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AI: Add debug toggle - This node is hard to use

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mcDandy
2026-04-21 16:16:46 +02:00
parent cd1e380901
commit 0a1e1bc1f6
1 changed files with 50 additions and 18 deletions
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more_math/SelectiveGuiderMathNode.py
+50 -18
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@@ -10,7 +10,6 @@ from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
from .helper_functions import checkLazyNew, generate_dim_variables, parse_expr, as_tensor
from .Stack import MrmthStack
from .ParseTree import MrmthParseTree
from comfy.ldm.modules.attention import optimized_attention
class SelectiveGuiderMathNode(io.ComfyNode):
@@ -42,7 +41,8 @@ class SelectiveGuiderMathNode(io.ComfyNode):
default="all"
),
MrmthStack.Input(id="stack", tooltip="Access stack between nodes", optional=True),
io.Boolean.Input(id="remember_stack", default=False, display_name="Remember stack across batch")
io.Boolean.Input(id="remember_stack", default=False, display_name="Remember stack across batch"),
io.Boolean.Input(id="debug_hooks", default=False, display_name="Debug hook logs"),
],
outputs=[io.Guider.Output(), MrmthStack.Output()],
)
@@ -56,6 +56,7 @@ class SelectiveGuiderMathNode(io.ComfyNode):
layer_x=0,
hook_target="all",
remember_stack=False,
debug_hooks=False,
stack={},
):
return checkLazyNew(Expression, {}, F)
@@ -69,6 +70,7 @@ class SelectiveGuiderMathNode(io.ComfyNode):
layer_x=0,
hook_target="all",
remember_stack=False,
debug_hooks=False,
stack=None,
):
if V is None or not hasattr(V, "model_options"):
@@ -77,7 +79,6 @@ class SelectiveGuiderMathNode(io.ComfyNode):
stack = stack if remember_stack else (copy.deepcopy(stack) if stack is not None else {})
tree = parse_expr(Expression) if isinstance(Expression, str) else Expression
MAX_HOOK_INDEX = 999
hit_flags = {
"attn1": False,
"attn2": False,
@@ -88,6 +89,26 @@ class SelectiveGuiderMathNode(io.ComfyNode):
"unet": False,
}
debug_hook_events = []
def dbg(msg: str):
if debug_hooks:
logging.warning(f"[SelectiveGuiderMathNode] {msg}")
def record_hook(kind: str, side: str, layer_id=None, stage=None, key=None):
if not debug_hooks:
return
parts = [f"hook={kind}", f"side={side}"]
if stage is not None:
parts.append(f"stage={stage}")
if layer_id is not None:
parts.append(f"layer_id={layer_id}")
if key is not None:
parts.append(f"key={key}")
line = " ".join(parts)
debug_hook_events.append(line)
dbg(f"HOOK {line}")
def resolve_attn_kind(transformer_options: dict, q: torch.Tensor | None = None, k: torch.Tensor | None = None, v: torch.Tensor | None = None) -> str:
# Flux / MM-DiT
btype = transformer_options.get("block_type", None)
@@ -280,7 +301,8 @@ class SelectiveGuiderMathNode(io.ComfyNode):
visitor = UnifiedMathVisitor(variables, inp.shape, inp.device, state_storage=stack)
out = visitor.visit(tree)
return as_tensor(out, inp.shape).to(inp.device)
# preserve dtype/device (float8-safe vs float16 promotion side effects)
return as_tensor(out, inp.shape).to(device=inp.device, dtype=inp.dtype)
def register_dit(patched_dict, forced_side=None):
topts = patched_dict["transformer_options"]
@@ -292,8 +314,8 @@ class SelectiveGuiderMathNode(io.ComfyNode):
if not match_index(idx, total_blocks, "dit", args.get("transformer_options", {}), "dit_block"):
return extra_args["original_block"](args)
if not hit_flags["dit"]:
logging.warning(f"[SelectiveGuiderMathNode] DIT HIT idx={idx} total={total_blocks}")
hit_flags["dit"] = True
dbg(f"DIT HIT idx={idx} total={total_blocks}")
hit_flags["dit"] = True
btype = args.get("transformer_options", {}).get("block_type", "dit")
co = args.get("transformer_options", {}).get("cond_or_uncond", None)
auto_side, auto_idx = side_from_cond_or_uncond(co)
@@ -374,12 +396,11 @@ class SelectiveGuiderMathNode(io.ComfyNode):
idx_side = 0 if side == "positive" else (1 if side == "negative" else auto_idx)
if not hit_flags.get(attn_kind, False):
logging.warning(f"[SelectiveGuiderMathNode] {attn_kind.upper()} HIT stage={stage} layer_id={layer_id} side={side}")
dbg(f"{attn_kind.upper()} HIT stage={stage} layer_id={layer_id} side={side}")
hit_flags[attn_kind] = True
act_shape = transformer_options.get("activations_shape", [])
if not isinstance(act_shape, (list, tuple)):
act_shape = []
meta = {
"hook_kind": attn_kind,
@@ -399,7 +420,7 @@ class SelectiveGuiderMathNode(io.ComfyNode):
"k": k,
"v": v,
"heads": float(heads),
"dim_head": float(q.shape[-1] // heads) if heads > 0 else 0.0,
"dim_head": float(q.shape[-1] // heads) if heads > 0 : 0.0,
"layer_key": f"{stage}.{layer_id}.{attn_kind}.{t_index}",
"cond_side": side,
"cond_index": float(idx_side),
@@ -440,7 +461,7 @@ class SelectiveGuiderMathNode(io.ComfyNode):
idx_side = 0 if side == "positive" else (1 if side == "negative" else auto_idx)
if not hit_flags["unet"]:
logging.warning(f"[SelectiveGuiderMathNode] UNET BLOCK HIT stage={stage} layer_id={idx} side={side}")
dbg(f"UNET BLOCK HIT stage={stage} layer_id={idx} side={side}")
hit_flags["unet"] = True
meta = {
@@ -574,6 +595,7 @@ class SelectiveGuiderMathNode(io.ComfyNode):
V.original_conds[side_name] = new_conds
# po parse_expr(...)
if hasattr(V, "original_conds"):
def make_shallow_cond_copy(conds_dict):
new_dict = {}
@@ -581,7 +603,7 @@ class SelectiveGuiderMathNode(io.ComfyNode):
# Kondice obvykle obsahují fragmentová pole (slovníky nebo listy)
new_dict[k] = [item.copy() if hasattr(item, "copy") else copy.copy(item) for item in v_list]
return new_dict
if not hasattr(V, "_mrmth_base_original_conds"):
V._mrmth_base_original_conds = make_shallow_cond_copy(V.original_conds)
else:
@@ -590,11 +612,11 @@ class SelectiveGuiderMathNode(io.ComfyNode):
if hasattr(V, "original_conds"):
attach_side_hook("positive")
attach_side_hook("negative")
logging.warning(f"[SelectiveGuiderMathNode] has original_conds: {list(V.original_conds.keys())}")
dbg(f"has original_conds: {list(V.original_conds.keys())}")
for side in ("positive", "negative"):
if side in V.original_conds:
cnt = sum(1 for c in V.original_conds[side] if c.get("hooks", None) is not None)
logging.warning(f"[SelectiveGuiderMathNode] side={side} conds_with_hooks={cnt}/{len(V.original_conds[side])}")
dbg(f"side={side} conds_with_hooks={cnt}/{len(V.original_conds[side])}")
else:
patched = comfy.model_patcher.create_model_options_clone(V.model_options)
patched.setdefault("transformer_options", {})
@@ -614,17 +636,17 @@ class SelectiveGuiderMathNode(io.ComfyNode):
reg_len = len(registered) if registered is not None else 0
in_len = len(hooks) if hooks is not None else 0
tr_count = len(registered.get_type(comfy.hooks.EnumHookType.TransformerOptions)) if registered is not None else 0
logging.warning(f"[SelectiveGuiderMathNode] REGISTER hooks_in={in_len} registered={reg_len} transformer_registered={tr_count}")
dbg(f"REGISTER hooks_in={in_len} registered={reg_len} transformer_registered={tr_count}")
if model_options is not None:
rh = model_options.get("registered_hooks", None)
logging.warning(f"[SelectiveGuiderMathNode] model_options.registered_hooks={'set' if rh is not None else 'None'}")
dbg(f"model_options.registered_hooks={'set' if rh is not None else 'None'}")
except Exception as e:
logging.warning(f"[SelectiveGuiderMathNode] REGISTER debug error: {e}")
def _on_apply_hooks(model_patcher, hooks):
try:
hl = len(hooks) if hooks is not None else 0
logging.warning(f"[SelectiveGuiderMathNode] APPLY hooks_len={hl}")
dbg(f"APPLY hooks_len={hl}")
except Exception as e:
logging.warning(f"[SelectiveGuiderMathNode] APPLY debug error: {e}")
@@ -641,4 +663,14 @@ class SelectiveGuiderMathNode(io.ComfyNode):
V._mrmth_debug_callbacks_added = True
stack = stack if remember_stack else copy.deepcopy(stack)
return (V, stack)
result = (V, stack)
# 6) Before return add compact summary:
if debug_hooks:
uniq = sorted(set(debug_hook_events))
dbg(f"HOOK SUMMARY total={len(debug_hook_events)} unique={len(uniq)}")
for item in uniq:
dbg(f"HOOK SUMMARY ITEM {item}")
return result