[None][feat] Add AD custom model for DeepSeek-V3.2 family

examples/auto_deploy/model_registry/models.yaml

@@ -241,11 +241,11 @@ models:
   yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml']
 # --- DeepSeek V3.2 (Dec 2025) ---
 - name: deepseek-ai/DeepSeek-V3.2
-  yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'num_hidden_layers_5.yaml']
+  yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'num_hidden_layers_5.yaml', 'deepseek_v2_ep.yaml']
 - name: deepseek-ai/DeepSeek-V3.2-Speciale
-  yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'num_hidden_layers_5.yaml']
+  yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'num_hidden_layers_5.yaml', 'deepseek_v2_ep.yaml']
 - name: nvidia/DeepSeek-V3.2-NVFP4
-  yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'num_hidden_layers_5.yaml']
+  yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml', 'num_hidden_layers_5.yaml', 'deepseek_v2_ep.yaml']
 # --- GLM-4.6 (Sep 2025) ---
 - name: zai-org/GLM-4.6
   yaml_extra: ['dashboard_default.yaml', 'world_size_8.yaml']

tensorrt_llm/_torch/auto_deploy/models/custom/__init__.py

@@ -1,6 +1,7 @@
 from .modeling_decilm import DeciLMForCausalLM
 from .modeling_deepseek import DeepSeekV3ForCausalLM
 from .modeling_deepseek_v2 import DeepSeekV2ForCausalLM
+from .modeling_deepseek_v32 import DeepSeekV32ForCausalLM
 from .modeling_glm4_moe_lite import Glm4MoeLiteForCausalLM
 from .modeling_granite_moe_hybrid import GraniteMoeHybridForCausalLM
 from .modeling_hunyuan_dense_v1 import HunYuanDenseV1ForCausalLM
@@ -16,6 +17,7 @@
     "DeciLMForCausalLM",
     "DeepSeekV2ForCausalLM",
     "DeepSeekV3ForCausalLM",
+    "DeepSeekV32ForCausalLM",
     "Glm4MoeLiteForCausalLM",
     "HunYuanDenseV1ForCausalLM",
     "GraniteMoeHybridForCausalLM",

tensorrt_llm/_torch/auto_deploy/models/custom/mla_rope_utils.py

@@ -3,14 +3,21 @@
 """Shared MLA RoPE utilities for auto_deploy custom models.
 
 Contains helper functions for RoPE weight de-interleaving,
-used by DeepSeek V3 and GLM4 MoE Lite model implementations.
+used by DeepSeek V3, V3.2, and GLM4 MoE Lite model implementations.
 """
 
 from typing import Dict
 
 import torch
 
 
+def _permute_with_fp8_support(tensor: torch.Tensor, perm: torch.Tensor, dim: int) -> torch.Tensor:
+    """Index-select along a dimension, casting through float32 for FP8 tensors."""
+    if tensor.dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+        return tensor.to(torch.float32).index_select(dim, perm).to(tensor.dtype)
+    return tensor.index_select(dim, perm)
+
+
 def _rope_deinterleave_load_hook(
     state_dict: Dict[str, torch.Tensor],
     prefix: str,
@@ -43,7 +50,7 @@ def _rope_deinterleave_load_hook(
             w = w.view(num_heads, qk_head_dim, -1)
             w_nope = w[:, :qk_nope_head_dim, :]
             w_rope = w[:, qk_nope_head_dim:, :]
-            w_rope = w_rope[:, perm, :]
+            w_rope = _permute_with_fp8_support(w_rope, perm, dim=1)
             w = torch.cat([w_nope, w_rope], dim=1)
             state_dict[q_key] = w.view(-1, w.shape[-1])
 
@@ -53,7 +60,7 @@ def _rope_deinterleave_load_hook(
             w = state_dict[kv_key]
             w_kv = w[:kv_lora_rank, :]
             w_pe = w[kv_lora_rank:, :]
-            w_pe = w_pe[perm, :]
+            w_pe = _permute_with_fp8_support(w_pe, perm, dim=0)
             state_dict[kv_key] = torch.cat([w_kv, w_pe], dim=0)
 
         # --- kv_a_proj_with_mqa.bias (if present) ---
@@ -62,5 +69,5 @@ def _rope_deinterleave_load_hook(
             b = state_dict[kv_bias_key]
             b_kv = b[:kv_lora_rank]
             b_pe = b[kv_lora_rank:]
-            b_pe = b_pe[perm]
+            b_pe = _permute_with_fp8_support(b_pe, perm, dim=0)
             state_dict[kv_bias_key] = torch.cat([b_kv, b_pe])