[BugFix] Fix prefix caching V0 MLA (#14255)

Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>
Co-authored-by: Ying Zhong <zhongyingmatrix@gmail.com>

vllm/attention/backends/mla/common.py

@@ -313,9 +313,10 @@ class MLACommonState(AttentionState, Generic[T]):
         cache_config = runner.cache_config
 
         self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
+        self.enable_prefix_caching = cache_config.enable_prefix_caching
 
-        if self.chunked_prefill_enabled:
-            self.chunked_prefill_workspace_size = min(
+        if self.chunked_prefill_enabled or self.enable_prefix_caching:
+            self.context_chunk_workspace_size = min(
                 # Max sure there is enough for 8 full length request or at least
                 # 4 pages of cache per request
                 max(
@@ -330,7 +331,7 @@ class MLACommonState(AttentionState, Generic[T]):
                 #   2*(192*128)*(64*1024) = 3gb
                 # (assuming 192 QK head dim, 128 heads, and fp16)
                 128 * 1024)
-            assert self.chunked_prefill_workspace_size >= \
+            assert self.context_chunk_workspace_size >= \
                 scheduler_config.max_num_seqs * cache_config.block_size
 
     @contextmanager
@@ -430,23 +431,23 @@ class MLACommonState(AttentionState, Generic[T]):
                 "TritonMLAState does not support encoder/decoder yet")
 
     def begin_forward(self, model_input):
-        if self.chunked_prefill_enabled:
-            if not hasattr(self, "chunked_prefill_workspace"):
+        if self.chunked_prefill_enabled or self.enable_prefix_caching:
+            if not hasattr(self, "context_chunk_workspace"):
                 # not self.runner.device does not return the correct device
                 # for this process, (init_device sets the correct device but
                 # only on the Worker). The only way Ive figured out to get the
                 # correct device is to allocate the workspace on the first call
                 # to begin_forward and use the device of the input tokens
                 assert model_input.input_tokens is not None
-                self.chunked_prefill_workspace = torch.empty(
-                    (self.chunked_prefill_workspace_size,
+                self.context_chunk_workspace = torch.empty(
+                    (self.context_chunk_workspace_size,
                      self.model_config.get_head_size()),
                     dtype=self.model_config.dtype,
                     device=model_input.input_tokens.device,
                 )
 
-            model_input.attn_metadata.chunked_prefill_workspace = \
-                self.chunked_prefill_workspace
+            model_input.attn_metadata.context_chunk_workspace = \
+                self.context_chunk_workspace
 
 
 @dataclass
@@ -537,7 +538,7 @@ class MLACommonMetadata(AttentionMetadata):
     context_chunk_seq_tot: Optional[List[int]] = None
     context_chunk_max_seq_lens: Optional[List[int]] = None
     # Set by MLAAttentionState in `begin_forward` so it doesn't get broadcasted
-    chunked_prefill_workspace: Optional[torch.Tensor] = None
+    context_chunk_workspace: Optional[torch.Tensor] = None
 
     def __post_init__(self):
         supported_head_sizes = MLACommonBackend.get_supported_head_sizes()
@@ -747,11 +748,13 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[T], Generic[T]):
         self.block_size = input_builder.block_size
         self.chunked_prefill_enabled = \
             self.runner.scheduler_config.chunked_prefill_enabled
+        self.enable_prefix_caching = \
+            self.runner.cache_config.enable_prefix_caching
 
-        if self.chunked_prefill_enabled:
+        if self.chunked_prefill_enabled or self.enable_prefix_caching:
             attn_state = self.input_builder.runner.attn_state
-            self.chunked_prefill_workspace_size = \
-                attn_state.chunked_prefill_workspace_size
+            self.context_chunk_workspace_size = \
+                attn_state.context_chunk_workspace_size
             self.page_size = self.runner.block_size
 
     def prepare(self):
@@ -920,7 +923,8 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[T], Generic[T]):
         context_chunk_seq_tot = None
         context_chunk_max_seq_lens = None
 
-        if self.chunked_prefill_enabled and self.num_prefills > 0 \
+        if (self.chunked_prefill_enabled or self.enable_prefix_caching) \
+            and self.num_prefills > 0 \
             and context_lens_tensor is not None \
             and context_lens_tensor[:self.num_prefills].max() > 0:
 
@@ -936,7 +940,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[T], Generic[T]):
             # algorithm here and allocate more workspace to prefills with
             # longer context lengths
             max_context_chunk = \
-                self.chunked_prefill_workspace_size // num_prefills_with_context
+                self.context_chunk_workspace_size // num_prefills_with_context
 
             # align max_context_chunk to page_size by rounding down,
             # currently the `gather_cache` kernel cannot handle
@@ -965,7 +969,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[T], Generic[T]):
                 chunk_seq_lens.max(dim=1).values.tolist()
             context_chunk_seq_tot = chunk_seq_lens.sum(dim=1).tolist()
             assert max(context_chunk_seq_tot) <= \
-                self.chunked_prefill_workspace_size
+                self.context_chunk_workspace_size
 
         return self.runner.attn_backend.make_metadata(
             # Required by ModelRunner
@@ -1288,8 +1292,8 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
         # Fetch from attn_metadata directly, since it late bound by
         # MLAAttentionState, grabbing it directly `attn_metadata` can avoid
         # any weirdness around prefill_metadata caching
-        assert attn_metadata.chunked_prefill_workspace is not None
-        workspace = attn_metadata.chunked_prefill_workspace
+        assert attn_metadata.context_chunk_workspace is not None
+        workspace = attn_metadata.context_chunk_workspace
 
         for i in range(iters):
             toks = prefill_metadata.context_chunk_seq_tot[i]
@@ -1502,12 +1506,12 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
                 "output is not yet supported for MLAImplBase")
 
         if attn_metadata.is_profile_run and \
-            attn_metadata.chunked_prefill_workspace is not None:
+            attn_metadata.context_chunk_workspace is not None:
             # During the profile run try to simulate to worse case output size
             # for `self.kv_b_proj(kv_c_normed)` in `_compute_prefill_context`
             # since this can be large
             _ = torch.empty(
-                (attn_metadata.chunked_prefill_workspace.shape[0],
+                (attn_metadata.context_chunk_workspace.shape[0],
                  self.num_heads, self.qk_nope_head_dim + self.v_head_dim),
                 device=k_c_normed.device,
                 dtype=k_c_normed.dtype,