适配在bmz上的mla的kvcache_e5m2和e4m3量化的支持

vllm/model_executor/layers/attention/mla_attention.py

@@ -215,6 +215,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     get_and_maybe_dequant_weights,
 )
 from vllm.platforms import current_platform
+from vllm.platforms.rocm import get_gcn_arch_name
 from vllm.utils.flashinfer import has_nvidia_artifactory
 from vllm.utils.math_utils import cdiv, round_down
 from vllm.v1.attention.backend import (
@@ -2115,7 +2116,7 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
                 scale=layer._k_scale,
             )
 
-        if fp8_attention:
+        if fp8_attention and get_gcn_arch_name() == "gfx938":
             kv_cache = kv_cache.view(current_platform.fp8_dtype())
 
         if has_prefill:
@@ -2185,7 +2186,7 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
                 # Convert from (N, B, L) to (B, N, L)
                 decode_ql_nope = decode_ql_nope.transpose(0, 1)
 
-            if fp8_attention:
+            if fp8_attention and get_gcn_arch_name() == "gfx938":
                 assert decode_ql_nope.shape[0] == decode_q_pe.shape[0]
                 assert decode_ql_nope.shape[1] == decode_q_pe.shape[1]
                 decode_q = self._decode_concat_quant_fp8_op(

vllm/model_executor/layers/sparse_attn_indexer.py

@@ -49,7 +49,7 @@ def sparse_attn_indexer(
     if not isinstance(attn_metadata, dict):
         # Reserve workspace for indexer during profiling run
         current_workspace_manager().get_simultaneous(
-            ((total_seq_lens, head_dim), fp8_dtype if not current_platform.is_rocm() or torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0] == "gfx938" else torch.bfloat16),
+            ((total_seq_lens, head_dim), fp8_dtype if not current_platform.is_rocm() or torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0] == "gfx938" else k.dtype,),
             ((total_seq_lens, 4), torch.uint8),
         )
         return sparse_attn_indexer_fake(

vllm/platforms/rocm.py

@@ -121,6 +121,10 @@ def on_gfx9() -> bool:
     GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
     return any(arch in GPU_ARCH for arch in ["gfx90a", "gfx942", "gfx950", "gfx928", "gfx936", "gfx938"])
 
+@cache
+def get_gcn_arch_name() -> str:
+    GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
+    return GPU_ARCH.split(':')[0]
 
 @cache
 def on_gfx942() -> bool:

vllm/v1/attention/backends/mla/flashmla.py

@@ -310,6 +310,7 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
                 causal=True,
                 descale_q=layer._q_scale.reshape(1),
                 descale_k=layer._k_scale.reshape(1),
+                kv_cache_dtype=self.kv_cache_dtype,
             )
         else:
             o, lse = flash_mla_with_kvcache(

vllm/v1/attention/ops/flashmla.py

@@ -6,7 +6,7 @@ import torch
 
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-
+from vllm.platforms.rocm import get_gcn_arch_name
 logger = init_logger(__name__)
 
 if current_platform.is_cuda():
@@ -136,7 +136,7 @@ def get_mla_metadata_dense_fp8(
             cache_seqlens,
             num_q_tokens_per_head_k,
             num_heads_k, 
-            16,
+            # 16,
         )
     else:
         return torch.ops._flashmla_extension_C.get_mla_decoding_metadata_dense_fp8(
@@ -158,12 +158,14 @@ def flash_mla_with_kvcache_fp8(
     causal: bool = False,
     descale_q: torch.Tensor | None = None,
     descale_k: torch.Tensor | None = None,
+    kv_cache_dtype: str | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     if not _is_flashmla_available()[0]:
         _raise_flashmla_unavailable()
     if softmax_scale is None:
         softmax_scale = q.shape[-1] ** (-0.5)
     if current_platform.is_rocm():
+        if get_gcn_arch_name() == "gfx938":
             out, softmax_lse = flash_mla_cuda.fwd_kvcache_mla_fp8(
                 q,
                 k_cache,
@@ -178,6 +180,21 @@ def flash_mla_with_kvcache_fp8(
                 descale_q,
                 descale_k,
             )
+        else:
+            out, softmax_lse = flash_mla_cuda.fwd_kvcache_quantization_mla(
+                q,
+                k_cache,
+                None,
+                head_dim_v,
+                cache_seqlens,
+                block_table,
+                softmax_scale,
+                causal,
+                tile_scheduler_metadata,
+                num_splits,
+                descale_k,
+                kv_cache_dtype,
+            )             
     else:
         out, softmax_lse = torch.ops._flashmla_extension_C.fwd_kvcache_mla_fp8(
             q,