[Model] Support Step-3.5-Flash

docs/source/tutorials/index.md

@@ -11,4 +11,5 @@ multi_xpu_GLM-4.5
 multi_xpu_Qwen3-Coder-480B-A35B(W8A8)
 multi_xpu_DeepSeek-V3.2-Exp-w8a8
 multi_xpu_GLM-5-W8A8-INT8
+multi_xpu_Step-3.5-Flash
 :::

docs/source/tutorials/multi_xpu_Step-3.5-Flash.md

@@ -0,0 +1,240 @@
+# Multi XPU (Step-3.5-Flash)
+
+## Run vllm-kunlun0.15.1-dev on Multi XPU
+
+Setup environment using container:
+
+```bash
+# !/bin/bash
+# rundocker.sh
+XPU_NUM=8
+DOCKER_DEVICE_CONFIG=""
+if [ $XPU_NUM -gt 0 ]; then
+    for idx in $(seq 0 $((XPU_NUM-1))); do
+        DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpu${idx}:/dev/xpu${idx}"
+    done
+    DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpuctrl:/dev/xpuctrl"
+fi
+
+export build_image="xxxxxxxxxxxxxxxxx"
+
+docker run -itd ${DOCKER_DEVICE_CONFIG} \
+    --net=host \
+    --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
+    --tmpfs /dev/shm:rw,nosuid,nodev,exec,size=32g \
+    --cap-add=SYS_PTRACE \
+    -v /home/users/vllm-kunlun:/home/vllm-kunlun \
+    -v /usr/local/bin/xpu-smi:/usr/local/bin/xpu-smi \
+    --name "$1" \
+    -w /workspace \
+    "$build_image" /bin/bash
+```
+
+### Offline Inference on Multi XPU
+
+Start the server in a container:
+
+```bash
+# export system variable
+# unset XPU_DUMMY_EVENT
+# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+# export XFT_USE_FAST_SWIGLU=1 #使用快速swiglu实现
+# export XPU_USE_FAST_SWIGLU=1 #使用moe算子中快速swiglu实现
+# export XMLIR_CUDNN_ENABLED=1
+# export XPU_USE_DEFAULT_CTX=1
+# export XMLIR_FORCE_USE_XPU_GRAPH=1
+# export XPU_USE_MOE_SORTED_THRES=128 
+# export VLLM_HOST_IP=127.0.0.1
+# export XMLIR_ENABLE_MOCK_TORCH_COMPILE=false 
+# export VLLM_USE_V1=1
+# export USE_ORI_ROPE=1
+# export KUNLUN_DISABLE_SMALL_MOE=1 #step-3.5-flash temporary fix
+
+# python /workspace/offline.py
+
+from vllm import LLM, SamplingParams
+
+llm = LLM(
+    model="/models/Step-3.5-Flash",
+    tensor_parallel_size=8,
+    dtype="bfloat16",
+    max_model_len=32768,
+    gpu_memory_utilization=0.9,
+    trust_remote_code=True,
+    distributed_executor_backend="mp",
+    block_size=128,
+    max_num_seqs=128,
+    max_num_batched_tokens=32768,
+    enable_prefix_caching=False,
+    enable_chunked_prefill=False,
+)
+
+sampling_params = SamplingParams(
+    temperature=0.7,
+    top_p=0.9,
+    top_k=10,
+    max_tokens=512,
+    stop=["<|end|>", "</s>"]
+)
+
+prompt = """
+<|user|>
+你好，请介绍一下你自己
+<|assistant|>
+"""
+
+outputs = llm.generate([prompt], sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+:::::
+If you run this script successfully, you can see the info shown below:
+
+```bash
+==================================================
+你好！我是 **Step**，由 **阶跃星辰（StepFun）** 开发的多模态大语言模型。  
+我具备自然语言理解与生成、图像分析、视觉推理、数理逻辑、知识问答等多种能力。不仅能理解和处理文字信息，还能结合图片进行多模态推理与分析。
+
+我的核心原则是：诚实可靠、有用友善、尊重隐私、促进积极交流、保持客观中立、拒绝有害内容。  
+简单来说，我的目标是为你提供准确、有帮助、温暖的智能支持。
+
+如果你愿意，可以告诉我你的兴趣或需求，我会尽力帮你实现目标 😊  
+你想先了解我在哪些方面能帮到你吗？
+==================================================
+```
+
+### Online Serving on Multi XPU
+
+Start the vLLM server on a multi XPU:
+
+```bash
+unset XPU_DUMMY_EVENT
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export XFT_USE_FAST_SWIGLU=1 #使用快速swiglu实现
+export XPU_USE_FAST_SWIGLU=1 #使用moe算子中快速swiglu实现
+export XMLIR_CUDNN_ENABLED=1
+export XPU_USE_DEFAULT_CTX=1
+export XMLIR_FORCE_USE_XPU_GRAPH=1
+export XPU_USE_MOE_SORTED_THRES=128 
+export VLLM_HOST_IP=127.0.0.1
+export XMLIR_ENABLE_MOCK_TORCH_COMPILE=false 
+export VLLM_USE_V1=1
+export USE_ORI_ROPE=1
+export KUNLUN_DISABLE_SMALL_MOE=1 #step-3.5-flash temporary fix
+
+python -m vllm.entrypoints.openai.api_server \
+      --host 0.0.0.0 \
+      --port 8356 \
+      --model /models/Step-3.5-Flash \
+      --gpu-memory-utilization 0.9 \
+      --trust-remote-code \
+      --max-model-len 32768 \
+      --tensor-parallel-size 8 \
+      --dtype bfloat16 \
+      --max_num_seqs 128 \
+      --max_num_batched_tokens 32768 \
+      --block-size 128 \
+      --no-enable-prefix-caching \
+      --no-enable-chunked-prefill \
+      --distributed-executor-backend mp \
+      --served-model-name Step-3.5-Flash \
+      --reasoning-parser step3p5 \
+      --enable-auto-tool-choice \
+      --tool-call-parser step3p5 \
+```
+
+If your service start successfully, you can see the info shown below:
+
+```bash
+(APIServer pid=133800) INFO:     Started server process [133800]
+(APIServer pid=133800) INFO:     Waiting for application startup.
+(APIServer pid=133800) INFO:     Application startup complete.
+```
+
+Once your server is started, you can query the model with input prompts:
+
+```bash
+curl http://127.0.0.1:8356/v1/chat/completions   
+    -H "Content-Type: application/json"   
+    -d '{
+    "model": "Step-3.5-Flash",
+    "messages": [
+      {"role": "user", "content": "你好，简单介绍一下你自己"}
+    ],
+    "max_tokens":200,
+    "temperature": 0.7
+  }'
+```
+
+Or use a Python script
+
+```python
+import requests
+import json
+import re
+
+URL = "http://127.0.0.1:8356/v1/chat/completions"
+
+payload = {
+    "model": "Step-3.5-Flash",
+    "messages": [
+        {"role": "user", "content": "你好，请介绍一下你自己"}
+    ],
+    "max_tokens": 500,
+    "top_p": 0.8,
+    "top_k": 10,
+    "temperature": 0.7,
+    # "presence_penalty": 0.3,
+    # "repetition_penalty": 1.05,
+    # At present, the model’s responses occasionally suffer from accuracy issues; you may wish to try adjusting the sampling parameters.
+
+}
+
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": "Bearer EMPTY"
+}
+
+resp = requests.post(URL, headers=headers, json=payload)
+data = resp.json()
+
+choice = data["choices"][0]
+content = choice["message"]["content"]
+
+answer = content
+
+print("\n===== ANSWER =====\n")
+print(answer)
+```
+
+If you query the server successfully, you can see the info shown below (client):
+
+```bash
+{"id":"chatcmpl-93112d4d8e047a9c","object":"chat.completion","created":1776166074,"model":"Step-3.5-Flash","choices":[{"index":0,"message":{"role":"assistant","content":"你好！我是 **Step**，由 **阶跃星辰（StepFun）** 开发的多\n","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning":"好的，用户让我简单介绍一下自己。首先我得明确身份，我是Step，由阶跃星辰（StepFun）开发。用户可能刚接触我，需要基础信息，比如功能、特点以及使用原则。\n\n然后考虑用户的需求场景，可能是第一次使用AI助手，或者想比较不同的AI。需要突出我的多模态能力，比如处理文字和图片，还有逻辑推理、知识问答这些核心功能。同时要强调中文","reasoning_content":"好的，用户让我简单介绍一下自己。首先我得明确身份，我是Step，由阶跃星辰（StepFun）开发。用户可能刚接触我，需要基础信息，比如功能、特点以及使用原则。\n\n然后考虑用户的需求场景，可能是第一次使用AI助手，或者想比较不同的AI。需要突出我的多模态能力，比如处理文字和图片，还有逻辑推理、知识问答这些核心功能。同时要强调中文"},"logprobs":null,"finish_reason":"stop","stop_reason":1,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":17,"total_tokens":131,"completion_tokens":114,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}
+
+# python script
+===== ANSWER =====
+
+你好！我是 **Step**，由 **阶跃星辰（StepFun）** 开发的大语言模型。  
+
+我具备以下主要能力和特点：  
+- 🧠 **自然语言理解与生成**：能够流畅地进行多轮对话、写作、总结、翻译等；  
+- 👁️ **多模态推理**：不仅能处理文字，还能理解和分析图片内容，进行视觉推理；  
+- 📚 **知识问答与逻辑推理**：擅长基于事实回答问题，并解决数学、逻辑类任务；  
+- 💡 **创意表达**：可辅助创作故事、诗歌、策划方案等富有创意的内容；  
+- 🌍 **多语言支持**：能用多种语言与用户交流；  
+- 🤝 **安全可靠**：遵循诚实、友善、尊重隐私的原则，保持客观中立。  
+
+我目前是 **完全免费使用** 的，不收集或存储你的个人隐私信息。你可以随时向我提问、讨论、创作或探索各种主题～  
+
+你想先了解我在哪方面最擅长吗？
+```
+
+Logs of the vllm server:
+
+```bash
+(APIServer pid=182858) INFO 04-14 19:45:26 [loggers.py:257] Engine 000: Avg prompt throughput: 1.7 tokens/s, Avg generation throughput: 19.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
+(APIServer pid=182858) INFO:     127.0.0.1:12670 - "POST /v1/chat/completions HTTP/1.1" 200 OK
+(APIServer pid=182858) INFO 04-14 19:45:36 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 24.2 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
+(APIServer pid=182858) INFO 04-14 19:45:46 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
+```

vllm_kunlun/ops/_kunlun_ops.py

@@ -18,13 +18,15 @@
 """kunlun custom op entry"""
 
 from typing import Optional
-
+import os
 import cocopod  # noqa
 import torch
 import xspeedgate_ops  # noqa
 from vllm.logger import init_logger
 from vllm.v1.worker.workspace import current_workspace_manager
 
+DISABLE_SMALL_MOE = os.environ.get("KUNLUN_DISABLE_SMALL_MOE", "0") == "1"
+
 logger = init_logger(__name__)
 
 try:
@@ -391,6 +393,7 @@ def fused_moe(
         w2_bias: Optional[torch.Tensor] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        routed_scaling_factor: float = 1.0,
     ) -> torch.Tensor:
         """fused_moe"""
         global_num_experts, up_gate_size, _ = w1.shape
@@ -419,15 +422,28 @@ def fused_moe(
                 stable=False,
             )
         elif scoring_func == "sigmoid":
+            if use_grouped_topk:
+                if num_expert_group is None or topk_group is None:
+                    raise ValueError("num_expert_group and topk_group must be set")
+                n_group = int(num_expert_group)
+                n_topk_group = int(topk_group)
+                if n_group <= 0 or n_topk_group <= 0:
+                    raise ValueError("num_expert_group and topk_group must be positive")
+                if n_topk_group > n_group:
+                    raise ValueError("topk_group must be less than or equal to num_expert_group")
+            else:
+                n_group = 1
+                n_topk_group = 1
+
             torch.ops._C.moe_sigmoid_group_topk_norm(
                 x=router_logits,
                 topk_index=topk_ids,
                 norm_score=normed_score,
                 block_static=block_statistic,
                 bias=e_score_correction_bias,
-                scale=1.0,
-                n_group=num_expert_group,
-                topk_group=topk_group,
+                scale=routed_scaling_factor,
+                n_group=n_group,
+                topk_group=n_topk_group,
             )
 
         if w1_bias is not None or w2_bias is not None:
@@ -475,7 +491,7 @@ def fused_moe(
             #     attn_metadata = attn_metadata[prefix]
 
             # if attn_metadata is None or attn_metadata.num_prefills > 0 or :
-            if M * moe_top_k < 400:
+            if M * moe_top_k < 400 and not DISABLE_SMALL_MOE:
                 sorted_tokens_idx, sorted_tokens_num_lod, moe_expand = (
                     torch.ops.xspeedgate_ops.moe_pre_small(
                         topk_ids, global_num_experts, False, False, hidden_states

vllm_kunlun/ops/fused_moe/layer.py

@@ -38,15 +38,24 @@ def apply_monolithic(
         layer,
         x: torch.Tensor,
         router_logits: torch.Tensor,
+        routed_scaling_factor: float = 1.0,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         """
         Monolithic mode entry point.
         When is_monolithic=True, FusedMoE.forward_impl calls this method
         directly with (layer, hidden_states, router_logits), bypassing
         the default routing logic.
+
+        Note: upstream forward_impl does not pass routed_scaling_factor,
+        so we read it from the layer attribute (consistent with the CPU
+        monolithic path in upstream vLLM).
         """
         from vllm_kunlun.ops._kunlun_ops import KunlunOps as ops
 
+        scaling_factor = getattr(
+            layer, "routed_scaling_factor", routed_scaling_factor
+        )
+
         if self.moe.use_ep:
             return ops.fused_moe_ep(
                 x,
@@ -78,4 +87,5 @@ def apply_monolithic(
                 e_score_correction_bias=layer.e_score_correction_bias,
                 w1_bias=getattr(layer, "w13_bias", None),
                 w2_bias=getattr(layer, "w2_bias", None),
+                router_scaling_factor=scaling_factor,
             )