Merge branch 'main' of http://10.6.10.68/dcutoolkit/deeplearing/vllm

vllm/__init__.py

@@ -13,9 +13,10 @@ from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.version import __dcu_version__
 
-__version__ = "0.5.0"
+from .version import __version__
 
 __all__ = [
+    "__version__",
     "LLM",
     "ModelRegistry",
     "PromptStrictInputs",

vllm/_custom_ops.py

 import contextlib
+import functools
 from typing import List, Optional, Tuple, Type
 
 import torch
 
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
 try:
     import vllm._C
 except ImportError as e:
-    from vllm.logger import init_logger
-    logger = init_logger(__name__)
     logger.warning("Failed to import from vllm._C with %r", e)
 
 with contextlib.suppress(ImportError):
@@ -23,6 +26,25 @@ def is_custom_op_supported(op_name: str) -> bool:
     return op is not None
 
 
+def hint_on_error(fn):
+
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        try:
+            return fn(*args, **kwargs)
+        except AttributeError as e:
+            msg = (
+                "Error in calling custom op %s: %s\n"
+                "Possibly you have built or installed an obsolete version of vllm.\n"
+                "Please try a clean build and install of vllm,"
+                "or remove old built files such as vllm/*cpython*.so and build/ ."
+            )
+            logger.error(msg, fn.__name__, e)
+            raise e
+
+    return wrapper
+
+
 # activation ops
 def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
     torch.ops._C.silu_and_mul(out, x)
@@ -190,9 +212,9 @@ def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
 
 
 # cutlass
-def cutlass_scaled_mm_dq(a: torch.Tensor, b: torch.Tensor,
-                         scale_a: torch.Tensor, scale_b: torch.Tensor,
-                         out_dtype: Type[torch.dtype]) -> torch.Tensor:
+def cutlass_scaled_mm(a: torch.Tensor, b: torch.Tensor, scale_a: torch.Tensor,
+                      scale_b: torch.Tensor,
+                      out_dtype: Type[torch.dtype]) -> torch.Tensor:
     assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
     assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
 
@@ -200,8 +222,7 @@ def cutlass_scaled_mm_dq(a: torch.Tensor, b: torch.Tensor,
     n = b.shape[1]
     out = torch.empty((m, n), dtype=out_dtype, device=a.device)
 
-    torch.ops._C.cutlass_scaled_mm_dq(out, a, b, scale_a, scale_b)
-
+    torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b)
     return out
 
 
@@ -459,3 +480,25 @@ def dispatch_bgmv_low_level(
         h_out,
         y_offset,
     )
+
+
+# temporary fix for https://github.com/vllm-project/vllm/issues/5456
+# TODO: remove this in v0.6.0
+names_and_values = globals()
+names_and_values_to_update = {}
+# prepare variables to avoid dict size change during iteration
+k, v, arg = None, None, None
+fn_type = type(lambda x: x)
+for k, v in names_and_values.items():
+    # find functions that are defined in this file and have torch.Tensor
+    # in their annotations. `arg == "torch.Tensor"` is used to handle
+    # the case when users use `import __annotations__` to turn type
+    # hints into strings.
+    if isinstance(v, fn_type) \
+        and v.__code__.co_filename == __file__ \
+        and any(arg is torch.Tensor or arg == "torch.Tensor"
+                   for arg in v.__annotations__.values()):
+        names_and_values_to_update[k] = hint_on_error(v)
+
+names_and_values.update(names_and_values_to_update)
+del names_and_values_to_update, names_and_values, v, k, fn_type

vllm/attention/backends/flash_attn.py

@@ -317,7 +317,7 @@ class FlashAttentionImpl(AttentionImpl):
                 # normal attention
                 # When block_tables are not filled, it means q and k are the
                 # prompt, and they have the same length.
-                flash_attn_varlen_func(
+                out = flash_attn_varlen_func(
                     q=query,
                     k=key,
                     v=value,
@@ -329,13 +329,14 @@ class FlashAttentionImpl(AttentionImpl):
                     causal=True,
                     window_size=self.sliding_window,
                     alibi_slopes=self.alibi_slopes,
-                    out=output[:num_prefill_tokens],
                 )
+                assert output[:num_prefill_tokens].shape == out.shape
+                output[:num_prefill_tokens] = out
             else:
                 # prefix-enabled attention
                 assert prefill_meta.seq_lens is not None
                 max_seq_len = max(prefill_meta.seq_lens)
-                flash_attn_varlen_func(
+                output[:num_prefill_tokens] = flash_attn_varlen_func(
                     q=query,
                     k=key_cache,
                     v=value_cache,
@@ -347,12 +348,11 @@ class FlashAttentionImpl(AttentionImpl):
                     causal=True,
                     alibi_slopes=self.alibi_slopes,
                     block_table=prefill_meta.block_tables,
-                    out=output[:num_prefill_tokens],
                 )
 
         if decode_meta := attn_metadata.decode_metadata:
             # Decoding run.
-            flash_attn_with_kvcache(
+            output[num_prefill_tokens:] = flash_attn_with_kvcache(
                 decode_query.unsqueeze(1),
                 key_cache,
                 value_cache,
@@ -361,8 +361,7 @@ class FlashAttentionImpl(AttentionImpl):
                 softmax_scale=self.scale,
                 causal=True,
                 alibi_slopes=self.alibi_slopes,
-                out=output[num_prefill_tokens:].unsqueeze(1),
-            )
+            ).squeeze(1)
 
         # Reshape the output tensor.
         return output.view(num_tokens, hidden_size)

vllm/attention/backends/pallas.py

+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Type
+
+import torch
+import torch_xla.experimental.custom_kernel  # Required to register custom ops.
+import torch_xla.experimental.dynamo_set_buffer_donor
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata)
+
+
+class PallasAttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_impl_cls() -> Type["PallasAttentionBackendImpl"]:
+        return PallasAttentionBackendImpl
+
+    @staticmethod
+    def make_metadata(*args, **kwargs) -> "PallasMetadata":
+        return PallasMetadata(*args, **kwargs)
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return (num_kv_heads, num_blocks, block_size, head_size)
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: Dict[int, int],
+    ) -> None:
+        raise NotImplementedError("swap_blocks is not implemented.")
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: Dict[int, List[int]],
+    ) -> None:
+        # TODO(woosuk): Implement this.
+        raise NotImplementedError("copy_blocks is not implemented.")
+
+
+@dataclass
+class PallasMetadata(AttentionMetadata):
+
+    # Currently, input sequences can only contain all prefills
+    # or all decoding.
+    block_tables: Optional[torch.Tensor]
+    context_lens: Optional[torch.Tensor]
+
+    @property
+    def prefill_metadata(self) -> Optional["PallasMetadata"]:
+        if self.num_prefills == 0:
+            return None
+
+        assert self.num_decode_tokens == 0
+        assert self.block_tables is None
+        assert self.context_lens is None
+        return self
+
+    @property
+    def decode_metadata(self) -> Optional["PallasMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+
+        assert self.num_prefills == 0
+        assert self.num_prefill_tokens == 0
+        assert self.block_tables is not None
+        assert self.context_lens is not None
+        return self
+
+
+class PallasAttentionBackendImpl(AttentionImpl):
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: Optional[List[float]],
+        sliding_window: Optional[int],
+        kv_cache_dtype: str,
+        blocksparse_params: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
+
+        assert self.num_heads % self.num_kv_heads == 0
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        if head_size % 128 != 0:
+            raise NotImplementedError("Head size must be a multiple of 128.")
+        if alibi_slopes is not None:
+            raise NotImplementedError("Alibi slopes is not supported.")
+        if sliding_window is not None:
+            raise NotImplementedError("Sliding window is not supported.")
+        if kv_cache_dtype != "auto":
+            raise NotImplementedError("FP8 KV cache dtype is not supported.")
+        if blocksparse_params is not None:
+            raise NotImplementedError("Blocksparse is not supported.")
+
+        if torch_xla.tpu.version() < 4:
+            raise NotImplementedError("TPU version must be 4 or higher.")
+
+        self.megacore_mode = None
+        tpu_type = torch_xla.tpu.get_tp_groupu_env()["TYPE"].lower()
+        if not tpu_type.endswith("lite"):
+            if self.num_kv_heads % 2 == 0:
+                self.megacore_mode = "kv_head"
+            else:
+                # NOTE(woosuk): If the batch size is not a multiple of 2, the
+                # megacore mode will be None.
+                self.megacore_mode = "batch"
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: Tuple[Optional[torch.Tensor], Optional[torch.Tensor]],
+        attn_metadata: PallasMetadata,
+        kv_scale: float = 1.0,
+    ) -> torch.Tensor:
+        """Forward pass with Pallas attention.
+
+        Args:
+            query: shape = [batch_size, seq_len, num_heads * head_size]
+            key: shape = [batch_size, seq_len, num_kv_heads * head_size]
+            value: shape = [batch_size, seq_len, num_kv_heads * head_size]
+            key_cache = [num_kv_heads, num_blocks, block_size, head_size]
+            value_cache = [num_kv_heads, num_blocks, block_size, head_size]
+            attn_metadata: Metadata for attention.
+        Returns:
+            shape = [batch_size, seq_len, num_heads * head_size]
+        """
+        assert kv_scale == 1.0
+        batch_size, seq_len, hidden_size = query.shape
+        query = query.view(batch_size, seq_len, self.num_heads, self.head_size)
+        key = key.view(batch_size, seq_len, self.num_kv_heads, self.head_size)
+        value = value.view(batch_size, seq_len, self.num_kv_heads,
+                           self.head_size)
+
+        if kv_cache[0] is not None:
+            slot_mapping = attn_metadata.slot_mapping
+            key_cache, value_cache = kv_cache
+            write_to_kv_cache(key, value, key_cache, value_cache, slot_mapping)
+
+        query = query * self.scale
+        if attn_metadata.num_prefills > 0:
+            assert seq_len % 16 == 0, (
+                "Pallas FlashAttention kernel requires seq_len to be a "
+                f"multiple of 16 but got {seq_len}")
+
+            # Handle GQA/MQA.
+            if self.num_kv_heads != self.num_heads:
+                key = key.repeat_interleave(self.num_queries_per_kv, dim=-2)
+                key = key.view(batch_size, seq_len, self.num_heads,
+                               self.head_size)
+                value = value.repeat_interleave(self.num_queries_per_kv,
+                                                dim=-2)
+                value = value.view(batch_size, seq_len, self.num_heads,
+                                   self.head_size)
+            # FlashAttention requires [batch_size, num_heads, seq_len, d_model]
+            # while the input is [batch_size, seq_len, num_heads, d_model].
+            # Permute the input to match the required format.
+            output = torch.ops.xla.flash_attention(
+                query.permute(0, 2, 1, 3),
+                key.permute(0, 2, 1, 3),
+                value.permute(0, 2, 1, 3),
+                True,
+            )
+            output = output.permute(0, 2, 1, 3)
+        else:
+            # Decoding run.
+            assert kv_cache is not None
+
+            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),
+                    key_cache,
+                    value_cache,
+                    attn_metadata.context_lens,
+                    attn_metadata.block_tables,
+                    pages_per_compute_block,
+                    megacore_mode=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,
+                )
+
+        # Reshape the output tensor.
+        return output.reshape(batch_size, seq_len, hidden_size)
+
+
+def write_to_kv_cache(
+    key: torch.Tensor,
+    value: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    slot_mapping: torch.Tensor,
+) -> None:
+    torch.ops.xla.dynamo_set_buffer_donor_(key_cache, True)
+    torch.ops.xla.dynamo_set_buffer_donor_(value_cache, True)
+
+    key = key.flatten(0, 2)
+    value = value.flatten(0, 2)
+    key_cache = key_cache.flatten(0, 2)
+    value_cache = value_cache.flatten(0, 2)
+    key_cache.index_copy_(0, slot_mapping, key)
+    value_cache.index_copy_(0, slot_mapping, value)

vllm/attention/backends/torch_sdpa.py

@@ -8,8 +8,16 @@ from torch.nn.functional import scaled_dot_product_attention
 
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata)
-from vllm.attention.ops.paged_attn import (PagedAttention,
-                                           PagedAttentionMetadata)
+from vllm.attention.ops.paged_attn import PagedAttentionMetadata
+from vllm.utils import is_cpu
+
+if is_cpu():
+    try:
+        from vllm.attention.ops.ipex_attn import PagedAttention
+    except ImportError:
+        from vllm.attention.ops.paged_attn import PagedAttention
+else:
+    from vllm.attention.ops.paged_attn import PagedAttention
 
 
 class TorchSDPABackend(AttentionBackend):
@@ -197,13 +205,14 @@ class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
                                          attn_metadata.attn_bias):
                     end = start + seq_len
                     sub_out = scaled_dot_product_attention(
-                        query[:, start:end, :],
-                        key[:, start:end, :],
-                        value[:, start:end, :],
+                        query[None, :, start:end, :],
+                        key[None, :, start:end, :],
+                        value[None, :, start:end, :],
                         attn_mask=mask,
                         dropout_p=0.0,
                         is_causal=not self.need_mask,
-                        scale=self.scale).movedim(query.dim() - 2, 0)
+                        scale=self.scale).squeeze(0).movedim(
+                            query.dim() - 2, 0)
                     output[start:end, :, :] = sub_out
                     start = end
             else:
@@ -248,7 +257,7 @@ def _make_alibi_bias(
 
         num_heads = alibi_slopes.shape[0]
         bias = bias[None, :].repeat((num_heads, 1, 1))
-        bias.mul_(alibi_slopes[:, None, None])
+        bias.mul_(alibi_slopes[:, None, None]).unsqueeze_(0)
         inf_mask = torch.empty(
             (1, seq_len, seq_len),
             dtype=bias.dtype).fill_(-torch.inf).triu_(diagonal=1)

vllm/attention/ops/ipex_attn.py

+from typing import Dict, List, Optional, Tuple
+
+import intel_extension_for_pytorch.llm.modules as ipex_modules
+import torch
+
+from vllm import _custom_ops as ops
+
+
+class PagedAttention:
+
+    @staticmethod
+    def get_supported_head_sizes() -> List[int]:
+        return [64, 80, 96, 112, 128, 256]
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        *args,
+    ) -> Tuple[int, ...]:
+        return (2, num_blocks, block_size * num_kv_heads * head_size)
+
+    @staticmethod
+    def split_kv_cache(
+        kv_cache: torch.Tensor,
+        num_kv_heads: int,
+        head_size: int,
+        *args,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        num_blocks = kv_cache.shape[1]
+
+        key_cache = kv_cache[0]
+        key_cache = key_cache.view(num_blocks, num_kv_heads, -1, head_size)
+        value_cache = kv_cache[1]
+        value_cache = value_cache.view(num_blocks, num_kv_heads, -1, head_size)
+        return key_cache, value_cache
+
+    @staticmethod
+    def write_to_paged_cache(
+        key: torch.Tensor,
+        value: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        slot_mapping: torch.Tensor,
+        kv_cache_dtype: str,
+        kv_scale: float,
+        *args,
+    ) -> None:
+        ipex_modules.PagedAttention.reshape_and_cache(
+            key, value, key_cache, value_cache,
+            slot_mapping.flatten().int())
+
+    @staticmethod
+    def forward_decode(
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        block_tables: torch.Tensor,
+        context_lens: torch.Tensor,
+        max_context_len: int,
+        kv_cache_dtype: str,
+        num_kv_heads: int,
+        scale: float,
+        alibi_slopes: Optional[torch.Tensor],
+        kv_scale: float,
+        *args,
+    ) -> torch.Tensor:
+        output = torch.empty_like(query)
+        block_size = value_cache.shape[2]
+        head_mapping = torch.arange(
+            0,
+            num_kv_heads,
+            device="cpu",
+            dtype=torch.int32,
+        ).view(num_kv_heads,
+               1).repeat_interleave(query.size(1) // num_kv_heads).flatten()
+        ipex_modules.PagedAttention.single_query_cached_kv_attention(
+            output, query.contiguous(), key_cache, value_cache, head_mapping,
+            scale, block_tables, context_lens, block_size, max_context_len,
+            alibi_slopes)
+
+        return output
+
+    @staticmethod
+    def forward_prefix(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        block_tables: torch.Tensor,
+        subquery_start_loc: torch.Tensor,
+        prompt_lens_tensor: torch.Tensor,
+        context_lens: torch.Tensor,
+        max_subquery_len: int,
+        alibi_slopes: Optional[torch.Tensor],
+        *args,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: Dict[int, int],
+        *args,
+    ) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: Dict[int, List[int]],
+        *args,
+    ) -> None:
+        key_caches = [kv_cache[0] for kv_cache in kv_caches]
+        value_caches = [kv_cache[1] for kv_cache in kv_caches]
+        ops.copy_blocks(key_caches, value_caches, src_to_dists)

vllm/attention/ops/triton_flash_attention.py

@@ -213,7 +213,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 256,
                 "BLOCK_N": 64,
-                "waves_per_eu": 2,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,
@@ -223,7 +223,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 128,
                 "BLOCK_N": 128,
-                "waves_per_eu": 2,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,
@@ -233,7 +233,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 256,
                 "BLOCK_N": 128,
-                "waves_per_eu": 2,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,
@@ -243,7 +243,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 128,
                 "BLOCK_N": 64,
-                "waves_per_eu": 1,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,
@@ -253,7 +253,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 128,
                 "BLOCK_N": 64,
-                "waves_per_eu": 3,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": True,
             },
             num_stages=1,
@@ -263,7 +263,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 128,
                 "BLOCK_N": 64,
-                "waves_per_eu": 3,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,
@@ -273,7 +273,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 64,
                 "BLOCK_N": 64,
-                "waves_per_eu": 4,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,
@@ -283,7 +283,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 32,
                 "BLOCK_N": 32,
-                "waves_per_eu": 4,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,
@@ -296,7 +296,7 @@ def _attn_fwd_inner(
             {
                 "BLOCK_M": 16,
                 "BLOCK_N": 16,
-                "waves_per_eu": 1,
+                "waves_per_eu": 0,
                 "PRE_LOAD_V": False,
             },
             num_stages=1,

vllm/attention/selector.py

@@ -7,7 +7,7 @@ import torch
 import vllm.envs as envs
 from vllm.attention.backends.abstract import AttentionBackend
 from vllm.logger import init_logger
-from vllm.utils import is_cpu, is_hip
+from vllm.utils import is_cpu, is_hip, is_tpu
 
 logger = init_logger(__name__)
 
@@ -18,6 +18,7 @@ class _Backend(enum.Enum):
     ROCM_FLASH = enum.auto()
     TORCH_SDPA = enum.auto()
     FLASHINFER = enum.auto()
+    PALLAS = enum.auto()
 
 
 @lru_cache(maxsize=None)
@@ -57,6 +58,9 @@ def get_attn_backend(
             ROCmFlashAttentionBackend)
         return ROCmFlashAttentionBackend
     elif backend == _Backend.TORCH_SDPA:
+        # TODO: make XPU backend available here.
+        assert is_cpu(), RuntimeError(
+            "Torch SDPA backend is only used for the CPU device.")
         logger.info("Using Torch SDPA backend.")
         from vllm.attention.backends.torch_sdpa import TorchSDPABackend
         return TorchSDPABackend
@@ -66,6 +70,10 @@ def get_attn_backend(
                        "Please make sure --enforce-eager is set.")
         from vllm.attention.backends.flashinfer import FlashInferBackend
         return FlashInferBackend
+    elif backend == _Backend.PALLAS:
+        logger.info("Using Pallas backend.")
+        from vllm.attention.backends.pallas import PallasAttentionBackend
+        return PallasAttentionBackend
     else:
         raise ValueError("Invalid attention backend.")
 
@@ -80,7 +88,6 @@ def which_attn_to_use(
     block_size: int,
 ) -> _Backend:
     """Returns which flash attention backend to use."""
-
     # Default case.
     selected_backend = _Backend.FLASH_ATTN
 
@@ -100,6 +107,11 @@ def which_attn_to_use(
             logger.info("Cannot use %s backend on CPU.", selected_backend)
         return _Backend.TORCH_SDPA
 
+    if is_tpu():
+        if selected_backend != _Backend.PALLAS:
+            logger.info("Cannot use %s backend on TPU.", selected_backend)
+        return _Backend.PALLAS
+
     if is_hip():
         # AMD GPUs.
         selected_backend = (_Backend.ROCM_FLASH if selected_backend

vllm/config.py

@@ -11,7 +11,8 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.model_executor.models import ModelRegistry
 from vllm.transformers_utils.config import get_config, get_hf_text_config
-from vllm.utils import get_cpu_memory, is_cpu, is_hip, is_neuron
+from vllm.utils import (cuda_device_count_stateless, get_cpu_memory, is_cpu,
+                        is_hip, is_neuron, is_tpu)
 
 if TYPE_CHECKING:
     from ray.util.placement_group import PlacementGroup
@@ -212,7 +213,7 @@ class ModelConfig:
                     f"{self.quantization} quantization is currently not "
                     f"supported in ROCm.")
             if (self.quantization
-                    not in ["marlin", "gptq_marlin_24", "gptq_marlin"]):
+                    not in ("fp8", "marlin", "gptq_marlin_24", "gptq_marlin")):
                 logger.warning(
                     "%s quantization is not fully "
                     "optimized yet. The speed can be slower than "
@@ -605,12 +606,11 @@ class ParallelConfig:
         if self.distributed_executor_backend is None and self.world_size > 1:
             # We use multiprocessing by default if world_size fits on the
             # current node and we aren't in a ray placement group.
-            from torch.cuda import device_count
 
             from vllm.executor import ray_utils
             backend = "mp"
             ray_found = ray_utils.ray is not None
-            if device_count() < self.world_size:
+            if cuda_device_count_stateless() < self.world_size:
                 if not ray_found:
                     raise ValueError("Unable to load Ray which is "
                                      "required for multi-node inference")
@@ -748,6 +748,8 @@ class DeviceConfig:
             # Automated device type detection
             if is_neuron():
                 self.device_type = "neuron"
+            elif is_tpu():
+                self.device_type = "tpu"
             elif is_cpu():
                 self.device_type = "cpu"
             else:
@@ -761,6 +763,8 @@ class DeviceConfig:
         # Some device types require processing inputs on CPU
         if self.device_type in ["neuron"]:
             self.device = torch.device("cpu")
+        elif self.device_type in ["tpu"]:
+            self.device = None
         else:
             # Set device with device type
             self.device = torch.device(self.device_type)

vllm/core/scheduler.py

@@ -50,8 +50,8 @@ class SchedulingBudget:
     """
     token_budget: int
     max_num_seqs: int
-    _requeset_ids_num_batched_tokens: Set[str] = field(default_factory=set)
-    _requeset_ids_num_curr_seqs: Set[str] = field(default_factory=set)
+    _request_ids_num_batched_tokens: Set[str] = field(default_factory=set)
+    _request_ids_num_curr_seqs: Set[str] = field(default_factory=set)
     _num_batched_tokens: int = 0
     _num_curr_seqs: int = 0
 
@@ -65,28 +65,28 @@ class SchedulingBudget:
         return self.token_budget - self.num_batched_tokens
 
     def add_num_batched_tokens(self, req_id: str, num_batched_tokens: int):
-        if req_id in self._requeset_ids_num_batched_tokens:
+        if req_id in self._request_ids_num_batched_tokens:
             return
 
-        self._requeset_ids_num_batched_tokens.add(req_id)
+        self._request_ids_num_batched_tokens.add(req_id)
         self._num_batched_tokens += num_batched_tokens
 
     def subtract_num_batched_tokens(self, req_id: str,
                                     num_batched_tokens: int):
-        if req_id in self._requeset_ids_num_batched_tokens:
-            self._requeset_ids_num_batched_tokens.remove(req_id)
+        if req_id in self._request_ids_num_batched_tokens:
+            self._request_ids_num_batched_tokens.remove(req_id)
             self._num_batched_tokens -= num_batched_tokens
 
     def add_num_seqs(self, req_id: str, num_curr_seqs: int):
-        if req_id in self._requeset_ids_num_curr_seqs:
+        if req_id in self._request_ids_num_curr_seqs:
             return
 
-        self._requeset_ids_num_curr_seqs.add(req_id)
+        self._request_ids_num_curr_seqs.add(req_id)
         self._num_curr_seqs += num_curr_seqs
 
     def subtract_num_seqs(self, req_id: str, num_curr_seqs: int):
-        if req_id in self._requeset_ids_num_curr_seqs:
-            self._requeset_ids_num_curr_seqs.remove(req_id)
+        if req_id in self._request_ids_num_curr_seqs:
+            self._request_ids_num_curr_seqs.remove(req_id)
             self._num_curr_seqs -= num_curr_seqs
 
     @property

vllm/distributed/communication_op.py

-from collections import namedtuple
-from contextlib import contextmanager, nullcontext
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Union
 
 import torch
-from torch.distributed import ProcessGroup
+import torch.distributed
 
-from .parallel_state import (get_cpu_world_group, get_pp_pynccl_communicator,
-                             get_tensor_model_parallel_group,
-                             get_tensor_model_parallel_rank,
-                             get_tensor_model_parallel_world_size,
-                             get_tp_ca_communicator,
-                             get_tp_pynccl_communicator)
-
-
-@dataclass
-class GraphCaptureContext:
-    stream: torch.cuda.Stream
-
-
-@contextmanager
-def graph_capture():
-    """
-    `graph_capture` is a context manager which should surround the code that
-    is capturing the CUDA graph. Its main purpose is to ensure that the
-    some operations will be run after the graph is captured, before the graph
-    is replayed. It returns a `GraphCaptureContext` object which contains the
-    necessary data for the graph capture. Currently, it only contains the
-    stream that the graph capture is running on. This stream is set to the
-    current CUDA stream when the context manager is entered and reset to the
-    default stream when the context manager is exited. This is to ensure that
-    the graph capture is running on a separate stream from the default stream,
-    in order to explicitly distinguish the kernels to capture
-    from other kernels possibly launched on background in the default stream.
-    """
-    stream = torch.cuda.Stream()
-    graph_capture_context = GraphCaptureContext(stream)
-    ca_comm = get_tp_ca_communicator()
-    maybe_ca_context = nullcontext() if ca_comm is None else ca_comm.capture()
-    with torch.cuda.stream(stream), maybe_ca_context:
-        # In graph mode, we have to be very careful about the collective
-        # operations. The current status is:
-        #     allreduce \ Mode   |  Eager  |  Graph  |
-        # --------------------------------------------
-        # custom allreduce       | enabled | enabled |
-        # PyNccl                 | disabled| enabled |
-        # torch.distributed      | enabled | disabled|
-        #
-        # Note that custom allreduce will have a runtime check, if the tensor
-        #  size is too large, it will fallback to the next available option.
-        # In summary: When using CUDA graph, we use
-        # either custom all-reduce kernel or pynccl. When not using CUDA
-        # graph, we use either custom all-reduce kernel or PyTorch NCCL.
-        # We always prioritize using custom all-reduce kernel but fall back
-        # to PyTorch or pynccl if it is disabled or not supported.
-        tp_pynccl_comm = get_tp_pynccl_communicator()
-        pp_pynccl_comm = get_pp_pynccl_communicator()
-        if not tp_pynccl_comm:
-            maybe_tp_pynccl_context = nullcontext()
-        else:
-            maybe_tp_pynccl_context = tp_pynccl_comm.change_state(
-                enable=True, stream=torch.cuda.current_stream())
-        if not pp_pynccl_comm:
-            maybe_pp_pynccl_context = nullcontext()
-        else:
-            maybe_pp_pynccl_context = pp_pynccl_comm.change_state(
-                enable=True, stream=torch.cuda.current_stream())
-        with maybe_tp_pynccl_context, maybe_pp_pynccl_context:
-            yield graph_capture_context
+from .parallel_state import get_tp_group
 
 
 def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:
-    """All-reduce the input tensor across model parallel group.
-
-    NOTE: This operation will be applied in-place on the input tensor if
-    disable_custom_all_reduce is set to True. Otherwise, this operation may or
-    may not be applied in place depending on whether custom all reduce is
-    invoked for a particular tensor, which further depends on the tensor size
-    and GPU topology.
-
-    TLDR: always assume this function modifies its input, but use the return
-    value as the output.
-    """
-    ca_comm = get_tp_ca_communicator()
-
-    # Bypass the function if we are using only 1 GPU.
-    if get_tensor_model_parallel_world_size() == 1:
-        return input_
-    if ca_comm is not None:
-        out = ca_comm.custom_all_reduce(input_)
-        if out is not None:
-            return out
-    pynccl_comm = get_tp_pynccl_communicator()
-    if (pynccl_comm is not None and not pynccl_comm.disabled):
-        pynccl_comm.all_reduce(input_)
-    else:
-        torch.distributed.all_reduce(input_,
-                                     group=get_tensor_model_parallel_group())
-    return input_
+    """All-reduce the input tensor across model parallel group."""
+    return get_tp_group().all_reduce(input_)
 
 
 def tensor_model_parallel_all_gather(input_: torch.Tensor,
                                      dim: int = -1) -> torch.Tensor:
     """All-gather the input tensor across model parallel group."""
-    world_size = get_tensor_model_parallel_world_size()
-    # Bypass the function if we are using only 1 GPU.
-    if world_size == 1:
-        return input_
-    assert -input_.dim() <= dim < input_.dim(), (
-        f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
-    if dim < 0:
-        # Convert negative dim to positive.
-        dim += input_.dim()
-    input_size = input_.size()
-    # Allocate output tensor.
-    output_tensor = torch.empty((world_size, ) + input_size,
-                                dtype=input_.dtype,
-                                device=input_.device)
-    # All-gather.
-    torch.distributed.all_gather_into_tensor(
-        output_tensor, input_, group=get_tensor_model_parallel_group())
-    # Reshape
-    output_tensor = output_tensor.movedim(0, dim)
-    output_tensor = output_tensor.reshape(input_size[:dim] +
-                                          (world_size * input_size[dim], ) +
-                                          input_size[dim + 1:])
-    return output_tensor
+    return get_tp_group().all_gather(input_, dim)
 
 
 def tensor_model_parallel_gather(input_: torch.Tensor,
                                  dst: int = 0,
                                  dim: int = -1) -> torch.Tensor:
-    """Gather the input tensor across model parallel group.
-
-    NOTE: We assume that the input tensor is on the same device across
-    all the ranks.
-    """
-    world_size = get_tensor_model_parallel_world_size()
-    # Bypass the function if we are using only 1 GPU.
-    if world_size == 1:
-        return input_
-    assert -input_.dim() <= dim < input_.dim(), (
-        f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
-    if dim < 0:
-        # Convert negative dim to positive.
-        dim += input_.dim()
-    # Allocate output tensor.
-    if get_tensor_model_parallel_rank() == dst:
-        gather_list = [torch.empty_like(input_) for _ in range(world_size)]
-    else:
-        gather_list = None
-    # Gather.
-    torch.distributed.gather(input_,
-                             gather_list,
-                             dst=dst,
-                             group=get_tensor_model_parallel_group())
-    if get_tensor_model_parallel_rank() == dst:
-        output_tensor = torch.cat(gather_list, dim=dim)
-    else:
-        output_tensor = None
-    return output_tensor
-
-
-def broadcast(input_: torch.Tensor,
-              src: int = 0,
-              group: Optional[ProcessGroup] = None):
-    """Broadcast the input tensor."""
-    group = group or torch.distributed.group.WORLD
-    ranks = torch.distributed.get_process_group_ranks(group)
-    assert src in ranks, f"Invalid src rank ({src})"
-
-    # Bypass the function if we are using only 1 GPU.
-    world_size = torch.distributed.get_world_size(group=group)
-    if world_size == 1:
-        return input_
-    # Broadcast.
-    torch.distributed.broadcast(input_, src=src, group=group)
-    return input_
+    """Gather the input tensor across model parallel group."""
+    return get_tp_group().gather(input_, dst, dim)
 
 
-def broadcast_object_list(obj_list: List[Any],
-                          src: int = 0,
-                          group: Optional[ProcessGroup] = None):
-    """Broadcast the input object list."""
-    group = group or torch.distributed.group.WORLD
-    ranks = torch.distributed.get_process_group_ranks(group)
-    assert src in ranks, f"Invalid src rank ({src})"
-
-    # Bypass the function if we are using only 1 GPU.
-    world_size = torch.distributed.get_world_size(group=group)
-    if world_size == 1:
-        return obj_list
-    # Broadcast.
-    torch.distributed.broadcast_object_list(obj_list, src=src, group=group)
-    return obj_list
-
-
-TensorMetadata = namedtuple("TensorMetadata", ["device", "dtype", "size"])
-
-
-def _split_tensor_dict(
-    tensor_dict: Dict[Any, Union[torch.Tensor, Any]]
-) -> Tuple[List[Tuple[str, Any]], List[torch.Tensor]]:
-    """Split the tensor dictionary into two parts:
-    1. A list of (key, value) pairs. If the value is a tensor, it is replaced
-         by its metadata.
-    2. A list of tensors.
-    """
-    metadata_list = []
-    tensor_list = []
-    for key, value in tensor_dict.items():
-        if isinstance(value, torch.Tensor):
-            # Note: we cannot use `value.device` here,
-            # because it contains not only the device type but also the device
-            # index (e.g. "cuda:0"). We only need the device type.
-            # receiving side will set the device index.
-            device = "cpu" if value.is_cpu else "cuda"
-            metadata_list.append(
-                (key, TensorMetadata(device, value.dtype, value.size())))
-            tensor_list.append(value)
-        else:
-            metadata_list.append((key, value))
-    return metadata_list, tensor_list
-
-
-def broadcast_tensor_dict(
-    tensor_dict: Optional[Dict[Any, Union[torch.Tensor, Any]]] = None,
-    src: int = 0,
-    group: Optional[ProcessGroup] = None,
-    metadata_group: Optional[ProcessGroup] = None
-) -> Optional[Dict[Any, Union[torch.Tensor, Any]]]:
-    """Broadcast the input tensor dictionary.
-    `group` is used to broadcast the tensors, while `metadata_group` is used
-     to broadcast the metadata of the dict (e.g. dict structure, tensor sizes,
-     dtypes).
-    """
-    # Bypass the function if we are using only 1 GPU.
-    if (not torch.distributed.is_initialized()
-            or torch.distributed.get_world_size(group=group) == 1):
+def broadcast_tensor_dict(tensor_dict: Optional[Dict[Any, Union[torch.Tensor,
+                                                                Any]]] = None,
+                          src: int = 0):
+    if not torch.distributed.is_initialized():
         return tensor_dict
-
-    group = group or torch.distributed.group.WORLD
-    metadata_group = metadata_group or get_cpu_world_group()
-    ranks = torch.distributed.get_process_group_ranks(group)
-    assert src in ranks, f"Invalid src rank ({src})"
-
-    rank = torch.distributed.get_rank()
-    if rank == src:
-        metadata_list: List[Tuple[Any, Any]] = []
-        assert isinstance(
-            tensor_dict,
-            dict), (f"Expecting a dictionary, got {type(tensor_dict)}")
-        metadata_list, tensor_list = _split_tensor_dict(tensor_dict)
-        # `metadata_list` lives in CPU memory.
-        # `broadcast_object_list` involves serialization and deserialization,
-        # all happening on CPU. Therefore, we can use the CPU group.
-        torch.distributed.broadcast_object_list([metadata_list],
-                                                src=src,
-                                                group=metadata_group)
-        async_handles = []
-        for tensor in tensor_list:
-            if tensor.numel() == 0:
-                # Skip broadcasting empty tensors.
-                continue
-            if tensor.is_cpu:
-                # use metadata_group for CPU tensors
-                handle = torch.distributed.broadcast(tensor,
-                                                     src=src,
-                                                     group=metadata_group,
-                                                     async_op=True)
-            else:
-                # use group for GPU tensors
-                handle = torch.distributed.broadcast(tensor,
-                                                     src=src,
-                                                     group=group,
-                                                     async_op=True)
-            async_handles.append(handle)
-        for async_handle in async_handles:
-            async_handle.wait()
-
-    else:
-        recv_metadata_list = [None]
-        torch.distributed.broadcast_object_list(recv_metadata_list,
-                                                src=src,
-                                                group=metadata_group)
-        assert recv_metadata_list[0] is not None
-        tensor_dict = {}
-        async_handles = []
-        for key, value in recv_metadata_list[0]:
-            if isinstance(value, TensorMetadata):
-                tensor = torch.empty(value.size,
-                                     dtype=value.dtype,
-                                     device=value.device)
-                if tensor.numel() == 0:
-                    # Skip broadcasting empty tensors.
-                    tensor_dict[key] = tensor
-                    continue
-                if tensor.is_cpu:
-                    # use metadata_group for CPU tensors
-                    handle = torch.distributed.broadcast(tensor,
-                                                         src=src,
-                                                         group=metadata_group,
-                                                         async_op=True)
-                else:
-                    # use group for GPU tensors
-                    handle = torch.distributed.broadcast(tensor,
-                                                         src=src,
-                                                         group=group,
-                                                         async_op=True)
-                async_handles.append(handle)
-                tensor_dict[key] = tensor
-            else:
-                tensor_dict[key] = value
-        for async_handle in async_handles:
-            async_handle.wait()
-    return tensor_dict
+    return get_tp_group().broadcast_tensor_dict(tensor_dict, src)

vllm/distributed/device_communicators/custom_all_reduce.py

@@ -9,9 +9,9 @@ import vllm.envs as envs
 from vllm import _custom_ops as ops
 from vllm.distributed.device_communicators.custom_all_reduce_utils import (
     gpu_p2p_access_check)
-from vllm.distributed.parallel_state import (
-    get_local_rank, get_tensor_model_parallel_cpu_group, is_in_the_same_node)
+from vllm.distributed.parallel_state import is_in_the_same_node
 from vllm.logger import init_logger
+from vllm.utils import cuda_device_count_stateless
 
 try:
     import pynvml
@@ -86,8 +86,8 @@ class CustomAllreduce:
 
     # max_size: max supported allreduce size
     def __init__(self,
-                 group: Optional[ProcessGroup] = None,
-                 device: Optional[Union[int, str, torch.device]] = None,
+                 group: ProcessGroup,
+                 device: Union[int, str, torch.device],
                  max_size=8192 * 1024) -> None:
         """
         Args:
@@ -107,7 +107,6 @@ class CustomAllreduce:
             # e.g. in a non-cuda environment
             return
 
-        group = group or get_tensor_model_parallel_cpu_group()
         self.group = group
 
         assert dist.get_backend(group) != dist.Backend.NCCL, (
@@ -134,10 +133,7 @@ class CustomAllreduce:
                 world_size, str(CustomAllreduce._SUPPORTED_WORLD_SIZES))
             return
 
-        if device is None:
-            local_rank = get_local_rank()
-            device = torch.device(f"cuda:{local_rank}")
-        elif isinstance(device, int):
+        if isinstance(device, int):
             device = torch.device(f"cuda:{device}")
         elif isinstance(device, str):
             device = torch.device(device)
@@ -149,7 +145,7 @@ class CustomAllreduce:
         if cuda_visible_devices:
             device_ids = list(map(int, cuda_visible_devices.split(",")))
         else:
-            device_ids = list(range(torch.cuda.device_count()))
+            device_ids = list(range(cuda_device_count_stateless()))
 
         physical_device_id = device_ids[device.index]
         tensor = torch.tensor([physical_device_id],

vllm/distributed/device_communicators/custom_all_reduce_utils.py

@@ -11,8 +11,8 @@ import torch.distributed as dist
 import torch.multiprocessing as mp
 
 import vllm.envs as envs
-from vllm.distributed.parallel_state import get_cpu_world_group, get_local_rank
 from vllm.logger import init_logger
+from vllm.utils import cuda_device_count_stateless
 
 logger = init_logger(__name__)
 
@@ -153,7 +153,7 @@ def gpu_p2p_access_check(i: int, j: int) -> bool:
 
     is_distributed = dist.is_initialized()
 
-    num_dev = torch.cuda.device_count()
+    num_dev = cuda_device_count_stateless()
     cuda_visible_devices = envs.CUDA_VISIBLE_DEVICES
     if cuda_visible_devices is None:
         cuda_visible_devices = ",".join(str(i) for i in range(num_dev))
@@ -162,7 +162,8 @@ def gpu_p2p_access_check(i: int, j: int) -> bool:
         f"{VLLM_CONFIG_ROOT}/vllm/gpu_p2p_access_cache_for_{cuda_visible_devices}.json"
     )
     os.makedirs(os.path.dirname(path), exist_ok=True)
-    if ((not is_distributed or get_local_rank() == 0)
+    from vllm.distributed.parallel_state import get_world_group
+    if ((not is_distributed or get_world_group().local_rank == 0)
             and (not os.path.exists(path))):
         # only the local master process (with local_rank == 0) can
         #  enter this block to calculate the cache
@@ -174,8 +175,7 @@ def gpu_p2p_access_check(i: int, j: int) -> bool:
         with open(path, "w") as f:
             json.dump(cache, f, indent=4)
     if is_distributed:
-        cpu_world_group = get_cpu_world_group()
-        dist.barrier(cpu_world_group)
+        get_world_group().barrier()
     logger.info("reading GPU P2P access cache from %s", path)
     with open(path, "r") as f:
         cache = json.load(f)

vllm/distributed/device_communicators/pynccl.py

@@ -9,7 +9,6 @@ from torch.distributed import ProcessGroup, ReduceOp
 from vllm.distributed.device_communicators.pynccl_wrapper import (
     NCCLLibrary, buffer_type, cudaStream_t, ncclComm_t, ncclDataTypeEnum,
     ncclRedOpTypeEnum, ncclUniqueId)
-from vllm.distributed.parallel_state import get_cpu_world_group, get_local_rank
 from vllm.logger import init_logger
 
 logger = init_logger(__name__)
@@ -19,8 +18,8 @@ class PyNcclCommunicator:
 
     def __init__(
         self,
-        group: Optional[ProcessGroup] = None,
-        device: Optional[Union[int, str, torch.device]] = None,
+        group: ProcessGroup,
+        device: Union[int, str, torch.device],
         library_path: Optional[str] = None,
     ):
         """
@@ -35,7 +34,6 @@ class PyNcclCommunicator:
         is bind to a unique device.
         """
         assert dist.is_initialized()
-        group = get_cpu_world_group() if group is None else group
         assert dist.get_backend(group) != dist.Backend.NCCL, (
             "PyNcclCommunicator should be attached to a non-NCCL group.")
         self.group = group
@@ -77,10 +75,7 @@ class PyNcclCommunicator:
         byte_list = tensor.tolist()
         for i, byte in enumerate(byte_list):
             self.unique_id.internal[i] = byte
-        if device is None:
-            local_rank = get_local_rank()
-            device = torch.device(f"cuda:{local_rank}")
-        elif isinstance(device, int):
+        if isinstance(device, int):
             device = torch.device(f"cuda:{device}")
         elif isinstance(device, str):
             device = torch.device(device)

vllm/engine/arg_utils.py

@@ -504,7 +504,7 @@ class EngineArgs:
         parser.add_argument("--device",
                             type=str,
                             default=EngineArgs.device,
-                            choices=["auto", "cuda", "neuron", "cpu"],
+                            choices=["auto", "cuda", "neuron", "cpu", "tpu"],
                             help='Device type for vLLM execution.')
 
         # Related to Vision-language models such as llava

vllm/engine/async_llm_engine.py

@@ -375,6 +375,9 @@ class AsyncLLMEngine:
         if engine_config.device_config.device_type == "neuron":
             from vllm.executor.neuron_executor import NeuronExecutorAsync
             executor_class = NeuronExecutorAsync
+        elif engine_config.device_config.device_type == "tpu":
+            from vllm.executor.tpu_executor import TPUExecutorAsync
+            executor_class = TPUExecutorAsync
         elif engine_config.device_config.device_type == "cpu":
             assert distributed_executor_backend is None, (
                 "Distributed execution is not supported with the CPU backend.")

vllm/engine/llm_engine.py

@@ -6,7 +6,6 @@ from typing import Type, TypeVar, Union
 
 from transformers import GenerationConfig, PreTrainedTokenizer
 
-import vllm
 from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoadConfig,
                          LoRAConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig, SpeculativeConfig,
@@ -38,6 +37,7 @@ from vllm.transformers_utils.tokenizer_group import (BaseTokenizerGroup,
 from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
                                   usage_message)
 from vllm.utils import Counter
+from vllm.version import __version__ as VLLM_VERSION
 
 logger = init_logger(__name__)
 _LOCAL_LOGGING_INTERVAL_SEC = 5
@@ -169,7 +169,7 @@ class LLMEngine:
             "enforce_eager=%s, kv_cache_dtype=%s, "
             "quantization_param_path=%s, device_config=%s, "
             "decoding_config=%r, seed=%d, served_model_name=%s)",
-            vllm.__version__,
+            VLLM_VERSION,
             model_config.model,
             speculative_config,
             model_config.tokenizer,
@@ -341,6 +341,9 @@ class LLMEngine:
         if engine_config.device_config.device_type == "neuron":
             from vllm.executor.neuron_executor import NeuronExecutor
             executor_class = NeuronExecutor
+        elif engine_config.device_config.device_type == "tpu":
+            from vllm.executor.tpu_executor import TPUExecutor
+            executor_class = TPUExecutor
         elif engine_config.device_config.device_type == "cpu":
             from vllm.executor.cpu_executor import CPUExecutor
             executor_class = CPUExecutor

vllm/entrypoints/llm.py

@@ -545,11 +545,13 @@ class LLM:
                 total=num_requests,
                 desc="Processed prompts",
                 dynamic_ncols=True,
-                postfix=f"Generation Speed: {0:.2f} toks/s",
+                postfix=(f"est. speed input: {0:.2f} toks/s, "
+                         f"output: {0:.2f} toks/s"),
             )
         # Run the engine.
         outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = []
-        total_toks = 0
+        total_in_toks = 0
+        total_out_toks = 0
         while self.llm_engine.has_unfinished_requests():
             step_outputs = self.llm_engine.step()
             for output in step_outputs:
@@ -558,10 +560,15 @@ class LLM:
                     if use_tqdm:
                         if isinstance(output, RequestOutput):
                             # Calculate tokens only for RequestOutput
-                            total_toks += sum(
+                            total_in_toks += len(output.prompt_token_ids)
+                            in_spd = total_in_toks / pbar.format_dict["elapsed"]
+                            total_out_toks += sum(
                                 len(stp.token_ids) for stp in output.outputs)
-                            spd = total_toks / pbar.format_dict["elapsed"]
-                            pbar.postfix = f"Generation Speed: {spd:.2f} toks/s"
+                            out_spd = total_out_toks / pbar.format_dict[
+                                "elapsed"]
+                            pbar.postfix = (
+                                f"est. speed input: {in_spd:.2f} toks/s, "
+                                f"output: {out_spd:.2f} toks/s")
                         pbar.update(1)
         if use_tqdm:
             pbar.close()

vllm/entrypoints/openai/api_server.py

@@ -15,7 +15,6 @@ from fastapi.responses import JSONResponse, Response, StreamingResponse
 from prometheus_client import make_asgi_app
 from starlette.routing import Mount
 
-import vllm
 import vllm.envs as envs
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -29,6 +28,7 @@ from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
 from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
 from vllm.logger import init_logger
 from vllm.usage.usage_lib import UsageContext
+from vllm.version import __version__ as VLLM_VERSION
 
 TIMEOUT_KEEP_ALIVE = 5  # seconds
 
@@ -93,7 +93,7 @@ async def show_available_models():
 
 @app.get("/version")
 async def show_version():
-    ver = {"version": vllm.__version__}
+    ver = {"version": VLLM_VERSION}
     return JSONResponse(content=ver)
 
 
@@ -174,7 +174,7 @@ if __name__ == "__main__":
             raise ValueError(f"Invalid middleware {middleware}. "
                              f"Must be a function or a class.")
 
-    logger.info("vLLM API server version %s", vllm.__version__)
+    logger.info("vLLM API server version %s", VLLM_VERSION)
     logger.info("args: %s", args)
 
     if args.served_model_name is not None: