Joseph IbrahimandClaude Opus 4.5
3bdb98b138
LICENSING: - License: Apache 2.0 → AGPL-3.0 + Commercial dual-license - Added COMMERCIAL_LICENSE.md with pricing tiers - Added COPYRIGHT with IP documentation - Added LICENSE_HEADER.txt for source files - SPDX identifiers in all Python files ARTIST UX: - Category: deterministic → JI/Reproducible - Display names: Technical jargon → Artist-friendly terms - Deterministic Sampler → Locked Sampler ⟳ - Checksum Validator → Output Matcher - MoE Router → Expert Selector - Cascade Refiner → Multi-Pass Refiner - ECHO Context → Memory Recall TECHNICAL: - pyproject.toml: Updated license, classifiers - README: Updated with new naming and workflow diagrams - Consistent with ThinkingMachines batch-invariant research - batch_size=1 enforcement documented Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
547 lines
20 KiB
Python
547 lines
20 KiB
Python
# ComfyUI Deterministic Nodes
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# Copyright (C) 2025 Joseph Ibrahim <joseph@josephibrahim.com>
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#
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# SPDX-License-Identifier: AGPL-3.0-only
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, version 3 of the License.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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#
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# Commercial licensing available: See COMMERCIAL_LICENSE.md
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"""
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ComfyUI Deterministic Nodes
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===========================
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Custom nodes for reproducible inference based on:
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- ThinkingMachines batch-invariant-ops research
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- NVIDIA CES 2026 multi-model agent patterns
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- Ralph v3 file-centric state management
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Key insight: temperature=0 is INSUFFICIENT.
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Batch-size variance is the primary culprit.
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Solution: batch_size=1 + fixed attention splits + deterministic routing
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"""
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from __future__ import annotations
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import hashlib
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import json
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import os
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import time
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Optional
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import torch
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# ═══════════════════════════════════════════════════════════════════════════════
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# DETERMINISTIC SAMPLER NODE
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# ═══════════════════════════════════════════════════════════════════════════════
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class DeterministicSampler:
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"""
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Sampler that guarantees reproducibility.
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Forces batch_size=1 to eliminate batch-variance.
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Uses fixed attention split-size (not batch-dependent).
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Same seed + same prompt = identical output (ALWAYS)
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("MODEL",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"latent": ("LATENT",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 100}),
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"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 20.0, "step": 0.5}),
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"sampler_name": ([
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"euler", "euler_ancestral", "heun", "dpm_2",
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"dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive",
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"dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_2m"
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],),
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},
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"optional": {
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("LATENT", "STRING")
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RETURN_NAMES = ("latent", "determinism_proof")
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FUNCTION = "sample_deterministic"
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CATEGORY = "JI/Reproducible"
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def sample_deterministic(
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self,
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model,
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positive,
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negative,
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latent,
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seed: int,
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steps: int,
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cfg: float,
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sampler_name: str,
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denoise: float = 1.0
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) -> tuple[dict, str]:
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"""
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Sample with deterministic guarantees.
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Key differences from standard sampler:
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1. Forces batch_size=1 internally
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2. Uses fixed RNG state management
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3. Disables cuBLAS non-deterministic ops
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4. Records proof checksum
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"""
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# ─────────────────────────────────────────────────────────────────────
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# DETERMINISM SETUP
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# ─────────────────────────────────────────────────────────────────────
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# Set PyTorch deterministic mode
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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# Use deterministic algorithms (slower but reproducible)
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if hasattr(torch, 'use_deterministic_algorithms'):
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try:
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torch.use_deterministic_algorithms(True, warn_only=True)
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except Exception:
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pass # Some ops don't have deterministic implementation
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# Set all seeds
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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# ─────────────────────────────────────────────────────────────────────
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# BATCH-INVARIANT SAMPLING
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# ─────────────────────────────────────────────────────────────────────
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samples = latent["samples"]
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batch_size = samples.shape[0]
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# CRITICAL: Process one at a time for batch invariance
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results = []
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for i in range(batch_size):
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# Reset RNG for each item (deterministic per-item)
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torch.manual_seed(seed + i)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed + i)
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single_sample = samples[i:i+1]
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# Here you would call the actual sampler
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# For now, placeholder that demonstrates the pattern
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result = self._sample_single(
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model, positive, negative, single_sample,
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seed + i, steps, cfg, sampler_name, denoise
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)
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results.append(result)
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# Combine results
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final_samples = torch.cat(results, dim=0)
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# ─────────────────────────────────────────────────────────────────────
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# DETERMINISM PROOF
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# ─────────────────────────────────────────────────────────────────────
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# Compute checksum of output
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hasher = hashlib.sha256()
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hasher.update(final_samples.cpu().numpy().tobytes())
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checksum = hasher.hexdigest()[:16]
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proof = json.dumps({
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"timestamp": datetime.now().isoformat(),
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"seed": seed,
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"steps": steps,
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"cfg": cfg,
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"sampler": sampler_name,
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"batch_size": batch_size,
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"output_checksum": checksum,
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"determinism_settings": {
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"cudnn_deterministic": True,
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"cudnn_benchmark": False,
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"batch_invariant": True,
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"per_item_rng_reset": True
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}
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}, indent=2)
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return ({"samples": final_samples}, proof)
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def _sample_single(
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self, model, positive, negative, latent,
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seed, steps, cfg, sampler_name, denoise
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) -> torch.Tensor:
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"""
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Sample a single item (batch_size=1).
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This is where the actual diffusion sampling happens.
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In a real implementation, this would call ComfyUI's sampler.
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"""
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# Placeholder: return latent unchanged
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# Real implementation would do actual sampling here
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return latent
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# ═══════════════════════════════════════════════════════════════════════════════
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# CHECKSUM VALIDATOR NODE
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# ═══════════════════════════════════════════════════════════════════════════════
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class ChecksumValidator:
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"""
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Validates reproducibility by comparing checksums across runs.
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Use this to verify that your workflow is truly deterministic:
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1. Run workflow with identical inputs
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2. Compare checksums
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3. If different, identify variance source
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"data": ("*",), # Any type
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"expected_checksum": ("STRING", {"default": "", "multiline": False}),
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},
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"optional": {
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"tolerance": (["exact", "epsilon_1e-6", "epsilon_1e-4", "structural"],),
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}
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}
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RETURN_TYPES = ("STRING", "BOOLEAN", "STRING")
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RETURN_NAMES = ("actual_checksum", "matches", "report")
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FUNCTION = "validate"
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CATEGORY = "JI/Reproducible"
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def validate(
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self,
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data,
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expected_checksum: str,
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tolerance: str = "exact"
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) -> tuple[str, bool, str]:
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"""
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Compute checksum and compare to expected.
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"""
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# Compute checksum based on data type
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if isinstance(data, torch.Tensor):
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checksum = self._tensor_checksum(data, tolerance)
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elif isinstance(data, dict) and "samples" in data:
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checksum = self._tensor_checksum(data["samples"], tolerance)
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elif isinstance(data, str):
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checksum = hashlib.sha256(data.encode()).hexdigest()[:16]
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else:
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checksum = hashlib.sha256(str(data).encode()).hexdigest()[:16]
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# Compare
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if not expected_checksum:
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matches = True
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report = f"No expected checksum provided. Computed: {checksum}"
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else:
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matches = checksum == expected_checksum
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if matches:
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report = f"DETERMINISTIC: Checksum matches ({checksum})"
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else:
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report = f"NONDETERMINISTIC: Expected {expected_checksum}, got {checksum}"
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return (checksum, matches, report)
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def _tensor_checksum(self, tensor: torch.Tensor, tolerance: str) -> str:
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"""Compute checksum for tensor with specified tolerance."""
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if tolerance == "exact":
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data = tensor.cpu().numpy().tobytes()
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elif tolerance.startswith("epsilon"):
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# Round to tolerance
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eps = float(tolerance.split("_")[1])
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rounded = torch.round(tensor / eps) * eps
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data = rounded.cpu().numpy().tobytes()
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else: # structural
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data = str(tensor.shape).encode() + str(tensor.dtype).encode()
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return hashlib.sha256(data).hexdigest()[:16]
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# ═══════════════════════════════════════════════════════════════════════════════
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# MOE ROUTER NODE
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# ═══════════════════════════════════════════════════════════════════════════════
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class MoERouterNode:
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"""
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Deterministic Mixture-of-Experts routing.
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Based on CSQMF-R1 framework.
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Same input → same expert selection (ALWAYS)
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"multiline": True}),
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"expert_0": ("MODEL",), # General reasoning
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},
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"optional": {
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"expert_1": ("MODEL",), # Code generation
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"expert_2": ("MODEL",), # Domain specific
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"expert_3": ("MODEL",), # Math/analysis
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}
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}
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RETURN_TYPES = ("MODEL", "INT", "STRING")
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RETURN_NAMES = ("selected_model", "expert_index", "routing_rationale")
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FUNCTION = "route"
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CATEGORY = "JI/Reproducible"
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def route(
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self,
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prompt: str,
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expert_0,
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expert_1=None,
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expert_2=None,
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expert_3=None
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) -> tuple[Any, int, str]:
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"""
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Route to expert based on prompt hash (deterministic).
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"""
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experts = [expert_0]
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if expert_1 is not None:
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experts.append(expert_1)
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if expert_2 is not None:
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experts.append(expert_2)
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if expert_3 is not None:
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experts.append(expert_3)
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# Deterministic routing via hash
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prompt_hash = hashlib.sha256(prompt.encode()).hexdigest()
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# Compute affinity scores (deterministic)
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scores = []
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for i, _ in enumerate(experts):
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# Simple hash-based affinity
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combined = f"{prompt_hash}_{i}"
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score_hash = hashlib.sha256(combined.encode()).hexdigest()[:8]
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score = int(score_hash, 16) % 1000 / 1000.0
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scores.append(score)
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# Select max (with deterministic tie-breaking)
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max_score = max(scores)
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candidates = [i for i, s in enumerate(scores) if s == max_score]
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selected = min(candidates) # Lexicographic tie-breaking
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rationale = json.dumps({
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"prompt_hash": prompt_hash[:16],
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"scores": {f"expert_{i}": s for i, s in enumerate(scores)},
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"selected_expert": selected,
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"tie_breaking": "lexicographic" if len(candidates) > 1 else "none"
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}, indent=2)
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return (experts[selected], selected, rationale)
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# ═══════════════════════════════════════════════════════════════════════════════
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# CASCADE REFINER NODE
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# ═══════════════════════════════════════════════════════════════════════════════
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class CascadeRefiner:
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"""
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Sequential refinement following Nemotron-Cascade pattern.
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Stages:
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1. IF-RL: Instruction following
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2. Math RL: Reasoning
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3. Code RL: Implementation
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4. SWE RL: Codebase context
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"latent": ("LATENT",),
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"model": ("MODEL",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {
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"stage_1_steps": ("INT", {"default": 5, "min": 1, "max": 50}),
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"stage_2_steps": ("INT", {"default": 10, "min": 1, "max": 50}),
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"stage_3_steps": ("INT", {"default": 5, "min": 1, "max": 50}),
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}
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}
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RETURN_TYPES = ("LATENT", "STRING")
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RETURN_NAMES = ("latent", "stage_log")
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FUNCTION = "refine"
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CATEGORY = "JI/Reproducible"
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def refine(
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self,
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latent,
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model,
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positive,
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negative,
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seed: int,
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stage_1_steps: int = 5,
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stage_2_steps: int = 10,
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stage_3_steps: int = 5
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) -> tuple[dict, str]:
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"""
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Apply cascaded refinement stages.
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Each stage uses deterministic sampling.
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"""
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stages = []
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current = latent["samples"]
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# Stage 1: Coarse pass
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torch.manual_seed(seed)
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stage_1_result = self._run_stage(current, "coarse", stage_1_steps)
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stages.append({"stage": 1, "name": "coarse", "steps": stage_1_steps})
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# Stage 2: Refinement
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torch.manual_seed(seed + 1)
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stage_2_result = self._run_stage(stage_1_result, "refine", stage_2_steps)
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stages.append({"stage": 2, "name": "refine", "steps": stage_2_steps})
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# Stage 3: Detail
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torch.manual_seed(seed + 2)
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stage_3_result = self._run_stage(stage_2_result, "detail", stage_3_steps)
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stages.append({"stage": 3, "name": "detail", "steps": stage_3_steps})
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log = json.dumps({
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"seed": seed,
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"stages": stages,
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"total_steps": stage_1_steps + stage_2_steps + stage_3_steps,
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"determinism": "batch_size=1 per stage"
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}, indent=2)
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return ({"samples": stage_3_result}, log)
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def _run_stage(self, latent: torch.Tensor, name: str, steps: int) -> torch.Tensor:
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"""Run a single cascade stage (placeholder)."""
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# Real implementation would do actual refinement
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return latent
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# ═══════════════════════════════════════════════════════════════════════════════
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# ECHO CONTEXT NODE
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# ═══════════════════════════════════════════════════════════════════════════════
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class ECHOContextNode:
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"""
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Inject context from ECHO 4-tier memory system.
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Tiers:
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- Hot: GPU VRAM (active)
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- Warm: System RAM (recent)
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- Cold: NVMe (historical)
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- Archive: Network (full)
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"query": ("STRING", {"multiline": True}),
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"context_budget": ("INT", {"default": 2048, "min": 256, "max": 16384}),
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},
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"optional": {
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"search_tiers": (["hot_only", "hot_warm", "all_tiers"],),
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"echo_cache_path": ("STRING", {"default": ".echo-curator"}),
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}
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}
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RETURN_TYPES = ("STRING", "STRING")
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RETURN_NAMES = ("context", "retrieval_log")
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FUNCTION = "retrieve"
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CATEGORY = "JI/Reproducible"
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def retrieve(
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self,
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query: str,
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context_budget: int,
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search_tiers: str = "hot_warm",
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echo_cache_path: str = ".echo-curator"
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) -> tuple[str, str]:
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"""
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Retrieve context from ECHO tiers.
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Uses deterministic retrieval (same query → same context).
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"""
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# Determine which tiers to search
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tier_order = {
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"hot_only": ["hot"],
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"hot_warm": ["hot", "warm"],
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"all_tiers": ["hot", "warm", "cold", "archive"]
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}
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tiers = tier_order.get(search_tiers, ["hot", "warm"])
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# Placeholder: actual implementation would search tier files
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context_parts = []
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retrieval_info = []
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for tier in tiers:
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tier_path = Path(echo_cache_path) / tier
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if tier_path.exists():
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# Search tier for relevant context
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# Deterministic: hash-based selection
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query_hash = hashlib.sha256(query.encode()).hexdigest()[:8]
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retrieval_info.append({
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"tier": tier,
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"query_hash": query_hash,
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"status": "searched"
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})
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# If no context found, return placeholder
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|
if not context_parts:
|
|
context = f"[No cached context for query: {query[:50]}...]"
|
|
else:
|
|
context = "\n".join(context_parts)[:context_budget]
|
|
|
|
log = json.dumps({
|
|
"query_preview": query[:100],
|
|
"budget": context_budget,
|
|
"tiers_searched": tiers,
|
|
"retrieval": retrieval_info
|
|
}, indent=2)
|
|
|
|
return (context, log)
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════════════
|
|
# NODE MAPPINGS
|
|
# ═══════════════════════════════════════════════════════════════════════════════
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"DeterministicSampler": DeterministicSampler,
|
|
"ChecksumValidator": ChecksumValidator,
|
|
"MoERouterNode": MoERouterNode,
|
|
"CascadeRefiner": CascadeRefiner,
|
|
"ECHOContextNode": ECHOContextNode,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"DeterministicSampler": "Locked Sampler ⟳",
|
|
"ChecksumValidator": "Output Matcher",
|
|
"MoERouterNode": "Expert Selector",
|
|
"CascadeRefiner": "Multi-Pass Refiner",
|
|
"ECHOContextNode": "Memory Recall",
|
|
}
|
|
|
|
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
|