except Exception:
|
|
config_obj = SimpleNamespace(**config)
|
|
replica = model_class(config_obj)
|
|
replica = replica.to(target_device, non_blocking=False)
|
|
else:
|
|
raise
|
|
|
|
print(f"[ParallelAnything] Loading parameters incrementally...")
|
|
supports_fp8 = device_supports_float8(target_device)
|
|
|
|
with torch.no_grad():
|
|
for key in list(state_dict.keys()):
|
|
try:
|
|
param = state_dict[key]
|
|
if '.' in key:
|
|
parts = key.split('.')
|
|
module = replica
|
|
for part in parts[:-1]:
|
|
module = getattr(module, part)
|
|
target_param = getattr(module, parts[-1])
|
|
else:
|
|
target_param = getattr(replica, key)
|
|
|
|
if isinstance(target_param, (torch.nn.Parameter, torch.Tensor)):
|
|
param_data = param.to(target_device, non_blocking=False)
|
|
if is_float8_dtype(param_data.dtype) and not supports_fp8:
|
|
param_data = param_data.half()
|
|
target_param.data.copy_(param_data)
|
|
|
|
del state_dict[key]
|
|
del param
|
|
|
|
if len(state_dict) % 50 == 0 and torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
except Exception as e:
|
|
print(f"[ParallelAnything] Warning: Could not load {key}: {e}")
|
|
|
|
del state_dict
|
|
if src_device.type != 'cpu':
|
|
del model_cpu
|
|
gc.collect()
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
method_used = "incremental_gpu"
|
|
|
|
except Exception as recon_error:
|
|
print(f"[ParallelAnything] Config reconstruction failed: {recon_error}")
|
|
print(f"[ParallelAnything] Attempting manual recursive cloning...")
|
|
|
|
replica = clone_module_simple(model_cpu, target_device)
|
|
method_used = "manual_recursive_clone"
|
|
|
|
if src_device.type != 'cpu':
|
|
del model_cpu
|
|
gc.collect()
|
|
|
|
if replica is None:
|
|
raise RuntimeError("All clone methods failed")
|
|
|
|
clear_flux_caches(replica)
|
|
|
|
if not device_supports_float8(target_device):
|
|
print(f"[ParallelAnything] Converting FP8 weights to FP16 for {target_device}...")
|
|
def convert_fp8_recursive(m):
|
|
for name, param in m.named_parameters():
|
|
if is_float8_dtype(param.dtype):
|
|
param.data = param.data.half()
|
|
for name, buffer in m.named_buffers():
|
|
if is_float8_dtype(buffer.dtype):
|
|
buffer.data = buffer.data.half()
|
|
for child in m.children():
|
|
convert_fp8_recursive(child)
|
|
convert_fp8_recursive(replica)
|
|
|
|
if hasattr(replica, 'gradient_checkpointing'):
|
|
replica.gradient_checkpointing = False
|
|
if hasattr(replica, '_gradient_checkpointing_func'):
|
|
replica._gradient_checkpointing_func = None
|
|
|
|
if hasattr(replica, 'hooks'):
|
|
replica.hooks = []
|
|
if hasattr(replica, '_hf_hook'):
|
|
replica._hf_hook = None
|
|
|
|
replica.eval()
|
|
for param in replica.parameters():
|
|
param.requires_grad = False
|
|
|
|
for buffer in replica.buffers():
|
|
if buffer.device != target_device:
|
|
buffer.data = buffer.data.to(target_device)
|
|
|
|
if disable_flash:
|
|
disable_flash_xformers(replica)
|
|
|
|
print(f"[ParallelAnything] Cloned via {method_used} to {target_device}")
|
|
return replica
|
|
|
|
def get_free_vram(device_name):
|
|
try:
|
|
if device_name.startswith("cuda"):
|
|
idx = int(device_name.split(":")[-1])
|
|
torch.cuda.set_device(idx)
|
|
free_memory = (torch.cuda.get_device_properties(idx).total_memory -
|
|
torch.cuda.memory_allocated(idx))
|
|
return free_memory / (1024 ** 2)
|
|
except Exception:
|
|
pass
|
|
return 0
|
|
|
|
def auto_split_batch(batch_size, devices, weights):
|
|
if not any(d.startswith("cuda") for d in devices):
|
|
return [max(1, int(batch_size * w)) for w in weights]
|
|
|
|
vram_avail = []
|
|
for d in devices:
|
|
if d.startswith("cuda"):
|
|
vram_avail.append(get_free_vram(d))
|
|
else:
|
|
vram_avail.append(0)
|
|
|
|
total_vram = sum(vram_avail)
|
|
if total_vram == 0:
|
|
return [max(1, int(batch_size * w)) for w in weights]
|
|
|
|
adjusted_weights = []
|
|
for i, (w, vram) in enumerate(zip(weights, vram_avail)):
|
|
if vram > 0:
|
|
vram_weight = vram / total_vram
|
|
adjusted = 0.7 * w + 0.3 * vram_weight
|
|
else:
|
|
adjusted = w
|
|
adjusted_weights.append(adjusted)
|
|
|
|
total = sum(adjusted_weights)
|
|
adjusted_weights = [w/total for w in adjusted_weights]
|
|
|
|
split_sizes = [max(1, int(batch_size * w)) for w in adjusted_weights]
|
|
split_sizes[-1] = batch_size - sum(split_sizes[:-1])
|
|
|
|
return split_sizes
|
|
|
|
class ParallelDevice:
|
|
@classmethod
|
|
def get_available_devices(cls):
|
|
devices = ["cpu"]
|
|
if torch.cuda.is_available():
|
|
for i in range(torch.cuda.device_count()):
|
|
devices.append(f"cuda:{i}")
|
|
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
|
devices.append("mps")
|
|
if hasattr(torch, 'xpu') and torch.xpu.is_available():
|
|
for i in range(torch.xpu.device_count()):
|
|
devices.append(f"xpu:{i}")
|
|
try:
|
|
import torch_directml
|
|
for i in range(torch_directml.device_count()):
|
|
devices.append(f"privateuseone:{i}")
|
|
except ImportError:
|
|
pass
|
|
return devices
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
available = s.get_available_devices()
|
|
default = "cuda:0" if any(d.startswith("cuda:0") for d in available) else available[0]
|
|
return {
|
|
"required": {
|
|
"device_id": (available, {
|
|
"default": default,
|
|
"tooltip": "Select available compute device (CPU/CUDA/MPS/XPU)"
|
|
}),
|
|
"percentage": ("FLOAT", {
|
|
"default": 50.0,
|
|
"min": 1.0,
|
|
"max": 100.0,
|
|
"step": 1.0,
|
|
"tooltip": "Percentage of batch (or layers for batch=1) to process on this device"
|
|
}),
|
|
},
|
|
"optional": {
|
|
"previous_devices": ("DEVICE_CHAIN", {
|
|
"tooltip": "Connect from another ParallelDevice node to chain multiple GPUs"
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("DEVICE_CHAIN",)
|
|
RETURN_NAMES = ("device_chain",)
|
|
FUNCTION = "add_device"
|
|
CATEGORY = "utils/hardware"
|
|
DESCRIPTION = "Add a GPU/CPU/MPS/XPU device to the parallel processing chain"
|
|
|
|
def add_device(self, device_id, percentage, previous_devices=None):
|
|
if previous_devices is None:
|
|
previous_devices = []
|
|
|
|
config = {
|
|
"device": device_id,
|
|
"percentage": float(percentage),
|
|
"weight": float(percentage) / 100.0
|
|
}
|
|
|
|
new_chain = previous_devices.copy()
|
|
new_chain.append(config)
|
|
|
|
return (new_chain,)
|
|
|
|
class ParallelDeviceList:
|
|
@classmethod
|
|
def get_available_devices(cls):
|
|
devices = ["cpu"]
|
|
if torch.cuda.is_available():
|
|
for i in range(torch.cuda.device_count()):
|
|
devices.append(f"cuda:{i}")
|
|
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
|
devices.append("mps")
|
|
if hasattr(torch, 'xpu') and torch.xpu.is_available():
|
|
for i in range(torch.xpu.device_count()):
|
|
devices.append(f"xpu:{i}")
|
|
return devices
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
devices = s.get_available_devices()
|
|
def_dev = "cuda:0" if "cuda:0" in devices else devices[0]
|
|
return {
|
|
"required": {
|
|
"device_1": (devices, {"default": def_dev}),
|
|
"pct_1": ("FLOAT", {"default": 50.0, "min": 1.0, "max": 100.0, "step": 1.0}),
|
|
"device_2": (devices, {"default": devices[1] if len(devices) > 1 else def_dev}),
|
|
"pct_2": ("FLOAT", {"default": 50.0, "min": 0.0, "max": 100.0, "step": 1.0}),
|
|
},
|
|
"optional": {
|
|
"device_3": (devices, {"default": devices[2] if len(devices) > 2 else "cpu"}),
|
|
"pct_3": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 1.0}),
|
|
"device_4": (devices, {"default": devices[3] if len(devices) > 3 else "cpu"}),
|
|
"pct_4": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 1.0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("DEVICE_CHAIN",)
|
|
RETURN_NAMES = ("device_chain",)
|
|
FUNCTION = "create_list"
|
|
CATEGORY = "utils/hardware"
|
|
|
|
def create_list(self, device_1, pct_1, device_2, pct_2,
|
|
device_3="cpu", pct_3=0, device_4="cpu", pct_4=0):
|
|
chain = []
|
|
devices = [(device_1, pct_1), (device_2, pct_2),
|
|
(device_3, pct_3), (device_4, pct_4)]
|
|
|
|
for dev_str, pct in devices:
|
|
if pct > 0:
|
|
chain.append({
|
|
"device": dev_str,
|
|
"percentage": float(pct),
|
|
"weight": float(pct) / 100.0
|
|
})
|
|
|
|
return (chain,)
|
|
|
|
class ParallelAnything:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
"device_chain": ("DEVICE_CHAIN", {"tooltip": "Connect from ParallelDevice nodes"}),
|
|
},
|
|
"optional": {
|
|
"workload_split": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Enable multi-device processing"
|
|
}),
|
|
"auto_vram_balance": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Automatically adjust batch split based on available VRAM"
|
|
}),
|
|
"purge_cache": ("BOOLEAN", {
|
|
"default": True,
|
|
"tooltip": "Purge CUDA cache when cleaning up parallel resources"
|
|
}),
|
|
"purge_models": ("BOOLEAN", {
|
|
"default": False,
|
|
"tooltip": "Unload all models from VRAM when cleaning up (aggressive memory clearing)"
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL",)
|
|
RETURN_NAMES = ("model",)
|
|
FUNCTION = "setup_parallel"
|
|
CATEGORY = "utils/hardware"
|
|
|
|
def setup_parallel(self, model, device_chain, workload_split=True,
|
|
auto_vram_balance=False, purge_cache=True, purge_models=False):
|
|
if model is None or not device_chain:
|
|
return (model,)
|
|
|
|
target_model = model.model.diffusion_model
|
|
|
|
if hasattr(target_model, "_true_parallel_active"):
|
|
print("[ParallelAnything] Cleaning up previous parallel setup...")
|
|
cleanup_parallel_model(target_model)
|
|
gc.collect()
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
comfy.model_management.soft_empty_cache()
|
|
|
|
print("[ParallelAnything] Freeing ComfyUI model cache for cloning...")
|
|
comfy.model_management.unload_all_models()
|
|
aggressive_cleanup()
|
|
|
|
total_pct = sum(item["percentage"] for item in device_chain)
|
|
if total_pct <= 0:
|
|
return (model,)
|
|
|
|
device_names = []
|
|
weights = []
|
|
for item in device_chain:
|
|
weights.append(item["percentage"] / total_pct)
|
|
device_names.append(item["device"])
|
|
|
|
print(f"[ParallelAnything] Setup: {list(zip(device_names, [f'{w*100:.1f}%' for w in weights]))}")
|
|
|
|
devices_needing_safe_attention = {}
|
|
for dev_name in device_names:
|
|
if not check_sm80_support(dev_name):
|
|
devices_needing_safe_attention[dev_name] = True
|
|
print(f"[ParallelAnything] {dev_name} < SM_80, disabling Flash/xFormers")
|
|
|
|
for dev in device_names:
|
|
try:
|
|
torch.device(dev)
|
|
except Exception:
|
|
print(f"[ParallelAnything] Invalid device: {dev}")
|
|
return (model,)
|
|
|
|
try:
|
|
original_device = next(target_model.parameters()).device
|
|
original_device_str = str(original_device)
|
|
except StopIteration:
|
|
original_device = torch.device('cpu')
|
|
original_device_str = "cpu"
|
|
|
|
replicas = {}
|
|
streams = {}
|
|
successful_devices = []
|
|
successful_weights = []
|
|
|
|
try:
|
|
print(f"[ParallelAnything] Cloning to {len(device_names)} devices...")
|
|
clear_flux_caches(target_model)
|
|
|
|
needs_cpu_transition = original_device.type == 'cuda' and original_device_str not in device_names
|
|
if needs_cpu_transition:
|
|
print(f"[ParallelAnything] Moving model to CPU for safe cloning...")
|
|
target_model = target_model.cpu()
|
|
aggressive_cleanup()
|
|
|
|
for i, dev_name in enumerate(device_names):
|
|
dev = torch.device(dev_name)
|
|
need_safe = dev_name in devices_needing_safe_attention
|
|
|
|
if dev_name == original_device_str and not needs_cpu_transition:
|
|
print(f"[ParallelAnything] Using original model for {dev_name} (skipping clone)")
|
|
replicas[dev_name] = target_model
|
|
if dev.type == 'cuda':
|
|
streams[dev_name] = torch.cuda.Stream(dev)
|
|
successful_devices.append(dev_name)
|
|
successful_weights.append(weights[i])
|
|
continue
|
|
|
|
try:
|
|
print(f"[ParallelAnything] Cloning to {dev_name} ({i+1}/{len(device_names)})...")
|
|
|
|
if dev.type == 'cuda':
|
|
idx = dev.index if dev.index is not None else int(dev_name.split(":")[-1])
|
|
torch.cuda.synchronize(idx)
|
|
free_mem = torch.cuda.mem_get_info(idx)[0] / 1024**3
|
|
print(f"[ParallelAnything] VRAM available on {dev_name}: {free_mem:.2f}GB")
|
|
|
|
replica = safe_model_clone(target_model, dev, disable_flash=need_safe)
|
|
clear_flux_caches(replica)
|
|
|
|
replicas[dev_name] = replica
|
|
if dev.type == 'cuda':
|
|
streams[dev_name] = torch.cuda.Stream(dev)
|
|
|
|
print(f"[ParallelAnything] ✓ {dev_name}" + (" (Safe mode)" if need_safe else ""))
|
|
successful_devices.append(dev_name)
|
|
successful_weights.append(weights[i])
|
|
|
|
aggressive_cleanup()
|
|
|
|
except RuntimeError as e:
|
|
if "out of memory" in str(e).lower():
|
|
print(f"[ParallelAnything] OOM cloning to {dev_name}, skipping...")
|
|
aggressive_cleanup()
|
|
continue
|
|
else:
|
|
raise
|
|
|
|
if len(successful_devices) < len(device_names):
|
|
if len(successful_devices) == 0:
|
|
raise RuntimeError("No devices available for parallel processing - all OOM")
|
|
print(f"[ParallelAnything] Reducing to {len(successful_devices)} devices due to OOM")
|
|
device_names = successful_devices
|
|
total_weight = sum(successful_weights)
|
|
weights = [w/total_weight for w in successful_weights]
|
|
|
|
if needs_cpu_transition and original_device_str not in replicas:
|
|
try:
|
|
print(f"[ParallelAnything] Restoring original model to {original_device}")
|
|
target_model = target_model.to(original_device)
|
|
except Exception as e:
|
|
print(f"[ParallelAnything] Warning: Could not restore original model: {e}")
|
|
|
|
except Exception as e:
|
|
print(f"[ParallelAnything] Error during cloning: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
for dev_name, r in replicas.items():
|
|
try:
|
|
if r is not target_model:
|
|
r.cpu()
|
|
del r
|
|
except Exception:
|
|
pass
|
|
aggressive_cleanup()
|
|
return (model,)
|
|
|
|
print("[ParallelAnything] Configuring Model/Pipeline Parallelism for Batch=1...")
|
|
|
|
lead_device_name = device_names[0]
|
|
lead_replica = replicas[lead_device_name]
|
|
|
|
block_lists = ['double_blocks', 'single_blocks', 'transformer_blocks', 'layers']
|
|
|
|
for list_name in block_lists:
|
|
if hasattr(lead_replica, list_name):
|
|
local_blocks = getattr(lead_replica, list_name)
|
|
if not isinstance(local_blocks, nn.ModuleList):
|
|
continue
|
|
|
|
num_blocks = len(local_blocks)
|
|
if num_blocks == 0:
|
|
continue
|
|
|
|
print(f"[ParallelAnything] Configuring {list_name} ({num_blocks} blocks) for pipeline execution...")
|
|
|
|
device_assignments = []
|
|
current_block = 0
|
|
for i, weight in enumerate(weights):
|
|
count = int(round(weight * num_blocks))
|
|
if i == len(weights) - 1:
|
|
count = num_blocks - current_block
|
|
|
|
target_dev = torch.device(device_names[i])
|
|
for _ in range(count):
|
|
if current_block < num_blocks:
|
|
device_assignments.append(target_dev)
|
|
current_block += 1
|
|
|
|
for idx in range(num_blocks):
|
|
assigned_dev = device_assignments[idx]
|
|
|
|
peers = {}
|
|
for d_name, r in replicas.items():
|
|
if hasattr(r, list_name):
|
|
peers[d_name] = getattr(r, list_name)[idx]
|
|
|
|
original_block = local_blocks[idx]
|
|
is_last = (idx == num_blocks - 1)
|
|
|
|
wrapper = ParallelBlock(
|
|
local_block=original_block,
|
|
block_idx=idx,
|
|
owner_device=assigned_dev,
|
|
peers=peers,
|
|
is_last_block=is_last,
|
|
lead_device=torch.device(lead_device_name)
|
|
)
|
|
|
|
local_blocks[idx] = wrapper
|
|
|
|
target_model._parallel_purge_cache = purge_cache
|
|
target_model._parallel_purge_models = purge_models
|
|
|
|
replicas_ref = replicas
|
|
devices_ref = tuple(device_names)
|
|
weights_ref = tuple(weights)
|
|
lead_device = torch.device(device_names[0])
|
|
streams_ref = streams
|
|
auto_balance_ref = auto_vram_balance
|
|
|
|
def get_batch_size(x):
|
|
if isinstance(x, torch.Tensor):
|
|
return x.shape[0]
|
|
elif isinstance(x, (list, tuple)) and len(x) > 0:
|
|
if isinstance(x[0], torch.Tensor):
|
|
batch_sizes = [t.shape[0] for t in x if isinstance(t, torch.Tensor)]
|
|
if batch_sizes:
|
|
return batch_sizes[0]
|
|
return len(x)
|
|
else:
|
|
return 1
|
|
|
|
def split_batch(x, split_sizes):
|
|
if isinstance(x, torch.Tensor):
|
|
return torch.split(x, split_sizes, dim=0)
|
|
elif isinstance(x, (list, tuple)):
|
|
split_lists = []
|
|
for t in x:
|
|
if isinstance(t, torch.Tensor):
|
|
split_lists.append(torch.split(t, split_sizes, dim=0))
|
|
else:
|
|
split_lists.append([t] * len(split_sizes))
|
|
result = []
|
|
for i in range(len(split_sizes)):
|
|
result.append(type(x)(sl[i] for sl in split_lists))
|
|
return result
|
|
else:
|
|
return [x] * len(split_sizes)
|
|
|
|
def move_to_device(x, device, non_blocking=False):
|
|
if isinstance(x, torch.Tensor):
|
|
if x.device != torch.device(device):
|
|
x = x.to(device, non_blocking=non_blocking)
|
|
if is_float8_dtype(x.dtype) and not device_supports_float8(device):
|
|
x = x.half()
|
|
return x
|
|
elif isinstance(x, (list, tuple)):
|
|
moved = [move_to_device(t, device, non_blocking) for t in x]
|
|
return type(x)(moved)
|
|
else:
|
|
return x
|
|
|
|
def split_kwargs(kwargs, split_sizes, total_batch):
|
|
split_kwargs_list = [{} for _ in range(len(split_sizes))]
|
|
for key, value in kwargs.items():
|
|
if isinstance(value, torch.Tensor) and value.shape[0] == total_batch:
|
|
chunks = torch.split(value, split_sizes, dim=0)
|
|
for i, chunk in enumerate(chunks):
|
|
split_kwargs_list[i][key] = chunk
|
|
elif isinstance(value, (list, tuple)) and len(value) > 0 and isinstance(value[0], torch.Tensor):
|
|
if all(isinstance(t, torch.Tensor) and t.shape[0] == total_batch for t in value):
|
|
split_lists = [torch.split(t, split_sizes, dim=0) for t in value]
|
|
for i in range(len(split_sizes)):
|
|
split_kwargs_list[i][key] = type(value)(sl[i] for sl in split_lists)
|
|
else:
|
|
for i in range(len(split_sizes)):
|
|
split_kwargs_list[i][key] = value
|
|
return split_kwargs_list
|
|
|
|
def concatenate_results(results, dim=0):
|
|
if len(results) == 0:
|
|
return results
|
|
first = results[0]
|
|
if isinstance(first, torch.Tensor):
|
|
return torch.cat(results, dim=dim)
|
|
elif isinstance(first, (list, tuple)):
|
|
concatenated = []
|
|
for i in range(len(first)):
|
|
if isinstance(first[i], torch.Tensor):
|
|
to_concat = [r[i] for r in results]
|
|
concatenated.append(torch.cat(to_concat, dim=dim))
|
|
else:
|
|
concatenated.append(first[i])
|
|
return type(first)(concatenated)
|
|
else:
|
|
return results
|
|
|
|
def parallel_forward(self, x, timesteps, context=None, **kwargs):
|
|
try:
|
|
try:
|
|
batch_size = get_batch_size(x)
|
|
except ValueError as e:
|
|
print(f"[ParallelAnything] Error: {e}")
|
|
raise
|
|
|
|
if batch_size == 1 and workload_split:
|
|
set_pipeline_mode(True)
|
|
try:
|
|
lead_replica = replicas_ref[devices_ref[0]]
|
|
if hasattr(lead_replica, '_original_forward'):
|
|
return lead_replica._original_forward(x, timesteps, context=context, **kwargs)
|
|
else:
|
|
return lead_replica(x, timesteps, context=context, **kwargs)
|
|
finally:
|
|
set_pipeline_mode(False)
|
|
|
|
if batch_size < len(devices_ref) or not workload_split:
|
|
set_pipeline_mode(False)
|
|
with torch.no_grad():
|
|
first_replica = replicas_ref[devices_ref[0]]
|
|
if hasattr(first_replica, '_original_forward'):
|
|
return first_replica._original_forward(x, timesteps, context=context, **kwargs)
|
|
else:
|
|
return first_replica(x, timesteps, context=context, **kwargs)
|
|
|
|
if auto_balance_ref:
|
|
split_sizes = auto_split_batch(batch_size, list(devices_ref), list(weights_ref))
|
|
else:
|
|
split_sizes = [max(1, int(batch_size * w)) for w in weights_ref]
|
|
split_sizes[-1] = batch_size - sum(split_sizes[:-1])
|
|
|
|
active = []
|
|
for idx, (dev_name, size) in enumerate(zip(devices_ref, split_sizes)):
|
|
if size > 0:
|
|
active.append({
|
|
'idx': idx,
|
|
'dev_name': dev_name,
|
|
'device': torch.device(dev_name),
|
|
'replica': replicas_ref[dev_name],
|
|
'stream': streams_ref.get(dev_name),
|
|
'size': size,
|
|
})
|
|
|
|
if len(active) == 0:
|
|
raise RuntimeError("No active devices available")
|
|
|
|
if len(active) == 1:
|
|
set_pipeline_mode(False)
|
|
with torch.no_grad():
|
|
replica = active[0]['replica']
|
|
if hasattr(replica, '_original_forward'):
|
|
return replica._original_forward(x, timesteps, context=context, **kwargs)
|
|
else:
|
|
return replica(x, timesteps, context=context, **kwargs)
|
|
|
|
x_chunks = split_batch(x, [a['size'] for a in active])
|
|
t_chunks = split_batch(timesteps, [a['size'] for a in active])
|
|
if context is not None:
|
|
c_chunks = split_batch(context, [a['size'] for a in active])
|
|
else:
|
|
c_chunks = [None] * len(active)
|
|
|
|
kwargs_chunks = split_kwargs(kwargs, [a['size'] for a in active], batch_size)
|
|
|
|
results = [None] * len(active)
|
|
exceptions = []
|
|
|
|
def worker(task_idx):
|
|
set_pipeline_mode(False)
|
|
task = active[task_idx]
|
|
dev = task['device']
|
|
replica = task['replica']
|
|
stream = task['stream']
|
|
|
|
try:
|
|
if dev.type == 'cuda':
|
|
torch.cuda.set_device(dev)
|
|
|
|
x_in = move_to_device(x_chunks[task_idx], dev)
|
|
t_in = move_to_device(t_chunks[task_idx], dev)
|
|
c_in = move_to_device(c_chunks[task_idx], dev) if c_chunks[task_idx] is not None else None
|
|
|
|
k_in = {}
|
|
for k, v in kwargs_chunks[task_idx].items():
|
|
k_in[k] = move_to_device(v, dev, non_blocking=False)
|
|
|
|
if hasattr(replica, '_original_forward'):
|
|
forward_fn = replica._original_forward
|
|
else:
|
|
forward_fn = replica.forward
|
|
|
|
if dev.type == 'cuda' and stream is not None:
|
|
with torch.cuda.device(dev):
|
|
with torch.cuda.stream(stream):
|
|
torch.cuda.synchronize(dev)
|
|
with torch.no_grad():
|
|
out = forward_fn(x_in, t_in, context=c_in, **k_in)
|
|
torch.cuda.synchronize(dev)
|
|
elif dev.type == 'cuda':
|
|
with torch.cuda.device(dev):
|
|
torch.cuda.synchronize(dev)
|
|
with torch.no_grad():
|
|
out = forward_fn(x_in, t_in, context=c_in, **k_in)
|
|
torch.cuda.synchronize(dev)
|
|
elif dev.type == 'xpu':
|
|
with torch.xpu.device(dev):
|
|
torch.xpu.synchronize(dev)
|
|
with torch.no_grad():
|
|
out = forward_fn(x_in, t_in, context=c_in, **k_in)
|
|
torch.xpu.synchronize(dev)
|
|
else:
|
|
with torch.no_grad():
|
|
out = forward_fn(x_in, t_in, context=c_in, **k_in)
|
|
|
|
out = move_to_device(out, lead_device, non_blocking=False)
|
|
return task_idx, out
|
|
except Exception as e:
|
|
return task_idx, e
|
|
|
|
with ThreadPoolExecutor(max_workers=len(active)) as executor:
|
|
futures = [executor.submit(worker, i) for i in range(len(active))]
|
|
for future in as_completed(futures):
|
|
idx, result = future.result()
|
|
if isinstance(result, Exception):
|
|
exceptions.append((active[idx]['dev_name'], result))
|
|
results[idx] = None
|
|
else:
|
|
results[idx] = result
|
|
|
|
if exceptions:
|
|
for dev_name, exc in exceptions:
|
|
print(f"[ParallelAnything] Error on {dev_name}: {exc}")
|
|
raise exceptions[0][1]
|
|
|
|
if any(r is None for r in results):
|
|
missing = [active[i]['dev_name'] for i, r in enumerate(results) if r is None]
|
|
raise RuntimeError(f"Missing results from devices: {missing}")
|
|
|
|
return concatenate_results(results, dim=0)
|
|
|
|
except RuntimeError as e:
|
|
if "out of memory" in str(e).lower():
|
|
print("[ParallelAnything] OOM in parallel forward, attempting emergency cleanup...")
|
|
aggressive_cleanup()
|
|
print("[ParallelAnything] Falling back to single device processing...")
|
|
set_pipeline_mode(False)
|
|
with torch.no_grad():
|
|
first_replica = replicas_ref[devices_ref[0]]
|
|
if hasattr(first_replica, '_original_forward'):
|
|
return first_replica._original_forward(x, timesteps, context=context, **kwargs)
|
|
else:
|
|
return first_replica(x, timesteps, context=context, **kwargs)
|
|
else:
|
|
raise
|
|
|
|
target_model._original_forward = target_model.forward
|
|
target_model.forward = types.MethodType(parallel_forward, target_model)
|
|
|
|
target_model._true_parallel_active = True
|
|
target_model._parallel_replicas = replicas
|
|
target_model._parallel_devices = device_names
|
|
target_model._parallel_streams = streams
|
|
target_model._parallel_weights = weights
|
|
target_model._auto_vram_balance = auto_vram_balance
|
|
|
|
weakref.finalize(model, cleanup_parallel_model, weakref.ref(target_model))
|
|
|
|
model.load_device = lead_device
|
|
|
|
print(f"[ParallelAnything] Parallel setup complete. Devices: {device_names}")
|
|
return (model,)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ParallelAnything": ParallelAnything,
|
|
"ParallelDevice": ParallelDevice,
|
|
"ParallelDeviceList": ParallelDeviceList,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ParallelAnything": "Parallel Anything (True Multi-GPU)",
|
|
"ParallelDevice": "Parallel Device Config",
|
|
"ParallelDeviceList": "Parallel Device List (1-4x)",
|
|
}
|