[CP]Glm45 air 2.2 (#4073)

* [Feature] Support zai-org/GLM-4.5-Air BF16 model (#3928)

* support glm45_air

* [Feature] GLM-45-AIR Support Mix Quantization(Dense wfp8afp8 and wint8 triton_moe_backend) (#4051)

* check

* fix v1 load for mix and wint8

* check --quantizations 'None'

* check

* support RL rollout

* check v1 loader

* check glm rollout_model, change wfp8afp8 per_token_cast_to_fp8 to native impl

* check rollout moe gate begin layer_id

* check rollout e_score_correction_bias

* delete infer_to_train_mapping={}

* code check

custom_ops/gpu_ops/append_attn/decoder_write_cache_with_rope_impl.cuh

@@ -381,6 +381,142 @@ __global__ void append_decode_cache_T_rope_kernel(
   }
 }
 
+template <typename T, int VecSize = 1>
+__global__ void append_decode_cache_T_neox_partial_rope_kernel(
+    const T* __restrict__ qkv,    // [bsz, num_heads + 2 * kv_num_heads,
+                                  // head_size]
+    T* __restrict__ key_cache,    // [num_blocks, kv_num_heads, block_size,
+                                  // head_size // 2]
+    T* __restrict__ value_cache,  // [num_blocks, kv_num_heads, block_size,
+                                  // head_size // 2]
+    T* __restrict__ qkv_out,
+    const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
+    const int* __restrict__ cu_seqlens_q,
+    const int* __restrict__ seq_lens,          // [bsz]
+    const int* __restrict__ seq_lens_encoder,  // [bsz]
+    const float* __restrict__ cos_emb,         // [2, 1, max_model_len, 1, rotary_dim/2]
+    const float* __restrict__ sin_emb,         // [2, 1, max_model_len, 1, rotary_dim/2]
+    const int max_seq_len,
+    const int max_blocks_per_seq,
+    const int num_heads,
+    const int head_size,
+    const int rotary_dim,
+    const int block_size,
+    const uint32_t elem_cnt,
+    const int kv_num_heads,
+    const bool rope_3d) {
+  using LoadT = AlignedVector<T, VecSize>;
+  using LoadBiasT = AlignedVector<T, VecSize>;
+  using LoadKVT = AlignedVector<T, VecSize>;
+  constexpr int HalfVecSize = VecSize / 2;
+  using LoadEmbT = AlignedVector<float, VecSize>;
+
+  LoadT left_vec, right_vec;
+  LoadBiasT left_bias_vec, right_bias_vec;
+  LoadKVT left_cache_vec, right_cache_vec;
+  LoadEmbT cos_emb_vec;
+  LoadEmbT sin_emb_vec;
+
+  int64_t global_thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+  const int half_head_size = head_size / 2;
+  const int half_rotary_dim = rotary_dim / 2;
+  const int64_t hidden_size = (num_heads + 2 * kv_num_heads) * head_size;
+  const int64_t half_hidden_size = hidden_size / 2;
+  // const int64_t offset = 2 * hidden_size;
+
+  for (int32_t linear_index = global_thread_idx * VecSize,
+               step = gridDim.x * blockDim.x * VecSize;
+       linear_index < elem_cnt;
+       linear_index += step) {
+    const int ori_bi = linear_index / half_hidden_size;
+    const int bias = linear_index % half_hidden_size;
+    const int hi = bias / half_head_size;  // q + k + v
+    const int h_bias = bias % half_head_size;
+    if (hi < num_heads && h_bias >= half_rotary_dim){
+      continue;
+    }
+    if (seq_lens_encoder[ori_bi] > 0) continue;
+    const int write_seq_id = seq_lens[ori_bi];
+    if (write_seq_id == 0) continue;
+    const int start_token_idx = cu_seqlens_q[ori_bi];
+
+    const int* block_table_now = nullptr;
+
+    block_table_now = block_tables + ori_bi * max_blocks_per_seq;
+    const int block_idx = block_table_now[write_seq_id / block_size];
+    const int block_offset = write_seq_id % block_size;
+    uint32_t ori_idx_left =
+        start_token_idx * hidden_size + hi * head_size + h_bias;
+    uint32_t ori_idx_right = ori_idx_left + half_head_size;
+    if (hi < num_heads){
+      ori_idx_right = ori_idx_left + half_rotary_dim;
+    }else if (hi < num_heads + kv_num_heads){
+      if (h_bias < half_rotary_dim){
+        ori_idx_right = ori_idx_left + half_rotary_dim;
+      }else{
+        ori_idx_left = ori_idx_left + half_rotary_dim;
+        ori_idx_right = ori_idx_left + half_rotary_dim;
+      }
+    }
+
+    Load<T, VecSize>(&qkv[ori_idx_left], &left_vec);
+    Load<T, VecSize>(&qkv[ori_idx_right], &right_vec);
+
+    if (hi < num_heads + kv_num_heads) {
+      // q k rope
+      const uint32_t emb_idx = write_seq_id * half_rotary_dim + h_bias;
+      uint32_t new_emb_idx = rope_3d ? emb_idx + ori_bi * max_seq_len * head_size * 2 : emb_idx;
+      if (h_bias < half_rotary_dim){
+        Load<float, VecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+        Load<float, VecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
+      }
+    }
+#pragma unroll
+    for (int i = 0; i < VecSize; i++) {
+      // rope
+      float input_left = static_cast<float>(left_vec[i]);
+      float input_right = static_cast<float>(right_vec[i]);
+      if (hi < num_heads + kv_num_heads && h_bias < half_rotary_dim) {
+        const float cos_tmp = cos_emb_vec[i];
+        const float sin_tmp = sin_emb_vec[i];
+        left_bias_vec[i] =
+            static_cast<T>(input_left * cos_tmp - input_right * sin_tmp);
+        right_bias_vec[i] =
+            static_cast<T>(input_right * cos_tmp + input_left * sin_tmp);
+      } else {
+        left_bias_vec[i] = static_cast<T>(input_left);
+        right_bias_vec[i] = static_cast<T>(input_right);
+      }
+    }
+    if (hi < num_heads) {
+      // write q
+      Store<T, VecSize>(left_bias_vec, &qkv_out[ori_idx_left]);
+      Store<T, VecSize>(right_bias_vec, &qkv_out[ori_idx_right]);
+    } else {
+      // write k/v
+      const uint32_t kv_head_idx = (hi - num_heads) % kv_num_heads;
+      uint32_t tgt_idx_left =
+          block_idx * kv_num_heads * block_size * head_size +
+          kv_head_idx * block_size * head_size + block_offset * head_size +
+          h_bias;
+      uint32_t tgt_idx_right = tgt_idx_left + half_head_size;
+      if (hi < num_heads + kv_num_heads) {
+        if (h_bias < half_rotary_dim) {
+          tgt_idx_right = tgt_idx_left + half_rotary_dim;
+        }else{
+          tgt_idx_left = tgt_idx_left + half_rotary_dim;
+          tgt_idx_right = tgt_idx_left + half_rotary_dim;
+        }
+        Store<T, VecSize>(left_bias_vec, &key_cache[tgt_idx_left]);
+        Store<T, VecSize>(right_bias_vec, &key_cache[tgt_idx_right]);
+      } else {
+        Store<T, VecSize>(left_bias_vec, &value_cache[tgt_idx_left]);
+        Store<T, VecSize>(right_bias_vec, &value_cache[tgt_idx_right]);
+      }
+    }
+  }
+}
+
 template <typename T, int VecSize = 1>
 __global__ void append_decode_cache_T_neox_rope_kernel(
     const T* __restrict__ qkv,    // [bsz, num_heads + 2 * kv_num_heads,

custom_ops/gpu_ops/append_attn/decoder_write_cache_with_rope_kernel.cu

@@ -97,6 +97,7 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
                               const int num_heads,
                               const int kv_num_heads,
                               const int dim_head,
+                              const int rotary_dim,
                               const int block_size,
                               const int bsz,
                               const cudaStream_t& stream,
@@ -137,6 +138,28 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
               kv_num_heads,
               rope_3d);
     } else {
+      if (rotary_dim < dim_head){
+        append_decode_cache_T_neox_partial_rope_kernel<T, PackSize>
+          <<<grid_size, blocksize, 0, stream>>>(reinterpret_cast<const T*>(qkv),
+                                                key_cache,
+                                                value_cache,
+                                                qkv_out,
+                                                block_tables,
+                                                cu_seqlens_q,
+                                                seq_lens,
+                                                seq_lens_encoder,
+                                                cos_emb,
+                                                sin_emb,
+                                                max_seq_len,
+                                                max_blocks_per_seq,
+                                                num_heads,
+                                                dim_head,
+                                                rotary_dim,
+                                                block_size,
+                                                elem_nums,
+                                                kv_num_heads,
+                                                rope_3d);
+      }else{
         append_decode_cache_T_neox_rope_kernel<T, PackSize>
           <<<grid_size, blocksize, 0, stream>>>(reinterpret_cast<const T*>(qkv),
                                                 key_cache,
@@ -158,6 +181,7 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
                                                 kv_num_heads,
                                                 rope_3d);
       }
+    }
   } else {
     if (qkv_out_scales) {
       append_decode_cache_T_rope_kernel<T, PackSize>
@@ -534,11 +558,20 @@ void DecoderWriteCacheWithRoPEKernel(
   const float* cos_emb =
       rotary_embs ? rotary_embs.get().data<float>() : nullptr;
   const float* sin_emb;
+  int rotary_dim = dim_head;
   if (rotary_embs) {
     sin_emb =
         use_neox_rotary_style
             ? rotary_embs.get().data<float>() + max_seq_len * dim_head
             : rotary_embs.get().data<float>() + max_seq_len * dim_head / 2;
+    rotary_dim = rotary_embs.get().dims()[rotary_embs.get().dims().size()-1] * 2;
+    if(rotary_dim < dim_head){
+      if (!use_neox_rotary_style || qkv_out_scales || q_norm_weight || k_norm_weight|| cache_quant_type_str != "none"){
+        PADDLE_THROW(phi::errors::Fatal(
+          "partial_rotary_factor < 1.0 only supports neox_rotary_style=True, qkv_out_scales is None, q_norm_weight/k_norm_weight) is None, and cache_quant_type_str is 'none'."));
+      }
+      sin_emb = rotary_embs.get().data<float>() + max_seq_len * rotary_dim / 2;
+    }
   }
 
   if (q_norm_weight && k_norm_weight) {
@@ -599,6 +632,7 @@ void DecoderWriteCacheWithRoPEKernel(
           num_heads,
           kv_num_heads,
           dim_head,
+          rotary_dim,
           block_size,
           bsz,
           stream,

custom_ops/gpu_ops/append_attn/encoder_write_cache_with_rope_impl.cuh

@@ -900,6 +900,74 @@ __global__ void GQANeoxVariableLengthRotaryKernel(
   }
 }
 
+template <typename T, int VecSize = 1>
+__global__ void GQANeoxVariableLengthPartialRotaryKernel(
+    const T *qkv,
+    const float *cos_emb,
+    const float *sin_emb,
+    const int *batch_id_per_token,
+    const int *cu_seqlens_q,
+    const int *seq_lens,
+    const int *seq_lens_decoder,
+    const float *qkv_out_scales,
+    const T *qkv_biases,
+    T *qkv_out,
+    const int64_t elem_cnt,
+    const int q_num_head,
+    const int kv_num_head,
+    const int seq_len,
+    const int head_dim,
+    const int rotary_dim,
+    const bool rope_3d) {
+  using LoadT = AlignedVector<T, VecSize>;
+  using LoadEmbT = AlignedVector<float, VecSize>;
+  LoadT left_vec;
+  LoadT right_vec;
+  LoadEmbT cos_emb_vec;
+  LoadEmbT sin_emb_vec;
+  int64_t global_thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+  const int rotary_dim_half = rotary_dim / 2;
+  const int offset = (q_num_head + kv_num_head) * rotary_dim_half;
+  for (int64_t linear_index = global_thread_idx * VecSize,
+               step = gridDim.x * blockDim.x * VecSize;
+       linear_index < elem_cnt;
+       linear_index += step) {
+    const int token_idx = linear_index / offset;
+    const int ori_bi = batch_id_per_token[token_idx];
+    if (seq_lens && seq_lens[ori_bi] == 0) continue;
+    const int bias = linear_index % offset;
+    const int hi = bias / rotary_dim_half;
+    const int h_bias = bias % rotary_dim_half;
+
+    const int ori_seq_id = (token_idx - cu_seqlens_q[ori_bi]) + seq_lens_decoder[ori_bi];
+
+    const int emb_idx = ori_seq_id * rotary_dim_half + h_bias;
+    int64_t new_emb_idx = rope_3d ? emb_idx + ori_bi * head_dim * seq_len * 2 : emb_idx;
+    const int base_idx_left =
+        token_idx * (q_num_head + 2 * kv_num_head) * head_dim + hi * head_dim +
+        h_bias;
+    const int base_idx_right = base_idx_left + rotary_dim_half;
+
+    Load<T, VecSize>(&qkv[base_idx_left], &left_vec);
+    Load<T, VecSize>(&qkv[base_idx_right], &right_vec);
+    Load<float, VecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+    Load<float, VecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
+#pragma unroll
+    for (int i = 0; i < VecSize; i++) {
+      const float input_left = static_cast<float>(left_vec[i]);
+      const float input_right = static_cast<float>(right_vec[i]);
+      const float cos_tmp = cos_emb_vec[i];
+      const float sin_tmp = sin_emb_vec[i];
+      left_vec[i] =
+          static_cast<T>(input_left * cos_tmp - input_right * sin_tmp);
+      right_vec[i] =
+          static_cast<T>(input_right * cos_tmp + input_left * sin_tmp);
+    }
+    Store<T, VecSize>(left_vec, &qkv_out[base_idx_left]);
+    Store<T, VecSize>(right_vec, &qkv_out[base_idx_right]);
+  }
+}
+
 template <typename T, int VecSize = 1>
 __global__ void cache_kernel(
     const T *__restrict__ qkv,    // [num_tokens, num_heads + 2 * kv_num_heads,
@@ -1755,6 +1823,7 @@ void gqa_rotary_qk_variable(
     const int seq_len,
     const int input_output_len,
     const int dim_head,
+    const int rotary_dim,
     const cudaStream_t &stream,
     bool use_neox_style = false,
     bool rope_3d = false) {
@@ -1835,6 +1904,37 @@ void gqa_rotary_qk_variable(
               dim_head,
               rope_3d);
     } else {
+      if (rotary_dim < dim_head){
+        PD_CHECK((rotary_dim / 2) % PackSize == 0);
+        elem_nums =
+            qkv_out_scales
+                ? token_num * (num_heads + 2 * kv_num_heads) * rotary_dim
+                : token_num * (num_heads + kv_num_heads) * rotary_dim;  // for all q k v
+        if (use_neox_style) {
+          elem_nums /= 2;
+        }
+        const int pack_num_new = elem_nums / PackSize;
+        GetNumBlocks<128>(pack_num_new, &grid_size);
+        GQANeoxVariableLengthPartialRotaryKernel<T, PackSize>
+            <<<grid_size, blocksize, 0, stream>>>(
+                reinterpret_cast<const T *>(qkv_input),
+                cos_emb,
+                rotary_emb + input_output_len * rotary_dim / 2,
+                batch_id_per_token,
+                cu_seqlens_q,
+                seq_lens,
+                seq_lens_decoder,
+                qkv_out_scales,
+                qkv_bias,
+                qkv_out,
+                elem_nums,
+                num_heads,
+                kv_num_heads,
+                seq_len,
+                dim_head,
+                rotary_dim,
+                rope_3d);
+      }else{
         GQANeoxVariableLengthRotaryKernel<T, PackSize>
           <<<grid_size, blocksize, 0, stream>>>(
               reinterpret_cast<const T *>(qkv_input),
@@ -1855,6 +1955,7 @@ void gqa_rotary_qk_variable(
               rope_3d);
       }
     }
+  }
 }
 
 template <typename T, typename QKV_TYPE>

custom_ops/gpu_ops/append_attn/encoder_write_cache_with_rope_kernel.h

@@ -55,9 +55,19 @@ void EncoderWriteCacheWithRopeKernel(
   auto kv_num_heads = meta_data.kv_num_heads;
   auto head_dim = meta_data.head_dims;
   bool is_scale_channel_wise = false;
+  int rotary_dim = head_dim;
   if (cache_k_scale && cache_k_scale.get().dims()[0] == head_dim * kv_num_heads) {
     is_scale_channel_wise = true;
   }
+  if (rotary_embs){
+    rotary_dim = rotary_embs.get().dims()[rotary_embs.get().dims().size()-1] * 2;
+    if(rotary_dim < head_dim){
+      if (!use_neox_style || q_norm_weight || k_norm_weight || num_heads == kv_num_heads || is_scale_channel_wise){
+        PADDLE_THROW(phi::errors::Fatal(
+          "partial_rotary_factor < 1.0 only supports use_neox_rotary_style=True, q_norm_weight/k_norm_weight) is None, GQA and is_scale_channel_wise=false."));
+      }
+    }
+  }
 
   if (q_norm_weight && k_norm_weight) {
     if (num_heads != kv_num_heads && !is_scale_channel_wise && !use_neox_style) {
@@ -125,6 +135,7 @@ void EncoderWriteCacheWithRopeKernel(
           max_seq_len,
           rope_3d ? rotary_embs.get().dims()[3] : rotary_embs.get().dims()[2],
           head_dim,
+          rotary_dim,
           stream,
           use_neox_style,
           rope_3d);

fastdeploy/config.py

@@ -132,6 +132,7 @@ class ModelConfig:
         self.eos_tokens_lens: int = 2
         self.lm_head_fp32: bool = False
         self.model_format = "auto"
+        self.partial_rotary_factor: float = 1.0
         for key, value in args.items():
             if hasattr(self, key):
                 setattr(self, key, value)
@@ -396,7 +397,7 @@ class SpeculativeConfig:
         # model for mtp/eagle/draft_model
         self.model: Optional[str] = None
         # quantization of model
-        self.quantization: Optional[str] = None
+        self.quantization: Optional[Dict[str, Any]] = None
         # allocate more blocks to prevent mtp from finishing the block earlier than the main model
         # Fixed now
         self.num_gpu_block_expand_ratio: Optional[float] = 1

fastdeploy/engine/args_utils.py

@@ -41,6 +41,7 @@ from fastdeploy.utils import (
     DeprecatedOptionWarning,
     FlexibleArgumentParser,
     is_port_available,
+    parse_quantization,
 )
 
 
@@ -138,7 +139,7 @@ class EngineArgs:
     """
     dynamic load weight strategy
     """
-    quantization: str = None
+    quantization: Optional[Dict[str, Any]] = None
     guided_decoding_backend: str = "off"
     """
     Guided decoding backend.
@@ -550,7 +551,7 @@ class EngineArgs:
         )
         model_group.add_argument(
             "--quantization",
-            type=str,
+            type=parse_quantization,
             default=EngineArgs.quantization,
             help="Quantization name for the model, currentlly support "
             "'wint8', 'wint4',"

fastdeploy/engine/engine.py

@@ -16,6 +16,7 @@
 
 from __future__ import annotations
 
+import json
 import multiprocessing
 import os
 import re
@@ -463,7 +464,7 @@ class LLMEngine:
             f" --kv_cache_ratio {self.cfg.cache_config.kv_cache_ratio}"
             f" --expert_parallel_size {self.cfg.parallel_config.expert_parallel_size}"
             f" --data_parallel_size {self.cfg.parallel_config.data_parallel_size}"
-            f" --quantization {self.cfg.model_config.quantization}"
+            f" --quantization '{json.dumps(self.cfg.model_config.quantization)}'"
             f" --ori_vocab_size {ori_vocab_size}"
             f" --speculative_config '{self.cfg.speculative_config.to_json_string()}'"
             f" --graph_optimization_config '{self.cfg.graph_opt_config.to_json_string()}'"

fastdeploy/model_executor/layers/moe/ep.py

@@ -28,38 +28,9 @@ except:
 
 import fastdeploy
 from fastdeploy.config import MoEPhase
+from fastdeploy.model_executor.layers.moe.moe import get_moe_scores
 from fastdeploy.utils import singleton
 
-try:
-    from fastdeploy.model_executor.ops.gpu import noaux_tc
-except:
-    logger.warning("import noaux_tc Failed!")
-
-
-def get_moe_scores(
-    gating_output: paddle.Tensor,
-    n_group,
-    topk_group,
-    top_k,
-    routed_scaling_factor,
-    e_score_correction_bias,
-) -> paddle.Tensor:
-    """
-    compute moe scores using e_score_correction_bias.
-    """
-    scores = paddle.nn.functional.sigmoid(gating_output)
-    assert e_score_correction_bias is not None, "e_score_correction_bias is none!"
-    scores_with_bias = scores + e_score_correction_bias
-    scores, topk_values, topk_idx = noaux_tc(
-        scores,
-        scores_with_bias,
-        n_group if n_group > 0 else 1,
-        topk_group if topk_group > 0 else 1,
-        top_k,
-        routed_scaling_factor,
-    )
-    return scores, topk_values, topk_idx
-
 
 @singleton
 class DeepEPEngine:

fastdeploy/model_executor/layers/moe/fused_moe_cutlass_backend.py

@@ -27,11 +27,8 @@ from ..utils import get_tensor
 from .fused_moe_backend_base import UnquantizedFusedMoEMethod
 
 if current_platform.is_cuda():
-    from fastdeploy.model_executor.ops.gpu import (
+    from fastdeploy.model_executor.layers.moe.moe import get_moe_scores
-        moe_expert_dispatch,
+    from fastdeploy.model_executor.ops.gpu import moe_expert_dispatch, moe_expert_reduce
-        moe_expert_reduce,
-        noaux_tc,
-    )
 
     try:
         from fastdeploy.model_executor.ops.gpu import w4afp8_gemm_scale_permute
@@ -46,31 +43,6 @@ elif current_platform.is_iluvatar():
 from fastdeploy.model_executor.utils import TensorTracker, free_tensor, set_weight_attrs
 
 
-# used for deepseek_v3
-def get_moe_scores(
-    gating_output: paddle.Tensor,
-    n_group,
-    topk_group,
-    top_k,
-    routed_scaling_factor,
-    e_score_correction_bias,
-) -> paddle.Tensor:
-    """
-    compute moe scores using e_score_correction_bias.
-    """
-    scores = paddle.nn.functional.sigmoid(gating_output)
-    scores_with_bias = scores + e_score_correction_bias
-    scores, topk_values, topk_idx = noaux_tc(
-        scores,
-        scores_with_bias,
-        n_group,
-        topk_group,
-        top_k,
-        routed_scaling_factor,
-    )
-    return scores, topk_values, topk_idx
-
-
 class CutlassMoEMethod(UnquantizedFusedMoEMethod):
     """
     Use Cutlass Group Gemm to compute Fused MoE.

fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py

@@ -481,7 +481,7 @@ class DeepGemmFusedMoeMethod(MoEMethodBase):
         gate_out = gate(x.cast("float32"))
 
         if layer.topk_method == "noaux_tc":
-            from .ep import get_moe_scores
+            from fastdeploy.model_executor.layers.moe.moe import get_moe_scores
 
             _, topk_weights, topk_ids = get_moe_scores(
                 gate_out,

fastdeploy/model_executor/layers/moe/fused_moe_marlin_backend.py

@@ -19,39 +19,15 @@ from paddle import nn
 
 import fastdeploy
 from fastdeploy.distributed.communication import tensor_model_parallel_all_reduce
+from fastdeploy.model_executor.layers.moe.moe import get_moe_scores
 from fastdeploy.model_executor.ops.gpu import (
     MoeWna16MarlinGemmApi,
-    noaux_tc,
     tritonmoe_preprocess_func,
 )
 
 from ..quantization.quant_base import QuantMethodBase
 
 
-def get_moe_scores(
-    gating_output: paddle.Tensor,
-    n_group,
-    topk_group,
-    top_k,
-    routed_scaling_factor,
-    e_score_correction_bias,
-) -> paddle.Tensor:
-    """
-    compute moe scores using e_score_correction_bias.
-    """
-    scores = paddle.nn.functional.sigmoid(gating_output)
-    scores_with_bias = scores + e_score_correction_bias.unsqueeze(0)
-    scores, topk_values, topk_idx = noaux_tc(
-        scores,
-        scores_with_bias,
-        n_group,
-        topk_group,
-        top_k,
-        routed_scaling_factor,
-    )
-    return scores, topk_values, topk_idx
-
-
 def gptq_marlin_moe_repack(
     b_q_weight: paddle.Tensor,
     perm: paddle.Tensor,

fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py

@@ -24,7 +24,6 @@ from fastdeploy.model_executor.utils import TensorTracker, set_weight_attrs
 from fastdeploy.utils import ceil_div
 
 from ..quantization.quant_base import QuantMethodBase
-from .ep import get_moe_scores
 
 try:
     from fastdeploy.model_executor.ops.gpu import tritonmoe_preprocess_func
@@ -32,6 +31,7 @@ try:
     from .triton_moe_kernels import fused_moe_kernel_paddle
 except ImportError:
     pass
+from fastdeploy.model_executor.layers.moe.moe import get_moe_scores
 
 
 class TritonWeightOnlyMoEMethod(QuantMethodBase):
@@ -72,6 +72,33 @@ class TritonWeightOnlyMoEMethod(QuantMethodBase):
             layer.moe_intermediate_size,
             layer.hidden_size,
         ]
+        if self.quant_config.is_checkpoint_bf16:
+            layer.up_gate_proj_weight = layer.create_parameter(
+                shape=self.up_gate_proj_weight_shape,
+                dtype=layer.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            )
+
+            layer.down_proj_weight = layer.create_parameter(
+                shape=self.down_proj_weight_shape,
+                dtype=layer.weight_dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            )
+            set_weight_attrs(
+                layer.up_gate_proj_weight,
+                {
+                    **extra_weight_attrs,
+                    "tensor_track": TensorTracker(shape=layer.up_gate_proj_weight.shape, output_dim=True),
+                },
+            )
+            set_weight_attrs(
+                layer.down_proj_weight,
+                {
+                    **extra_weight_attrs,
+                    "tensor_track": TensorTracker(shape=layer.down_proj_weight.shape, output_dim=False),
+                },
+            )
+        else:
             setattr(
                 layer,
                 up_gate_proj_weight_name,
@@ -151,6 +178,62 @@ class TritonWeightOnlyMoEMethod(QuantMethodBase):
             getattr(layer, weight_name).set_value(quanted_weight)
             getattr(layer, scale_name).set_value(quanted_weight_scale)
 
+    def process_weights_after_loading(self, layer):
+        """ """
+        if not self.quant_config.is_checkpoint_bf16:
+            return
+
+        algo = layer.quant_method.quant_config.name()
+        assert algo == "wint8"
+        max_bound = 127
+        weight_id_map = {"gate_up": 0, "down": 1}
+        if (
+            hasattr(layer.up_gate_proj_weight, "tensor_track")
+            and layer.up_gate_proj_weight.tensor_track is not None
+            and layer.up_gate_proj_weight.tensor_track.is_fully_copied()
+        ):
+            weight_type = "gate_up"
+            layer.up_gate_proj_weight.tensor_track = None
+        else:
+            weight_type = "down"
+            layer.down_proj_weight.tensor_track = None
+
+        # weight
+        weight_name = self.added_weight_attrs[weight_id_map[weight_type]]
+        # scale
+        scale_name = self.added_scale_attrs[weight_id_map[weight_type]]
+
+        weight_tensor = getattr(layer, weight_name)
+        quanted_weight_scale = weight_tensor.abs().max(axis=1)
+        quanted_weight = weight_tensor / quanted_weight_scale[:, None, :] * max_bound
+        quanted_weight = paddle.round(quanted_weight).astype("int8")
+        quanted_weight_scale = quanted_weight_scale / max_bound
+
+        getattr(layer, weight_name).value().get_tensor()._clear()
+
+        # create weight
+        setattr(
+            layer,
+            weight_name,
+            layer.create_parameter(
+                shape=weight_tensor.shape,
+                dtype=quanted_weight.dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        # create scale
+        setattr(
+            layer,
+            scale_name,
+            layer.create_parameter(
+                shape=quanted_weight_scale.shape,
+                dtype=quanted_weight_scale.dtype,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            ),
+        )
+        getattr(layer, weight_name).copy_(quanted_weight, False)
+        getattr(layer, scale_name).copy_(quanted_weight_scale, False)
+
     def apply(
         self,
         layer: nn.Layer,
@@ -164,12 +247,11 @@ class TritonWeightOnlyMoEMethod(QuantMethodBase):
         token_num = x.shape[0]
         top_k = layer.top_k
         num_local_experts = layer.num_local_experts
-        top_k = layer.top_k
         moe_intermediate_size = layer.moe_intermediate_size
         hidden_size = layer.hidden_size
 
         if layer.topk_method == "noaux_tc":
-            _, topk_weights, topk_ids = get_moe_scores(
+            gate_out, topk_weights, topk_ids = get_moe_scores(
                 gate_out,
                 layer.n_group,
                 layer.topk_group,
@@ -177,15 +259,15 @@ class TritonWeightOnlyMoEMethod(QuantMethodBase):
                 layer.routed_scaling_factor,
                 layer.gate_correction_bias,
             )
+            topk_weights, topk_ids = paddle.topk(gate_out, k=layer.top_k, axis=-1, sorted=False)
         else:
             topk_ids, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
                 gate_out,
                 layer.gate_correction_bias,
-                layer.top_k,
+                top_k,
-                True,  # apply_norm_weight
+                True,  # apply_norm_weight,
                 False,
             )
-
         up_gate_proj_out = paddle.empty(
             [token_num * top_k, moe_intermediate_size * 2],
             dtype=x.dtype,
@@ -302,6 +384,9 @@ class TritonWeightOnlyMoEMethod(QuantMethodBase):
 
         down_proj_out.reshape_([token_num, top_k, hidden_size])
         out = down_proj_out.sum(axis=1)
+        if layer.reduce_results and layer.tp_size > 1:
+            tensor_model_parallel_all_reduce(out)
+
         return out
 
 
@@ -432,7 +517,6 @@ class TensorWiseFP8MoEMethod(QuantMethodBase):
         hidden_size = layer.hidden_size
 
         if layer.topk_method == "noaux_tc":
-
             _, topk_weights, topk_ids = get_moe_scores(
                 gate_out,
                 layer.n_group,

fastdeploy/model_executor/layers/moe/moe.py

@@ -27,6 +27,11 @@ from fastdeploy.model_executor.utils import slice_fn
 from fastdeploy.platforms import current_platform
 from fastdeploy.worker.experts_manager import RedundantExpertManger
 
+try:
+    from fastdeploy.model_executor.ops.gpu import noaux_tc
+except:
+    logger.warning("import noaux_tc Failed!")
+
 
 def get_moe_method():
     """
@@ -54,6 +59,31 @@ def get_moe_method():
     raise NotImplementedError
 
 
+def get_moe_scores(
+    gating_output: paddle.Tensor,
+    n_group,
+    topk_group,
+    top_k,
+    routed_scaling_factor,
+    e_score_correction_bias,
+) -> paddle.Tensor:
+    """
+    compute moe scores using e_score_correction_bias.
+    """
+    scores = paddle.nn.functional.sigmoid(gating_output)
+    assert e_score_correction_bias is not None, "e_score_correction_bias is none!"
+    scores_with_bias = scores + e_score_correction_bias
+    scores, topk_values, topk_idx = noaux_tc(
+        scores,
+        scores_with_bias,
+        n_group if n_group > 0 else 1,
+        topk_group if topk_group > 0 else 1,
+        top_k,
+        routed_scaling_factor,
+    )
+    return scores, topk_values, topk_idx
+
+
 class FusedMoE(nn.Layer):
     """
     FusedMoE is a layer that performs MoE (Mixture of Experts) computation.

fastdeploy/model_executor/layers/quantization/mix_quant.py

@@ -76,13 +76,13 @@ class MixQuantConfig(QuantConfigBase):
             if layer.moe_tag == "Image":
                 return (
                     get_quantization_config(self.image_moe_quant_type)
-                    .from_config({"is_permuted": self.is_permuted, "self.is_checkpoint_bf16": self.is_checkpoint_bf16})
+                    .from_config({"is_permuted": self.is_permuted, "is_checkpoint_bf16": self.is_checkpoint_bf16})
                     .get_quant_method(layer)
                 )
             else:
                 return (
                     get_quantization_config(self.moe_quant_type)
-                    .from_config({"is_permuted": self.is_permuted, "self.is_checkpoint_bf16": self.is_checkpoint_bf16})
+                    .from_config({"is_permuted": self.is_permuted, "is_checkpoint_bf16": self.is_checkpoint_bf16})
                     .get_quant_method(layer)
                 )
         elif isinstance(layer, Attention):
@@ -97,6 +97,6 @@ class MixQuantConfig(QuantConfigBase):
         else:
             return (
                 get_quantization_config(self.dense_quant_type)
-                .from_config({"self.is_checkpoint_bf16": self.is_checkpoint_bf16})
+                .from_config({"is_checkpoint_bf16": self.is_checkpoint_bf16})
                 .get_quant_method(layer)
             )

fastdeploy/model_executor/layers/quantization/weight_only.py

@@ -44,6 +44,7 @@ class WeightOnlyConfig(QuantConfigBase):
     def __init__(
         self,
         algo: str,
+        is_checkpoint_bf16: bool = False,
     ) -> None:
         super().__init__()
         self.algo = algo
@@ -55,6 +56,7 @@ class WeightOnlyConfig(QuantConfigBase):
         self.quant_max_bound = 0
         self.quant_min_bound = 0
         self.quant_round_type = 0
+        self.is_checkpoint_bf16 = is_checkpoint_bf16
 
     def name(self) -> str:
         return "weight_only"
@@ -62,7 +64,8 @@ class WeightOnlyConfig(QuantConfigBase):
     @classmethod
     def from_config(cls, config: dict) -> "WeightOnlyConfig":
         algo = config["algo"]
-        return cls(algo)
+        is_checkpoint_bf16 = config.get("is_checkpoint_bf16", False)
+        return cls(algo, is_checkpoint_bf16=is_checkpoint_bf16)
 
     def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
         if current_platform.is_xpu():
@@ -153,12 +156,13 @@ class WINT8Config(WeightOnlyConfig):
     weight only int8 config
     """
 
-    def __init__(self) -> None:
+    def __init__(self, is_checkpoint_bf16: bool = False) -> None:
-        super().__init__("weight_only_int8")
+        super().__init__("weight_only_int8", is_checkpoint_bf16)
 
     @classmethod
     def from_config(cls, config: dict) -> "WINT8Config":
-        return cls()
+        is_checkpoint_bf16 = config.get("is_checkpoint_bf16", False)
+        return cls(is_checkpoint_bf16)
 
     def name(self) -> str:
         return "wint8"

fastdeploy/model_executor/layers/quantization/wfp8afp8.py

@@ -14,10 +14,15 @@
 # limitations under the License.
 """
 
+import copy
 from typing import Optional
 
 import paddle
 
+from fastdeploy.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+)
 from fastdeploy.model_executor.layers.quantization.ops import (
     cutlass_scaled_mm,
     scaled_fp8_quant,
@@ -26,6 +31,8 @@ from fastdeploy.model_executor.layers.quantization.quant_base import (
     QuantConfigBase,
     QuantMethodBase,
 )
+from fastdeploy.model_executor.layers.utils import per_token_cast_to_fp8
+from fastdeploy.model_executor.utils import TensorTracker, set_weight_attrs
 
 
 class WFP8AFP8Config(QuantConfigBase):
@@ -33,13 +40,19 @@ class WFP8AFP8Config(QuantConfigBase):
     Quantization config for weight and activation with FP8.
     """
 
-    def __init__(self, weight_scale_dict, act_scale_dict) -> None:
+    def __init__(
+        self,
+        activation_scheme: str = "dynamic",
+        weight_block_size: list[int] = [-1, 1],
+        is_checkpoint_bf16: bool = False,
+    ) -> None:
         super().__init__()
-        self.weight_scale_dict = weight_scale_dict
-        self.act_scale_dict = act_scale_dict
         self.quant_max_bound = 448
         self.quant_min_bound = -448
         self.quant_round_type = 1
+        self.activation_scheme = activation_scheme
+        self.weight_block_size = weight_block_size
+        self.is_checkpoint_bf16 = is_checkpoint_bf16
 
     def name(self) -> str:
         """ """
@@ -48,9 +61,8 @@ class WFP8AFP8Config(QuantConfigBase):
     @classmethod
     def from_config(cls, config: dict) -> "WFP8AFP8Config":
         """ """
-        weight_scale_dict = config.get("weight_scale_dict", None)
+        is_checkpoint_bf16 = config.get("is_checkpoint_bf16", False)
-        act_scale_dict = config.get("act_scale_dict", None)
+        return cls(is_checkpoint_bf16=is_checkpoint_bf16)
-        return cls(weight_scale_dict, act_scale_dict)
 
     def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
         """ """
@@ -68,26 +80,85 @@ class WFP8AFP8LinearMethod(QuantMethodBase):
     ) -> None:
         super().__init__()
         self.quant_config = quant_config
+        self.use_per_token_if_dynamic = True
 
     def create_weights(self, layer, **extra_weight_attrs):
         """ """
+        weight_shape = layer.weight_shape
+        weight_block_size = self.quant_config.weight_block_size
+        assert len(weight_shape) == 2 and len(weight_block_size) == 2
+        scale_shape = copy.deepcopy(weight_shape)
+        for i in range(len(weight_shape)):
+            scale_shape[i] = (
+                (weight_shape[i] + weight_block_size[i] - 1) // weight_block_size[i] if weight_block_size[i] > 0 else 1
+            )
+        scale_shape = scale_shape[::-1]
+        if self.quant_config.is_checkpoint_bf16:
+            self.use_per_token_if_dynamic = True
+            layer.weight = layer.create_parameter(
+                shape=weight_shape,
+                dtype=layer.weight_dtype,
+                is_bias=False,
+                default_initializer=paddle.nn.initializer.Constant(0),
+            )
+            quant_attrs = extra_weight_attrs
+            if isinstance(layer, MergedColumnParallelLinear) or isinstance(layer, QKVParallelLinear):
+                quant_attrs = {
+                    **extra_weight_attrs,
+                    "tensor_track": TensorTracker(
+                        shape=layer.weight_shape, output_dim=extra_weight_attrs.get("output_dim")
+                    ),
+                }
+            set_weight_attrs(
+                layer.weight,
+                quant_attrs,
+            )
+        else:
             layer.weight_shape.reverse()
             layer.weight_dtype = "float8_e4m3fn"
             # TODO(YuanRisheng): set weight logic should be moved to process_loaded_weights func
             self.skip_quant = False
-        layer.create_parameter(
+            layer.weight = layer.create_parameter(
                 shape=layer.weight_shape,
                 dtype=layer.weight_dtype,
                 is_bias=False,
                 default_initializer=paddle.nn.initializer.Constant(0),
             )
             layer.weight_scale = layer.create_parameter(
-            shape=[1],
+                shape=scale_shape,
                 dtype="float32",
                 is_bias=False,
                 default_initializer=paddle.nn.initializer.Constant(0),
             )
 
+    def process_weights_after_loading(self, layer) -> None:
+        if not self.quant_config.is_checkpoint_bf16:
+            return
+        weight_tensor = layer.weight.transpose([1, 0]).contiguous()
+        assert self.quant_config.weight_block_size == [-1, 1]
+        qweight, weight_scale = per_token_cast_to_fp8(weight_tensor)
+
+        if hasattr(layer.weight, "tensor_track"):
+            layer.weight.tensor_track = None
+        layer.weight.value().get_tensor()._clear()
+        del layer.weight
+
+        layer.weight = layer.create_parameter(
+            shape=qweight.shape,
+            dtype="float8_e4m3fn",
+            is_bias=False,
+            default_initializer=paddle.nn.initializer.Constant(0),
+        )
+        layer.weight_scale = layer.create_parameter(
+            shape=weight_scale.shape,
+            dtype="float32",
+            is_bias=False,
+            default_initializer=paddle.nn.initializer.Constant(0),
+        )
+
+        layer.weight.copy_(qweight, False)
+        layer.weight_scale.copy_(weight_scale, False)
+
     def process_loaded_weights(self, layer, weights) -> None:
         """ """
         if self.skip_quant:
@@ -97,18 +168,12 @@ class WFP8AFP8LinearMethod(QuantMethodBase):
         if weights.dtype != paddle.float8_e4m3fn:
             self.use_per_token_if_dynamic = True
         weight_tensor = weights.transpose([1, 0]).contiguous()
-        qweight, weight_scale = scaled_fp8_quant(
+        qweight, weight_scale = per_token_cast_to_fp8(weight_tensor)
-            weight_tensor,
-            use_per_token_if_dynamic=False,
-        )
         layer.weight.copy_(qweight, False)
         layer.weight_scale.set_value(weight_scale)
 
     def apply(self, layer, x):
         """ """
-        if self.skip_quant:
-            linear_out = paddle.matmul(x, layer.weight, False, True)
-            return linear_out
         if self.use_per_token_if_dynamic:
             out_type = x.dtype
             a_q, a_scales = scaled_fp8_quant(x, use_per_token_if_dynamic=self.use_per_token_if_dynamic)

fastdeploy/model_executor/layers/rotary_embedding.py

@@ -73,6 +73,30 @@ class ErnieRotaryEmbedding:
             return rot_emb
 
 
+class GlmRotaryEmbedding:
+    def __init__(self, rotary_dim, base, partial_rotary_factor):
+        """
+        Pre-calculate rotary position embedding for position_ids.
+        """
+        self.rotary_dim = rotary_dim
+        self.base = base
+        if partial_rotary_factor < 1.0:
+            self.rotary_dim = int(self.rotary_dim * partial_rotary_factor)
+
+    def __call__(self, position_ids):
+        bsz, max_seq_len = position_ids.shape[:2]
+        inv_freq = self.base ** (-paddle.arange(0, self.rotary_dim, 2, dtype="float32") / self.rotary_dim)
+        freqs = paddle.einsum("ij,k->ijk", position_ids.cast("float32"), inv_freq)
+        # shape: [B, S, D/2]
+        rot_emb = paddle.zeros((2, bsz, max_seq_len, 1, self.rotary_dim // 2), dtype="float32")
+        emb = paddle.stack([freqs], axis=-1).reshape((bsz, max_seq_len, self.rotary_dim // 2))
+        # shape: [B, S, 1, D]
+        emb = paddle.unsqueeze(emb, 2)
+        rot_emb[0] = paddle.cos(emb)
+        rot_emb[1] = paddle.sin(emb)
+        return rot_emb
+
+
 class QwenRotaryEmbedding:
     def __init__(self, rotary_dim, base, partial_rotary_factor):
         """
@@ -246,6 +270,9 @@ def get_rope_impl(
     if model_config is None or architecture.startswith("Qwen"):
         rotary_emb_layer = QwenRotaryEmbedding(rotary_dim, base, partial_rotary_factor)
         rotary_emb = rotary_emb_layer(position_ids)
+    elif architecture.startswith("Glm"):
+        rotary_emb_layer = GlmRotaryEmbedding(rotary_dim, base, partial_rotary_factor)
+        rotary_emb = rotary_emb_layer(position_ids)
     else:
         rotary_emb_layer = ErnieRotaryEmbedding(rotary_dim, base, partial_rotary_factor)
         rotary_emb = rotary_emb_layer(position_ids)

fastdeploy/model_executor/layers/utils.py

@@ -77,6 +77,17 @@ def per_block_cast_to_fp8(x: Tensor, block_size: list = [128, 128]) -> Tuple[Ten
     )
 
 
+def per_token_cast_to_fp8(x: Tensor) -> Tuple[Tensor, Tensor]:
+    """
+    Per token cast to float8_e4m3fn used in wfp8apf8
+    """
+    x_abs = paddle.abs(x).astype(paddle.float32)
+    x_max = x_abs.max(axis=-1, keepdim=True).clip_(min=1e-4)
+    x_s = x_max / 448.0
+    x_q = paddle.clip(x / x_s, -448.0, 448.0).astype(paddle.float8_e4m3fn)
+    return x_q, x_s
+
+
 # for distributed tensor model parallel
 def _set_var_distributed(var: Tensor, split_axis: int):
     """

fastdeploy/model_executor/models/glm4_moe.py

@@ -0,0 +1,579 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from __future__ import annotations
+
+import re
+from functools import partial
+
+import paddle
+from paddle import nn
+from paddleformers.transformers import PretrainedModel
+from paddleformers.utils.log import logger
+
+from fastdeploy.config import FDConfig
+from fastdeploy.distributed.communication import tensor_model_parallel_all_reduce
+from fastdeploy.model_executor.forward_meta import ForwardMeta
+from fastdeploy.model_executor.graph_optimization.decorator import (
+    support_graph_optimization,
+)
+from fastdeploy.model_executor.layers.activation import SiluAndMul
+from fastdeploy.model_executor.layers.attention.attention import Attention
+from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
+from fastdeploy.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
+from fastdeploy.model_executor.layers.moe.moe import FusedMoE
+from fastdeploy.model_executor.layers.normalization import RMSNorm
+from fastdeploy.model_executor.models.model_base import ModelForCasualLM
+
+
+class Glm4MoeMLP(nn.Layer):
+    """ """
+
+    def __init__(
+        self,
+        fd_config: FDConfig,
+        intermediate_size: int,
+        prefix: str = "",
+        reduce_results: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.up_gate_proj = MergedColumnParallelLinear(
+            fd_config=fd_config,
+            prefix=f"{prefix}.up_gate_proj",
+            input_size=fd_config.model_config.hidden_size,
+            output_size=intermediate_size * 2,
+            with_bias=False,
+            activation=fd_config.model_config.hidden_act,
+        )
+
+        self.down_proj = RowParallelLinear(
+            fd_config=fd_config,
+            prefix=f"{prefix}.down_proj",
+            input_size=intermediate_size,
+            output_size=fd_config.model_config.hidden_size,
+            with_bias=False,
+            reduce_results=reduce_results,
+        )
+
+        self.act_fn = SiluAndMul(
+            fd_config=fd_config,
+            bias=None,
+            act_method=fd_config.model_config.hidden_act,
+        )
+
+    def forward(self, x):
+        """ """
+        gate_up_out = self.up_gate_proj(x)
+        act_out = self.act_fn(gate_up_out)
+        down_out = self.down_proj(act_out)
+        return down_out
+
+
+class Glm4Moe(nn.Layer):
+    def __init__(
+        self,
+        fd_config: FDConfig,
+        layer_id: int,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.expert_parallel_size = fd_config.parallel_config.expert_parallel_size
+        self.tensor_parallel_size = fd_config.parallel_config.tensor_parallel_size
+        self.tensor_parallel_rank = fd_config.parallel_config.tensor_parallel_rank
+        self.tp_group = fd_config.parallel_config.tp_group
+
+        self.use_ep = self.expert_parallel_size > 1
+        self.use_tp = self.tensor_parallel_size > 1
+
+        self.n_routed_experts: int = fd_config.model_config.n_routed_experts
+        self.n_shared_experts: int = fd_config.model_config.n_shared_experts
+
+        weight_key_map = {
+            "gate_correction_bias_key": f"{prefix}.gate.e_score_correction_bias",
+            "up_gate_proj_expert_weight_key": f"{prefix}.experts.{{}}.up_gate_proj.weight",
+            "down_proj_expert_weight_key": f"{prefix}.experts.{{}}.down_proj.weight",
+        }
+
+        self.gate = ReplicatedLinear(
+            fd_config=fd_config,
+            prefix=f"{prefix}.gate",
+            input_size=fd_config.model_config.hidden_size,
+            output_size=fd_config.model_config.n_routed_experts,
+            with_bias=False,
+            skip_quant=True,
+            weight_dtype="float32",
+        )
+        self.gate.e_score_correction_bias = self.create_parameter(
+            shape=[1, fd_config.model_config.n_routed_experts],
+            dtype="float32",
+            default_initializer=paddle.nn.initializer.Constant(0),
+        )
+
+        self.experts = FusedMoE(
+            fd_config,
+            reduce_results=False,
+            moe_intermediate_size=fd_config.model_config.moe_intermediate_size,
+            num_experts=fd_config.model_config.n_routed_experts,
+            top_k=fd_config.model_config.num_experts_per_tok,
+            topk_method="noaux_tc",
+            topk_group=fd_config.model_config.topk_group,
+            n_group=fd_config.model_config.n_group,
+            routed_scaling_factor=fd_config.model_config.routed_scaling_factor,
+            layer_idx=layer_id,
+            gate_correction_bias=self.gate.e_score_correction_bias,
+            weight_key_map=weight_key_map,
+        )
+
+        shared_experts_intermediate_size = self.n_shared_experts * fd_config.model_config.moe_intermediate_size
+
+        self.shared_experts = Glm4MoeMLP(
+            fd_config=fd_config,
+            intermediate_size=shared_experts_intermediate_size,
+            prefix=f"{prefix}.shared_experts",
+            reduce_results=False,
+        )
+
+    def forward(self, x):
+        shared_experts_out = self.shared_experts(x)
+        out = self.experts(x, self.gate)
+        out = out + shared_experts_out
+        # We do to TP all reduce after the sum of experts.
+        if self.tensor_parallel_size > 1:
+            tensor_model_parallel_all_reduce(out)
+        return out
+
+
+class Glm4MoeAttention(nn.Layer):
+    """ """
+
+    def __init__(self, fd_config: FDConfig, layer_id: int, prefix: str = "") -> None:
+        super().__init__()
+
+        tp_size = fd_config.parallel_config.tensor_parallel_size
+        self.fd_config = fd_config
+        self.head_dim = fd_config.model_config.head_dim
+        self.num_heads = fd_config.model_config.num_attention_heads // tp_size
+        self.num_kv_heads = fd_config.model_config.num_key_value_heads // tp_size
+        self.attention_bias = fd_config.model_config.attention_bias
+        self.use_qk_norm = fd_config.model_config.use_qk_norm
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.qkv_proj = QKVParallelLinear(fd_config, prefix=f"{prefix}.qkv_proj", with_bias=self.attention_bias)
+
+        self.o_proj = RowParallelLinear(
+            fd_config,
+            prefix=f"{prefix}.o_proj",
+            input_size=fd_config.model_config.num_attention_heads * fd_config.model_config.head_dim,
+            output_size=fd_config.model_config.hidden_size,
+        )
+
+        self.attn = Attention(
+            fd_config,
+            layer_id=layer_id,
+            prefix=prefix,
+            use_neox_rotary_style=True,
+            rms_norm_eps=fd_config.model_config.rms_norm_eps,
+        )
+        if self.use_qk_norm:
+            self.q_norm = RMSNorm(
+                fd_config,
+                hidden_size=self.head_dim,
+                eps=fd_config.model_config.rms_norm_eps,
+                prefix=f"{prefix}.q_norm",
+                begin_norm_axis=2,
+            )
+            self.k_norm = RMSNorm(
+                fd_config,
+                hidden_size=self.head_dim,
+                eps=fd_config.model_config.rms_norm_eps,
+                prefix=f"{prefix}.k_norm",
+                begin_norm_axis=2,
+            )
+
+    def forward(
+        self,
+        forward_meta: ForwardMeta,
+        hidden_states: paddle.Tensor,
+    ):
+        """ """
+        qkv_out = self.qkv_proj(hidden_states)
+
+        if self.use_qk_norm:
+            q, k, v = qkv_out.split([self.q_size, self.kv_size, self.kv_size], axis=-1)
+            q = self.q_norm(q.reshape([-1, self.num_heads, self.head_dim])).reshape(q.shape)
+            k = self.k_norm(k.reshape([-1, self.num_kv_heads, self.head_dim])).reshape(k.shape)
+            qkv_out = paddle.concat([q, k, v], axis=-1)
+
+        atten_out = self.attn(
+            qkv=qkv_out,
+            forward_meta=forward_meta,
+        )
+        output = self.o_proj(atten_out)
+        return output
+
+
+class Glm4MoeDecoderLayer(nn.Layer):
+    """ """
+
+    def __init__(
+        self,
+        fd_config: FDConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        layer_id = int(prefix.split(sep=".")[-1])
+        self.self_attn = Glm4MoeAttention(
+            fd_config=fd_config,
+            layer_id=layer_id,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        if (
+            fd_config.model_config.n_routed_experts is not None
+            and layer_id >= fd_config.model_config.first_k_dense_replace
+        ):
+            self.mlp = Glm4Moe(fd_config, layer_id, prefix=f"{prefix}.mlp")
+        else:
+            self.mlp = Glm4MoeMLP(
+                fd_config,
+                intermediate_size=fd_config.model_config.intermediate_size,
+                prefix=f"{prefix}.mlp",
+            )
+
+        self.input_layernorm = RMSNorm(
+            fd_config,
+            hidden_size=fd_config.model_config.hidden_size,
+            eps=fd_config.model_config.rms_norm_eps,
+            prefix=f"{prefix}.input_layernorm",
+        )
+
+        self.post_attention_layernorm = RMSNorm(
+            fd_config,
+            hidden_size=fd_config.model_config.hidden_size,
+            eps=fd_config.model_config.rms_norm_eps,
+            prefix=f"{prefix}.post_attention_layernorm",
+        )
+
+    def forward(
+        self,
+        forward_meta: ForwardMeta,
+        hidden_states: paddle.Tensor,
+        residual: paddle.Tensor = None,
+    ):
+        """ """
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            forward_meta=forward_meta,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+
+        hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_graph_optimization
+class Glm4MoeModel(nn.Layer):
+    """ """
+
+    def __init__(
+        self,
+        fd_config: FDConfig = None,
+    ):
+        """
+        Initializer for the Qwen2Model class.
+
+        Args:
+
+        """
+        super().__init__()
+
+        self.num_layers = fd_config.model_config.num_hidden_layers
+        fd_config.model_config.pretrained_config.prefix_name = "model"
+
+        self.embed_tokens = VocabParallelEmbedding(
+            fd_config,
+            num_embeddings=fd_config.model_config.vocab_size,
+            embedding_dim=fd_config.model_config.hidden_size,
+            params_dtype=paddle.get_default_dtype,
+            prefix=(f"{fd_config.model_config.pretrained_config.prefix_name}.embed_tokens"),
+        )
+
+        self.layers = nn.LayerList(
+            [
+                Glm4MoeDecoderLayer(
+                    fd_config,
+                    prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}",
+                )
+                for i in range(self.num_layers)
+            ]
+        )
+
+        self.norm = RMSNorm(
+            fd_config,
+            hidden_size=fd_config.model_config.hidden_size,
+            eps=fd_config.model_config.rms_norm_eps,
+            prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.norm",
+        )
+
+    def forward(
+        self,
+        ids_remove_padding: paddle.Tensor,
+        forward_meta: ForwardMeta,
+    ):
+        """ """
+        hidden_states = self.embed_tokens(ids_remove_padding=ids_remove_padding)
+
+        residual = None
+
+        for i in range(self.num_layers):
+            hidden_states, residual = self.layers[i](forward_meta, hidden_states, residual)
+        hidden_states = hidden_states + residual
+
+        out = self.norm(hidden_states)
+
+        return out
+
+
+class Glm4MoeForCausalLM(ModelForCasualLM):
+    """
+    Glm4MoeForCausalLM
+    """
+
+    def __init__(self, fd_config: FDConfig):
+        """
+        Args:
+            fd_config (FDConfig): Configurations for the LLM model.
+        """
+        super(Glm4MoeForCausalLM, self).__init__(fd_config)
+
+        self.model = Glm4MoeModel(fd_config)
+
+        self.ori_vocab_size = fd_config.model_config.ori_vocab_size
+
+        self.lm_head = ParallelLMHead(
+            fd_config,
+            embedding_dim=fd_config.model_config.hidden_size,
+            num_embeddings=fd_config.model_config.vocab_size,
+            prefix="lm_head",
+        )
+
+    @classmethod
+    def name(self):
+        """ """
+        return "Glm4MoeForCausalLM"
+
+    @paddle.no_grad()
+    def load_weights(self, weights_iterator) -> None:
+        """
+        Load model parameters from a given weights_iterator object.
+
+        Args:
+            weights_iterator (Iterator): An iterator yielding (name, weight) pairs.
+        """
+
+        from fastdeploy.model_executor.utils import (
+            default_weight_loader,
+            process_weights_after_loading,
+        )
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("up_gate_proj", "gate_proj", "gate"),
+            ("up_gate_proj", "up_proj", "up"),
+            ("embed_tokens.embeddings", "embed_tokens", None),
+            ("lm_head.linear", "lm_head", None),
+            ("experts.gate_correction_bias", "gate.e_score_correction_bias", None),
+        ]
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            num_experts=self.fd_config.model_config.n_routed_experts,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            param_gate_up_proj_name="experts.up_gate_proj_",
+            param_down_proj_name="experts.down_proj_",
+        )
+        params_dict = dict(self.named_parameters())
+        process_weights_after_loading_fn = process_weights_after_loading(dict(self.named_sublayers()))
+        for loaded_weight_name, loaded_weight in weights_iterator:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in loaded_weight_name:
+                    continue
+                if "mlp.experts" in loaded_weight_name:
+                    continue
+                model_param_name = loaded_weight_name.replace(weight_name, param_name)
+                if model_param_name not in params_dict:
+                    continue
+                param = params_dict[model_param_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in loaded_weight_name:
+                        continue
+                    model_param_name = loaded_weight_name.replace(weight_name, param_name)
+                    if model_param_name not in params_dict:
+                        continue
+                    param = params_dict[model_param_name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id=shard_id, expert_id=expert_id)
+                    break
+                else:
+                    model_param_name = loaded_weight_name
+                    if model_param_name not in params_dict:
+                        continue
+                    param = params_dict[model_param_name]
+                    weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
+                    weight_loader(param, loaded_weight)
+
+            model_sublayer_name = re.sub(r"\.(up_gate_proj_weight|down_proj_weight|weight)$", "", model_param_name)
+            process_weights_after_loading_fn(model_sublayer_name, param)
+
+    @paddle.no_grad()
+    def set_state_dict(self, state_dict):
+        """
+        glm4_moe only support loader_v1.
+        """
+        assert False, "glm4_moe only support --load_choices default_v1."
+
+    def compute_logits(self, hidden_states: paddle.Tensor):
+        """ """
+        logits = self.lm_head(hidden_states)
+        logits = logits.astype(paddle.float32)
+        logits[:, self.ori_vocab_size :] = -float("inf")
+
+        return logits
+
+    def forward(
+        self,
+        ids_remove_padding: paddle.Tensor,
+        forward_meta: ForwardMeta,
+    ):
+        """ """
+        hidden_states = self.model(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)
+
+        return hidden_states
+
+    def clear_grpah_opt_backend(self):
+        """Clear graph optimization backend, the captured cuda graph will be cleaned"""
+        self.model.clear_grpah_opt_backend(fd_config=self.fd_config)
+
+
+class Glm4MoePretrainedModel(PretrainedModel):
+    """
+    Glm4MoePretrainedModel
+    """
+
+    config_class = FDConfig
+
+    def _init_weight(self, layer):
+        """
+        _init_weight
+        """
+        return None
+
+    @classmethod
+    def arch_name(self):
+        return "Glm4MoeForCausalLM"
+
+    @classmethod
+    def _get_tensor_parallel_mappings(cls, config, is_split=True):
+
+        logger.info("Glm4Moe inference model _get_tensor_parallel_mappings")
+
+        from fastdeploy.model_executor.models.tp_utils import split_or_merge_func_v1
+
+        fn = split_or_merge_func_v1(
+            is_split=is_split,
+            tensor_parallel_degree=config.tensor_parallel_degree,
+            tensor_parallel_rank=config.tensor_parallel_rank,
+            num_attention_heads=config.num_attention_heads,
+            num_key_value_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+        )
+
+        def get_tensor_parallel_split_mappings(num_layers):
+            final_actions = {}
+
+            base_actions = {
+                "lm_head.weight": partial(fn, is_column=True),
+                "embed_tokens.weight": partial(fn, is_column=False),
+                "layers.0.self_attn.o_proj.weight": partial(fn, is_column=False),
+            }
+
+            # Self Attention Layer which are need TP.
+            base_actions["layers.0.self_attn.q_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.0.self_attn.k_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.0.self_attn.v_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.0.self_attn.q_proj.bias"] = partial(fn, is_column=True)
+            base_actions["layers.0.self_attn.k_proj.bias"] = partial(fn, is_column=True)
+            base_actions["layers.0.self_attn.v_proj.bias"] = partial(fn, is_column=True)
+
+            # MLP Layer
+            base_actions["layers.0.mlp.gate_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.0.mlp.up_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.0.mlp.down_proj.weight"] = partial(fn, is_column=False)
+
+            # Moe Layer
+            for expert_idx in range(config.n_routed_experts):
+                base_actions[f"layers.0.mlp.experts.{expert_idx}.up_proj.weight"] = partial(fn, is_column=True)
+                base_actions[f"layers.0.mlp.experts.{expert_idx}.gate_proj.weight"] = partial(fn, is_column=True)
+                base_actions[f"layers.0.mlp.experts.{expert_idx}.down_proj.weight"] = partial(fn, is_column=False)
+
+            # Shared Expert Layer
+            base_actions["layers.0.mlp.shared_experts.up_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.0.mlp.shared_experts.gate_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.0.mlp.shared_experts.down_proj.weight"] = partial(fn, is_column=False)
+
+            # MTP parts
+            base_actions["layers.46.embed_tokens.weight"] = partial(fn, is_column=False)
+            base_actions["layers.46.eh_proj.weight"] = partial(fn, is_column=True)
+            base_actions["layers.46.shared_head.head.weight"] = partial(fn, is_column=True)
+
+            for key, action in base_actions.items():
+                if "layers.0." in key:
+                    for i in range(num_layers):
+                        final_actions[key.replace("layers.0.", f"layers.{i}.")] = action
+                final_actions[key] = action
+
+            return final_actions
+
+        mappings = get_tensor_parallel_split_mappings(config.num_hidden_layers)
+        return mappings

fastdeploy/rl/rollout_config.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """
 
+from typing import Any, Dict, Optional
+
 from fastdeploy.worker.worker_process import initialize_fd_config
 
 
@@ -52,7 +54,7 @@ class RolloutModelConfig:
         expert_parallel_size: int = 1,
         enable_expert_parallel: bool = False,
         ori_vocab_size: int = None,
-        quantization: str = "None",
+        quantization: Optional[Dict[str, Any]] = None,
         guided_decoding_backend: str = "off",
         disable_any_whitespace: bool = True,
         enable_logprob: bool = False,

fastdeploy/rl/rollout_model.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 """
 
+import copy
 from typing import Dict
 
 import paddle
@@ -28,6 +29,10 @@ from fastdeploy.model_executor.models.ernie4_5_vl.ernie4_5_vl_moe import (
     Ernie4_5_VLMoeForConditionalGeneration,
     Ernie4_5_VLPretrainedModel,
 )
+from fastdeploy.model_executor.models.glm4_moe import (
+    Glm4MoeForCausalLM,
+    Glm4MoePretrainedModel,
+)
 from fastdeploy.model_executor.models.model_base import ModelRegistry
 from fastdeploy.model_executor.models.qwen2 import (
     Qwen2ForCausalLM,
@@ -529,3 +534,83 @@ class Qwen2_5_VLForConditionalGenerationRL(Qwen2_5_VLForConditionalGeneration, B
         self._complete_missing_mappings()
 
         return self.infer_to_train_mapping
+
+
+class Glm4MoeForCausalLMRL(Glm4MoeForCausalLM, BaseRLModel):
+    """
+    Glm4MoeForCausalLMRL
+    """
+
+    _get_tensor_parallel_mappings = Glm4MoePretrainedModel._get_tensor_parallel_mappings
+
+    def __init__(self, fd_config: FDConfig):
+        """
+        Args:
+            fd_config (FDConfig): Configurations for the LLM model.
+        """
+        super(Glm4MoeForCausalLMRL, self).__init__(fd_config)
+
+    @classmethod
+    def name(self) -> str:
+        """name"""
+        return "Glm4MoeForCausalLMRL"
+
+    def get_name_mappings_to_training(self, trainer_degree=None) -> Dict[str, str]:
+        """Generate mapping between inference and training parameter for RL(donot delete!)."""
+        if self._mappings_built:
+            return self.infer_to_train_mapping
+
+        self.infer_to_train_mapping = {}
+        self._mappings_built = True
+        # Prepare placeholders
+        place_holders = ["weight"]
+
+        # Initialize mapping dictionary
+        self._update_base_mappings("model")
+
+        base_name = "model.layers"
+
+        # Helper function to add layer mappings
+        def _add_layer_mappings(layer_idx: int):
+            # MoE specific mappings
+            self.infer_to_train_mapping[f"{base_name}.{layer_idx}.mlp.gate.weight"] = (
+                f"{base_name}.{layer_idx}.mlp.gate.weight"
+            )
+
+            self.infer_to_train_mapping[f"{base_name}.{layer_idx}.mlp.gate.e_score_correction_bias"] = (
+                f"{base_name}.{layer_idx}.mlp.gate.e_score_correction_bias"
+            )
+
+            # MoE experts mappings
+            for expert_idx in range(self.fd_config.model_config.n_routed_experts):
+                for ph in place_holders:
+                    # up_gate_proj (up_gate_proj)
+                    up_gate_proj_key = f"{base_name}.{layer_idx}.mlp.experts.up_gate_proj_weight"
+                    if up_gate_proj_key not in self.infer_to_train_mapping:
+                        self.infer_to_train_mapping[up_gate_proj_key] = []
+                    self.infer_to_train_mapping[up_gate_proj_key].append(
+                        f"{base_name}.{layer_idx}.mlp.experts.{expert_idx}.up_gate_proj.{ph}"
+                    )
+
+                    # down_proj (down_proj)
+                    down_proj_key = f"{base_name}.{layer_idx}.mlp.experts.down_proj_weight"
+                    if down_proj_key not in self.infer_to_train_mapping:
+                        self.infer_to_train_mapping[down_proj_key] = []
+                    self.infer_to_train_mapping[down_proj_key].append(
+                        f"{base_name}.{layer_idx}.mlp.experts.{expert_idx}.down_proj.{ph}"
+                    )
+
+        # Process MoE layers
+        for layer_idx in range(
+            self.fd_config.model_config.first_k_dense_replace,
+            self.fd_config.model_config.num_hidden_layers,
+        ):
+            _add_layer_mappings(layer_idx)
+
+        self._complete_missing_mappings()
+        infer_to_train_mapping_copy = copy.deepcopy(self.infer_to_train_mapping)
+        for key in infer_to_train_mapping_copy.keys():
+            if "mlp.experts.gate_correction_bias" in key:
+                self.infer_to_train_mapping.pop(key)
+
+        return self.infer_to_train_mapping

fastdeploy/utils.py

@@ -18,6 +18,7 @@ import argparse
 import asyncio
 import codecs
 import importlib
+import json
 import logging
 import os
 import random
@@ -757,6 +758,18 @@ class StatefulSemaphore:
         }
 
 
+def parse_quantization(value: str):
+    """
+    Parse a JSON string into a dictionary.
+    """
+    try:
+        return json.loads(value)
+    except ValueError:
+        if value is None or value.lower() == "none":
+            return None
+        return {"quantization": value}
+
+
 # 日志使用全局访问点（兼容原有使用方式）
 def get_logger(name, file_name=None, without_formater=False, print_to_console=False):
     """全局函数包装器，保持向后兼容"""

fastdeploy/worker/gpu_model_runner.py

@@ -740,6 +740,7 @@ class GPUModelRunner(ModelRunnerBase):
                 position_ids=tmp_position_ids,
                 base=self.model_config.rope_theta,
                 model_config=self.model_config,
+                partial_rotary_factor=self.model_config.partial_rotary_factor,
             )
 
         # Set block tables
@@ -1589,7 +1590,7 @@ class GPUModelRunner(ModelRunnerBase):
         # 2. Dummy run
         self._dummy_run(
             num_tokens=self.parallel_config.max_num_batched_tokens,
-            batch_size=min(self.parallel_config.max_num_seqs, 3),
+            batch_size=self.parallel_config.max_num_seqs,
         )
 
         # 3. gc

fastdeploy/worker/worker_process.py

@@ -44,7 +44,7 @@ from fastdeploy.inter_communicator import EngineWorkerQueue as TaskQueue
 from fastdeploy.inter_communicator import IPCSignal
 from fastdeploy.model_executor.layers.quantization import get_quantization_config
 from fastdeploy.platforms import current_platform
-from fastdeploy.utils import get_logger
+from fastdeploy.utils import get_logger, parse_quantization
 from fastdeploy.worker.worker_base import WorkerBase
 
 logger = get_logger("worker_process", "worker_process.log")
@@ -545,9 +545,9 @@ def parse_args():
 
     parser.add_argument(
         "--quantization",
-        type=str,
+        type=json.loads,
-        default="None",
+        default=None,
-        help="Quantization name for the model, currentlly support "
+        help="Quantization name for the model, currently support "
         "'wint4', 'wint8',"
         "default is None. The priority of this configuration "
         "is lower than that of the config file. "
@@ -635,6 +635,9 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
     Returns:
         FDConfig: Initialized FastDeploy configuration object
     """
+    # RL rollout
+    if args.quantization is not None and isinstance(args.quantization, str):
+        args.quantization = parse_quantization(args.quantization)
     paddle.set_default_dtype(args.dtype)
     model_config = ModelConfig(vars(args))
     device_config = DeviceConfig(vars(args))
@@ -704,12 +707,16 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
 
     if quantization_config is not None:
         quant_config_name = quantization_config["quantization"]
-    elif args.quantization != "None":
+    elif args.quantization is not None:
         quantization_config = {}
-        quant_config_name = args.quantization
+        try:
+            quantization_config.update(args.quantization)
+            quant_config_name = quantization_config["quantization"]
+        except:
+            quant_config_name = args.quantization["quantization"]
             quantization_config["quantization"] = quant_config_name
         # Only v1 loader sets is_checkpoint_bf16=True during dynamic quantization.
-        if load_config.load_choices == "default_v1":
+        if load_config.load_choices == "default_v1" and not load_config.dynamic_load_weight:
             quantization_config["is_checkpoint_bf16"] = True
         # Special handling for Ernie models
         is_ernie = ErnieArchitectures.contains_ernie_arch(model_config.architectures)

tests/e2e/test_fake_Glm45_AIR_serving.py

@@ -0,0 +1,223 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import shutil
+import signal
+import socket
+import subprocess
+import sys
+import time
+
+import pytest
+import requests
+
+# Read ports from environment variables; use default values if not set
+FD_API_PORT = int(os.getenv("FD_API_PORT", 8188))
+FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8133))
+FD_METRICS_PORT = int(os.getenv("FD_METRICS_PORT", 8233))
+FD_CACHE_QUEUE_PORT = int(os.getenv("FD_CACHE_QUEUE_PORT", 8333))
+
+# List of ports to clean before and after tests
+PORTS_TO_CLEAN = [FD_API_PORT, FD_ENGINE_QUEUE_PORT, FD_METRICS_PORT, FD_CACHE_QUEUE_PORT]
+
+
+def is_port_open(host: str, port: int, timeout=1.0):
+    """
+    Check if a TCP port is open on the given host.
+    Returns True if connection succeeds, False otherwise.
+    """
+    try:
+        with socket.create_connection((host, port), timeout):
+            return True
+    except Exception:
+        return False
+
+
+def kill_process_on_port(port: int):
+    """
+    Kill processes that are listening on the given port.
+    Uses `lsof` to find process ids and sends SIGKILL.
+    """
+    try:
+        output = subprocess.check_output(f"lsof -i:{port} -t", shell=True).decode().strip()
+        current_pid = os.getpid()
+        parent_pid = os.getppid()
+        for pid in output.splitlines():
+            pid = int(pid)
+            if pid in (current_pid, parent_pid):
+                print(f"Skip killing current process (pid={pid}) on port {port}")
+                continue
+            os.kill(pid, signal.SIGKILL)
+            print(f"Killed process on port {port}, pid={pid}")
+    except subprocess.CalledProcessError:
+        pass
+
+
+def clean_ports():
+    """
+    Kill all processes occupying the ports listed in PORTS_TO_CLEAN.
+    """
+    for port in PORTS_TO_CLEAN:
+        kill_process_on_port(port)
+    time.sleep(2)
+
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_and_run_server():
+    """
+    Pytest fixture that runs once per test session:
+    - Cleans ports before tests
+    - Starts the API server as a subprocess
+    - Waits for server port to open (up to 30 seconds)
+    - Tears down server after all tests finish
+    """
+    print("Pre-test port cleanup...")
+    clean_ports()
+    print("log dir clean ")
+    if os.path.exists("log") and os.path.isdir("log"):
+        shutil.rmtree("log")
+    base_path = os.getenv("MODEL_PATH")
+    if base_path:
+        model_path = os.path.join(base_path, "GLM-4.5-Air-Fake")
+    else:
+        model_path = "./GLM-4.5-Air-Fake"
+
+    log_path = "server.log"
+    cmd = [
+        sys.executable,
+        "-m",
+        "fastdeploy.entrypoints.openai.api_server",
+        "--model",
+        model_path,
+        "--port",
+        str(FD_API_PORT),
+        "--tensor-parallel-size",
+        "1",
+        "--engine-worker-queue-port",
+        str(FD_ENGINE_QUEUE_PORT),
+        "--metrics-port",
+        str(FD_METRICS_PORT),
+        "--cache-queue-port",
+        str(FD_CACHE_QUEUE_PORT),
+        "--max-model-len",
+        "32768",
+        "--max-num-seqs",
+        "32",
+        "--graph-optimization-config",
+        '{"use_cudagraph":true}',
+        "--load_choices",
+        "default_v1",
+        "--lm_head-fp32",
+        "--quantization",
+        '{"quantization":"mix_quant","dense_quant_type":"wfp8afp8","moe_quant_type":"wint8"}',
+    ]
+    env = os.environ.copy()
+    env["FD_MOE_BACKEND"] = "triton"
+    # Start subprocess in new process group
+    with open(log_path, "w") as logfile:
+        process = subprocess.Popen(
+            cmd,
+            env=env,
+            stdout=logfile,
+            stderr=subprocess.STDOUT,
+            start_new_session=True,  # Enables killing full group via os.killpg
+        )
+
+    # Wait up to 300 seconds for API server to be ready
+    for _ in range(300):
+        if is_port_open("127.0.0.1", FD_API_PORT):
+            print(f"API server is up on port {FD_API_PORT}")
+            break
+        time.sleep(1)
+    else:
+        print("[TIMEOUT] API server failed to start in 5 minutes. Cleaning up...")
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except Exception as e:
+            print(f"Failed to kill process group: {e}")
+        raise RuntimeError(f"API server did not start on port {FD_API_PORT}")
+
+    yield  # Run tests
+
+    print("\n===== Post-test server cleanup... =====")
+    try:
+        os.killpg(process.pid, signal.SIGTERM)
+        print(f"API server (pid={process.pid}) terminated")
+    except Exception as e:
+        print(f"Failed to terminate API server: {e}")
+
+
+@pytest.fixture(scope="session")
+def api_url(request):
+    """
+    Returns the API endpoint URL for chat completions.
+    """
+    return f"http://0.0.0.0:{FD_API_PORT}/v1/chat/completions"
+
+
+@pytest.fixture(scope="session")
+def metrics_url(request):
+    """
+    Returns the metrics endpoint URL.
+    """
+    return f"http://0.0.0.0:{FD_METRICS_PORT}/metrics"
+
+
+@pytest.fixture
+def headers():
+    """
+    Returns common HTTP request headers.
+    """
+    return {"Content-Type": "application/json"}
+
+
+@pytest.fixture
+def consistent_payload():
+    """
+    Returns a fixed payload for consistency testing,
+    including a fixed random seed and temperature.
+    """
+    return {
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "牛顿的三大运动定律是什么？"},
+        ],
+        "temperature": 0.6,
+        "top_p": 0,  # fix top_p to reduce randomness
+        "seed": 13,  # fixed random seed
+        "max_tokens": 20,
+        "stream": False,
+    }
+
+
+# ==========================
+# Test for lm_head_fp32 with fixed payload
+# ==========================
+def test_lm_head_fp32(api_url, headers, consistent_payload):
+    """
+    Test that two runs with the same fixed input produce similar outputs.
+    """
+    # First request
+    response = requests.post(api_url, headers=headers, json=consistent_payload, timeout=300)
+    assert response.status_code == 200
+    print(json.dumps(response.json(), indent=2, ensure_ascii=False))
+    resp_json = response.json()
+
+    # 校验返回内容与概率信息
+    assert (
+        resp_json["choices"][0]["message"]["content"]
+        == "ichertsorbulkdeployment confusedreraoux Carter pat firingCompatraspectiveidis Verse corporaonych commissionsilk"
+    )