fix weight_loading_mode for deepseek

Signed-off-by: Anthony Chang <[email protected]>

Author: rosenrodt

tensorrt_llm/_torch/models/modeling_deepseekv3.py

@@ -45,6 +45,7 @@
 from tensorrt_llm.llmapi.utils import enable_llm_debug
 from tensorrt_llm.mapping import Mapping
 from tensorrt_llm.models.modeling_utils import QuantConfig
+from tensorrt_llm.quantization.mode import QuantAlgo
 from tensorrt_llm.quantization.utils.fp8_utils import (
     resmooth_to_fp8_e8m0, transform_sf_into_required_layout)
 
@@ -457,10 +458,13 @@ def __init__(self,
             layer_idx=layer_idx,
             # DS-R1 W4A8 is only supported through custom quantization script from
             # examples/quantization/quantize_mixed_precision_moe.py
-            weight_loading_mode=(MoEWeightLoadingMode.W4A8_CUSTOM
-                                 if model_config.quant_config.quant_mode.
-                                 is_int4_weight_only_per_group() else
-                                 MoEWeightLoadingMode.VANILLA))
+            weight_loading_mode=(
+                MoEWeightLoadingMode.W4A8_CUSTOM
+                if self._get_experts_quant_config(
+                    model_config,
+                    layer_idx).layer_quant_mode.is_int4_weight_only_per_group()
+                else MoEWeightLoadingMode.VANILLA),
+        )
 
         self.mapping = model_config.mapping
 
@@ -525,6 +529,13 @@ def _compute_shared_expert_tp_size(self, intermediate_size: int,
 
         return shared_tp_size, shared_output_scale
 
+    @staticmethod
+    def _get_experts_quant_config(model_config, layer_idx: int):
+        if model_config.quant_config_dict is None:
+            return None
+        return model_config.quant_config_dict.get(
+            f"model.layers.{layer_idx}.mlp.experts", model_config.quant_config)
+
     def compute_routed_output(self, hidden_states, hidden_states_fp4,
                               all_rank_num_tokens, all_rank_max_num_tokens,
                               do_finalize):
@@ -635,6 +646,9 @@ def __init__(self, model_config: ModelConfig[PretrainedConfig],
         quant_config = self._get_decoder_layer_quant_config(
             model_config, layer_idx)
         self.is_nvfp4 = quant_config.layer_quant_mode.has_nvfp4()
+        assert (
+            quant_config.quant_algo
+            is not QuantAlgo.MIXED_PRECISION), "MIXED_PRECISION is ambiguous"
 
         has_tp = mapping.has_tp()