[Speculative decoding 7/9] Speculative decoding end-to-end correctness tests. (#3951)

vllm/spec_decode/multi_step_worker.py

@@ -6,8 +6,7 @@ import torch
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
 from vllm.spec_decode.interfaces import (SpeculativeProposals,
                                          SpeculativeProposer)
-from vllm.spec_decode.util import (maybe_mock_device_tensors,
-                                   sampler_output_to_torch)
+from vllm.spec_decode.util import sampler_output_to_torch
 from vllm.worker.worker import Worker
 
 
@@ -329,12 +328,15 @@ class DraftModelTop1Proposer(SpeculativeProposer):
         """
         if maybe_sampler_output is None:
             # If no speculative tokens, the sampler output will be None.
-            # In this case we return empty tensors.
-            proposal_tokens = torch.zeros(0,
+            # In this case we return empty proposals.
+            proposal_tokens = torch.full(size=(
+                batch_size,
                 max_proposal_len,
+            ),
+                                         fill_value=-1,
                                          dtype=torch.long,
                                          device=self._device)
-            proposal_probs = torch.zeros(0,
+            proposal_probs = torch.zeros(batch_size,
                                          max_proposal_len,
                                          self._vocab_size,
                                          dtype=torch.float32,
@@ -345,17 +347,6 @@ class DraftModelTop1Proposer(SpeculativeProposer):
             return proposal_tokens, proposal_probs, proposal_lens_tensor
 
         sampler_output = maybe_sampler_output
-
-        # We mock the device tensors until PR 7/9 is merged (e2e correctness).
-        # https://docs.google.com/document/d/1rE4pr3IdspRw97XbImY4fS9IWYuJJ3HGtL7AdIKGrw8/edit#heading=h.qijw1sdidrer
-        for step_output in sampler_output:
-            maybe_mock_device_tensors(
-                sampler_output=step_output,
-                batch_size=len(proposal_lens),
-                vocab_size=self._vocab_size,
-                device=self._device,
-            )
-
         proposal_tokens, proposal_probs = sampler_output_to_torch(
             sampler_output)
 

vllm/spec_decode/spec_decode_worker.py

@@ -111,6 +111,32 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
             device=self.device,
             vocab_size=self._vocab_size)
 
+        self._configure_model_sampler_for_spec_decode()
+
+    def _configure_model_sampler_for_spec_decode(self):
+        """Configure model sampler to emit GPU tensors. This allows spec decode
+        to keep data on device without transferring to CPU and serializing,
+        which significantly reduces overhead of rejection sampling.
+
+        NOTE(cade): This breaks abstraction boundaries pretty badly. The better
+        design is to have the "move to CPU and serialize" sampling decision be
+        done outside of the model/sampler; this way the "last-mile" worker
+        object which interfaces with the scheduler can serialize and incur the
+        performance hit as necessary. This allows us to run the worker several
+        iterations in a row without incurring the "move to CPU and serialize"
+        performance penalty.
+
+        Since this requires a large change to vLLM, we defer it to later and
+        temporarily accept this broken abstraction boundary.
+
+        NOTE(cade): This will require a special check if the proposer worker
+        does not have a sampler (e.g. ngram speculation).
+        """
+        (self.scorer_worker.model_runner.model.sampler.include_gpu_probs_tensor
+         ) = True
+        (self.proposer_worker.model_runner.model.sampler.
+         include_gpu_probs_tensor) = True
+
     def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of cache blocks to use.
 
@@ -286,15 +312,26 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
             select_proposal_len_zero=True)
         original_indices = spec_indices + non_spec_indices
 
-        proposal_probs = proposal_scores.probs[spec_indices, :-1]
-        bonus_token_ids = proposal_scores.token_ids[spec_indices, -1:]
+        # Get probabilities of target model, excluding bonus token.
+        proposal_verifier_probs = proposal_scores.probs[spec_indices, :-1]
+
+        # Get non-speculative sampled tokens from target model.
         non_spec_token_ids = proposal_scores.token_ids[non_spec_indices]
 
+        # Get bonus tokens from target model.
+        bonus_token_ids = proposal_scores.token_ids[spec_indices, -1:]
+
+        # Get probabilities according to proposal method.
+        proposal_probs = proposals.proposal_probs[spec_indices]
+
+        # Get proposed tokens.
+        proposal_token_ids = proposals.proposal_token_ids[spec_indices]
+
         accepted_token_ids = self.rejection_sampler(
-            proposal_probs,
-            bonus_token_ids,
-            proposals.proposal_probs,
-            proposals.proposal_token_ids,
+            target_probs=proposal_verifier_probs,
+            bonus_token_ids=bonus_token_ids,
+            draft_probs=proposal_probs,
+            draft_token_ids=proposal_token_ids,
         )
 
         # Append output tokens from non-speculative sequences to