[Fix] Fix trtllm_mla backend + fp8 kv cache without rope (#32181)

python/sglang/kernels/ops/attention/utils.py

@@ -177,6 +177,18 @@ def mla_quantize_and_rope_for_fp8(
     return q_out, k_nope_out, k_rope_out
 
 
+def mla_quantize_without_rope_for_fp8(
+    q_nope: torch.Tensor,
+    q_rope: torch.Tensor,
+    k_nope: torch.Tensor,
+    k_rope: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Quantize MLA components to FP8 without applying rotary embeddings."""
+    attn_dtype = torch.float8_e4m3fn
+    q = concat_mla_absorb_q_general(q_nope, q_rope).to(attn_dtype)
+    return q, k_nope.to(attn_dtype), k_rope.to(attn_dtype)
+
+
 def concat_mla_absorb_q_general(q_nope, q_rope):
     if _is_cuda and q_nope.shape[-1] == 512 and q_rope.shape[-1] == 64:
         return concat_mla_absorb_q(q_nope, q_rope)

python/sglang/srt/layers/attention/trtllm_mla_backend.py

@@ -22,6 +22,7 @@ from sglang.kernels.ops.attention.pad import (
 from sglang.kernels.ops.attention.utils import (
     concat_mla_absorb_q_general,
     mla_quantize_and_rope_for_fp8,
+    mla_quantize_without_rope_for_fp8,
 )
 from sglang.kernels.ops.kvcache.kv_indices import (
     create_flashmla_kv_indices_triton,
@@ -760,22 +761,23 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
         """Run forward for decode using TRTLLM MLA kernel."""
         merge_query = q_rope is not None
         if self.data_type == torch.float8_e4m3fn:
-            # For FP8 path, we quantize the query and rope parts and merge them into a single tensor
-            # Note: rope application in deepseek_v2.py:forward_absorb_prepare is skipped for FP8 decode path of this trtllm_mla backend
-            assert all(
-                x is not None for x in [q_rope, k_rope, cos_sin_cache]
-            ), "For FP8 path and using flashinfer.rope.mla_rope_quantize we need all of q_rope, k_rope and cos_sin_cache to be not None."
-            q, k, k_rope = mla_quantize_and_rope_for_fp8(
-                q,
-                q_rope,
-                k.squeeze(1),
-                k_rope.squeeze(1),
-                forward_batch.positions,
-                cos_sin_cache,
-                is_neox,
-                self.kv_lora_rank,
-                self.qk_rope_head_dim,
-            )
+            assert q_rope is not None and k_rope is not None
+            if cos_sin_cache is None:
+                q, k, k_rope = mla_quantize_without_rope_for_fp8(
+                    q, q_rope, k.squeeze(1), k_rope.squeeze(1)
+                )
+            else:
+                q, k, k_rope = mla_quantize_and_rope_for_fp8(
+                    q,
+                    q_rope,
+                    k.squeeze(1),
+                    k_rope.squeeze(1),
+                    forward_batch.positions,
+                    cos_sin_cache,
+                    is_neox,
+                    self.kv_lora_rank,
+                    self.qk_rope_head_dim,
+                )
             merge_query = False
 
         # Save KV cache if requested
@@ -868,22 +870,23 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
         if (
             self.data_type == torch.float8_e4m3fn
         ) and forward_batch.forward_mode.is_target_verify():
-            # For FP8 path, we quantize the query and rope parts and merge them into a single tensor
-            # Note: rope application in deepseek_v2.py:forward_absorb_prepare is skipped for FP8 decode path of this trtllm_mla backend
-            assert all(
-                x is not None for x in [q_rope, k_rope, cos_sin_cache]
-            ), "For FP8 path and using flashinfer.rope.mla_rope_quantize we need all of q_rope, k_rope and cos_sin_cache to be not None."
-            q, k, k_rope = mla_quantize_and_rope_for_fp8(
-                q,
-                q_rope,
-                k.squeeze(1),
-                k_rope.squeeze(1),
-                forward_batch.positions,
-                cos_sin_cache,
-                is_neox,
-                self.kv_lora_rank,
-                self.qk_rope_head_dim,
-            )
+            assert q_rope is not None and k_rope is not None
+            if cos_sin_cache is None:
+                q, k, k_rope = mla_quantize_without_rope_for_fp8(
+                    q, q_rope, k.squeeze(1), k_rope.squeeze(1)
+                )
+            else:
+                q, k, k_rope = mla_quantize_and_rope_for_fp8(
+                    q,
+                    q_rope,
+                    k.squeeze(1),
+                    k_rope.squeeze(1),
+                    forward_batch.positions,
+                    cos_sin_cache,
+                    is_neox,
+                    self.kv_lora_rank,
+                    self.qk_rope_head_dim,
+                )
             merge_query = False
 
         # Save KV cache if requested

python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mla.py

@@ -995,7 +995,8 @@ class DeepseekMLAForwardMixin:
             ) and get_attn_backend().kv_cache_dtype == torch.float8_e4m3fn
 
         return (
-            self.current_attention_backend
+            self.rotary_emb is not None
+            and self.current_attention_backend
             in ("trtllm_mla", "tokenspeed_mla", "cutedsl_mla")
             and (
                 forward_batch.forward_mode.is_decode_or_idle()