[FEAT] [ROCm] [Embedding] Add encoder-only model support into ROCm Flash...

[FEAT] [ROCm] [Embedding] Add encoder-only model support into ROCm Flash Attention to enable embedding models.

vllm/attention/backends/rocm_flash_attn.py

 # SPDX-License-Identifier: Apache-2.0
 """Attention layer ROCm GPUs."""
+import itertools
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type
 
@@ -351,28 +352,27 @@ def _get_seq_len_block_table_args(
     Decoder attn -> select entirely decoder self-attention-related fields
     Encoder/decoder cross-attn -> select encoder sequence lengths
     Encoder attn -> select encoder sequence lengths fields
+    Encoder-only attn -> select prefill sequence lengths with 
+        bidirectional attention
     
     Arguments:
 
     * attn_metadata: Attention metadata structure associated with attention op
     * attn_type: encoder attention, decoder self-attention,
-                encoder/decoder cross-attention
+                encoder/decoder cross-attention, encoder-only
 
     Returns:
 
     * Appropriate sequence-lengths tensors for query and key
     * Appropriate max sequence-length scalar
+    * Causal masking flag
     '''
 
-    partial_prefix_sum = 0
     if attn_type == AttentionType.ENCODER:
         assert attn_metadata.encoder_seq_lens is not None
         assert attn_metadata.encoder_seq_lens_tensor is not None
         query_seq_start_loc = torch.tensor(
-            [0] + [
-                partial_prefix_sum := partial_prefix_sum + i
-                for i in attn_metadata.encoder_seq_lens
-            ],
+            list(itertools.accumulate([0] + attn_metadata.encoder_seq_lens)),
             device=attn_metadata.encoder_seq_lens_tensor.device,
             dtype=attn_metadata.encoder_seq_lens_tensor.dtype)
         causal_mask = False
@@ -381,16 +381,27 @@ def _get_seq_len_block_table_args(
         return (query_seq_start_loc, attn_metadata.max_encoder_seq_len,
                 query_seq_start_loc, attn_metadata.max_encoder_seq_len,
                 attn_metadata.encoder_seq_lens, causal_mask)
+    elif attn_type == AttentionType.ENCODER_ONLY:
+        # For encoder-only models, we use the prefill sequence lengths
+        assert attn_metadata.seq_lens is not None
+        assert attn_metadata.seq_lens_tensor is not None
+        query_seq_start_loc = torch.tensor(
+            list(itertools.accumulate([0] + attn_metadata.seq_lens)),
+            device=attn_metadata.seq_lens_tensor.device,
+            dtype=attn_metadata.seq_lens_tensor.dtype)
+        max_seq_len = attn_metadata.max_prefill_seq_len
+        # Encoder-only models typically use bidirectional attention
+        causal_mask = False
+
+        return (query_seq_start_loc, max_seq_len, query_seq_start_loc,
+                max_seq_len, attn_metadata.seq_lens, causal_mask)
     elif attn_type == AttentionType.DECODER:
         # Decoder self-attention
         # Choose max_seq_len based on whether we are in prompt_run
         assert attn_metadata.seq_lens is not None
         assert attn_metadata.seq_lens_tensor is not None
         query_seq_start_loc = torch.tensor(
-            [0] + [
-                partial_prefix_sum := partial_prefix_sum + i
-                for i in attn_metadata.seq_lens
-            ],
+            list(itertools.accumulate([0] + attn_metadata.seq_lens)),
             device=attn_metadata.seq_lens_tensor.device,
             dtype=attn_metadata.seq_lens_tensor.dtype)
         max_seq_len = attn_metadata.max_prefill_seq_len
@@ -402,21 +413,14 @@ def _get_seq_len_block_table_args(
         assert attn_metadata.seq_lens is not None
         assert attn_metadata.encoder_seq_lens_tensor is not None
         query_start_loc = torch.tensor(
-            [0] + [
-                partial_prefix_sum := partial_prefix_sum + i
-                for i in attn_metadata.seq_lens
-            ],
+            list(itertools.accumulate([0] + attn_metadata.seq_lens)),
             device=attn_metadata.encoder_seq_lens_tensor.device,
             dtype=attn_metadata.encoder_seq_lens_tensor.dtype)
 
-        partial_prefix_sum = 0
         assert attn_metadata.encoder_seq_lens is not None
         assert attn_metadata.seq_lens_tensor is not None
         key_seq_start_loc = torch.tensor(
-            [0] + [
-                partial_prefix_sum := partial_prefix_sum + i
-                for i in attn_metadata.encoder_seq_lens
-            ],
+            list(itertools.accumulate([0] + attn_metadata.encoder_seq_lens)),
             device=attn_metadata.seq_lens_tensor.device,
             dtype=attn_metadata.seq_lens_tensor.dtype)
         causal_mask = False
@@ -598,6 +602,8 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                 will match encoder sequence lengths, pass encoder sequence
                 attributes to kernel (encoder_seq_lens/encoder_seq_lens_tensor/
                 max_encoder_seq_len)
+            * ENCODER_ONLY: bidirectional attention with no KV caching;
+                use prefill sequence attributes
 
         Args:
             query: shape = [num_tokens, num_heads * head_size]
@@ -622,7 +628,11 @@ class ROCmFlashAttentionImpl(AttentionImpl):
         else:
             assert value is None
 
-        if self.attn_type != AttentionType.ENCODER and kv_cache.numel() > 0:
+        # Only update KV cache for decoder self-attention
+        # and encoder-decoder cross-attention
+        if self.attn_type not in [
+                AttentionType.ENCODER, AttentionType.ENCODER_ONLY
+        ] and kv_cache.numel() > 0:
             key_cache, value_cache = PagedAttention.split_kv_cache(
                 kv_cache, self.num_kv_heads, self.head_size)
 
@@ -646,6 +656,9 @@ class ROCmFlashAttentionImpl(AttentionImpl):
 
         if self.attn_type != AttentionType.ENCODER:
             num_prefill_tokens = attn_metadata.num_prefill_tokens
+        elif self.attn_type == AttentionType.ENCODER_ONLY:
+            # For encoder-only models, all tokens are processed in one go
+            num_prefill_tokens = query.shape[0]
         else:
             assert attn_metadata.num_encoder_tokens is not None
             num_prefill_tokens = attn_metadata.num_encoder_tokens
@@ -656,8 +669,13 @@ class ROCmFlashAttentionImpl(AttentionImpl):
         # QKV for prefill.
         query = query[:num_prefill_tokens]
 
+        # For encoder-only and encoder models,
+        # we process all tokens at once
+        # For decoder and encoder-decoder,
+        # we may need to limit key/value to prefill tokens
         if key is not None and value is not None \
-            and self.attn_type != AttentionType.ENCODER_DECODER:
+            and self.attn_type not in [AttentionType.ENCODER_DECODER,
+                                       AttentionType.ENCODER_ONLY]:
             key = key[:num_prefill_tokens]
             value = value[:num_prefill_tokens]
 
@@ -692,7 +710,7 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                             self.alibi_slopes,
                             query.dtype,
                             seq_lens,
-                            make_attn_mask=False)  # type: ignore
+                            make_attn_mask=causal_mask)  # type: ignore
                     out, _ = self.attn_func(
                         query,
                         key,
@@ -718,7 +736,7 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                             self.alibi_slopes,
                             query.dtype,
                             attn_metadata.seq_lens,
-                            make_attn_mask=True)  # type: ignore
+                            make_attn_mask=causal_mask)  # type: ignore
                     query = query.movedim(0, query.dim() - 2)
                     key = key.movedim(0, key.dim() - 2)
                     value = value.movedim(0, value.dim() - 2)
@@ -745,7 +763,7 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                         max_seqlen_q=prefill_meta.max_prefill_seq_len,
                         max_seqlen_k=key_max_seq_len,
                         softmax_scale=self.scale,
-                        causal=True,
+                        causal=causal_mask,
                         window_size=self.sliding_window,
                         alibi_slopes=self.alibi_slopes,
                         softcap=self.logits_soft_cap,
@@ -759,7 +777,10 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                     output = out
             else:
                 # prefix-enabled attention
-                output[:num_prefill_tokens] = PagedAttention.forward_prefix(
+                # not applicable for encoder-only models
+                if self.attn_type != AttentionType.ENCODER_ONLY:
+                    output[:
+                           num_prefill_tokens] = PagedAttention.forward_prefix(
                                query,
                                key,
                                value,
@@ -769,7 +790,6 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                                prefill_meta.block_tables,
                                prefill_meta.query_start_loc,
                                prefill_meta.seq_lens_tensor,
-                    prefill_meta.context_lens_tensor,
                                prefill_meta.max_query_len,
                                self.alibi_slopes,
                                self.sliding_window[0],
@@ -777,7 +797,9 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                                layer._v_scale,
                            )
 
-        if decode_meta := attn_metadata.decode_metadata:
+        # Skip decode phase for encoder-only models
+        if (decode_meta := attn_metadata.decode_metadata) and (
+                self.attn_type != AttentionType.ENCODER_ONLY):
             # Decoding run.
             # Whether to use rocm custom paged attention or not
             num_seqs, num_heads, head_size = decode_query.shape