[TPU] Fix TPU SMEM OOM by Pallas paged attention kernel (vllm-project…

…#9350)

Signed-off-by: Alvant <[email protected]>

vllm/attention/backends/pallas.py

@@ -208,35 +208,54 @@ def forward(
         else:
             # Decoding run.
             assert kv_cache[0].numel() > 0
-
+            query = query.squeeze(dim=1)
             pages_per_compute_block = 16  # TODO(woosuk): Tune this value.
-            if self.megacore_mode == "batch" and batch_size % 2 != 0:
-                megacore_mode = None
-            else:
-                megacore_mode = self.megacore_mode
-
-            # NOTE(woosuk): A temporary workaround to avoid the error:
-            # "xla::paged_attention() Expected a value of type 'str' for
-            # argument 'megacore_mode' but instead found type 'NoneType'."
-            if megacore_mode is not None:
-                output = torch.ops.xla.paged_attention(
-                    query.squeeze(dim=1),
+
+            assert attn_metadata.block_tables is not None
+            assert attn_metadata.context_lens is not None
+            # NOTE(woosuk): The PagedAttention Pallas kernel stores the entire
+            # block table in SMEM. Therefore, if the block table is too large,
+            # the kernel compilation will fail. To avoid this, we split the
+            # batch dimension into smaller chunks and run the kernel multiple
+            # times.
+            MAX_SMEM_USAGE = 512 * 1024
+            size_per_seq = 4 * attn_metadata.block_tables.shape[1]
+            max_num_seq = MAX_SMEM_USAGE // size_per_seq
+
+            if batch_size <= max_num_seq:
+                output = paged_attention(
+                    query,
                     key_cache,
                     value_cache,
                     attn_metadata.context_lens,
                     attn_metadata.block_tables,
                     pages_per_compute_block,
-                    megacore_mode=megacore_mode,
+                    self.megacore_mode,
                 )
             else:
-                output = torch.ops.xla.paged_attention(
-                    query.squeeze(dim=1),
-                    key_cache,
-                    value_cache,
-                    attn_metadata.context_lens,
-                    attn_metadata.block_tables,
-                    pages_per_compute_block,
-                )
+                chunk_size = max_num_seq
+                # Make sure the chunk size is a multiple of 2.
+                chunk_size = chunk_size // 2 * 2
+                num_chunks = (batch_size + chunk_size - 1) // chunk_size
+
+                output = torch.empty_like(query)
+                for chunk_idx in range(num_chunks):
+                    chunk_start = chunk_idx * chunk_size
+                    chunk_end = chunk_start + chunk_size
+                    # NOTE(woosuk): We skip this line because it causes Dynamo
+                    # compilation error. Instead, we rely on the slice operation
+                    # to handle the out-of-bound case.
+                    # chunk_end = min(chunk_end, batch_size)
+                    chunk_output = paged_attention(
+                        query[chunk_start:chunk_end],
+                        key_cache,
+                        value_cache,
+                        attn_metadata.context_lens[chunk_start:chunk_end],
+                        attn_metadata.block_tables[chunk_start:chunk_end],
+                        pages_per_compute_block,
+                        self.megacore_mode,
+                    )
+                    output[chunk_start:chunk_end] = chunk_output
 
         # Reshape the output tensor.
         return output.reshape(batch_size, seq_len, hidden_size)
@@ -258,3 +277,43 @@ def write_to_kv_cache(
     value_cache = value_cache.flatten(0, 2)
     key_cache.index_copy_(0, slot_mapping, key)
     value_cache.index_copy_(0, slot_mapping, value)
+
+
+def paged_attention(
+    query: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    context_lens: torch.Tensor,
+    block_tables: torch.Tensor,
+    pages_per_compute_block: int,
+    megacore_mode: Optional[str],
+) -> torch.Tensor:
+    batch_size = query.shape[0]
+    if megacore_mode == "batch" and batch_size % 2 != 0:
+        megacore_mode = None
+    else:
+        megacore_mode = megacore_mode
+
+    # NOTE(woosuk): A temporary workaround to avoid the error:
+    # "xla::paged_attention() Expected a value of type 'str' for
+    # argument 'megacore_mode' but instead found type 'NoneType'."
+    if megacore_mode is not None:
+        output = torch.ops.xla.paged_attention(
+            query,
+            key_cache,
+            value_cache,
+            context_lens,
+            block_tables,
+            pages_per_compute_block,
+            megacore_mode=megacore_mode,
+        )
+    else:
+        output = torch.ops.xla.paged_attention(
+            query,
+            key_cache,
+            value_cache,
+            context_lens,
+            block_tables,
+            pages_per_compute_block,
+        )
+    return output

vllm/worker/tpu_model_runner.py

@@ -123,6 +123,15 @@ def __init__(
         )
         self.cached_step_outputs: List[torch.Tensor] = []
 
+        smem_size = 512 * 1024
+        block_table_size = 4 * self.block_tables.size
+        if block_table_size >= smem_size:
+            logger.warning(
+                "The max_model_len (%d) is too large. This may degrade the "
+                "performance due to the insufficient smem size. Consider "
+                "setting --max-model-len to a smaller value.",
+                self.model_config.max_model_len)
+
     def load_model(self) -> None:
         self.device = self.device_config.device