[TPU] Use mark_dynamic to reduce compilation time (#7340)

Dockerfile.tpu

@@ -1,4 +1,4 @@
-ARG NIGHTLY_DATE="20240726"
+ARG NIGHTLY_DATE="20240808"
 ARG BASE_IMAGE="us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_$NIGHTLY_DATE"
 
 FROM $BASE_IMAGE

docs/source/getting_started/tpu-installation.rst

@@ -56,7 +56,7 @@ First, install the dependencies:
     $ pip uninstall torch torch-xla -y
 
     $ # Install PyTorch and PyTorch XLA.
-    $ export DATE="+20240726"
+    $ export DATE="+20240808"
     $ pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-nightly${DATE}-cp310-cp310-linux_x86_64.whl
     $ pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly${DATE}-cp310-cp310-linux_x86_64.whl
 
@@ -65,7 +65,7 @@ First, install the dependencies:
     $ pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
 
     $ # Install other build dependencies.
-    $ pip install packaging aiohttp
+    $ pip install -r requirements-tpu.txt
 
 
 Next, build vLLM from source. This will only take a few seconds:

vllm/worker/tpu_model_runner.py

@@ -147,19 +147,7 @@ class TPUModelRunner(ModelRunnerBase[ModelInputForTPU]):
             )
         model = model.eval()
         xm.wait_device_ops()
-
+        self.model = CompiledModelWrapper(model)
-        model = ModelWrapper(model)
-        # NOTE(woosuk): There are two stages of compilation: torch.compile and
-        # XLA compilation. Setting dynamic=True can reduce the torch.compile
-        # overhead by reusing the FX graph for different shapes.
-        # However, the XLA graph will still require static shapes and needs to
-        # be re-compiled for every different shapes. This overhead is inevitable
-        # in the first run, but can be skipped afterwards as we cache the XLA
-        # graphs in the disk (VLLM_XLA_CACHE_PATH).
-        self.model = torch.compile(model,
-                                   backend="openxla",
-                                   fullgraph=True,
-                                   dynamic=True)
 
     def _dummy_run(
         self,
@@ -697,6 +685,52 @@ class ModelWrapper(nn.Module):
         return next_token_ids
 
 
+class CompiledModelWrapper:
+
+    def __init__(self, model: nn.Module):
+        model = ModelWrapper(model)
+        self.model = torch.compile(model,
+                                   backend="openxla",
+                                   fullgraph=True,
+                                   dynamic=False)
+
+    def __call__(
+        self,
+        token_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+        input_lens: torch.Tensor,
+        t: torch.Tensor,
+        p: torch.Tensor,
+        num_samples: int,
+        kv_caches: List[Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]],
+    ) -> torch.Tensor:
+        # NOTE(woosuk): There are two stages of compilation: torch.compile and
+        # XLA compilation. Using `mark_dynamic` can reduce the torch.compile
+        # overhead by reusing the FX graph for different shapes.
+        # However, the XLA graph will still require static shapes and needs to
+        # be re-compiled for every different shapes. This overhead is inevitable
+        # in the first run, but can be skipped afterwards as we cache the XLA
+        # graphs in the disk (VLLM_XLA_CACHE_PATH).
+        if attn_metadata.num_prefills > 0:
+            # Prefll
+            torch._dynamo.mark_dynamic(token_ids, 1)
+            torch._dynamo.mark_dynamic(position_ids, 1)
+            torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 1)
+        else:
+            # Decode
+            torch._dynamo.mark_dynamic(token_ids, 0)
+            torch._dynamo.mark_dynamic(position_ids, 0)
+            torch._dynamo.mark_dynamic(input_lens, 0)
+            torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
+            torch._dynamo.mark_dynamic(attn_metadata.context_lens, 0)
+            torch._dynamo.mark_dynamic(attn_metadata.block_tables, 0)
+            torch._dynamo.mark_dynamic(t, 0)
+            torch._dynamo.mark_dynamic(p, 0)
+        return self.model(token_ids, position_ids, attn_metadata, input_lens,
+                          t, p, num_samples, kv_caches)
+
+
 def _get_padded_prefill_len(x: int) -> int:
     # NOTE(woosuk): The pallas FlashAttention kernel requires the sequence
     # length to be a multiple of 16. We pad the prompt length to the nearest