[SpecDecode] [Minor] Fix spec decode sampler tests (#7183)

tests/samplers/test_rejection_sampler.py

@@ -25,7 +25,7 @@ def mock_causal_accepted_tensor(
 
     accepted = (torch.arange(k).expand(batch_size, k) <=
                 last_accepted_indices.unsqueeze(-1).broadcast_to(
-                    batch_size, k)).to(device="cuda")
+                    batch_size, k))
 
     # Sprinkle accepted values after the contiguous initial accepted values.
     # This replicates the behavior of rejection sampling, which may "accept"
@@ -33,7 +33,7 @@ def mock_causal_accepted_tensor(
     sprinkle_candidates = (
         torch.arange(k).expand(batch_size, k) >
         last_accepted_indices.unsqueeze(-1).broadcast_to(batch_size, k) + 1)
-    sprinkle = torch.rand(batch_size, k, device="cuda") > 0.5
+    sprinkle = torch.rand(batch_size, k) > 0.5
     accepted[sprinkle_candidates] = sprinkle[sprinkle_candidates]
     return accepted
 
@@ -86,7 +86,7 @@ def test_correct_output_format(which_tokens_accepted: str,
 
     rejection_sampler = RejectionSampler(
         disable_bonus_tokens=disable_bonus_tokens)
-    rejection_sampler.init_gpu_tensors(rank=0)
+    rejection_sampler.init_gpu_tensors(device=device)
     output_token_ids = rejection_sampler._create_output(  # pylint: disable=protected-access
         accepted,
         recovered_token_ids,
@@ -138,7 +138,7 @@ def test_no_crash_with_varying_dims(k: int, vocab_size: int, batch_size: int,
                                     device: str):
     torch.set_default_device(device)
     rejection_sampler = RejectionSampler()
-    rejection_sampler.init_gpu_tensors(rank=0)
+    rejection_sampler.init_gpu_tensors(device=device)
 
     draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
@@ -167,7 +167,7 @@ def test_deterministic_when_seeded(k: int, vocab_size: int, batch_size: int,
                                    device: str):
     torch.set_default_device(device)
     rejection_sampler = RejectionSampler()
-    rejection_sampler.init_gpu_tensors(rank=0)
+    rejection_sampler.init_gpu_tensors(device=device)
 
     draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
@@ -211,7 +211,7 @@ def test_raises_when_vocab_oob(above_or_below_vocab_range: str,
     torch.set_default_device(device)
 
     rejection_sampler = RejectionSampler(strict_mode=True)
-    rejection_sampler.init_gpu_tensors(rank=0)
+    rejection_sampler.init_gpu_tensors(device=device)
 
     draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
@@ -339,7 +339,7 @@ class _CorrectnessTestHelper:
         self.vocab_size = vocab_size
         self.vocab_range = (0, vocab_size)
 
-        self.rejection_sampler.init_gpu_tensors(rank=0)
+        self.rejection_sampler.init_gpu_tensors(device=0)
 
         # Keep test simple, use k=1
         self.k = 1

tests/samplers/test_typical_acceptance_sampler.py

@@ -78,7 +78,7 @@ def test_no_crash_with_varying_dims(k: int, vocab_size: int, batch_size: int,
     """
     torch.set_default_device(device)
     typical_acceptance_sampler = get_acceptance_sampler()
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     bonus_token_ids = torch.randint(low=0,
                                     high=vocab_size,
@@ -111,7 +111,7 @@ def test_raises_when_vocab_oob(above_or_below_vocab_range: str,
     vocab_size = 30_000
     torch.set_default_device(device)
     typical_acceptance_sampler = get_acceptance_sampler(strict_mode=True)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     bonus_token_ids = torch.randint(low=0,
                                     high=vocab_size,
@@ -171,7 +171,7 @@ def test_uniform_target_distribution_accepts_all_tokens(
     torch.set_default_device(device)
     typical_acceptance_sampler = get_acceptance_sampler(
         strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     draft_token_ids = torch.randint(low=0,
                                     high=vocab_size,
@@ -225,7 +225,7 @@ def test_temperature_zero_target_distribution(seed: int,
 
     typical_acceptance_sampler = get_acceptance_sampler(
         strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     # Simulate temperature 0 probability distribution for target probabilities
     # and create target probabilities such that only 1 token id has
     # probability 1.0
@@ -285,7 +285,7 @@ def test_mixed_target_distribution(seed: int, disable_bonus_tokens: bool,
     torch.set_default_device(device)
     typical_acceptance_sampler = get_acceptance_sampler(
         strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     # For sequences 0 and 2 set the distribution to a temperature
     # zero distribution. For sequences 1 and 3 set it to a uniform
     # distribution.
@@ -352,7 +352,7 @@ def test_accept_tokens_partially(seed: int, disable_bonus_tokens: bool,
     torch.set_default_device(device)
     typical_acceptance_sampler = get_acceptance_sampler(
         strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     # Create a temperature zero target probability distribution and ensure
     # all draft token ids correspond to the tokens with 1.0 probability.
     # Verify that all of them are accepted.
@@ -414,7 +414,7 @@ def test_accept_tokens_set_non_default_posteriors(seed: int,
     torch.set_default_device(device)
     typical_acceptance_sampler = get_acceptance_sampler(
         strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     # Simulate temperature 0 probability distribution for target
     # probabilities and create target probabilities such that only 1 token
     # id has probability 1.0 and others have a very low probability of
@@ -447,7 +447,7 @@ def test_accept_tokens_set_non_default_posteriors(seed: int,
         disable_bonus_tokens=disable_bonus_tokens,
         posterior_threshold=0.0,
         posterior_alpha=0.0)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     output_token_ids = typical_acceptance_sampler(
         target_probs,
         bonus_token_ids,
@@ -485,7 +485,7 @@ def test_replacement_token_ids(seed: int, disable_bonus_tokens: bool,
     torch.set_default_device(device)
     typical_acceptance_sampler = get_acceptance_sampler(
         strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
-    typical_acceptance_sampler.init_gpu_tensors(rank=0)
+    typical_acceptance_sampler.init_gpu_tensors(device=device)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     expected_replacement_tokens = -torch.ones(
         (batch_size, k), dtype=torch.long)

vllm/model_executor/layers/spec_decode_base_sampler.py

 from abc import abstractmethod
-from typing import Dict, Optional
+from typing import Dict, Optional, Union
 
 import torch
 import torch.jit
@@ -36,9 +36,12 @@ class SpecDecodeBaseSampler(nn.Module):
         self.num_emitted_tokens: Optional[torch.Tensor] = None
         self.num_draft_tokens: int = 0
 
-    def init_gpu_tensors(self, rank: int) -> None:
+    def init_gpu_tensors(self, device: Union[int, str]) -> None:
         assert self.num_accepted_tokens is None
-        device = f"cuda:{rank}"
+        if isinstance(device, int):
+            device = f"cuda:{device}"
+        elif not isinstance(device, str):
+            raise ValueError(f"Device must be int or str, get {type(device)}")
         self.num_accepted_tokens = torch.tensor(0,
                                                 dtype=torch.long,
                                                 device=device)