[V0 deprecation] Remove VLLM_USE_V1 usage in platform and v1 module (vllm-project#27798)

Signed-off-by: wangxiyuan <[email protected]>
Co-authored-by: Harry Mellor <[email protected]>

Author: wangxiyuan

vllm/platforms/cuda.py

@@ -276,17 +276,12 @@ def get_attn_backend_cls(
                     "FLASHMLA, FLASH_ATTN_MLA, or TRITON_MLA. Alternatively, set "
                     "VLLM_MLA_DISABLE=1 to disable MLA for this model."
                 )
-            if not use_v1:
-                raise RuntimeError(
-                    "MLA attention backends require the V1 engine. "
-                    "Set VLLM_USE_V1=1 to enable them."
-                )
 
             from vllm.attention.ops.flashmla import is_flashmla_dense_supported
             from vllm.attention.utils.fa_utils import flash_attn_supports_mla
 
             if use_sparse:
-                logger.info_once("Using Sparse MLA backend on V1 engine.")
+                logger.info_once("Using Sparse MLA backend.")
                 return (
                     "vllm.v1.attention.backends.mla.flashmla_sparse."
                     "FlashMLASparseBackend"
@@ -313,15 +308,13 @@ def get_attn_backend_cls(
             )
 
             if use_cutlassmla:
-                logger.info_once(
-                    "Using Cutlass MLA backend on V1 engine.", scope="local"
-                )
+                logger.info_once("Using Cutlass MLA backend.", scope="local")
                 return "vllm.v1.attention.backends.mla.cutlass_mla.CutlassMLABackend"
             if use_flashinfermla:
                 from vllm.v1.attention.backends.utils import set_kv_cache_layout
 
                 set_kv_cache_layout("HND")
-                logger.info_once("Using FlashInfer MLA backend on V1 engine.")
+                logger.info_once("Using FlashInfer MLA backend.")
                 return (
                     "vllm.v1.attention.backends.mla.flashinfer_mla.FlashInferMLABackend"
                 )
@@ -333,116 +326,107 @@ def get_attn_backend_cls(
                         block_size,
                     )
                 else:
-                    logger.info_once("Using FlashMLA backend on V1 engine.")
+                    logger.info_once("Using FlashMLA backend.")
                     return "vllm.v1.attention.backends.mla.flashmla.FlashMLABackend"
             if use_flashattn:
-                logger.info_once("Using FlashAttention MLA backend on V1 engine.")
+                logger.info_once("Using FlashAttention MLA backend.")
                 return (
                     "vllm.v1.attention.backends.mla.flashattn_mla.FlashAttnMLABackend"
                 )
             if use_triton:
-                logger.info_once("Using Triton MLA backend on V1 engine.")
+                logger.info_once("Using Triton MLA backend.")
                 return "vllm.v1.attention.backends.mla.triton_mla.TritonMLABackend"
-        if use_v1:
-            FLASHINFER_V1 = "vllm.v1.attention.backends.flashinfer.FlashInferBackend"  # noqa: E501
-            FLEX_ATTENTION_V1 = (
-                "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"  # noqa: E501
-            )
-            TRITON_ATTN = (
-                "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"  # noqa: E501
-            )
-            FLASH_ATTN_V1 = (
-                "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"  # noqa: E501
-            )
-            TREE_ATTN_V1 = "vllm.v1.attention.backends.tree_attn.TreeAttentionBackend"  # noqa: E501
-            XFORMERS_V1 = "vllm.v1.attention.backends.xformers.XFormersAttentionBackend"  # noqa: E501
 
-            use_fp8_kv_cache = kv_cache_dtype is not None and kv_cache_dtype.startswith(
-                "fp8"
-            )
+        FLASHINFER_V1 = "vllm.v1.attention.backends.flashinfer.FlashInferBackend"  # noqa: E501
+        FLEX_ATTENTION_V1 = (
+            "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"  # noqa: E501
+        )
+        TRITON_ATTN = "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"  # noqa: E501
+        FLASH_ATTN_V1 = "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"  # noqa: E501
+        TREE_ATTN_V1 = "vllm.v1.attention.backends.tree_attn.TreeAttentionBackend"  # noqa: E501
+        XFORMERS_V1 = "vllm.v1.attention.backends.xformers.XFormersAttentionBackend"  # noqa: E501
 
-            if selected_backend == _Backend.FLASHINFER:
-                logger.info_once("Using FlashInfer backend on V1 engine.")
-                if cls.has_device_capability(100):
-                    from vllm.v1.attention.backends.utils import set_kv_cache_layout
+        use_fp8_kv_cache = kv_cache_dtype is not None and kv_cache_dtype.startswith(
+            "fp8"
+        )
 
-                    set_kv_cache_layout("HND")
-                return FLASHINFER_V1
-            elif selected_backend == _Backend.FLEX_ATTENTION:
-                logger.info_once("Using FlexAttention backend on V1 engine.")
-                return FLEX_ATTENTION_V1
-            elif selected_backend == _Backend.TRITON_ATTN:
-                logger.info_once("Using Triton backend on V1 engine.")
-                return TRITON_ATTN
-            elif selected_backend == _Backend.FLASH_ATTN:
-                logger.info_once("Using Flash Attention backend on V1 engine.")
-                return FLASH_ATTN_V1
-            elif selected_backend == _Backend.TREE_ATTN:
-                logger.info_once("Using Tree Attention backend on V1 engine.")
-                return TREE_ATTN_V1
-            elif selected_backend == _Backend.XFORMERS:
-                logger.info_once("Using XFormers backend on V1 engine.")
-                return XFORMERS_V1
+        if selected_backend == _Backend.FLASHINFER:
+            logger.info_once("Using FlashInfer backend.")
+            if cls.has_device_capability(100):
+                from vllm.v1.attention.backends.utils import set_kv_cache_layout
 
-            from vllm.attention.selector import is_attn_backend_supported
+                set_kv_cache_layout("HND")
+            return FLASHINFER_V1
+        elif selected_backend == _Backend.FLEX_ATTENTION:
+            logger.info_once("Using FlexAttention backend.")
+            return FLEX_ATTENTION_V1
+        elif selected_backend == _Backend.TRITON_ATTN:
+            logger.info_once("Using Triton backend.")
+            return TRITON_ATTN
+        elif selected_backend == _Backend.FLASH_ATTN:
+            logger.info_once("Using Flash Attention backend.")
+            return FLASH_ATTN_V1
+        elif selected_backend == _Backend.TREE_ATTN:
+            logger.info_once("Using Tree Attention backend.")
+            return TREE_ATTN_V1
+        elif selected_backend == _Backend.XFORMERS:
+            logger.info_once("Using XFormers backend.")
+            return XFORMERS_V1
+
+        from vllm.attention.selector import is_attn_backend_supported
+
+        # Default backends for V1 engine
+        # Prefer FlashInfer for Blackwell GPUs if installed
+        if cls.is_device_capability(100):
+            if is_default_backend_supported := is_attn_backend_supported(
+                FLASHINFER_V1, head_size, dtype
+            ):
+                from vllm.v1.attention.backends.utils import set_kv_cache_layout
 
-            # Default backends for V1 engine
-            # Prefer FlashInfer for Blackwell GPUs if installed
-            if cls.is_device_capability(100):
-                if is_default_backend_supported := is_attn_backend_supported(
-                    FLASHINFER_V1, head_size, dtype
-                ):
-                    from vllm.v1.attention.backends.utils import set_kv_cache_layout
-
-                    logger.info_once(
-                        "Using FlashInfer backend with HND KV cache layout on "
-                        "V1 engine by default for Blackwell (SM 10.0) GPUs."
-                    )
-                    set_kv_cache_layout("HND")
+                logger.info_once(
+                    "Using FlashInfer backend with HND KV cache layout on "
+                    "V1 engine by default for Blackwell (SM 10.0) GPUs."
+                )
+                set_kv_cache_layout("HND")
 
-                    return FLASHINFER_V1
+                return FLASHINFER_V1
 
-                if not is_default_backend_supported.can_import:
-                    logger.warning_once(
-                        "FlashInfer failed to import for V1 engine on "
-                        "Blackwell (SM 10.0) GPUs; it is recommended to "
-                        "install FlashInfer for better performance."
-                    )
+            if not is_default_backend_supported.can_import:
+                logger.warning_once(
+                    "FlashInfer failed to import on Blackwell (SM 10.0) GPUs; "
+                    "it is recommended to install FlashInfer for better "
+                    "performance."
+                )
 
-            # FlashAttention is the default for SM 8.0+ GPUs
-            if cls.has_device_capability(80):
-                if (has_sink or use_fp8_kv_cache) and not cls.is_device_capability(90):
-                    logger.info_once("Using Triton backend on V1 engine.")
-                    return TRITON_ATTN
-                elif is_default_backend_supported := is_attn_backend_supported(
-                    FLASH_ATTN_V1, head_size, dtype, allow_import_error=False
-                ):
-                    logger.info_once("Using Flash Attention backend on V1 engine.")
-                    return FLASH_ATTN_V1
-
-            # FlexAttention is the default for older GPUs
-            else:
-                logger.info_once("Using FlexAttention backend on V1 engine.")
-                return FLEX_ATTENTION_V1
+        # FlashAttention is the default for SM 8.0+ GPUs
+        if cls.has_device_capability(80):
+            if (has_sink or use_fp8_kv_cache) and not cls.is_device_capability(90):
+                logger.info_once("Using Triton backend.")
+                return TRITON_ATTN
+            elif is_default_backend_supported := is_attn_backend_supported(
+                FLASH_ATTN_V1, head_size, dtype, allow_import_error=False
+            ):
+                logger.info_once("Using Flash Attention backend.")
+                return FLASH_ATTN_V1
 
-            assert not is_default_backend_supported
+        # FlexAttention is the default for older GPUs
+        else:
+            logger.info_once("Using FlexAttention backend.")
+            return FLEX_ATTENTION_V1
 
-            use_flex_attention_reason = {}
-            if not is_default_backend_supported.head_size:
-                use_flex_attention_reason["head_size"] = head_size
-            if not is_default_backend_supported.dtype:
-                use_flex_attention_reason["dtype"] = dtype
+        assert not is_default_backend_supported
 
-            logger.info_once(
-                "Using FlexAttention backend for %s on V1 engine.",
-                ", ".join(f"{k}={v}" for k, v in use_flex_attention_reason.items()),
-            )
-            return FLEX_ATTENTION_V1
+        use_flex_attention_reason = {}
+        if not is_default_backend_supported.head_size:
+            use_flex_attention_reason["head_size"] = head_size
+        if not is_default_backend_supported.dtype:
+            use_flex_attention_reason["dtype"] = dtype
 
-        raise RuntimeError(
-            "V0 attention backends have been removed. Set VLLM_USE_V1=1 "
-            "to select a supported backend."
+        logger.info_once(
+            "Using FlexAttention backend for %s.",
+            ", ".join(f"{k}={v}" for k, v in use_flex_attention_reason.items()),
         )
+        return FLEX_ATTENTION_V1
 
     @classmethod
     def get_punica_wrapper(cls) -> str:

vllm/platforms/interface.py

@@ -467,14 +467,7 @@ def use_all_gather(cls) -> bool:
         """
         Whether to use allgather in LogitsProcessor to gather the logits.
         """
-        import vllm.envs as envs
-        from vllm.config import get_current_vllm_config
-
-        parallel_config = get_current_vllm_config().parallel_config
-        return (
-            envs.VLLM_USE_V1
-            or parallel_config.distributed_executor_backend == "external_launcher"
-        )
+        return True
 
     @classmethod
     def use_custom_allreduce(cls) -> bool:

vllm/platforms/rocm.py

@@ -149,7 +149,7 @@ def use_rocm_custom_paged_attention(
     # disabled due to observed numerical discrepancy.
     if ON_GFX9:
         return (
-            (not envs.VLLM_USE_V1 or sliding_window == 0 or sliding_window == (-1, -1))
+            (sliding_window == 0 or sliding_window == (-1, -1))
             and (qtype == torch.half or qtype == torch.bfloat16)
             and (head_size == 64 or head_size == 128)
             and (block_size == 16 or block_size == 32)
@@ -163,11 +163,7 @@ def use_rocm_custom_paged_attention(
     else:
         return (
             ON_GFX11_GFX12
-            and (
-                not envs.VLLM_USE_V1
-                or sliding_window == 0
-                or sliding_window == (-1, -1)
-            )
+            and (sliding_window == 0 or sliding_window == (-1, -1))
             and (qtype == torch.half or qtype == torch.bfloat16)
             and head_size == 128
             and block_size == 16
@@ -236,12 +232,6 @@ def get_attn_backend_cls(
         if use_sparse:
             raise NotImplementedError("Sparse Attention is not supported on ROCm.")
         if use_mla:
-            if not use_v1:
-                raise RuntimeError(
-                    "MLA attention backends require the V1 engine. "
-                    "Set VLLM_USE_V1=1 to enable them."
-                )
-
             from vllm.v1.attention.backends.mla.rocm_aiter_mla import (
                 is_aiter_mla_enabled,
             )
@@ -255,15 +245,15 @@ def get_attn_backend_cls(
 
             if selected_backend == _Backend.TRITON_MLA:
                 if block_size != 1:
-                    logger.info_once("Using Triton MLA backend on V1 engine.")
+                    logger.info_once("Using Triton MLA backend.")
                     return "vllm.v1.attention.backends.mla.triton_mla.TritonMLABackend"
                 raise ValueError(
                     f" The selected backend, {selected_backend.name},"
                     f"does not support block size {block_size}."
                 )
             if selected_backend == _Backend.ROCM_AITER_MLA:
                 if block_size == 1:
-                    logger.info("Using AITER MLA backend on V1 engine.")
+                    logger.info("Using AITER MLA backend.")
                     return (
                         "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
                     )
@@ -277,41 +267,33 @@ def get_attn_backend_cls(
                 f"is not MLA type while requested for MLA backend."
             )
 
-        if envs.VLLM_USE_V1:
-            if selected_backend == _Backend.FLEX_ATTENTION:
-                logger.info("Using FlexAttention backend on V1 engine.")
-                return "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"
-            if (
-                envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_MHA and on_gfx9()
-            ) or selected_backend == _Backend.ROCM_AITER_FA:
-                logger.info("Using Aiter Flash Attention backend on V1 engine.")
-                return (
-                    "vllm.v1.attention.backends."
-                    "rocm_aiter_fa.AiterFlashAttentionBackend"
-                )
-            if (
-                envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
-            ) or selected_backend == _Backend.ROCM_AITER_UNIFIED_ATTN:
-                logger.info("Using Aiter Unified Attention backend on V1 engine.")
-                return (
-                    "vllm.v1.attention.backends."
-                    "rocm_aiter_unified_attn.RocmAiterUnifiedAttentionBackend"
-                )
-            if (
-                envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
-                or selected_backend == _Backend.ROCM_ATTN
-            ):
-                # rocm specific backend, with aiter and/or
-                #   triton prefix-prefill
-                logger.info("Using Rocm Attention backend on V1 engine.")
-                return "vllm.v1.attention.backends.rocm_attn.RocmAttentionBackend"
-            # default case, using triton unified attention
-            logger.info("Using Triton Attention backend on V1 engine.")
-            return "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"
-        raise RuntimeError(
-            "V0 attention backends have been removed. Set VLLM_USE_V1=1 "
-            "to select a supported backend."
-        )
+        if selected_backend == _Backend.FLEX_ATTENTION:
+            logger.info("Using FlexAttention backend.")
+            return "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"
+        if (
+            envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_MHA and on_gfx9()
+        ) or selected_backend == _Backend.ROCM_AITER_FA:
+            logger.info("Using Aiter Flash Attention backend.")
+            return "vllm.v1.attention.backends.rocm_aiter_fa.AiterFlashAttentionBackend"
+        if (
+            envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
+        ) or selected_backend == _Backend.ROCM_AITER_UNIFIED_ATTN:
+            logger.info("Using Aiter Unified Attention backend.")
+            return (
+                "vllm.v1.attention.backends."
+                "rocm_aiter_unified_attn.RocmAiterUnifiedAttentionBackend"
+            )
+        if (
+            envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
+            or selected_backend == _Backend.ROCM_ATTN
+        ):
+            # rocm specific backend, with aiter and/or
+            #   triton prefix-prefill
+            logger.info("Using Rocm Attention backend.")
+            return "vllm.v1.attention.backends.rocm_attn.RocmAttentionBackend"
+        # default case, using triton unified attention
+        logger.info("Using Triton Attention backend.")
+        return "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"
 
     @classmethod
     def set_device(cls, device: torch.device) -> None:
@@ -372,7 +354,6 @@ def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
         parallel_config = vllm_config.parallel_config
         is_eager_execution = compilation_config == CUDAGraphMode.NONE
 
-        use_v1 = envs.VLLM_USE_V1
         use_aiter_rms_norm = (
             envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_RMSNORM
         )
@@ -384,8 +365,7 @@ def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
             parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker"
         #  Aiter rms norm perform best when CUDA Graph capture is enabled.
         if (
-            use_v1
-            and use_aiter_rms_norm
+            use_aiter_rms_norm
             and not is_eager_execution
             and "-rms_norm" not in compilation_config.custom_ops
         ):