[Log] Debugging Log with more Information (#20770)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -571,34 +571,42 @@ def _valid_cutlass_block_scaled_grouped_gemm(
 
     _, K, N = w2.size()
     if not _valid_cutlass_block_scaled_grouped_gemm_shape(N, K):
-        logger.debug(
-            "CutlassBlockScaledGroupedGemm disabled: unalinged problem size.")
+        logger.debug_once(
+            "CutlassBlockScaledGroupedGemm disabled: unaligned problem size. "
+            "N: %s, K: %s",
+            N,
+            K,
+        )
         return False
 
     if (w1.dtype != torch.float8_e4m3fn or w2.dtype != torch.float8_e4m3fn):
-        logger.debug(
-            "CutlassBlockScaledGroupedGemm disabled: invalid weight dtype(s).")
+        logger.debug_once(
+            "CutlassBlockScaledGroupedGemm disabled: invalid weight dtype(s). "
+            "w1.dtype: %s, w2.dtype: %s",
+            w1.dtype,
+            w2.dtype,
+        )
         return False
 
     if expert_map is not None:
-        logger.debug(
+        logger.debug_once(
             "CutlassBlockScaledGroupedGemm disabled: expert_parallel is"
             " not supported.")
         return False
 
     if activation != "silu":
-        logger.debug(
+        logger.debug_once(
             "CutlassBlockScaledGroupedGemm disabled: only activation silu is"
             " supported.")
         return False
 
     if apply_router_weight_on_input:
-        logger.debug("CutlassBlockScaledGroupedGemm disabled:"
+        logger.debug_once("CutlassBlockScaledGroupedGemm disabled:"
                           " apply_router_weight_on_input is not supported.")
         return False
 
     if inplace:
-        logger.debug(
+        logger.debug_once(
             "CutlassBlockScaledGroupedGemm disabled: inplace is not supported."
         )
         return False

vllm/model_executor/layers/fused_moe/deep_gemm_moe.py

@@ -50,17 +50,33 @@ def _valid_deep_gemm(hidden_states: torch.Tensor, w1: torch.Tensor,
     M = hidden_states.size(0)
     _, K, N = w2.size()
     if not _valid_deep_gemm_shape(M, N, K):
-        logger.debug("DeepGemm disabled: unaligned problem size.")
+        logger.debug_once(
+            "DeepGemm disabled: unaligned problem size. M: %s, N: %s, K: %s",
+            M,
+            N,
+            K,
+        )
         return False
 
     if (w1.dtype != torch.float8_e4m3fn or w2.dtype != torch.float8_e4m3fn):
-        logger.debug("DeepGemm disabled: invalid weight dtype(s).")
+        logger.debug_once(
+            "DeepGemm disabled: invalid weight dtype(s). "
+            "w1.dtype: %s, w2.dtype: %s",
+            w1.dtype,
+            w2.dtype,
+        )
         return False
 
     if (not hidden_states.is_contiguous() or not w1.is_contiguous()
             or not w2.is_contiguous()):
-        logger.debug(
-            "DeepGemm disabled: weights or activations not contiguous.")
+        logger.debug_once(
+            "DeepGemm disabled: weights or activations not contiguous. "
+            "hidden_states.is_contiguous(): %s, w1.is_contiguous(): %s, "
+            "w2.is_contiguous(): %s",
+            hidden_states.is_contiguous(),
+            w1.is_contiguous(),
+            w2.is_contiguous(),
+        )
         return False
 
     return True