[Hardware][TPU] Raise errors for unsupported sampling params (vllm-pr…

…oject#5850)

vllm/worker/tpu_model_runner.py

@@ -20,6 +20,8 @@
 logger = init_logger(__name__)
 
 _PAD_SLOT_ID = 0  # FIXME(woosuk)
+# FIXME(woosuk): Temporarily disabled top-p sampling since it's too slow.
+_ENABLE_TOP_P = False
 
 
 class TPUModelRunner:
@@ -339,9 +341,34 @@ def _prepare_sample(
             assert seq_group_metadata.sampling_params is not None
             sampling_params = seq_group_metadata.sampling_params
 
+            # NOTE(woosuk): Here we mimic argmax sampling by applying a very
+            # low temperature. This is not accurate.
             t.append(sampling_params.temperature
                      if sampling_params.temperature >= 1e-5 else 1e-5)
+            if sampling_params.top_p != 1 and not _ENABLE_TOP_P:
+                raise NotImplementedError(
+                    "Top-p sampling is currently disabled for the TPU backend "
+                    "due to performance issues.")
             p.append(sampling_params.top_p)
+            if sampling_params.top_k != -1:
+                raise NotImplementedError(
+                    "Top-k sampling is currently disabled for the TPU backend "
+                    "due to performance issues.")
+            if sampling_params.best_of > 1:
+                raise NotImplementedError(
+                    "best_of > 1 is not currently supported by the TPU "
+                    "backend.")
+            if sampling_params.use_beam_search:
+                raise NotImplementedError(
+                    "Beam search is not supported by the TPU backend.")
+            if sampling_params.logprobs is not None:
+                raise NotImplementedError(
+                    "logprobs is not currently supported by the TPU backend.")
+            if sampling_params.prompt_logprobs is not None:
+                raise NotImplementedError(
+                    "prompt_logprobs is not currently supported by the TPU "
+                    "backend.")
+
         num_paddings = padded_batch_size - len(seq_group_metadata_list)
         t += [1.0] * num_paddings
         p += [1.0] * num_paddings
@@ -350,35 +377,32 @@ def _prepare_sample(
         p = torch.tensor(p, dtype=torch.float32, device=self.device)
         return t, p
 
-    def prepare_inputs(
+    def _execute_model(
         self,
-        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
-    ):
-        assert seq_group_metadata_list is not None
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
+    ) -> List[CompletionSequenceGroupOutput]:
+        # Prepare inputs.
         assert len(seq_group_metadata_list) > 0
         # NOTE: We assume that all sequences in the group are all prompts or
         # all decodes.
-        if seq_group_metadata_list[0].is_prompt:
+        is_prompt = seq_group_metadata_list[0].is_prompt
+        if is_prompt:
             inputs = self._prepare_prompt(seq_group_metadata_list)
         else:
             inputs = self._prepare_decode(seq_group_metadata_list)
         padded_batch_size = inputs[0].shape[0]
-        sample_inputs = self._prepare_sample(seq_group_metadata_list,
-                                             padded_batch_size)
-        return inputs + sample_inputs
+        t, p = self._prepare_sample(seq_group_metadata_list, padded_batch_size)
 
-    def _execute_model(
-        self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
-        kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
-    ) -> List[CompletionSequenceGroupOutput]:
-        inputs = self.prepare_inputs(seq_group_metadata_list)
+        # Execute the model.
         next_token_ids = self.model(inputs[0], inputs[1], kv_caches,
-                                    *inputs[2:])
-        if not self.is_driver_worker:
-            return []
+                                    *inputs[2:], t, p)
+        # Retrieve the outputs to CPU.
         next_token_ids = next_token_ids.cpu().tolist()
 
+        # NOTE(woosuk): Minimal code to construct the sampler outputs.
+        # The TPU backend does not reuse the sampler, since the TPU backend
+        # does not support the advanced sampling parameters such as logprobs.
         i = 0
         sampler_outputs = []
         for seq_group_metadata in seq_group_metadata_list:
@@ -400,6 +424,7 @@ def execute_model(
         kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
     ) -> SamplerOutput:
         assert seq_group_metadata_list is not None
+        assert len(seq_group_metadata_list) > 0
         if seq_group_metadata_list[0].is_prompt:
             # NOTE(woosuk): To reduce the compilation time, we only compile the
             # prefill inputs with batch size 1. Because the scheduler is not
@@ -492,8 +517,8 @@ def forward(
         logits = self.model.compute_logits(hidden_states, sampling_metadata)
 
         logits = logits / t.unsqueeze(dim=1)
-        # FIXME(woosuk): Disabled top-p sampling since it's too slow.
-        # logits = _apply_top_p(logits, p.unsqueeze(dim=1))
+        if _ENABLE_TOP_P:
+            logits = _apply_top_p(logits, p.unsqueeze(dim=1))
         probs = torch.softmax(logits, dim=-1, dtype=torch.float32)
         # FIXME(woosuk): best_of > 1 is not supported.
         next_token_ids = torch.multinomial(probs, num_samples=1).squeeze(dim=1)