[V1][TPU] Speed up top-k on TPU by using torch.topk (#15242)

Signed-off-by: Hyesoo Yang <hyeygit@gmail.com>

tests/v1/tpu/test_sampler.py

@@ -39,7 +39,7 @@ def test_sampler_compilation(model_name: str, monkeypatch):
         sampling_params = SamplingParams(
             temperature=0.7,
             # top_p=0.6, # TODO too slow!
-            # top_k=10,
+            top_k=10,
             min_p=0.2,
             max_tokens=16)
         s = time()
@@ -49,6 +49,7 @@ def test_sampler_compilation(model_name: str, monkeypatch):
         # Second request with different params, but for which we
         # compiled for in previous eager iteration.
         sampling_params = SamplingParams(temperature=0.1,
+                                         top_k=12,
                                          min_p=0.8,
                                          max_tokens=24)
         s = time()

vllm/envs.py

@@ -95,6 +95,7 @@ if TYPE_CHECKING:
     VLLM_DP_MASTER_PORT: int = 0
     VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
     VLLM_V0_USE_OUTLINES_CACHE: bool = False
+    VLLM_TPU_DISABLE_TOPK_TOPP_OPTIMIZATION: bool = False
 
 
 def get_default_cache_root():
@@ -623,6 +624,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # an environment with potentially malicious users.
     "VLLM_V0_USE_OUTLINES_CACHE":
     lambda: os.environ.get("VLLM_V0_USE_OUTLINES_CACHE", "0") == "1",
+
+    # If set, disables TPU-specific optimization for top-k & top-p sampling
+    "VLLM_TPU_DISABLE_TOPK_TOPP_OPTIMIZATION":
+    lambda: bool(int(os.environ["VLLM_TPU_DISABLE_TOPK_TOPP_OPTIMIZATION"]))
+    if "VLLM_TPU_DISABLE_TOPK_TOPP_OPTIMIZATION" in os.environ else None,
 }
 
 # end-env-vars-definition

vllm/v1/sample/ops/topk_topp_sampler.py

@@ -66,7 +66,14 @@ class TopKTopPSampler(nn.Module):
                     "best performance, please install FlashInfer.")
                 self.forward = self.forward_native
         elif current_platform.is_tpu():
-            self.forward = self.forward_tpu
+            if envs.VLLM_TPU_DISABLE_TOPK_TOPP_OPTIMIZATION:
+                logger.warning(
+                    "TPU-specific optimization for top-k & top-p sampling are "
+                    "disabled, falling back to PyTorch-native implementation "
+                    "which could be very slow.")
+                self.forward = self.forward_native
+            else:
+                self.forward = self.forward_tpu
         else:
             self.forward = self.forward_native
 
@@ -105,8 +112,19 @@ class TopKTopPSampler(nn.Module):
         k: Optional[torch.Tensor],
         p: Optional[torch.Tensor],
     ) -> torch.Tensor:
-        # TODO Placeholder for TPU optimized topk/p kernel
-        # logits = apply_top_k_top_p(logits, k, p)
+        # If only top-k is specified, use pytorch's builtin topk op. This leads
+        # to significant speed up on TPU compared to using apply_top_k_top_p.
+        if k is not None and p is None:
+            topk_values, topk_indices = torch.topk(logits, k, dim=-1)
+
+            mask = torch.ones_like(logits, dtype=torch.bool)
+            mask.scatter_(-1, topk_indices, False)
+            logits.masked_fill_(mask, float('-inf'))
+        else:
+            # TODO Placeholder for TPU optimized topp kernel
+            # logits = apply_top_k_top_p(logits, k, p)
+            pass
+
         probs = logits.softmax(dim=-1, dtype=torch.float32)
         return random_sample(probs, generators)