[TPU] make ptxla not imported when using tpu_commons (#23081)

Signed-off-by: Chengji Yao <chengjiyao@gmail.com>
Signed-off-by: Chengji Yao <chengjiyao@google.com>
Co-authored-by: Chengji Yao <chengjiyao@gmail.com>

vllm/distributed/device_communicators/tpu_communicator.py

@@ -10,6 +10,7 @@ from torch.distributed import ProcessGroup
 from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.platforms.tpu import USE_TPU_COMMONS
 
 from .base_device_communicator import DeviceCommunicatorBase
 
@@ -18,16 +19,17 @@ USE_RAY = parallel_config = get_current_vllm_config(
 
 logger = init_logger(__name__)
 
-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.executor import ray_utils
+if not USE_TPU_COMMONS:
+    logger.info("tpu_commons 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.executor import ray_utils
 
 
 class TpuCommunicator(DeviceCommunicatorBase):
@@ -94,10 +96,7 @@ class TpuCommunicator(DeviceCommunicatorBase):
         return xm.all_gather(input_, dim=dim)
 
 
-try:
+if USE_TPU_COMMONS:
     from tpu_commons.distributed.device_communicators import (
         TpuCommunicator as TpuCommonsCommunicator)
     TpuCommunicator = TpuCommonsCommunicator  # type: ignore
-except ImportError:
-    logger.info("tpu_commons not found, using vLLM's TpuCommunicator")
-    pass

vllm/model_executor/layers/fused_moe/moe_pallas.py

@@ -3,7 +3,6 @@
 
 import torch
 import torch.nn.functional as F
-import torch_xla.experimental.custom_kernel  # noqa: F401
 
 
 def _histogram(input: torch.Tensor, min: int, max: int) -> torch.Tensor:
@@ -41,6 +40,7 @@ def fused_moe(
         gating_output: [*, num_experts]
     """
     assert expert_map is None, "expert_map is not supported for pallas MoE."
+    import torch_xla.experimental.custom_kernel  # noqa: F401
     orig_shape = hidden_states.shape
     hidden_size = hidden_states.shape[-1]
     num_tokens = hidden_states.shape[:-1].numel()

vllm/model_executor/model_loader/default_loader.py

@@ -207,16 +207,21 @@ class DefaultModelLoader(BaseModelLoader):
             )
 
         if current_platform.is_tpu():
-            # In PyTorch XLA, we should call `xm.mark_step` frequently so that
-            # not too many ops are accumulated in the XLA program.
-            import torch_xla.core.xla_model as xm
+            from vllm.platforms.tpu import USE_TPU_COMMONS
 
-            def _xla_weights_iterator(iterator: Generator):
-                for weights in iterator:
-                    yield weights
-                    xm.mark_step()
+            if not USE_TPU_COMMONS:
+                # In PyTorch XLA, we should call `xm.mark_step`
+                # requently so that not too many ops are accumulated
+                # in the XLA program. import torch_xla.core.xla_model
+                # as xm
+                import torch_xla.core.xla_model as xm
 
-            weights_iterator = _xla_weights_iterator(weights_iterator)
+                def _xla_weights_iterator(iterator: Generator):
+                    for weights in iterator:
+                        yield weights
+                        xm.mark_step()
+
+                weights_iterator = _xla_weights_iterator(weights_iterator)
 
         if self.counter_before_loading_weights == 0.0:
             self.counter_before_loading_weights = time.perf_counter()

vllm/platforms/tpu.py

@@ -24,6 +24,8 @@ else:
 
 logger = init_logger(__name__)
 
+USE_TPU_COMMONS = False
+
 
 class TpuPlatform(Platform):
     _enum = PlatformEnum.TPU
@@ -201,6 +203,7 @@ class TpuPlatform(Platform):
 try:
     from tpu_commons.platforms import TpuPlatform as TpuCommonsPlatform
     TpuPlatform = TpuCommonsPlatform  # type: ignore
+    USE_TPU_COMMONS = True
 except ImportError:
     logger.info("tpu_commons not found, using vLLM's TpuPlatform")
     pass

vllm/v1/attention/backends/pallas.py

@@ -5,12 +5,6 @@ from dataclasses import dataclass
 from typing import Optional
 
 import torch
-import torch_xla.core.xla_builder as xb
-import torch_xla.experimental.custom_kernel  # noqa: F401
-# Required to register custom ops.
-from torch.library import impl
-from torch_xla._internal.jax_workarounds import requires_jax
-from torch_xla.experimental.custom_kernel import XLA_LIB
 
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionLayer, AttentionType)
@@ -37,6 +31,57 @@ TPU_STR_DTYPE_TO_TORCH_DTYPE = {
     "uint8": torch.uint8,
 }
 
+try:
+    import tpu_commons  # 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):
 
@@ -313,46 +358,6 @@ def write_to_kv_cache(
     kv_cache.copy_(new_kv_cache)
 
 
-@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
-
-
 # We can move this function to a common utils file if it's also useful for other
 # hardware.
 def dtype_bits(dtype: torch.dtype):

vllm/v1/worker/tpu_worker.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """A TPU worker class."""
+
 import os
 from typing import Any, Optional
 
 import torch
 import torch.distributed
 import torch.nn as nn
-import torch_xla.core.xla_model as xm
-import torch_xla.debug.profiler as xp
-import torch_xla.runtime as xr
 
 import vllm.envs as envs
 from vllm.config import VllmConfig
@@ -21,19 +19,27 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
 from vllm.platforms import current_platform
+from vllm.platforms.tpu import USE_TPU_COMMONS
 from vllm.tasks import SupportedTask
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
-from vllm.v1.attention.backends.pallas import TPU_HEAD_SIZE_ALIGNMENT
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.kv_cache_interface import (AttentionSpec, KVCacheConfig,
                                         KVCacheSpec)
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.utils import report_usage_stats
-from vllm.v1.worker.tpu_model_runner import TPUModelRunner
 from vllm.v1.worker.utils import bind_kv_cache
 
 logger = init_logger(__name__)
 
+if not USE_TPU_COMMONS:
+    logger.info("tpu_commons 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:
 
@@ -325,9 +331,7 @@ class TPUWorker:
         ensure_kv_transfer_initialized(vllm_config)
 
 
-try:
+if USE_TPU_COMMONS:
     from tpu_commons.worker import TPUWorker as TPUCommonsWorker
+
     TPUWorker = TPUCommonsWorker  # type: ignore
-except ImportError:
-    logger.info("tpu_commons not found, using vLLM's TPUWorker.")
-    pass