feat(CRITICAL): 从 GitHub 扫描搬运 ixformer SDK + xllm 完整 GDN/MoE 代码

来源:
  1. Chranos/ixformer (GitHub) → ixformer_sdk/ (230 files, 70K lines)
     - inference/functions/vllm.py: vllm_moe_topk_softmax 完整实现 (2033 lines)
     - inference/functions/moe.py: MoE ops 完整实现 (1380 lines)
     - contrib/vllm_flash_attn/: FA2 Python 接口 (1018 lines)
     - contrib/tgi/fused_moe.py: TGI fused MoE (429 lines)
     - csrc/include/ixformer/: C++ kernel headers + cmake

  2. Deep-Spark/xllm (GitHub) → upstream_ref/xllm_latest/ (+15 files)
     - npu_torch/qwen3_5_decoder_layer_impl.cpp/.h
     - npu_torch/qwen3_5_gated_delta_net.cpp/.h
     - npu_torch/qwen3_next_*.cpp/.h (6 files)
     - npu_torch/attention.cpp/.h + fused_moe.cpp/.h + CMakeLists.txt
     - models/llm/qwen3_5.h + qwen3_5_mtp.h + qwen3_next.h
     - models/vlm/qwen3_5.h

调用链完整性:
  ixformer_sdk/inference/functions/vllm.py
    → ops.infer.moe_topk_softmax() (C++ 层)
    → 这就是 base 镜像 libixformer.so 里的实现

  upstream_ref/xllm_latest/core/layers/ilu/fused_moe.cpp
    → ixformer::infer::topk_softmax() (直接 C++ 调用)
    → ixformer::infer::group_gemm() → 完整 7-step MoE pipeline

ixformer_sdk/contrib/DeepCache/ddpm/ddim.py

@@ -0,0 +1,180 @@
+import argparse
+import traceback
+import shutil
+import logging
+import yaml
+import random
+import sys
+import os
+import torch
+import numpy as np
+
+from ddpm.utils.logging import Logger, EmptyLogger
+from ddpm.utils.tools import set_random_seed
+from accelerate import Accelerator, DistributedDataParallelKwargs
+
+torch.set_printoptions(sci_mode=False)
+
+def dict2namespace(config):
+    namespace = argparse.Namespace()
+    for key, value in config.items():
+        if isinstance(value, dict):
+            new_value = dict2namespace(value)
+        else:
+            new_value = value
+        setattr(namespace, key, new_value)
+    return namespace
+
+def parse_args_and_config():
+    parser = argparse.ArgumentParser(description=globals()["__doc__"])
+
+    parser.add_argument(
+        "--config", type=str, required=True, help="Path to the config file"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=1234, help="Random seed")
+    parser.add_argument(
+        "--exp", type=str, default="exp", help="Path for saving running related data."
+    )
+    
+    parser.add_argument(
+        "--test", action="store_true", help="Whether to test the model"
+    )
+    parser.add_argument(
+        "--sample", action="store_true", help="Whether to produce samples from the model",
+    )
+    parser.add_argument(
+        "--image_folder", type=str, default="images", help="folder name for storing the sampled images"
+    )
+
+    parser.add_argument(
+        "--fid", action="store_true"
+    )
+    parser.add_argument(
+        "--interpolation", action="store_true"
+    )
+    parser.add_argument(
+        "--resume_training", action="store_true", help="Whether to resume training"
+    )
+    parser.add_argument(
+        "--ni", action="store_true", help="No interaction. Suitable for Slurm Job launcher",
+    )
+    parser.add_argument(
+        "--use_pretrained", action="store_true"
+    )
+    parser.add_argument(
+        "--sample_type", type=str, default="generalized", help="sampling approach (generalized or ddpm_noisy)",
+    )
+    parser.add_argument(
+        "--skip_type", type=str, default="uniform", help="skip according to (uniform or quadratic)",
+    )
+    parser.add_argument(
+        "--timesteps", type=int, default=1000, help="number of steps involved"
+    )
+    parser.add_argument(
+        "--eta", type=float, default=0.0, help="eta used to control the variances of sigma",
+    )
+    parser.add_argument(
+        "--dyn", action="store_true", help="whether to activate the dynamic train/inference"
+    )
+    parser.add_argument(
+        "--sequence", action="store_true"
+    )
+    parser.add_argument(
+        "--select_step", type=int, default=None
+    )
+    parser.add_argument(
+        "--select_depth", type=int, default=None
+    )
+
+    parser.add_argument(
+        "--cache", action="store_true"
+    )
+    parser.add_argument(
+        "--cache_interval", type=int, default=None,
+    )
+    parser.add_argument(
+        "--non_uniform", action="store_true"
+    )
+    parser.add_argument(
+        "--pow", type=float, default=None,
+    )
+    parser.add_argument(
+        "--center", type=int, default=None,
+    )
+    parser.add_argument(
+        "--branch", type=int, default=None,
+    )
+    args = parser.parse_args()
+    # parse config file
+    with open(args.config, "r") as f:
+        config = yaml.safe_load(f)
+    new_config = dict2namespace(config)
+    new_config.select_step = args.select_step
+    new_config.select_depth = args.select_depth
+
+    torch.backends.cudnn.benchmark = True
+
+    return args, new_config
+
+
+def main():
+    args, config = parse_args_and_config()
+    
+    if args.dyn:
+        ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+        accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+    else:
+        accelerator = Accelerator()
+    args.accelerator = accelerator
+
+    #log_root_dir = "{}_runtime_log".format(args.config[8:-4])
+    log_root_dir = "runtime_log"
+    dataset = args.config[8:-4]
+    if args.cache:
+        if args.non_uniform:
+            sub_dir_name = "{}_{}_cache_{}_pow_{}_center_{}".format(dataset, args.exp, args.cache_interval, args.pow, args.center)
+        else:
+            sub_dir_name = "{}_{}_cache_{}".format(dataset, args.exp, args.cache_interval)
+    else:
+        sub_dir_name = "{}".format(args.exp)
+
+    if accelerator.is_main_process:
+        logger = Logger(
+            root_dir=log_root_dir,
+            sub_name=sub_dir_name,
+            config=args.__dict__, 
+            append=(args.sample == True)
+        )
+        args.logger = logger
+
+        args.logger.log("Writing log file to {}".format(args.logger.sub_dir))
+        args.logger.log("Exp instance PID = {}".format(os.getpid()))
+    else:
+        args.logger = EmptyLogger(
+            root_dir=log_root_dir,
+            sub_name=sub_dir_name,
+        )
+
+    args.image_folder = args.logger.setup_image_folder("{}".format(args.image_folder))
+
+    args.seed += accelerator.process_index      
+    # set random seed
+    set_random_seed(args.seed)
+    try:
+        if args.cache:
+            from ddpm.runners.deepcache import Diffusion
+            runner = Diffusion(args, config)
+            runner.sample()
+        else:
+            from ddpm.runners.diffusion import Diffusion
+            runner = Diffusion(args, config)
+            runner.sample()
+    except Exception:
+        logging.error(traceback.format_exc())
+
+    return 0
+
+
+if __name__ == "__main__":
+    main()