""" Usage: # Focus on mixers/MLP, show params/buffers, depth limit `filter` "self_attn|linear_attn|mlp" --show-params --max-depth 4 # Control MoE collapsing: # - collapse any ModuleList whose name matches the regex and whose children are numeric # - show the first N experts and collapse the rest `experts-regex` "(^|\\.)experts($|\\.)" --experts-show 1 `no-collapse-experts` # disable collapsing Notes: - Detects shared submodules and avoids re-printing them. - Collapsing is generic: any numeric-indexed ModuleList whose qualified name matches `experts-regex`. """ import re from typing import Optional, Set, Tuple from torch import nn def human_count(n: int) -> str: if n < 1_000: return str(n) units = ["K", "M", "B", "T"] k = 0 x = float(n) while x >= 1000 and k < len(units) - 1: x /= 1000.0 k += 1 return f"{x:.2f}{units[k]}" def _param_summary(mod: nn.Module, recurse: bool = False) -> Tuple[int, int]: p = sum(p.numel() for p in mod.parameters(recurse=recurse)) b = sum(bf.numel() for bf in mod.buffers(recurse=recurse)) return p, b def _format_line(prefix: str, trunk: str, name: str, mod: nn.Module, show_counts: bool, color: bool) -> str: cls = mod.__class__.__name__ p, b = _param_summary(mod, recurse=False) counts = f" (P={human_count(p)} B={human_count(b)})" if show_counts else "" if color: BLUE, CYAN, DIM, RESET = "\033[34m", "\033[36m", "\033[2m", "\033[0m" return f"{prefix}{trunk}{BLUE}{name}{RESET}: {CYAN}{cls}{RESET}{DIM}{counts}{RESET}" return f"{prefix}{trunk}{name}: {cls}{counts}" def _print_params(prefix: str, mod: nn.Module, include_buffers: bool, color: bool, max_items: int = 100): items = [] for n, p in mod.named_parameters(recurse=False): items.append(("param", n, tuple(p.shape), p.dtype)) if include_buffers: for n, b in mod.named_buffers(recurse=False): items.append(("buffer", n, tuple(b.shape), b.dtype)) if not items: return if color: DIM, RESET = "\033[2m", "\033[0m" else: DIM = RESET = "" for kind, n, shape, dt in items[:max_items]: print(f"{prefix}{DIM}• {kind}: {n:32s} shape={shape!s:>20s} dtype={str(dt)}{RESET}") if len(items) > max_items: print(f"{prefix}{DIM}• … {len(items)-max_items} more{RESET}") def print_module_tree( model: nn.Module, *, root_name: str = "model", max_depth: Optional[int] = None, filter_regex: Optional[str] = None, show_params: bool = False, show_buffers: bool = False, color: bool = True, # MoE collapsing controls: collapse_experts: bool = True, experts_regex: str = r"(^|\.)experts($|\.)", experts_show: int = 1, ): re.compile(filter_regex) if filter_regex else None experts_name_re = re.compile(experts_regex) if collapse_experts else None seen: Set[int] = set() total_p = sum(p.numel() for p in model.parameters()) total_b = sum(b.numel() for b in model.buffers()) def should_collapse(qual_name: str, container: nn.Module) -> bool: if not experts_name_re: return False if not experts_name_re.search(qual_name): return False if not isinstance(container, (nn.ModuleList, nn.Sequential)): return False # must have numerically indexed children names = [n for n, _ in container.named_children()] if not names: return False return all(n.isdigit() for n in names) and len(names) > max(0, experts_show) def rec(mod: nn.Module, name: str, depth: int, prefix: str, is_last: bool): full_name = name if max_depth is not None and depth > max_depth: return mod_id = id(mod) shared = "" if mod_id in seen: shared = " ↩ shared ref" else: seen.add(mod_id) trunk = "└─ " if is_last else "├─ " print(_format_line(prefix, trunk, full_name, mod, show_counts=True, color=color) + shared) if shared: return if show_params or show_buffers: _print_params(prefix + (" " if is_last else "│ "), mod, include_buffers=show_buffers, color=color) # Children children = list(mod.named_children()) n = len(children) for i, (child_name, child) in enumerate(children): last = (i == n - 1) child_prefix = prefix + (" " if is_last else "│ ") display_name = f"{full_name}.{child_name}" if full_name else child_name # Collapsing logic for MoE-like ModuleLists if should_collapse(display_name, child): # Print the container node itself print(_format_line(child_prefix, "└─ " if last else "├─ ", display_name, child, True, color)) # Materialize its numeric children sub_children = list(child.named_children()) total_k = len(sub_children) k_show = max(0, min(experts_show, total_k)) # Show first K experts fully for j, (sub_name, sub_mod) in enumerate(sub_children[:k_show]): sub_last = (j == k_show - 1) and (k_show == total_k) # if showing all, it's last sub_prefix = child_prefix + (" " if last else "│ ") sub_trunk = "└─ " if sub_last else "├─ " print(_format_line(sub_prefix, sub_trunk, f"{display_name}.{sub_name}", sub_mod, True, color)) # descend into the exemplar(s) rec( sub_mod, f"{display_name}.{sub_name}", depth + 2, child_prefix + (" " if last else "│ "), sub_last, ) # Collapsed summary for the remainder if k_show < total_k: # Estimate per-expert params/buffers from the first exemplar p_one, b_one = _param_summary(sub_children[0][1], recurse=True) p_rest = (total_k - k_show) * p_one b_rest = (total_k - k_show) * b_one if color: DIM, RESET = "\033[2m", "\033[0m" else: DIM = RESET = "" summary_prefix = child_prefix + (" " if last else "│ ") # Use a single summary line print( f"{summary_prefix}{DIM}• … collapsed (repeats {k_show}..{total_k-1}, " f"per-expert P={human_count(p_one)} B={human_count(b_one)}, " f"collapsed total P≈{human_count(p_rest)} B≈{human_count(b_rest)}){RESET}" ) continue # handled this container; do not regular-rec descend # Normal recursion rec(child, display_name, depth + 1, child_prefix, last) # Root root_prefix = "" print(_format_line(root_prefix, "", root_name, model, show_counts=True, color=color)) if show_params or show_buffers: _print_params(" ", model, include_buffers=show_buffers, color=color) # Descend from root children = list(model.named_children()) for i, (child_name, child) in enumerate(children): last = (i == len(children) - 1) rec(child, f"{root_name}.{child_name}", 1, "", last) # Footer print("\nTotal parameters:", human_count(total_p), " | Total buffers:", human_count(total_b)) trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) frozen = total_p - trainable print("Trainable:", human_count(trainable), " | Frozen:", human_count(frozen))