Fabian
540 lines
20 KiB
Python
540 lines
20 KiB
Python
"""
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BC-250 gfx1010 Comprehensive Monkey-Patch v11
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1. Softmax: manual impl for dim > threshold (VGPR overflow fix)
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2. SDPA: manual impl for large sequences
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3. GGUF: GPU dequant with weight cache (eliminates per-step dequant)
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4. Mmap: pre-clones non-GGUF tensor data before GPU transfer
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5. GPU: pre-warms context and caching allocator
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6. CLIP: forces text encoder to CPU (memory constraint)
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7. VAE: GPU fp16 decode with persistent caching (shared memory APU)
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8. Threads: all CPU cores for intra-op parallelism
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9. Preload: background warmup prompt on server start
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NOTE: mlockall REMOVED — on APU with shared memory, pinning 10GB of mmap'd
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GGUF files leaves no room for GPU GTT allocations → OOM kill.
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The kernel page cache handles this correctly without mlockall.
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v11 changes vs v10:
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- GPU dequant instead of CPU (GGUF dequant ops are pure PyTorch, run on GPU)
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- Weight cache: dequanted fp16 weights cached per-layer, reused across steps
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- mlockall() to pin process memory in RAM (no zram/swap penalty)
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- Removed duplicate preload section
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- Clean rewrite
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BC-250 APU / gfx1010: XNACK disabled, no GPU page fault handling.
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Place in ComfyUI root and import as first line of main.py.
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"""
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import torch
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import torch.nn.functional as F
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import os
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import sys
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import gc
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import logging
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import threading
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import json
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import time as _time
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logger = logging.getLogger(__name__)
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# === THREAD CONFIGURATION ===
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_NUM_THREADS = int(os.environ.get("BC250_NUM_THREADS", str(os.cpu_count() or 12)))
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torch.set_num_threads(_NUM_THREADS)
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logger.warning(f"[BC-250] Torch threads: intra-op={_NUM_THREADS}")
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SAFE_SOFTMAX_THRESHOLD = int(os.environ.get("BC250_SOFTMAX_THRESHOLD", "4096"))
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_original_softmax = torch.nn.functional.softmax
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_original_tensor_softmax = torch.Tensor.softmax
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_original_sdpa = torch.nn.functional.scaled_dot_product_attention
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# === MMAP PRE-CLONE PATCH ===
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_original_module_apply = torch.nn.Module._apply
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def _bc250_safe_apply(self, fn, recurse=True):
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"""Pre-clone mmap'd CPU tensor data before GPU transfer (XNACK workaround).
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Note: GGMLTensor.clone() returns self, so GGUF weights are unaffected.
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They're handled by GGMLTensor.to() which preserves metadata."""
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for key, param in self._parameters.items():
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if param is not None and param.device.type == 'cpu':
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param.data = param.data.clone()
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for key, buf in self._buffers.items():
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if buf is not None and buf.device.type == 'cpu':
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self._buffers[key] = buf.clone()
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return _original_module_apply(self, fn, recurse)
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# === SOFTMAX PATCH ===
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def _safe_softmax_impl(input, dim=-1):
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x_max = input.max(dim=dim, keepdim=True).values
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exp_x = torch.exp(input - x_max)
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return exp_x / exp_x.sum(dim=dim, keepdim=True)
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def patched_softmax(input, dim=None, _stacklevel=3, dtype=None):
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if dim is None:
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dim = -1
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if dtype is not None:
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input = input.to(dtype)
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if input.is_cuda and input.shape[dim] > SAFE_SOFTMAX_THRESHOLD:
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if not getattr(patched_softmax, '_logged', False):
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logger.warning(f"[BC-250] Manual F.softmax: shape={list(input.shape)}, dim={dim}")
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patched_softmax._logged = True
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return _safe_softmax_impl(input, dim)
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return _original_softmax(input, dim=dim)
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def patched_tensor_softmax(self, dim=-1, dtype=None):
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if dtype is not None:
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self = self.to(dtype)
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if self.is_cuda and self.shape[dim] > SAFE_SOFTMAX_THRESHOLD:
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if not getattr(patched_tensor_softmax, '_logged', False):
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logger.warning(f"[BC-250] Manual softmax: shape={list(self.shape)}, dim={dim}")
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patched_tensor_softmax._logged = True
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return _safe_softmax_impl(self, dim)
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return _original_tensor_softmax(self, dim=dim)
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def _safe_sdpa(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
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L, S = query.size(-2), key.size(-2)
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if scale is None:
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scale = query.size(-1) ** -0.5
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attn_weight = torch.matmul(query, key.transpose(-2, -1)) * scale
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if is_causal:
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causal_mask = torch.triu(torch.ones(L, S, dtype=torch.bool, device=query.device), diagonal=1)
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attn_weight = attn_weight.masked_fill(causal_mask, float('-inf'))
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if attn_mask is not None:
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if attn_mask.dtype == torch.bool:
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attn_weight = attn_weight.masked_fill(~attn_mask, float('-inf'))
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else:
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attn_weight = attn_weight + attn_mask
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attn_weight = _safe_softmax_impl(attn_weight, dim=-1)
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if dropout_p > 0.0:
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attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p)
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return torch.matmul(attn_weight, value)
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def patched_sdpa(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
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S = key.size(-2)
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if query.is_cuda and S > SAFE_SOFTMAX_THRESHOLD:
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if not getattr(patched_sdpa, '_logged', False):
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logger.warning(f"[BC-250] Manual SDPA: Q={list(query.shape)}, S={S}")
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patched_sdpa._logged = True
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return _safe_sdpa(query, key, value, attn_mask=attn_mask,
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dropout_p=dropout_p, is_causal=is_causal, scale=scale)
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return _original_sdpa(query, key, value, attn_mask=attn_mask,
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dropout_p=dropout_p, is_causal=is_causal, scale=scale)
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# === GGUF WEIGHT CACHE + GPU DEQUANT ===
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_gguf_patched = False
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_weight_cache = {}
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_weight_cache_bytes = 0
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_WEIGHT_CACHE_MB = int(os.environ.get("BC250_WEIGHT_CACHE_MB", "0"))
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def _try_patch_gguf():
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"""Patch GGMLLayer.cast_bias_weight: GPU dequant + weight caching."""
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global _gguf_patched
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if _gguf_patched:
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return True
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ops_mod = None
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dequant_mod = None
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for name, mod in sys.modules.items():
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if mod is None:
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continue
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if name.endswith('.ops') and 'GGUF' in name:
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ops_mod = mod
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if name.endswith('.dequant') and 'GGUF' in name:
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dequant_mod = mod
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if ops_mod is None or dequant_mod is None:
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return False
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GGMLLayer = getattr(ops_mod, 'GGMLLayer', None)
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is_quantized_fn = getattr(dequant_mod, 'is_quantized', None)
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if GGMLLayer is None or is_quantized_fn is None:
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return False
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_original_cast = getattr(GGMLLayer, 'cast_bias_weight', None)
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cache_budget = _WEIGHT_CACHE_MB * 1024 * 1024
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def _bc250_cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
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"""GPU dequant with optional weight caching.
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With --highvram, GGUF weights are already on GPU. Dequant happens
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via PyTorch tensor ops on GPU (parallel) instead of CPU (sequential).
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If weight cache is enabled (BC250_WEIGHT_CACHE_MB > 0), dequanted
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weights are cached per-layer to eliminate dequant on steps 2+.
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"""
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global _weight_cache_bytes
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import comfy.model_management
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import comfy.ops
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if input is not None:
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if dtype is None:
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dtype = getattr(input, "dtype", torch.float32)
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if bias_dtype is None:
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bias_dtype = dtype
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if device is None:
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device = input.device
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non_blocking = comfy.model_management.device_supports_non_blocking(device)
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# Check weight cache
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if cache_budget > 0:
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cache_key = id(s)
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cached = _weight_cache.get(cache_key)
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if cached is not None:
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return cached
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# Bias
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bias = None
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if s.bias is not None:
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bias = s.get_weight(s.bias.to(device), bias_dtype)
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bias = comfy.ops.cast_to(bias, bias_dtype, device, non_blocking=non_blocking, copy=False)
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# Weight: .to(device) moves GGMLTensor to GPU, get_weight dequants on GPU
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weight = s.get_weight(s.weight.to(device), dtype)
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weight = comfy.ops.cast_to(weight, dtype, device, non_blocking=non_blocking, copy=False)
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# Cache if within budget
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if cache_budget > 0:
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entry_bytes = weight.nelement() * weight.element_size()
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if bias is not None:
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entry_bytes += bias.nelement() * bias.element_size()
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if _weight_cache_bytes + entry_bytes <= cache_budget:
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_weight_cache[cache_key] = (weight, bias)
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_weight_cache_bytes += entry_bytes
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return weight, bias
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GGMLLayer.cast_bias_weight = _bc250_cast_bias_weight
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_gguf_patched = True
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cache_str = f", weight cache={_WEIGHT_CACHE_MB}MB" if cache_budget > 0 else ""
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logger.warning(f"[BC-250] GGUF patched: GPU dequant{cache_str}")
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return True
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# === IMPORT HOOK for deferred GGUF patching ===
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class _GGUFImportWatcher:
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def __init__(self):
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self.done = False
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def find_module(self, fullname, path=None):
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if self.done:
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return None
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if 'GGUF' in fullname and ('dequant' in fullname or 'ops' in fullname):
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return self
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return None
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def load_module(self, fullname):
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if self in sys.meta_path:
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sys.meta_path.remove(self)
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try:
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import importlib
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mod = importlib.import_module(fullname)
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finally:
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if self not in sys.meta_path:
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sys.meta_path.insert(0, self)
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if _try_patch_gguf():
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self.done = True
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return mod
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# === TEXT ENCODER CPU PATCH ===
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_te_patched = False
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def _try_patch_text_encoder_device():
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global _te_patched
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if _te_patched:
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return True
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mm = sys.modules.get('comfy.model_management')
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if mm is None:
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return False
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mm.text_encoder_device = lambda: torch.device("cpu")
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mm.text_encoder_offload_device = lambda: torch.device("cpu")
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_te_patched = True
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logger.warning("[BC-250] Text encoder forced to CPU")
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return True
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class _ModelMgmtWatcher:
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def __init__(self):
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self.done = False
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def find_module(self, fullname, path=None):
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if self.done:
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return None
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if fullname == 'comfy.model_management':
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return self
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return None
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def load_module(self, fullname):
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if self in sys.meta_path:
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sys.meta_path.remove(self)
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try:
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import importlib
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mod = importlib.import_module(fullname)
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finally:
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if self not in sys.meta_path:
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sys.meta_path.insert(0, self)
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if _try_patch_text_encoder_device():
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self.done = True
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return mod
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# === VAE GPU FP16 WITH CACHING ===
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# BC-250 = APU with shared memory. GPU VRAM = CPU RAM = same physical pool.
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# No OOM risk from "using VRAM" — it's all the same 16GB.
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# GPU fp16 VAE is ~10x faster than CPU float32.
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_vae_patched = False
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_vae_cached = False
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def _try_patch_vae_gpu():
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global _vae_patched
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if _vae_patched:
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return True
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sd_mod = sys.modules.get('comfy.sd')
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if sd_mod is None:
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return False
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VAE = getattr(sd_mod, 'VAE', None)
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if VAE is None:
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return False
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def _ensure_vae_on_gpu_f16(self):
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"""Move VAE to GPU fp16 once, keep it cached. Shared memory = no OOM risk."""
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global _vae_cached
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gpu = torch.device("cuda")
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try:
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p = next(self.first_stage_model.parameters())
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already_ready = _vae_cached and p.device.type == 'cuda' and p.dtype == torch.float16
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except StopIteration:
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already_ready = False
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if not already_ready:
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logger.warning("[BC-250] Loading VAE to GPU fp16 (shared memory, will stay cached)")
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# Bypass _bc250_safe_apply (mmap pre-clone) — VAE is safetensors, not GGUF
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old_apply = torch.nn.Module._apply
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torch.nn.Module._apply = _original_module_apply
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try:
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self.first_stage_model.half().cuda()
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finally:
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torch.nn.Module._apply = old_apply
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self.first_stage_model.eval()
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_vae_cached = True
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self.disable_offload = True
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def _bc250_vae_decode(self, samples_in, vae_options={}):
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t0 = _time.time()
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self.throw_exception_if_invalid()
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if self.latent_dim == 2 and samples_in.ndim == 5:
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samples_in = samples_in[:, :, 0]
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# Free GPU memory from UNET before loading VAE
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mm = sys.modules.get('comfy.model_management')
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if mm:
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mm.unload_all_models()
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gc.collect()
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torch.cuda.empty_cache()
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_ensure_vae_on_gpu_f16(self)
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pixel_samples = None
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with torch.no_grad():
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for x in range(samples_in.shape[0]):
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sample = samples_in[x:x+1].to(torch.float16).cuda()
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decoded = self.first_stage_model.decode(sample, **vae_options)
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if decoded.ndim == 5:
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decoded = decoded[:, :, 0]
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out = self.process_output(decoded.float().cpu())
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if pixel_samples is None:
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pixel_samples = torch.empty(
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(samples_in.shape[0],) + tuple(out.shape[1:]), device='cpu'
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)
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pixel_samples[x:x+1] = out
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del decoded, sample
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pixel_samples = pixel_samples.movedim(1, -1)
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elapsed = _time.time() - t0
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logger.warning(f"[BC-250] VAE decode (GPU fp16): {elapsed:.1f}s")
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return pixel_samples
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def _bc250_vae_encode(self, pixel_samples):
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t0 = _time.time()
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self.throw_exception_if_invalid()
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mm = sys.modules.get('comfy.model_management')
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if mm:
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mm.unload_all_models()
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gc.collect()
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torch.cuda.empty_cache()
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_ensure_vae_on_gpu_f16(self)
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with torch.no_grad():
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pixels_in = self.process_input(pixel_samples).to(torch.float16).cuda()
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result = self.first_stage_model.encode(pixels_in).float().cpu()
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logger.warning(f"[BC-250] VAE encode (GPU fp16): {_time.time() - t0:.1f}s")
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return result
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VAE.decode = _bc250_vae_decode
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VAE.encode = _bc250_vae_encode
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_vae_patched = True
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logger.warning("[BC-250] VAE patched: GPU fp16 (shared memory = zero OOM risk)")
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return True
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class _SDModuleWatcher:
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def __init__(self):
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self.done = False
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def find_module(self, fullname, path=None):
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if self.done:
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return None
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if fullname == 'comfy.sd':
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return self
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return None
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def load_module(self, fullname):
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if self in sys.meta_path:
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sys.meta_path.remove(self)
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try:
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import importlib
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mod = importlib.import_module(fullname)
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finally:
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if self not in sys.meta_path:
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sys.meta_path.insert(0, self)
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if _try_patch_vae_gpu():
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self.done = True
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return mod
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# === GPU MEMORY CLEANUP HOOK ===
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_load_patched = False
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def _try_patch_load_models():
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global _load_patched
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if _load_patched:
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return True
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mm = sys.modules.get('comfy.model_management')
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if mm is None:
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return False
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_original_load = getattr(mm, 'load_models_gpu', None)
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if _original_load is None:
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return False
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def _bc250_load_models_gpu(models, *args, **kwargs):
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gc.collect()
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torch.cuda.empty_cache()
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return _original_load(models, *args, **kwargs)
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mm.load_models_gpu = _bc250_load_models_gpu
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_load_patched = True
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logger.warning("[BC-250] GPU memory cleanup hook installed")
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return True
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# === STARTUP PRELOAD ===
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_PRELOAD_CLIP = os.environ.get("BC250_PRELOAD_CLIP", "Qwen_3_4b-Q8_0.gguf")
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_PRELOAD_UNET = os.environ.get("BC250_PRELOAD_UNET", "z_image_turbo-Q5_K_S.gguf")
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_PRELOAD_VAE = os.environ.get("BC250_PRELOAD_VAE", "ae.safetensors")
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_PRELOAD_PORT = int(os.environ.get("BC250_PRELOAD_PORT", "8188"))
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_PRELOAD_ENABLED = os.environ.get("BC250_PRELOAD", "1") == "1"
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def _preload_models():
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import urllib.request
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import urllib.error
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url = f"http://127.0.0.1:{_PRELOAD_PORT}"
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logger.warning("[BC-250] Preload: waiting for server...")
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for _ in range(240):
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try:
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urllib.request.urlopen(f"{url}/api/system_stats", timeout=2)
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break
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except (urllib.error.URLError, OSError, ConnectionRefusedError):
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_time.sleep(0.5)
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else:
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logger.warning("[BC-250] Preload: server not ready after 120s, skip")
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return
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logger.warning("[BC-250] Preload: server ready, submitting warmup...")
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warmup = {
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"1": {"class_type": "CLIPLoaderGGUF", "inputs": {"clip_name": _PRELOAD_CLIP, "type": "lumina2"}},
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"2": {"class_type": "CLIPTextEncode", "inputs": {"text": "warmup", "clip": ["1", 0]}},
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"3": {"class_type": "CLIPTextEncode", "inputs": {"text": "", "clip": ["1", 0]}},
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"4": {"class_type": "UnetLoaderGGUF", "inputs": {"unet_name": _PRELOAD_UNET}},
|
|
"5": {"class_type": "EmptyLatentImage", "inputs": {"width": 64, "height": 64, "batch_size": 1}},
|
|
"6": {"class_type": "KSampler", "inputs": {
|
|
"seed": 1, "steps": 1, "cfg": 1.0, "sampler_name": "euler",
|
|
"scheduler": "normal", "denoise": 1.0,
|
|
"model": ["4", 0], "positive": ["2", 0], "negative": ["3", 0], "latent_image": ["5", 0]
|
|
}},
|
|
"7": {"class_type": "VAELoader", "inputs": {"vae_name": _PRELOAD_VAE}},
|
|
"8": {"class_type": "VAEDecode", "inputs": {"samples": ["6", 0], "vae": ["7", 0]}},
|
|
"9": {"class_type": "SaveImage", "inputs": {"filename_prefix": "_warmup", "images": ["8", 0]}}
|
|
}
|
|
|
|
payload = json.dumps({"prompt": warmup}).encode("utf-8")
|
|
req = urllib.request.Request(
|
|
f"{url}/api/prompt", data=payload,
|
|
headers={"Content-Type": "application/json"}, method="POST"
|
|
)
|
|
|
|
try:
|
|
resp = urllib.request.urlopen(req, timeout=10)
|
|
data = json.loads(resp.read())
|
|
prompt_id = data.get("prompt_id", "unknown")
|
|
logger.warning(f"[BC-250] Preload: warmup queued (id={prompt_id})")
|
|
|
|
for _ in range(300):
|
|
_time.sleep(1)
|
|
try:
|
|
hist_resp = urllib.request.urlopen(f"{url}/api/history/{prompt_id}", timeout=5)
|
|
hist = json.loads(hist_resp.read())
|
|
if prompt_id in hist:
|
|
logger.warning("[BC-250] Preload: all models cached. Ready.")
|
|
return
|
|
except Exception:
|
|
pass
|
|
|
|
logger.warning("[BC-250] Preload: warmup timed out (5min)")
|
|
except Exception as e:
|
|
logger.warning(f"[BC-250] Preload failed: {e}")
|
|
|
|
|
|
def _start_preload_thread():
|
|
if not _PRELOAD_ENABLED:
|
|
logger.warning("[BC-250] Preload: disabled (BC250_PRELOAD=0)")
|
|
return
|
|
t = threading.Thread(target=_preload_models, daemon=True, name="BC250-Preload")
|
|
t.start()
|
|
logger.warning("[BC-250] Preload: background thread started")
|
|
|
|
# === INSTALL ===
|
|
|
|
def _prewarm_gpu():
|
|
try:
|
|
if not torch.cuda.is_available():
|
|
return
|
|
dummy = torch.zeros(1, device='cuda')
|
|
_ = dummy + 1
|
|
torch.cuda.synchronize()
|
|
del dummy
|
|
torch.cuda.empty_cache()
|
|
logger.warning("[BC-250] GPU pre-warmed")
|
|
except Exception as e:
|
|
logger.warning(f"[BC-250] GPU pre-warm failed: {e}")
|
|
|
|
|
|
def install():
|
|
# Mmap pre-clone patch
|
|
torch.nn.Module._apply = _bc250_safe_apply
|
|
logger.warning("[BC-250] Mmap pre-clone patch installed")
|
|
|
|
# Softmax patches
|
|
torch.nn.functional.softmax = patched_softmax
|
|
torch.Tensor.softmax = patched_tensor_softmax
|
|
torch.nn.functional.scaled_dot_product_attention = patched_sdpa
|
|
logger.warning(f"[BC-250] Softmax patch installed (threshold={SAFE_SOFTMAX_THRESHOLD})")
|
|
|
|
# Deferred patches via import hooks
|
|
sys.meta_path.insert(0, _GGUFImportWatcher())
|
|
sys.meta_path.insert(0, _ModelMgmtWatcher())
|
|
sys.meta_path.insert(0, _SDModuleWatcher())
|
|
|
|
# Try immediate patches
|
|
_try_patch_gguf()
|
|
_try_patch_text_encoder_device()
|
|
_try_patch_vae_gpu()
|
|
_try_patch_load_models()
|
|
|
|
_prewarm_gpu()
|
|
_start_preload_thread()
|
|
|
|
install()
|