[TRTLLM-6898][feat] make fused_moe_cute_dsl work on blackwell

tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py

@@ -13,7 +13,8 @@
 from ...model_config import ModelConfig
 from ...utils import Fp4QuantizedTensor
 from .fused_moe_cutlass import CutlassFusedMoE
-from .quantization import MoEWeightLoadingMode
+from .quantization import (DeepSeekFP8BlockScalesFusedMoEMethodDeepGemm,
+                           MoEWeightLoadingMode, UnquantizedFusedMoEMethod)
 from .routing import BaseMoeRoutingMethod
 
 
@@ -340,6 +341,18 @@ def __init__(
             layer_idx=layer_idx,
         )
 
+    def _get_quant_method(self):
+        if self.quant_config is not None and self.quant_config.layer_quant_mode.has_any_quant(
+                exclude_kv_cache=True):
+            if self.quant_config.layer_quant_mode.has_fp8_block_scales():
+                return DeepSeekFP8BlockScalesFusedMoEMethodDeepGemm()
+            else:
+                raise ValueError(
+                    f"Unsupported quantization mode: {self.quant_config.quant_mode}"
+                )
+        else:
+            return UnquantizedFusedMoEMethod()
+
     @nvtx_range("[DG] forward")
     def forward_chunk(
         self,

tensorrt_llm/_torch/modules/fused_moe/quantization.py

@@ -629,45 +629,8 @@ def create_weights(self, module: torch.nn.Module):
 
     def load_weights(self, module: torch.nn.Module, weights: List[Dict],
                      weight_loading_mode: MoEWeightLoadingMode):
-
-        if get_sm_version() == 100:
-            expert_ids = set(module.initial_local_expert_ids)
-            if self.need_load_shared_weights(module):
-                expert_ids.update(
-                    module.layer_load_balancer.get_load_expert_ids())
-            for name in list(weights.keys()):
-                if name.endswith("weight_scale_inv"):
-                    if int(name.split(".")[0]) not in expert_ids:
-                        continue
-                    weight_name = name.replace("weight_scale_inv", "weight")
-                    logger.debug(f"Resmoothing {weight_name}")
-                    weight = weights[weight_name][:]
-                    scale = weights[name][:]
-                    weights[weight_name], weights[name] = resmooth_to_fp8_e8m0(
-                        weight, scale)
         super().load_weights(module, weights, weight_loading_mode)
 
-        if get_sm_version() == 100:
-            transfromed_w3_w1_scale = transform_sf_into_required_layout(
-                module.quant_scales[0],
-                mn=module.w3_w1_weight.shape[1],
-                k=module.w3_w1_weight.shape[2],
-                recipe=(1, 128, 128),
-                num_groups=module.w3_w1_weight.shape[0],
-                is_sfa=False)
-            module.w3_w1_weight_scaling_factor = nn.Parameter(
-                transfromed_w3_w1_scale, requires_grad=False)
-            transfromed_w2_scale = transform_sf_into_required_layout(
-                module.quant_scales[1],
-                mn=module.w2_weight.shape[1],
-                k=module.w2_weight.shape[2],
-                recipe=(1, 128, 128),
-                num_groups=module.w3_w1_weight.shape[0],
-                is_sfa=False)
-            module.w2_weight_scaling_factor = nn.Parameter(transfromed_w2_scale,
-                                                           requires_grad=False)
-            self.setup_quant_scales(module)
-
     def setup_quant_scales(self, module: torch.nn.Module):
         module.quant_scales = FusedMoEQuantScalesDeepSeekFP8BlockScales(
             fc_weight_scales=module.w3_w1_weight_scaling_factor,
@@ -765,6 +728,50 @@ def load_quant_scales(self, module: torch.nn.Module, weights: Dict):
             })
 
 
+class DeepSeekFP8BlockScalesFusedMoEMethodDeepGemm(
+        DeepSeekFP8BlockScalesFusedMoEMethod):
+
+    def load_weights(self, module: torch.nn.Module, weights: List[Dict],
+                     weight_loading_mode: MoEWeightLoadingMode):
+        if get_sm_version() == 100:
+            expert_ids = set(module.initial_local_expert_ids)
+            if self.need_load_shared_weights(module):
+                expert_ids.update(
+                    module.layer_load_balancer.get_load_expert_ids())
+            for name in list(weights.keys()):
+                if name.endswith("weight_scale_inv"):
+                    if int(name.split(".")[0]) not in expert_ids:
+                        continue
+                    weight_name = name.replace("weight_scale_inv", "weight")
+                    logger.debug(f"Resmoothing {weight_name}")
+                    weight = weights[weight_name][:]
+                    scale = weights[name][:]
+                    weights[weight_name], weights[name] = resmooth_to_fp8_e8m0(
+                        weight, scale)
+        super().load_weights(module, weights, weight_loading_mode)
+
+        if get_sm_version() == 100:
+            transfromed_w3_w1_scale = transform_sf_into_required_layout(
+                module.quant_scales[0],
+                mn=module.w3_w1_weight.shape[1],
+                k=module.w3_w1_weight.shape[2],
+                recipe=(1, 128, 128),
+                num_groups=module.w3_w1_weight.shape[0],
+                is_sfa=False)
+            module.w3_w1_weight_scaling_factor = nn.Parameter(
+                transfromed_w3_w1_scale, requires_grad=False)
+            transfromed_w2_scale = transform_sf_into_required_layout(
+                module.quant_scales[1],
+                mn=module.w2_weight.shape[1],
+                k=module.w2_weight.shape[2],
+                recipe=(1, 128, 128),
+                num_groups=module.w3_w1_weight.shape[0],
+                is_sfa=False)
+            module.w2_weight_scaling_factor = nn.Parameter(transfromed_w2_scale,
+                                                           requires_grad=False)
+            self.setup_quant_scales(module)
+
+
 class WInt4AFP8FusedMoEMethod(FusedMoEMethodBase):
 
     def create_weights(self, module: torch.nn.Module):

tensorrt_llm/_torch/modules/gated_mlp.py

@@ -27,7 +27,8 @@ def __init__(self,
                  config: Optional[ModelConfig] = None,
                  overridden_tp_size: Optional[int] = None,
                  reduce_output: bool = True,
-                 layer_idx: Optional[int] = None):
+                 layer_idx: Optional[int] = None,
+                 use_cute_dsl_blockscaling_mm: bool = False):
         super().__init__()
         self.layer_idx = layer_idx
         self.hidden_size = hidden_size
@@ -64,7 +65,8 @@ def __init__(self,
             reduce_output=False,
             skip_create_weights_in_init=config.skip_create_weights_in_init,
             allreduce_strategy=config.allreduce_strategy,
-            force_dynamic_quantization=config.force_dynamic_quantization)
+            force_dynamic_quantization=config.force_dynamic_quantization,
+            use_cute_dsl_blockscaling_mm=use_cute_dsl_blockscaling_mm)
 
         self.down_lora = LoraLayer([LoraModuleType.MLP_4H_TO_H],
                                    [self.hidden_size])
@@ -81,7 +83,8 @@ def __init__(self,
             skip_create_weights_in_init=config.skip_create_weights_in_init,
             lora=self.down_lora,
             allreduce_strategy=config.allreduce_strategy,
-            force_dynamic_quantization=config.force_dynamic_quantization)
+            force_dynamic_quantization=config.force_dynamic_quantization,
+            use_cute_dsl_blockscaling_mm=use_cute_dsl_blockscaling_mm)
 
         # These two modules are mutually exclusive - either splitted_gate_up_lora or fused_gate_up_lora will be used,
         # but never both at the same time. splitted_gate_up_lora handles gate and up separately while fused_gate_up_lora

tensorrt_llm/_torch/modules/linear.py

@@ -583,21 +583,29 @@ def apply(self, module: Linear, input: torch.Tensor,
         assert input.dtype == torch.bfloat16
 
         if get_sm_version() == 100:
-            from tensorrt_llm import deep_gemm
-            a, a_sf = fp8_utils.per_token_quant_and_transform(input)
-            output = torch.empty((input.shape[0], module.weight.shape[0]),
-                                 device=input.device,
-                                 dtype=torch.bfloat16)
-            deep_gemm.fp8_gemm_nt((a, a_sf),
-                                  (module.weight, module.weight_scale),
-                                  output,
-                                  disable_ue8m0_cast=True)
+            if module.use_cute_dsl_blockscaling_mm:
+                # TODO (@lmin): replace with cute_dsl gemm
+                act_input_fp8, act_input_sf = torch.ops.trtllm.fp8_quantize_1x128(
+                    input)
+                output = torch.ops.trtllm.fp8_block_scaling_gemm(
+                    act_input_fp8, module.weight, act_input_sf,
+                    module.weight_scale)
+            else:
+                from tensorrt_llm import deep_gemm
+                a, a_sf = fp8_utils.per_token_quant_and_transform(input)
+                output = torch.empty((input.shape[0], module.weight.shape[0]),
+                                     device=input.device,
+                                     dtype=torch.bfloat16)
+                deep_gemm.fp8_gemm_nt((a, a_sf),
+                                      (module.weight, module.weight_scale),
+                                      output,
+                                      disable_ue8m0_cast=True)
         else:
             act_input_fp8, act_input_sf = torch.ops.trtllm.fp8_quantize_1x128(
                 input)
-
             output = torch.ops.trtllm.fp8_block_scaling_gemm(
                 act_input_fp8, module.weight, act_input_sf, module.weight_scale)
+
         if bias is not None:
             output = output + bias
         return output
@@ -1488,6 +1496,7 @@ def __init__(
         lora: Optional[LoraLayer] = None,
         allreduce_strategy: AllReduceStrategy = AllReduceStrategy.AUTO,
         force_dynamic_quantization: bool = False,
+        use_cute_dsl_blockscaling_mm: bool = False,
     ):
         from ..distributed import AllReduce
 
@@ -1504,6 +1513,7 @@ def __init__(
         self.tp_mode = tensor_parallel_mode
         self.gather_output = gather_output
         self.force_dynamic_quantization = force_dynamic_quantization
+        self.use_cute_dsl_blockscaling_mm = use_cute_dsl_blockscaling_mm
 
         local_in_features = in_features
         local_out_features = out_features

tensorrt_llm/evaluate/lm_eval.py

@@ -25,6 +25,7 @@
 
 try:
     from lm_eval.api.model import TemplateLM
+    from lm_eval.tasks import TaskManager
 except ImportError:
     TemplateLM = object
 
@@ -147,7 +148,7 @@ def __init__(self,
         self.dataset_path = dataset_path
         self.num_samples = num_samples
 
-        task_manager = lm_eval.tasks.TaskManager(
+        task_manager = TaskManager(
             include_path=f"{os.path.dirname(__file__)}/lm_eval_tasks")
         with self._patch_lm_eval():
             self.task_dict = lm_eval.tasks.get_task_dict(

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -1021,7 +1021,7 @@ def test_fp8_block_scales(self, mtp, fp8kv, attention_dp, cuda_graph,
             task = GSM8K(self.MODEL_NAME)
             task.evaluate(llm)
 
-    @skip_no_hopper
+    @skip_pre_blackwell
     @parametrize_with_ids("torch_compile", [False])
     @parametrize_with_ids(
         "fp8kv,attention_dp,cuda_graph,overlap_scheduler",
@@ -1171,7 +1171,7 @@ def test_fp8_block_scales_4gpus(self, tp_size, pp_size, ep_size, mtp_nextn,
             task.evaluate(llm)
 
     @pytest.mark.skip_less_device(4)
-    @skip_no_hopper
+    @skip_pre_blackwell
     @parametrize_with_ids("torch_compile", [False])
     @parametrize_with_ids(
         "fp8kv,attention_dp,cuda_graph,overlap_scheduler",