[FEAT] [ROCm] [Embedding] Add encoder-only model support into ROCm Flash Attention to enable embedding models. (vllm-project#14664)

Signed-off-by: tjtanaa <[email protected]>
Signed-off-by: Louis Ulmer <[email protected]>

Author: tjtanaa

CMakeLists.txt

@@ -561,6 +561,10 @@ set(VLLM_MOE_EXT_SRC
   "csrc/moe/moe_align_sum_kernels.cu"
   "csrc/moe/topk_softmax_kernels.cu")
 
+if(VLLM_GPU_LANG STREQUAL "CUDA")
+  list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")
+endif()
+
 set_gencode_flags_for_srcs(
   SRCS "${VLLM_MOE_EXT_SRC}"
   CUDA_ARCHS "${CUDA_ARCHS}")

csrc/moe/torch_bindings.cpp

@@ -52,6 +52,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "int moe_block_size, bool replicate_input, bool apply_weights)"
       " -> Tensor");
   // conditionally compiled so impl registration is in source file
+
 #endif
 }
 

tests/models/embedding/language/test_cls_models.py

@@ -7,6 +7,8 @@
 import torch
 from transformers import AutoModelForSequenceClassification
 
+from vllm.platforms import current_platform
+
 
 @pytest.mark.parametrize(
     "model",
@@ -15,14 +17,21 @@
                      marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
     ],
 )
-@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("dtype",
+                         ["half"] if current_platform.is_rocm() else ["float"])
 def test_classification_models(
     hf_runner,
     vllm_runner,
     example_prompts,
     model: str,
     dtype: str,
+    monkeypatch,
 ) -> None:
+    if current_platform.is_rocm():
+        # ROCm Triton FA does not currently support sliding window attention
+        # switch to use ROCm CK FA backend
+        monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")
+
     with vllm_runner(model, dtype=dtype) as vllm_model:
         vllm_outputs = vllm_model.classify(example_prompts)
 
@@ -43,4 +52,8 @@ def print_model(model):
         hf_output = torch.tensor(hf_output)
         vllm_output = torch.tensor(vllm_output)
 
-        assert torch.allclose(hf_output, vllm_output, 1e-3)
+        # the tolerance value of 1e-2 is selected based on the
+        # half datatype tests in
+        # tests/models/embedding/language/test_embedding.py
+        assert torch.allclose(hf_output, vllm_output,
+                              1e-3 if dtype == "float" else 1e-2)

tests/models/embedding/language/test_embedding.py

@@ -6,6 +6,7 @@
 import pytest
 
 from vllm.config import PoolerConfig
+from vllm.platforms import current_platform
 
 from ..utils import check_embeddings_close
 
@@ -18,15 +19,15 @@
                      marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
         pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
         pytest.param("intfloat/multilingual-e5-small"),
+        pytest.param("Alibaba-NLP/gte-Qwen2-7B-instruct"),
         # [Decoder-only]
         pytest.param("BAAI/bge-multilingual-gemma2",
                      marks=[pytest.mark.core_model]),
         pytest.param("intfloat/e5-mistral-7b-instruct",
                      marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
         pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
-        pytest.param("Alibaba-NLP/gte-Qwen2-7B-instruct"),
         pytest.param("ssmits/Qwen2-7B-Instruct-embed-base"),
-        # [Encoder-decoder]
+        # [Cross-Encoder]
         pytest.param("sentence-transformers/stsb-roberta-base-v2"),
     ],
 )
@@ -37,11 +38,19 @@ def test_models(
     example_prompts,
     model,
     dtype: str,
+    monkeypatch,
 ) -> None:
+
+    if model == "BAAI/bge-multilingual-gemma2" and current_platform.is_rocm():
+        # ROCm Triton FA does not currently support sliding window attention
+        # switch to use ROCm CK FA backend
+        monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")
+
     vllm_extra_kwargs = {}
     if model == "ssmits/Qwen2-7B-Instruct-embed-base":
         vllm_extra_kwargs["override_pooler_config"] = \
             PoolerConfig(pooling_type="MEAN")
+
     if model == "Alibaba-NLP/gte-Qwen2-1.5B-instruct":
         vllm_extra_kwargs["hf_overrides"] = {"is_causal": True}
 

tests/models/embedding/language/test_gritlm.py

@@ -15,8 +15,8 @@
 from ....utils import RemoteOpenAIServer
 
 # GritLM embedding implementation is only supported by XFormers backend.
-pytest.mark.skipif(not importlib.util.find_spec("xformers"),
-                   reason="GritLM requires XFormers")
+pytestmark = pytest.mark.skipif(not importlib.util.find_spec("xformers"),
+                                reason="GritLM requires XFormers")
 
 MODEL_NAME = "parasail-ai/GritLM-7B-vllm"
 MAX_MODEL_LEN = 4000

tests/models/embedding/vision_language/test_llava_next.py

@@ -4,10 +4,27 @@
 import torch.nn.functional as F
 from transformers import AutoModelForVision2Seq
 
+from vllm.platforms import current_platform
+
 from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
 from ....utils import large_gpu_test
 from ..utils import check_embeddings_close
 
+# Llava Next embedding implementation is only supported by CUDA.
+# If run on ROCm, hf_model.model.resize_token_embeddings will
+# cause the following error:
+#    RuntimeError: Calling torch.linalg.cholesky on a CUDA tensor
+#    requires compiling PyTorch with MAGMA. Please use PyTorch
+#    built with MAGMA support.
+# If run on CPU, hf_model.model.resize_token_embeddings will
+# cause the following error:
+#    RuntimeError: Calling torch.linalg.cholesky on a CPU tensor
+#    requires compiling PyTorch with LAPACK. Please use PyTorch
+#    built with LAPACK support.
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_cuda(),
+    reason="Llava Next model uses op that is only supported in CUDA")
+
 llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n'  # noqa: E501
 
 HF_TEXT_PROMPTS = [

vllm/attention/backends/rocm_flash_attn.py

@@ -1,5 +1,6 @@
 # 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
 
@@ -342,28 +343,27 @@ 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
@@ -372,16 +372,29 @@ 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
@@ -393,21 +406,14 @@ 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
@@ -584,6 +590,8 @@ def forward(
                 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]
@@ -608,7 +616,11 @@ def forward(
         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)
 
@@ -632,6 +644,9 @@ def forward(
 
         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
@@ -642,8 +657,13 @@ def forward(
         # 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]
 
@@ -678,7 +698,7 @@ def forward(
                             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,
@@ -703,7 +723,7 @@ def forward(
                             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)
@@ -729,7 +749,7 @@ def forward(
                         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,
@@ -742,25 +762,29 @@ def forward(
                 else:
                     output = out
             else:
-                # prefix-enabled attention
-                output[:num_prefill_tokens] = PagedAttention.forward_prefix(
-                    query,
-                    key,
-                    value,
-                    self.kv_cache_dtype,
-                    key_cache,
-                    value_cache,
-                    prefill_meta.block_tables,
-                    prefill_meta.query_start_loc,
-                    prefill_meta.seq_lens_tensor,
-                    prefill_meta.max_query_len,
-                    self.alibi_slopes,
-                    self.sliding_window[0],
-                    layer._k_scale,
-                    layer._v_scale,
-                )
-
-        if decode_meta := attn_metadata.decode_metadata:
+                # prefix-enabled attention -
+                # not applicable for encoder-only models
+                if self.attn_type != AttentionType.ENCODER_ONLY:
+                    output[:
+                           num_prefill_tokens] = PagedAttention.forward_prefix(
+                               query,
+                               key,
+                               value,
+                               self.kv_cache_dtype,
+                               key_cache,
+                               value_cache,
+                               prefill_meta.block_tables,
+                               prefill_meta.query_start_loc,
+                               prefill_meta.seq_lens_tensor,
+                               prefill_meta.max_query_len,
+                               self.alibi_slopes,
+                               self.sliding_window[0],
+                               layer._k_scale,
+                               layer._v_scale,
+                           )
+        # 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
@@ -885,4 +909,4 @@ def _use_rocm_custom_paged_attention(qtype: torch.dtype, head_size: int,
             and (qtype == torch.half or qtype == torch.bfloat16)
             and (head_size == 64 or head_size == 128)
             and (block_size == 16 or block_size == 32)
-            and (gqa_ratio >= 1 and gqa_ratio <= 16) and max_seq_len <= 32768)
+            and (gqa_ratio >= 1 and gqa_ratio <= 16) and max_seq_len <= 32768)