Claude
d1d83ebd8e
Settings > Engine > Sampling: pick a PTD adapter file from models/ptd and the run samples the file's own 4 to 8 hops with no CFG, whatever sampler and steps are chosen. Reads the EPFL fl2va and taobao-content-ai Ref2VA releases (bf16 or int8, full or pruned base); ordinary LoRAs stack. Ported from ComfyUI-PTD-Compact (Nynxz), ComfyUI-PTD (EPFL VITA Lab) and ComfyUI-MiniMaxH3-PTD (taobao-content-ai), all Apache-2.0: README Thank you plus NOTICE and licence text in the module. Proven on a tiny real H3 on CPU only. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0164nckPWA2L2ugVqiDcXzc3
385 lines
18 KiB
Python
385 lines
18 KiB
Python
"""Running a PTD adapter on a native MiniMax-H3 model: one expert set per hop, switched by hooks.
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Adapters are low-rank additions to linear outputs (forward hooks), so ordinary LoRAs, which are weight
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patches, stack with them in either order. The bank stays in system RAM; each layer copies only the
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current hop's factors to the GPU, and int8 row scales are folded into the small factors.
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Hooks live for one model call and are removed even when the call fails. Block arithmetic follows PTD's
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reference order (bf16 products rounded one by one, as the experts were trained) unless `fuse_blocks`
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asks for ComfyUI's fused native blocks.
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Controller, hooks, block maths and the model-call wrapper follow ComfyUI-PTD (EPFL VITA Lab, Apache-2.0),
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with the int8 factors, in-place residual and DynamicVRAM handling of ComfyUI-PTD-Compact (Nynxz,
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Apache-2.0) and the output-head fusion of ComfyUI-MiniMaxH3-PTD (taobao-content-ai, Apache-2.0).
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"""
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from contextlib import ExitStack, contextmanager
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from contextvars import ContextVar
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import copy
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from dataclasses import dataclass
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import math
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import threading
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import weakref
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import torch
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import torch.nn.functional as F
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from . import schedule
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_FORWARD = ContextVar("funpack_ptd_forward", default=None)
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_LOCKS = weakref.WeakKeyDictionary()
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_LOCKS_GUARD = threading.Lock()
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def _model_lock(model):
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with _LOCKS_GUARD:
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return _LOCKS.setdefault(model, threading.RLock())
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def _check_interrupt():
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from comfy.model_management import throw_exception_if_processing_interrupted
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throw_exception_if_processing_interrupted()
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# --- block arithmetic in PTD's reference order ------------------------------------------------------
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def _modulated(normed, shift, scale, segments):
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result = torch.empty_like(normed)
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for start, stop, row in segments:
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result[start:stop] = normed[start:stop] * (1.0 + scale[row].to(normed.dtype)) + shift[row].to(normed.dtype)
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return result
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def _residual(state, update, gate, segments):
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"""`state + update * gate`, added in place. The product is rounded on its own (no addcmul) as PTD was
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trained; adding into the stream keeps ComfyUI's allocation compiler from seeing a block output that
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outlives its block."""
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for start, stop, row in segments:
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state[start:stop].add_(update[start:stop] * gate[row].to(state.dtype))
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return state
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def _block_math(block, x, t_emb, mod_segments, rope_freqs, transformer_options=None, attention=None):
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options = {} if transformer_options is None else transformer_options
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shift_a, scale_a, gate_a, shift_m, scale_m, gate_m = block.adaln_proj(t_emb)
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normed = _modulated(block.norm1(x), shift_a, scale_a, mod_segments)
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update = (block.attn if attention is None else attention)(normed, rope_freqs=rope_freqs, transformer_options=options)
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x = _residual(x, update, gate_a, mod_segments)
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normed = _modulated(block.norm2(x), shift_m, scale_m, mod_segments)
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return _residual(x, block.mlp(normed), gate_m, mod_segments)
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def _split_half_rotation(x, table):
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cos = table[0, :, 0, :, 0, 0].repeat(1, 2).unsqueeze(1).to(x.dtype)
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sin = table[0, :, 0, :, 1, 0].repeat(1, 2).unsqueeze(1).to(x.dtype)
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width = cos.shape[-1]
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leading = x[..., :width]
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first, second = leading.chunk(2, dim=-1)
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# The two products and their sum stay separate: a fused RMS-RoPE would do the rotation in fp32 first.
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result = leading * cos + torch.cat((-second, first), dim=-1) * sin
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return torch.cat((result, x[..., width:]), dim=-1)
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def _attention_math(attention, x, rope_freqs=None, transformer_options=None):
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from comfy.ldm.modules.attention import AttentionTensorContainer, optimized_attention
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options = {} if transformer_options is None else transformer_options
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streams = attention.qkv_proj(x).chunk(3, dim=-1)
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q, k, v = [value.reshape(x.shape[0], attention.heads, attention.head_dim) for value in streams]
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q, k = attention.q_norm(q), attention.k_norm(k)
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if rope_freqs is not None:
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q, k = _split_half_rotation(q, rope_freqs), _split_half_rotation(k, rope_freqs)
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packed = [AttentionTensorContainer(value.transpose(0, 1).unsqueeze(0)) for value in (q, k, v)]
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result = optimized_attention(*packed, attention.heads, skip_reshape=True,
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preferred_attention=attention.comfy_attention, transformer_options=options)
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return attention.out_proj(result.squeeze(0))
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def _scope_forward(module, function, frame, originals):
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original = module.forward
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originals.append((module, "forward" in vars(module), original))
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def forward(*args, **kwargs):
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if _FORWARD.get() is not frame:
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return original(*args, **kwargs)
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return function(module, *args, **kwargs)
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module.forward = forward
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@contextmanager
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def _without_comfy_compiler():
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"""Switch off ComfyUI's allocation compiler (and its CUDA graphs) for one call. Used only for a run in
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which the compiler failed: its recording does not expect the extra tensors PTD's block maths allocates."""
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try:
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from comfy.cli_args import args
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except ImportError:
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yield
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return
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saved = getattr(args, "disable_comfy_compiler", False), getattr(args, "disable_cuda_graphs", False)
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args.disable_comfy_compiler = args.disable_cuda_graphs = True
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try:
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yield
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finally:
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args.disable_comfy_compiler, args.disable_cuda_graphs = saved
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@dataclass(frozen=True)
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class Hop:
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index: int
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sigma: float
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class Controller:
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"""The adapter bank of one loaded file, bound to one diffusion model. Hop selection is context-local;
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calls that share a backbone are serialised."""
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def __init__(self, native, adapter):
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self.native = native
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self.adapter = adapter
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self.hops = adapter.hops
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self.grid = torch.tensor(adapter.grid, dtype=torch.float64)
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self._lock = _model_lock(native)
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self._hop = ContextVar(f"funpack_ptd_hop_{id(self)}", default=None)
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self.fuse_blocks = False
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self.allocation_compiler = True
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@contextmanager
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def activate(self, hop, sigma):
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if isinstance(hop, bool) or not isinstance(hop, int) or not 0 <= hop < self.hops:
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raise ValueError(f"PTD hop must be in [0, {self.hops})")
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expected = float(self.grid[hop])
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if abs(float(sigma) - expected) > 2e-6:
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raise ValueError(f"Hop {hop} must run at its trained time {expected:.6f}, not {float(sigma):.6f}")
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token = self._hop.set(Hop(hop, expected))
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try:
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yield self
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finally:
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self._hop.reset(token)
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def current(self):
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hop = self._hop.get()
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if hop is None:
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raise RuntimeError("PTD adapters are on this model but no PTD hop is running: the model was called "
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"outside the PTD sampler")
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return hop
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def _linear_hook(self, frame, experts):
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# q/k/v share one input: their A factors run as one matmul. Each expert's delta is added into the
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# fresh linear output in place; int8 row scales are folded into the small factors.
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qkv = sorted((e for e in experts if e.qkv_index is not None), key=lambda e: e.qkv_index)
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plain = [e for e in experts if e.qkv_index is None]
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ranks = [e.a.shape[0] for e in qkv]
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def factors(e, dev, dt):
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a = e.a.to(dev, non_blocking=True).to(dt)
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b = e.b.to(dev, non_blocking=True).to(dt)
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if e.sa is not None:
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a = a * e.sa.to(dev, dt)[:, None]
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if e.sb is not None:
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b = b * e.sb.to(dev, dt)[:, None]
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return a, b
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def hook(module, args, kwargs, output):
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if _FORWARD.get() is not frame:
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return output
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_check_interrupt()
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x = args[0] if args else kwargs.get("input")
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dev, dt = x.device, output.dtype # the layer's compute dtype
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x = x.to(dt)
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for e in plain:
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a, b = factors(e, dev, dt)
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output.add_(F.linear(F.linear(x, a), b))
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if qkv:
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pairs = [factors(e, dev, dt) for e in qkv]
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hs = F.linear(x, torch.cat([a for a, _ in pairs])).split(ranks, dim=-1)
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width = output.shape[-1] // 3
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for e, (_, b), h in zip(qkv, pairs, hs):
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output[..., e.qkv_index * width:(e.qkv_index + 1) * width].add_(F.linear(h, b))
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return output
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return hook
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@contextmanager
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def forward_hooks(self, hop):
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"""Scope the adapters, the PTD clocks and (unless fuse_blocks) PTD's block maths to one model call."""
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native = self.native
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with self._lock, ExitStack() as stack:
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if not self.allocation_compiler:
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stack.enter_context(_without_comfy_compiler())
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frame = object()
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token = _FORWARD.set(frame)
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handles, forced, forwards = [], [], []
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try:
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if not self.fuse_blocks:
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for block in native.blocks:
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if all(hasattr(block, n) for n in ("norm1", "norm2", "adaln_proj", "attn", "mlp")):
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_scope_forward(block, _block_math, frame, forwards)
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_scope_forward(block.attn, _attention_math, frame, forwards)
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self._time_hooks(frame, hop, handles)
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heads = self.adapter.heads[hop.index] if self.adapter.heads else {}
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for name, (weight, bias) in heads.items():
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parent, _, attr = name.rpartition(".")
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handles.append(getattr(native.get_submodule(parent), attr).register_forward_hook(
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self._fused_head(frame, weight, bias)))
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for name, by_hop in self.adapter.bank.items():
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module = native.get_submodule(name)
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# Native SwiGLU may fuse activation and fc2, bypassing Module.__call__ and its hooks;
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# ComfyUI's public flag routes the call through the plain linear.
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if hasattr(module, "comfy_force_forward") or name.endswith(".mlp.fc2"):
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forced.append((module, "comfy_force_forward" in vars(module),
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getattr(module, "comfy_force_forward", None)))
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module.comfy_force_forward = True
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handles.append(module.register_forward_hook(
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self._linear_hook(frame, self.adapter.bank[name][hop.index]), with_kwargs=True))
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factor = self.adapter.audio[hop.index]
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def audio_clock(module, args, output):
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if _FORWARD.get() is not frame:
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return output
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# Scaled before native H3 casts to the latent dtype, like PTD's fp32 output heads.
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return output * torch.as_tensor(factor, dtype=output.dtype, device=output.device)
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handles.append(native.final_layer.audio_out.register_forward_hook(audio_clock))
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_check_interrupt()
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yield
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_check_interrupt()
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finally:
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for handle in reversed(handles):
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handle.remove()
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for module, had, value in reversed(forced):
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if had:
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module.comfy_force_forward = value
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else:
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del module.comfy_force_forward
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for module, had, original in reversed(forwards):
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if had:
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module.forward = original
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else:
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del module.forward
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_FORWARD.reset(token)
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@staticmethod
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def _fused_head(frame, weight, bias):
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def hook(module, args, output):
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if _FORWARD.get() is not frame:
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return output
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x = args[0]
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return F.linear(x.to(output.dtype), weight.to(x.device, output.dtype),
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None if bias is None else bias.to(x.device, output.dtype))
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return hook
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def _time_hooks(self, frame, hop, handles):
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native = self.native
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if not hasattr(native, "time_embedder"): # pruned (AdaLN-curve) base: no time embedder to steer
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return
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remembered = {}
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def exact_times(module, args):
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if _FORWARD.get() is not frame:
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return args
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times = args[0]
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if not isinstance(times, torch.Tensor) or times.ndim != 1:
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raise ValueError("PTD expects a vector of native H3 time labels")
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video_time = 1.0 - hop.sigma
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candidates = torch.tensor([video_time, 1.0 - schedule.audio_sigma(hop.sigma), max(video_time, .999)],
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device=times.device, dtype=torch.float32).unique()
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distance, indices = (times[:, None] - candidates[None]).abs().min(dim=1)
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if not bool((distance <= 2e-6).all()):
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raise ValueError("Native H3 time labels do not match the active PTD clocks")
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# Native H3 rounds sigma to fp32 first; PTD subtracts in float64. Keep the table order.
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return (candidates[indices],) + args[1:]
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def remember(module, args, output):
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if _FORWARD.get() is frame:
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remembered["fp32"] = output
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def precise_adaln_input(module, args):
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if _FORWARD.get() is not frame:
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return args
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if "fp32" not in remembered:
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raise RuntimeError("PTD AdaLN ran before the time embedder")
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# Diffusers applies SiLU to the fp32 time embedding before casting; native H3 casts first.
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return (F.silu(remembered["fp32"]).to(args[0].dtype),) + args[1:]
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handles.append(native.time_embedder.register_forward_pre_hook(exact_times))
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handles.append(native.time_embedder.register_forward_hook(remember))
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for layer in (*native.blocks, native.final_layer):
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adaln = getattr(layer, "adaln_proj", None)
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if adaln is not None and getattr(adaln, "apply_silu", True):
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handles.append(adaln.linear.register_forward_pre_hook(precise_adaln_input))
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class ModelCall:
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"""The model-function wrapper: PTD carries physical latents, so the native shifts are neutralised for the
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call and the adapters are switched on for it."""
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def __init__(self, controller, previous=None):
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self.controller = controller
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self.previous = previous
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def __call__(self, model_function, kwargs):
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hop = self.controller.current()
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sigma = kwargs.get("timestep")
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if not isinstance(sigma, torch.Tensor) or sigma.numel() == 0:
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raise ValueError("PTD needs a tensor sigma from the sampler")
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if not bool(torch.allclose(sigma, torch.full_like(sigma, hop.sigma), rtol=0.0, atol=2e-6)):
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raise ValueError("The model sigma does not match the active PTD hop")
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forwarded = dict(kwargs)
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conditions = dict(kwargs.get("c", {}))
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options = dict(conditions.get("transformer_options", {}))
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options["minimax_h3_sigma_shift_video"] = 1.0
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options["minimax_h3_sigma_shift_audio"] = schedule.native_audio_shift(hop.sigma)
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conditions["transformer_options"] = options
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payload = dict(conditions.get("minimax_payload", {}))
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payload["audio_scale"] = 1.0
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conditions["minimax_payload"] = payload
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forwarded["c"] = conditions
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with self.controller.forward_hooks(hop):
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if self.previous is not None:
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return self.previous(model_function, forwarded)
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return model_function(forwarded["input"], forwarded["timestep"], **conditions)
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def to(self, device):
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if self.previous is not None and hasattr(self.previous, "to"):
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self.previous = self.previous.to(device)
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return self
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def cleanup(self, **kwargs):
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if self.previous is not None and hasattr(self.previous, "cleanup"):
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self.previous.cleanup(**kwargs)
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def models(self):
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if self.previous is not None and hasattr(self.previous, "models"):
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return self.previous.models()
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return []
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def attach(patcher, controller, key, tag):
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"""Put `controller` on `patcher` (a clone the caller owns): raw text embeddings so each hop's refiner
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adapters run, PTD's audio shift, and the model-call wrapper. Everything is tagged `key` for removal."""
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native = controller.native
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if not all(hasattr(native, n) for n in ("blocks", "token_refiner", "condition_proj", "final_layer",
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"sigma_shift_video", "sigma_shift_audio")):
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raise ValueError("PTD needs a native MiniMax-H3 model")
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sampling = copy.deepcopy(patcher.get_model_object("model_sampling"))
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if not hasattr(sampling, "audio_shift") or not hasattr(sampling, "shift"):
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raise ValueError("PTD needs the native MiniMax-H3 audio/video flow sampling")
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sampling.audio_shift = sampling.shift
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patcher.add_object_patch("model_sampling", tag(sampling, key))
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text_dim = native.condition_proj.in_features
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def raw_text(text_states):
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if text_states.shape[-1] != text_dim:
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raise ValueError("PTD needs raw MiniMax-H3 text conditioning, not pre-refined embeddings")
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return text_states
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patcher.add_object_patch("diffusion_model.preprocess_text_embeds", tag(raw_text, key))
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previous = patcher.model_options.get("model_function_wrapper")
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wrapper = tag(ModelCall(controller, previous), key)
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wrapper._funpack_prev = previous
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patcher.set_model_unet_function_wrapper(wrapper)
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patcher.model_options[CONTROLLER_KEY] = tag(controller, key)
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CONTROLLER_KEY = "funpack_ptd_controller"
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