[Spec Decoding] Fix API error caused by upstream.

tests/layers/jax/sample/test_rejection_sampler.py

@@ -436,6 +436,9 @@ def run_rejection_sampler_test(
             batch_size=len(num_draft_tokens),
             padded_tokens_length=int(sum(num_draft_tokens)))
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         assert parsed_output == test_case.expected, \
             f"Test '{test_case.name}': Expected {test_case.expected}, got {parsed_output}"
 
@@ -512,6 +515,9 @@ def test_parse_output_basic(self, rejection_sampler):
             batch_size=len(num_draft_tokens),
             padded_tokens_length=int(sum(num_draft_tokens)))
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         expected = [[10, 20, 30, 40], [50, 60, 70]]
         assert parsed_output == expected, f"Expected {expected}, got {parsed_output}"
 
@@ -535,6 +541,9 @@ def test_parse_output_with_placeholders(self, rejection_sampler):
             batch_size=len(num_draft_tokens),
             padded_tokens_length=int(sum(num_draft_tokens)))
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         expected = [[10], [20, 30, 40]]
         assert parsed_output == expected, f"Expected {expected}, got {parsed_output}"
 
@@ -556,6 +565,9 @@ def test_parse_output_invalid_tokens(self, rejection_sampler):
             batch_size=len(num_draft_tokens),
             padded_tokens_length=int(sum(num_draft_tokens)))
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         expected = [[10, 20]]  # Invalid tokens filtered out
         assert parsed_output == expected, f"Expected {expected}, got {parsed_output}"
 
@@ -577,6 +589,9 @@ def test_parse_output_empty_sequences(self, rejection_sampler):
             batch_size=len(num_draft_tokens),
             padded_tokens_length=int(sum(num_draft_tokens)))
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         expected = [[50], [60]]  # Only bonus tokens
         assert parsed_output == expected, f"Expected {expected}, got {parsed_output}"
 
@@ -632,6 +647,9 @@ def test_extreme_padding(self, rejection_sampler, test_helper):
             batch_size=len(num_draft_tokens),
             padded_tokens_length=int(sum(num_draft_tokens)))
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         expected = [[1, 5]]  # Should ignore all padding
         assert parsed_output == expected, f"Expected {expected}, got {parsed_output}"
 
@@ -777,6 +795,9 @@ def test_single_long_sequence(self, rejection_sampler, test_helper):
             batch_size=len(num_draft_tokens),
             padded_tokens_length=int(sum(num_draft_tokens)))
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         expected = [list(range(1, 28)) + [99]]  # Tokens up to mismatch point
         assert parsed_output == expected, f"Expected {expected}, got {parsed_output}"
 
@@ -884,6 +905,9 @@ def test_non_greedy_deterministic_with_seed(self, rejection_sampler,
                 num_draft_tokens_cpu=np.asarray(num_draft_tokens),
                 batch_size=1,
                 padded_tokens_length=4)
+
+            # Convert numpy arrays to lists for comparison
+            parsed_output = [x.tolist() for x in parsed_output]
             outputs.append(parsed_output)
 
         # All outputs should be identical with same seed
@@ -1064,6 +1088,9 @@ def test_non_greedy_empty_sequence(self, rejection_sampler, test_helper):
             batch_size=2,
             padded_tokens_length=0)
 
+        # Convert numpy arrays to lists for comparison
+        parsed_output = [x.tolist() for x in parsed_output]
+
         # Should get bonus tokens for empty sequences
         expected = [[77], [88]]
         assert parsed_output == expected, f"Expected {expected}, got {parsed_output}"
@@ -1152,6 +1179,10 @@ def test_non_greedy_vs_greedy_same_perfect_case(self, rejection_sampler,
         non_greedy_parsed = rejection_sampler.parse_output(
             non_greedy_output, VOCAB_SIZE, np.asarray(num_draft_tokens), 1, 3)
 
+        # Convert numpy arrays to lists for comparison
+        greedy_parsed = [x.tolist() for x in greedy_parsed]
+        non_greedy_parsed = [x.tolist() for x in non_greedy_parsed]
+
         # For perfect match, greedy should have all tokens + bonus
         assert greedy_parsed == [[5, 15, 25, 99]]
 

tests/runner/test_speculative_decoding_manager.py

@@ -321,7 +321,7 @@ def test_propose_eagle3_draft_token_ids(self,
         )
 
         # Inputs
-        sampled_token_ids = [[1], [2]]
+        sampled_token_ids = [np.array([1]), np.array([2])]
         aux_hidden_states = MagicMock()
         attn_metadata = MagicMock()
         attn_metadata.seq_lens.shape = [2]

tpu_inference/layers/jax/sample/rejection_sampler.py

@@ -128,7 +128,7 @@ def parse_output(
         num_draft_tokens_cpu: np.ndarray,
         batch_size: int,
         padded_tokens_length: int,
-    ) -> list[list[int]]:
+    ) -> list[np.ndarray]:
         """Parse the output of the rejection sampler.
 
         Args:
@@ -177,7 +177,7 @@ def parse_output(
             else:
                 seq_tokens = valid_main_tokens
 
-            outputs.append(seq_tokens.tolist())
+            outputs.append(seq_tokens)
             start_idx = end_idx
 
         return outputs

tpu_inference/runner/speculative_decoding_manager.py

@@ -78,7 +78,7 @@ def propose_draft_token_ids(
 
     def propose_eagle3_draft_token_ids(
         self,
-        sampled_token_ids: list[list[int]],
+        sampled_token_ids: list[np.ndarray],
         aux_hidden_states: Optional[tuple[jnp.ndarray, ...]],
         attn_metadata: AttentionMetadata,
         spec_decode_metadata: Optional[SpecDecodeMetadata],
@@ -91,7 +91,7 @@ def propose_eagle3_draft_token_ids(
         req_ids = self.runner.input_batch.req_ids
         next_token_ids: list[int] = []
         for i, token_ids in enumerate(sampled_token_ids):
-            if token_ids:
+            if token_ids.size != 0:
                 # Common case.
                 next_token_id = token_ids[-1]
             else:

tpu_inference/runner/tpu_runner.py

@@ -935,7 +935,7 @@ def _sample_from_logits(
         # Append sampled tokens
         for req_idx, req_state, _ in request_seq_lens:
             sampled_ids = valid_sampled_token_ids[req_idx]
-            if not sampled_ids:
+            if sampled_ids.size == 0:
                 continue
 
             start_idx = self.input_batch.num_tokens_no_spec[req_idx]