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Remove tpu_inference fall back logic

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Signed-off-by: Wei-Yu Lin <weiyulin@google.com>
This commit is contained in:
Wei-Yu Lin 2025-12-11 19:59:02 +00:00 committed by Wei-Yu Lin
parent 254f6b9867
commit d5cab6f65c
5 changed files with 2 additions and 653 deletions
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14
vllm/distributed/device_communicators/tpu_communicator.py
View File

@ -19,20 +19,6 @@ USE_RAY = parallel_config = (
logger = init_logger(__name__) logger = init_logger(__name__)
if not USE_TPU_INFERENCE:
logger.info("tpu_inference not found, using vLLM's TpuCommunicator")
if current_platform.is_tpu():
import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.runtime as xr
from torch_xla._internal import pjrt
from torch_xla.distributed.xla_multiprocessing import (
create_optimized_replica_groups,
)
if USE_RAY:
from vllm.v1.executor import ray_utils
class TpuCommunicator(DeviceCommunicatorBase): class TpuCommunicator(DeviceCommunicatorBase):
def __init__( def __init__(

13
vllm/model_executor/model_loader/default_loader.py
View File

@ -244,19 +244,6 @@ class DefaultModelLoader(BaseModelLoader):
if current_platform.is_tpu(): if current_platform.is_tpu():
from vllm.platforms.tpu import USE_TPU_INFERENCE from vllm.platforms.tpu import USE_TPU_INFERENCE
if not USE_TPU_INFERENCE:
# In PyTorch XLA, we should call `torch_xla.sync`
# frequently so that not too many ops are accumulated
# in the XLA program.
import torch_xla
def _xla_weights_iterator(iterator: Generator):
for weights in iterator:
yield weights
torch_xla.sync(wait=False)
weights_iterator = _xla_weights_iterator(weights_iterator)
if self.counter_before_loading_weights == 0.0: if self.counter_before_loading_weights == 0.0:
self.counter_before_loading_weights = time.perf_counter() self.counter_before_loading_weights = time.perf_counter()
# Apply the prefix. # Apply the prefix.

253
vllm/platforms/tpu.py
View File

@ -31,257 +31,6 @@ else:
logger = init_logger(__name__) logger = init_logger(__name__)
USE_TPU_INFERENCE = False
class TpuPlatform(Platform):
_enum = PlatformEnum.TPU
device_name: str = "tpu"
device_type: str = "tpu"
dispatch_key: str = "XLA"
ray_device_key: str = "TPU"
dist_backend: str = "gloo"
device_control_env_var: str = "TPU_VISIBLE_CHIPS"
simple_compile_backend: str = "openxla"
supported_quantization: list[str] = ["fp8", "tpu_int8", "compressed-tensors"]
additional_env_vars: list[str] = ["TPU_CHIPS_PER_HOST_BOUNDS", "TPU_HOST_BOUNDS"]
@classmethod
def import_kernels(cls) -> None:
# Do not import vllm._C
with contextlib.suppress(ImportError):
import vllm._moe_C # noqa: F401
@classmethod
def get_attn_backend_cls(
cls,
selected_backend: "AttentionBackendEnum",
attn_selector_config: "AttentionSelectorConfig",
) -> str:
if attn_selector_config.use_sparse:
raise NotImplementedError("Sparse Attention is not supported on TPU.")
if selected_backend != AttentionBackendEnum.PALLAS:
logger.info("Cannot use %s backend on TPU.", selected_backend)
logger.info("Using Pallas V1 backend.")
return AttentionBackendEnum.PALLAS.get_path()
@classmethod
def get_supported_vit_attn_backends(cls) -> list["AttentionBackendEnum"]:
return [
AttentionBackendEnum.PALLAS,
]
@classmethod
def get_vit_attn_backend(
cls,
head_size: int,
dtype: torch.dtype,
backend: Optional["AttentionBackendEnum"] = None,
) -> "AttentionBackendEnum":
if backend is not None:
assert backend in cls.get_supported_vit_attn_backends(), (
f"Backend {backend} is not supported for vit attention"
f"Supported backends are: {cls.get_supported_vit_attn_backends()}."
)
logger.info_once(f"Using backend {backend} for vit attention.")
return backend
logger.info_once(
f"Using default backend {AttentionBackendEnum.PALLAS} for vit attention."
)
return AttentionBackendEnum.PALLAS
@classmethod
def set_device(cls, device: torch.device) -> None:
"""
Set the device for the current platform.
"""
torch.tpu.set_device(device)
@classmethod
def get_device_name(cls, device_id: int = 0) -> str:
chip_type, _ = device.get_local_chips()
return f"TPU {chip_type.name}"
@classmethod
def get_device_total_memory(cls, device_id: int = 0) -> int:
raise NotImplementedError
@classmethod
def get_punica_wrapper(cls) -> str:
return "vllm.lora.punica_wrapper.punica_tpu.PunicaWrapperTPU"
@classmethod
def get_infinity_values(cls, dtype: torch.dtype) -> tuple[float, float]:
return torch.finfo(dtype).min, torch.finfo(dtype).max
@classmethod
def can_update_inplace(cls):
return False
@classmethod
def get_lora_vocab_padding_size(cls) -> int:
return 1
@classmethod
def inference_mode(cls):
return torch.no_grad()
@classmethod
def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
from vllm.config import CompilationMode, CUDAGraphMode
cache_config = vllm_config.cache_config
# For v0, the default block size is 16.
if cache_config and cache_config.block_size is None:
cache_config.block_size = cast(BlockSize, 16)
compilation_config = vllm_config.compilation_config
# TPU only supports DYNAMO_TRACE_ONCE compilation mode
if compilation_config.mode != CompilationMode.DYNAMO_TRACE_ONCE:
logger.info(
"[TPU] Forcing DYNAMO_TRACE_ONCE compilation mode, and\
disabling cudagraph."
)
compilation_config.mode = CompilationMode.DYNAMO_TRACE_ONCE
if (
compilation_config.cudagraph_mode is None
or compilation_config.cudagraph_mode.max_cudagraph_mode()
!= CUDAGraphMode.NONE
):
logger.info(
"[TPU] CUDA graph is not supported on TPU, disabling cudagraphs."
)
compilation_config.cudagraph_mode = CUDAGraphMode.NONE
if compilation_config.backend == "":
compilation_config.backend = "openxla"
assert vllm_config.speculative_config is None, (
"TPU does not support speculative decoding"
)
model_config = vllm_config.model_config
if model_config is not None and model_config.dtype in (
torch.float16,
torch.float32,
):
logger.warning(
"The TPU backend currently does not support %s. "
"Using bfloat16 instead.",
model_config.dtype,
)
model_config.dtype = torch.bfloat16
from vllm.v1.attention.backends.pallas import PallasAttentionBackend
cache_config.block_size = PallasAttentionBackend.get_page_size(vllm_config) # type: ignore[assignment]
parallel_config = vllm_config.parallel_config
scheduler_config = vllm_config.scheduler_config
if parallel_config.worker_cls == "auto":
parallel_config.worker_cls = "vllm.v1.worker.tpu_worker.TPUWorker"
assert not vllm_config.speculative_config, (
"Speculative decoding is not yet supported for TPU backend"
)
if (
scheduler_config.is_multimodal_model
and not scheduler_config.disable_chunked_mm_input
):
logger.warning(
"TPU does not support running Multimodal models"
" without setting `--disable_chunked_mm_input`. "
"Forcing --disable_chunked_mm_input."
)
scheduler_config.disable_chunked_mm_input = True
if model_config and model_config.use_mla:
logger.info(
"MLA is enabled on a non-GPU platform; forcing chunked "
"prefill and prefix caching to be disabled."
)
vllm_config.scheduler_config.enable_chunked_prefill = False
vllm_config.scheduler_config.max_num_batched_tokens = max(
vllm_config.model_config.max_model_len,
vllm_config.scheduler_config.DEFAULT_MAX_NUM_BATCHED_TOKENS,
)
@classmethod
def is_pin_memory_available(cls):
logger.warning("Pin memory is not supported on TPU.")
return False
@classmethod
def get_device_communicator_cls(cls) -> str:
return "vllm.distributed.device_communicators.tpu_communicator.TpuCommunicator" # noqa
@classmethod
def validate_request(
cls,
prompt: PromptType,
params: ParamsType,
processed_inputs: ProcessorInputs,
) -> None:
"""Raises if this request is unsupported on this platform"""
from vllm.sampling_params import SamplingParams, SamplingType
if (
isinstance(params, SamplingParams)
and params.sampling_type == SamplingType.RANDOM_SEED
):
raise ValueError("Torch XLA does not support per-request seed.")
@classmethod
@torch.compile(backend="openxla")
def insert_blocks_to_device(
cls,
src_cache: torch.Tensor,
dst_cache: torch.Tensor,
src_block_indices: torch.Tensor,
dst_block_indices: torch.Tensor,
) -> None:
torch.ops.xla.dynamo_set_buffer_donor_(dst_cache, True)
dst_cache[dst_block_indices] = src_cache[src_block_indices].to(dst_cache.device)
@classmethod
@torch.compile(backend="openxla")
def swap_out_blocks_to_host(
cls,
src_cache: torch.Tensor,
dst_cache: torch.Tensor,
src_block_indices: torch.Tensor,
dst_block_indices: torch.Tensor,
) -> None:
"""tpu blocks to cpu blocks"""
torch.ops.xla.dynamo_set_buffer_donor_(src_cache, True)
dst_cache[dst_block_indices] = src_cache[src_block_indices].cpu()
@classmethod
def use_sync_weight_loader(cls) -> bool:
return True
@classmethod
def check_max_model_len(cls, max_model_len: int) -> int:
"""
Check max_model_len for the current platform.
"""
logger.warning(
"--max-model-len is not specified, "
"it's currently using model's default length %d, "
"which might be too large."
"Please input with --max-model-len based on your "
"request input length and output length, to avoid "
"unnecessary degradation.",
max_model_len,
)
return max_model_len
try: try:
from tpu_inference.platforms import ( from tpu_inference.platforms import (
@ -291,5 +40,5 @@ try:
TpuPlatform = TpuInferencePlatform # type: ignore TpuPlatform = TpuInferencePlatform # type: ignore
USE_TPU_INFERENCE = True USE_TPU_INFERENCE = True
except ImportError: except ImportError:
logger.info("tpu_inference not found, using vLLM's TpuPlatform") logger.error("tpu_inference not found, please install tpu_inference to run vllm on TPU")
pass pass

65
vllm/v1/attention/backends/pallas.py
View File

@ -33,70 +33,7 @@ TPU_STR_DTYPE_TO_TORCH_DTYPE = {
"uint8": torch.uint8, "uint8": torch.uint8,
} }
try: import tpu_inference # noqa: F401
import tpu_inference # noqa: F401
except ImportError:
# Lazy import torch_xla
import torch_xla.core.xla_builder as xb
import torch_xla.experimental.custom_kernel # noqa: F401
from torch.library import impl
from torch_xla._internal.jax_workarounds import requires_jax
from torch_xla.experimental.custom_kernel import XLA_LIB
@requires_jax
def kv_cache_update_op_impl(
kv: torch.Tensor,
slot_mapping: torch.Tensor,
kv_cache: torch.Tensor,
num_kv_update_slices: torch.Tensor,
page_size: int,
num_slices_per_block: int,
):
from vllm.attention.ops.pallas_kv_cache_update import kv_cache_update
new_kv_cache = xb.call_jax(
kv_cache_update,
(kv, slot_mapping, kv_cache, num_kv_update_slices),
{"page_size": page_size, "num_slices_per_block": num_slices_per_block},
)
return new_kv_cache
XLA_LIB.define(
"kv_cache_update_op(Tensor kv, Tensor slot_mapping,"
"Tensor kv_cache, Tensor num_kv_update_slices, int page_size,"
"int num_slices_per_block)"
"-> Tensor",
)
@impl(XLA_LIB, "kv_cache_update_op", "XLA")
def kv_cache_update_op_xla(
kv: torch.Tensor,
slot_mapping: torch.Tensor,
kv_cache: torch.Tensor,
num_kv_update_slices: torch.Tensor,
page_size: int,
num_slices_per_block: int,
) -> torch.Tensor:
new_kv_cache = kv_cache_update_op_impl(
kv,
slot_mapping,
kv_cache,
num_kv_update_slices,
page_size,
num_slices_per_block,
)
return new_kv_cache
@impl(XLA_LIB, "kv_cache_update_op", "CompositeExplicitAutograd")
def kv_cache_update_op_non_xla(
kv: torch.Tensor,
slot_mapping: torch.Tensor,
kv_cache: torch.Tensor,
num_kv_update_slices: torch.Tensor,
page_size: int,
num_slices_per_block: int,
) -> torch.Tensor:
return kv_cache
class PallasAttentionBackend(AttentionBackend): class PallasAttentionBackend(AttentionBackend):

310
vllm/v1/worker/tpu_worker.py
View File

@ -36,316 +36,6 @@ logger = init_logger(__name__)
_R = TypeVar("_R") _R = TypeVar("_R")
if not USE_TPU_INFERENCE:
logger.info("tpu_inference not found, using vLLM's TPUWorker.")
import torch_xla.core.xla_model as xm
import torch_xla.debug.profiler as xp
import torch_xla.runtime as xr
from vllm.v1.attention.backends.pallas import TPU_HEAD_SIZE_ALIGNMENT
from vllm.v1.worker.tpu_model_runner import TPUModelRunner
class TPUWorker:
def __init__(
self,
vllm_config: VllmConfig,
local_rank: int,
rank: int,
distributed_init_method: str,
is_driver_worker: bool = False,
):
self.is_driver_worker = is_driver_worker
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.cache_config = vllm_config.cache_config
self.lora_config = vllm_config.lora_config
self.load_config = vllm_config.load_config
self.parallel_config = vllm_config.parallel_config
self.use_spmd = envs.VLLM_XLA_USE_SPMD
self.original_parallel_config = None
if self.use_spmd:
# Under SPMD mode, distributed env is initialized as if there is
# only one worker/device.
self.original_parallel_config = self.parallel_config
self.parallel_config.tensor_parallel_size = 1
self.parallel_config.pipeline_parallel_size = 1
self.parallel_config.world_size = 1
self.scheduler_config = vllm_config.scheduler_config
self.device_config = vllm_config.device_config
self.speculative_config = vllm_config.speculative_config
self.observability_config = vllm_config.observability_config
self.parallel_config.rank = rank
self.local_rank = local_rank
self.rank = rank
self.distributed_init_method = distributed_init_method
if self.cache_config.cache_dtype == "auto":
self.cache_dtype = self.model_config.dtype
else:
self.cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[self.cache_config.cache_dtype]
if self.model_config.trust_remote_code:
# note: lazy import to avoid importing torch before initializing
from vllm.utils.import_utils import init_cached_hf_modules
init_cached_hf_modules()
# Delay profiler initialization to the start of the profiling.
# This is because in vLLM V1, MP runtime is initialized before the
# TPU Worker is initialized. The profiler server needs to start after
# MP runtime is initialized.
self.profiler = None
self.profile_dir = None
if vllm_config.profiler_config.profiler == "torch" and self.rank < 1:
# For TPU, we can only have 1 active profiler session for 1 profiler
# server. So we only profile on rank0.
self.profile_dir = vllm_config.profiler_config.torch_profiler_dir
logger.info(
"Profiling enabled. Traces will be saved to: %s", self.profile_dir
)
def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None:
self.cache_config.num_gpu_blocks = num_gpu_blocks
self.cache_config.num_cpu_blocks = num_cpu_blocks
def init_device(self):
os.environ["PJRT_DEVICE"] = "TPU"
# Note: Currently the XLA compiler wrongly uses 2D ring strategy on 1D
# ring, the xla tpu compiler flag
# `xla_tpu_force_1d_allreduce_at_chunk_count` is a temporary solution to
# fix this. It will be removed after the bug in XLA compiler is fixed.
os.environ["LIBTPU_INIT_ARGS"] = (
os.environ.get("LIBTPU_INIT_ARGS", "")
+ " --xla_tpu_force_1d_allreduce_at_chunk_count=1"
" --xla_jf_conv_input_fusion=False"
)
# --xla_jf_conv_input_fusion=False is used to improve the perf of
# quantized matmul.
torch.set_grad_enabled(False)
torch.set_default_dtype(self.model_config.dtype)
# Initialize the distributed environment.
self._init_tpu_worker_distributed_environment(
self.vllm_config, self.rank, self.distributed_init_method, self.local_rank
)
# Device initialization should happen after initializing
# the distributed runtime.
self.device = xm.xla_device()
self.device_config.device = self.device
# Set random seed.
set_random_seed(self.model_config.seed)
xm.set_rng_state(self.model_config.seed, self.device)
# Increase the cache size limit, which is the maximum number of
# dynamo graphs that can be compiled.
# TODO (NickLucche) On gsm we compile 80+ graphs.
# Re-evaluate limit, with MM we may get close to this limit.
torch._dynamo.config.cache_size_limit = 128
# Use persistent cache to avoid XLA recompilation.
# NOTE(woosuk): Set per-rank cache path since different ranks
# can have slightly different XLA graphs.
world_size = self.parallel_config.world_size
rank = xr.global_ordinal()
# The PyTorch/XLA compilation cache uses the Torch IR to generate keys.
# Consequently, changes in optimization flags, which affect compilation
# results, don't change the cache key. This can result in the wrong
# compilation being used. To prevent this, disabling the XLA compilation
# cache during development is recommended.We can disable it by
# `export VLLM_XLA_CACHE_PATH=`
if envs.VLLM_XLA_CACHE_PATH:
per_rank_path = os.path.join(
envs.VLLM_XLA_CACHE_PATH, f"tp{world_size}_rank{rank}"
)
xr.initialize_cache(per_rank_path, readonly=False)
# Init ModelRunner here, so that we have access to self.device.
self.model_runner = TPUModelRunner(
self.vllm_config, self.device, self.original_parallel_config
)
if rank == 0:
# If usage stat is enabled, collect relevant info.
report_usage_stats(self.vllm_config)
def determine_available_memory(self) -> int:
kv_caches: dict[str, torch.Tensor] = {}
kv_cache_spec = self.model_runner.get_kv_cache_spec()
for layer_name, layer_spec in kv_cache_spec.items():
if isinstance(layer_spec, AttentionSpec):
dtype = layer_spec.dtype
# Use an empty tensor instead of `None` to force Dynamo to pass
# it by reference, rather by specializing on the value `None`.
tpu_kv_cache = torch.tensor([], dtype=dtype).to(self.device)
kv_caches[layer_name] = tpu_kv_cache
else:
raise NotImplementedError(
f"Unsupported KV cache spec '{type(layer_spec)}'"
)
runner_kv_caches: list[torch.Tensor] = []
bind_kv_cache(
kv_caches,
self.vllm_config.compilation_config.static_forward_context,
runner_kv_caches,
)
# `max_num_tokens >= max_num_batched_tokens` due to padding.
with self.model_runner.maybe_setup_dummy_loras(self.lora_config):
self.model_runner.profile_run(self.model_runner.max_num_tokens)
# Synchronize before measuring the memory usage.
xm.wait_device_ops()
# During the profiling run, the model runs without KV cache. After
# the profiling run, the model always runs with KV cache. Here we clear
# the dynamo cache and cached bytecode to ensure the model always has
# one compiled bytecode. Having one FX graph/cached bytecode per
# compiled model is required for `support_torch_compile` decorator to
# skip dynamo guard.
with set_current_vllm_config(self.vllm_config):
self.model_runner.reset_dynamo_cache()
# Get the maximum amount of memory used by the model weights and
# intermediate activations.
if self.use_spmd:
# This is a workaround for the TPU SPMD mode. The get_memory_info
# API doesn't work with SPMD mode in PyTorch/XLA.
# TODO: use xm.get_memory_info for SPMD once it's supported in
# PyTorch/XLA.
import tpu_info
chip_type, _ = tpu_info.device.get_local_chips()
device_usage = tpu_info.metrics.get_chip_usage(chip_type)
total_memory_size = device_usage[0].total_memory
current_mem = device_usage[0].memory_usage
else:
m = xm.get_memory_info(self.device)
total_memory_size = m["bytes_limit"]
current_mem = m["bytes_used"]
# Ideally we would use profiled = m["peak_bytes_used"] to
# get weights + activations. But there is memory used during
# compilation / weight loading that impacts the peak and
# there is no way to reset peak memory in XLA, So we
# use the heuristic of 2% of weights.
profiled = current_mem * 1.02
# Calculate the TPU KV cache size based on profiling.
usable_memory_size = int(
total_memory_size * self.cache_config.gpu_memory_utilization
)
tpu_kv_cache_bytes = max(usable_memory_size - profiled, 0)
head_size = self.model_config.get_head_size()
if head_size > 0:
padded_head_size = (
cdiv(head_size, TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
)
if padded_head_size != head_size:
logger.warning_once("head size is padded to %d", padded_head_size)
# We adjust the usable memory size for the KV cache to prevent OOM
# errors, even after padding the head_size.
tpu_kv_cache_bytes = tpu_kv_cache_bytes * head_size // padded_head_size
return int(tpu_kv_cache_bytes)
def sample_tokens(self, grammar_output: "GrammarOutput") -> ModelRunnerOutput:
return self.model_runner.sample_tokens(grammar_output)
def execute_model(
self, scheduler_output: "SchedulerOutput"
) -> ModelRunnerOutput | None:
return self.model_runner.execute_model(scheduler_output)
def profile(self, is_start: bool = True):
if self.rank < 1:
if self.profile_dir is None:
raise RuntimeError("Profiler is not enabled.")
if is_start:
if self.profiler is None:
self.profiler = xp.start_server(9012)
xp.start_trace(self.profile_dir)
else:
xp.stop_trace()
def add_lora(self, lora_request: LoRARequest) -> bool:
return self.model_runner.add_lora(lora_request)
def load_model(self) -> None:
self.model_runner.load_model()
def update_config(self, overrides: dict[str, Any]) -> None:
self.model_runner.update_config(overrides)
def reload_weights(self) -> None:
self.model_runner.reload_weights()
def compile_or_warm_up_model(self) -> None:
if not self.model_config.enforce_eager:
self.model_runner.capture_model()
# Reset the seed to ensure that the random state is not affected by
# the model initialization and profiling.
set_random_seed(self.model_config.seed)
def reset_mm_cache(self) -> None:
self.model_runner.reset_mm_cache()
def get_model(self) -> nn.Module:
return self.model_runner.get_model()
def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
return self.model_runner.get_supported_tasks()
def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
return self.model_runner.get_kv_cache_spec()
def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None:
"""Allocate GPU KV cache with the specified kv_cache_config."""
self.model_runner.initialize_kv_cache(kv_cache_config)
def check_health(self) -> None:
# worker will always be healthy as long as it's running.
return
def _init_tpu_worker_distributed_environment(
self,
vllm_config: VllmConfig,
rank: int,
distributed_init_method: str | None = None,
local_rank: int = -1,
) -> None:
"""Initialize the distributed environment."""
if self.use_spmd:
xr.use_spmd()
# NOTE(woosuk): This is just to initialize the TP group and broadcast
# the input objects on CPU. The all-reduce and all-gather ops on TPU
# are invoked by `xm.all_reduce` and `xm.all_gather` which use their
# own context.
parallel_config = vllm_config.parallel_config
init_distributed_environment(
world_size=parallel_config.world_size,
rank=rank,
local_rank=local_rank,
distributed_init_method=distributed_init_method or "env://",
backend=current_platform.dist_backend,
)
ensure_model_parallel_initialized(
parallel_config.tensor_parallel_size, parallel_config.pipeline_parallel_size
)
ensure_kv_transfer_initialized(vllm_config)
def shutdown(self) -> None:
self.model_runner.ensure_kv_transfer_shutdown()
def apply_model(self, fn: Callable[[nn.Module], _R]) -> _R:
"""Apply a function on the model inside this worker."""
return fn(self.get_model())
if USE_TPU_INFERENCE: if USE_TPU_INFERENCE:
from tpu_inference.worker.tpu_worker import TPUWorker as TpuInferenceWorker from tpu_inference.worker.tpu_worker import TPUWorker as TpuInferenceWorker