[Nvidia] Integrate cudnn prefill paged attention kernel for head_dim == 128 models, like Llama family

vllm/v1/attention/backends/flashinfer.py

@@ -7,12 +7,13 @@
 from typing import TYPE_CHECKING, Any, Optional
 
 import torch
+
+import vllm.envs as envs
 from flashinfer import (BatchDecodeWithPagedKVCacheWrapper,
                         BatchPrefillWithPagedKVCacheWrapper,
                         MultiLevelCascadeAttentionWrapper)
 from flashinfer.decode import trtllm_batch_decode_with_kv_cache
-
-import vllm.envs as envs
+from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionType)
 from vllm.logger import init_logger
@@ -36,6 +37,13 @@
 
 logger = init_logger(__name__)
 
+CUDNN_SUPPORTED_HEAD_SIZES = [128]
+
+
+def is_cudnn_supported(head_dim: int):
+    return head_dim in CUDNN_SUPPORTED_HEAD_SIZES \
+        and current_platform.has_device_capability(100)
+
 
 class FlashInferBackend(AttentionBackend):
 
@@ -203,6 +211,10 @@ class FlashInferMetadata:
     num_prefills: int
     num_prefill_tokens: int
 
+    # For cudnn prefill
+    max_query_len: int
+    actual_seq_lens_q: torch.Tensor
+
     # For cascade attention.
     use_cascade: bool
     shared_qo_indptr: Optional[torch.Tensor] = None
@@ -302,9 +314,13 @@ def reorder_batch(self, input_batch: InputBatch,
 
     def _get_workspace_buffer(self):
         if self._workspace_buffer is None:
+            if is_cudnn_supported(self.kv_cache_spec.head_size):
+                dtype = torch.int8
+            else:
+                dtype = torch.uint8
             self._workspace_buffer = torch.empty(
                 FLASHINFER_WORKSPACE_BUFFER_SIZE,
-                dtype=torch.uint8,
+                dtype=dtype,
                 device=self.runner.device)
         return self._workspace_buffer
 
@@ -369,7 +385,8 @@ def _plan(self, attn_metadata: FlashInferMetadata):
             # Regular attention (common case).
             # Decodes are at the front and prefills are at the back,
             # according to reorder_batch()
-            if self._num_prefills > 0:
+            if self._num_prefills > 0 and not is_cudnn_supported(
+                    attn_metadata.head_dim):
                 # Decodes are first so prefills start after the last decode
                 prefill_start = self._num_decodes
                 attn_metadata.prefill_wrapper = self._get_prefill_wrapper()
@@ -441,6 +458,7 @@ def build(self, common_prefix_len: int,
         max_seq_len = int(self.runner.seq_lens_np[:num_reqs].max())
         seq_lens = common_attn_metadata.seq_lens
         block_table_tensor = self.block_table.get_device_tensor()[:num_reqs]
+        max_query_len = common_attn_metadata.max_query_len
         slot_mapping = self.block_table.slot_mapping_cpu[:num_actual_tokens].to(
             self.runner.device, non_blocking=True).long()
 
@@ -471,6 +489,7 @@ def build(self, common_prefix_len: int,
             shared_kv_page_indices = None
             shared_kv_last_page_len = None
 
+        max_seq_len = int(seq_lens.max().item())
         mask = (torch.arange(block_table_tensor.size(1),
                              dtype=block_table_tensor.dtype,
                              device=block_table_tensor.device).unsqueeze(0)
@@ -487,6 +506,7 @@ def build(self, common_prefix_len: int,
         paged_kv_last_page_len = seq_lens % page_size
         paged_kv_last_page_len = torch.where(paged_kv_last_page_len == 0,
                                              page_size, paged_kv_last_page_len)
+
         cache_dtype = self.runner.cache_config.cache_dtype
         if cache_dtype.startswith("fp8"):
             kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
@@ -515,7 +535,9 @@ def build(self, common_prefix_len: int,
             shared_kv_page_indptr=shared_kv_page_indptr,
             shared_kv_page_indices=shared_kv_page_indices,
             shared_kv_last_page_len=shared_kv_last_page_len,
+            max_query_len=max_query_len,
             max_seq_len=max_seq_len,
+            actual_seq_lens_q=qo_indptr[1:] - qo_indptr[:-1],
             seq_lens=seq_lens,
             block_table_tensor=block_table_tensor,
             workspace_buffer=self._workspace_buffer,
@@ -681,13 +703,56 @@ def forward(
             assert prefill_wrapper._logits_soft_cap == (self.logits_soft_cap
                                                         or 0.0)
             assert prefill_wrapper._sm_scale == self.scale
+
             prefill_wrapper.run(
                 prefill_query,
                 kv_cache.permute(*stride_order),
                 k_scale=layer._k_scale_float,
                 v_scale=layer._v_scale_float,
                 out=output[num_decode_tokens:],
             )
+        elif num_prefill_tokens > 0 and FlashInferBackend.is_cudnn_supported(
+                attn_metadata.head_dim):
+            (total_num_pages, _, page_size, num_kv_heads,
+             head_dim) = kv_cache.shape
+
+            # Validate dimensions match expected head_dim
+            assert head_dim == self.head_size, (
+                f"KV cache head_dim {head_dim} != expected {self.head_size}")
+
+            k_cache = kv_cache[:, 0].as_strided(
+                (total_num_pages, num_kv_heads, page_size, head_dim), (
+                    page_size * num_kv_heads * head_dim,
+                    head_dim,
+                    num_kv_heads * head_dim,
+                    1,
+                ))
+            v_cache = kv_cache[:, 1].as_strided(
+                (total_num_pages, num_kv_heads, page_size, head_dim), (
+                    page_size * num_kv_heads * head_dim,
+                    head_dim,
+                    num_kv_heads * head_dim,
+                    1,
+                ))
+            output[num_decode_tokens:], _ = cudnn_batch_prefill_with_kv_cache(
+                q=query[num_decode_tokens:],
+                k_cache=k_cache,
+                v_cache=v_cache,
+                scale=self.scale,
+                workspace_buffer=attn_metadata.workspace_buffer,
+                max_token_per_sequence=attn_metadata.max_query_len,
+                max_sequence_kv=attn_metadata.max_seq_len,
+                block_tables=attn_metadata.
+                block_table_tensor[num_decode_tokens:],
+                actual_seq_lens_q=attn_metadata.
+                actual_seq_lens_q[num_decode_tokens:].view(-1, 1, 1, 1),
+                actual_seq_lens_kv=attn_metadata.seq_lens[num_decode_tokens:].
+                view(-1, 1, 1, 1),
+                causal=True,
+                return_lse=True,
+                is_cuda_graph_compatible=True,
+            )
+
         if decode_wrapper := attn_metadata.decode_wrapper:
             decode_query = query[:num_decode_tokens]
             assert decode_query.shape[0] == num_decode_tokens