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[Hardware][TPU] Optionally import for TPU backend (#18269)
Signed-off-by: Siyuan Liu <lsiyuan@google.com> Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com> Co-authored-by: Carol Zheng <cazheng@google.com> Co-authored-by: Jade Zheng <zheng.shoujian@outlook.com> Co-authored-by: Hongmin Fan <fanhongmin@google.com>
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@ -91,3 +91,12 @@ class TpuCommunicator(DeviceCommunicatorBase):
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def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
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assert dim == -1, "TPUs only support dim=-1 for all-gather."
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return xm.all_gather(input_, dim=dim)
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try:
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from tpu_commons.distributed.device_communicators import (
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TpuCommunicator as TpuCommonsCommunicator)
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TpuCommunicator = TpuCommonsCommunicator # type: ignore
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except ImportError:
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logger.info("tpu_commons not found, using vLLM's TpuCommunicator")
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pass
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@ -194,3 +194,11 @@ class TpuPlatform(Platform):
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if params.sampling_type == SamplingType.RANDOM_SEED:
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raise ValueError(
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"Torch XLA does not support per-request seed.")
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try:
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from tpu_commons.platforms import TpuPlatform as TpuCommonsPlatform
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TpuPlatform = TpuCommonsPlatform # type: ignore
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except ImportError:
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logger.info("tpu_commons not found, using vLLM's TpuPlatform")
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pass
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@ -267,3 +267,11 @@ def init_tpu_worker_distributed_environment(
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ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
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parallel_config.pipeline_parallel_size,
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parallel_config.enable_expert_parallel)
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try:
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from tpu_commons.worker import TPUWorker as TPUCommonsWorker
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TPUWorker = TPUCommonsWorker # type: ignore
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except ImportError:
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logger.info("tpu_commons not found, using vLLM's TPUWorker.")
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pass
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