[Misc][Refactor] Introduce ExecuteModelData (vllm-project#4540)

tests/spec_decode/test_multi_step_worker.py

@@ -5,13 +5,12 @@
 import torch
 
 from vllm.model_executor.utils import set_random_seed
-from vllm.sequence import SamplerOutput
+from vllm.sequence import ExecuteModelRequest, SamplerOutput
 from vllm.spec_decode.multi_step_worker import MultiStepWorker
 from vllm.spec_decode.top1_proposer import Top1Proposer
 from vllm.worker.worker import Worker
 
 from .utils import (assert_logprobs_dict_allclose, create_batch,
-                    create_execute_model_data,
                     create_seq_group_metadata_from_prompts, create_worker,
                     patch_execute_model_with_seeds, zero_kv_cache)
 
@@ -105,31 +104,32 @@ def test_same_output_for_single_step():
 
     final_prompt_lens = [len(prompt) + num_steps for prompt in prompts]
 
-    multi_step_execute_model_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    single_step_execute_model_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
+    multi_step_seq_group = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
 
     zero_kv_cache(multi_step_worker.cache_engine)
     set_random_seed(seed)
     actual_output, _ = multi_step_worker.sampler_output(
-        **multi_step_execute_model_data.to_dict(), sample_len=num_steps)
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=multi_step_seq_group),
+        sample_len=num_steps)
     assert len(actual_output) == num_steps
     actual_output = actual_output[0]
 
+    single_step_seq_group = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
     zero_kv_cache(worker.cache_engine)
     set_random_seed(seed)
     expected_output = worker.execute_model(
-        **single_step_execute_model_data.to_dict(), )[0]
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=single_step_seq_group))[0]
 
     actual_token_ids = [
         output.samples[0].output_token for output in actual_output
@@ -193,19 +193,20 @@ def test_same_output_for_multi_step():
     worker.execute_model = patch_execute_model_with_seeds(worker, rand_seeds)
 
     continuations = [[1] for _ in prompts]
-    execute_model_data = create_execute_model_data(
-        create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            continuations=continuations,
-            final_prompt_lens=final_prompt_lens), )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        continuations=continuations,
+        final_prompt_lens=final_prompt_lens)
 
     # Run multi-step.
     zero_kv_cache(multi_step_worker.cache_engine)
     set_random_seed(seed)
     multi_step_output, _ = multi_step_worker.sampler_output(
-        **execute_model_data.to_dict(), sample_len=num_steps)
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list),
+        sample_len=num_steps)
 
     # Run single-step repeatedly.
     zero_kv_cache(worker.cache_engine)
@@ -215,16 +216,16 @@ def test_same_output_for_multi_step():
 
     for _ in multi_step_output:
 
-        execute_model_data = create_execute_model_data(
-            create_seq_group_metadata_from_prompts(
-                prompts,
-                num_gpu_blocks,
-                block_size,
-                continuations=continuations,
-                final_prompt_lens=final_prompt_lens))
+        seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+            prompts,
+            num_gpu_blocks,
+            block_size,
+            continuations=continuations,
+            final_prompt_lens=final_prompt_lens)
 
         single_step_output.extend(
-            worker.execute_model(**execute_model_data.to_dict(), ))
+            worker.execute_model(execute_model_req=ExecuteModelRequest(
+                seq_group_metadata_list=seq_group_metadata_list)))
 
         # Append output tokens to new sequence data.
         for i, seq_group_output in enumerate(single_step_output[-1]):
@@ -304,12 +305,11 @@ def test_draft_proposals_full_speculation_len():
         ) for _ in range(k)
     ], True
 
-    execute_model_data, _, _ = create_batch(batch_size, k)
+    seq_group_metadata_list, _, _ = create_batch(batch_size, k)
 
-    proposals = proposer.get_proposals(
-        **execute_model_data.to_dict(),
-        proposal_len=k,
-    )
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -340,14 +340,13 @@ def test_draft_proposals_no_speculations():
         max_proposal_len=prompt_len + k - 1,
     )
 
-    execute_model_data, _, _ = create_batch(batch_size,
-                                            k,
-                                            prompt_len=prompt_len)
+    seq_group_metadata_list, _, _ = create_batch(batch_size,
+                                                 k,
+                                                 prompt_len=prompt_len)
 
-    proposals = proposer.get_proposals(
-        **execute_model_data.to_dict(),
-        proposal_len=k,
-    )
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -409,17 +408,16 @@ def test_draft_proposals_mixed_k():
         ) for _ in range(k)
     ], True
 
-    execute_model_data, _, _ = create_batch(
+    seq_group_metadata_list, _, _ = create_batch(
         batch_size,
         k,
         prompt_len=prompt_len,
         prev_output_token_len=prev_output_token_len,
     )
 
-    proposals = proposer.get_proposals(
-        **execute_model_data.to_dict(),
-        proposal_len=k,
-    )
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=k), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)

tests/spec_decode/test_ngram_worker.py

@@ -1,10 +1,10 @@
 import torch
 
+from vllm.sequence import ExecuteModelRequest
 from vllm.spec_decode.ngram_worker import NGramWorker
 from vllm.spec_decode.top1_proposer import Top1Proposer
 
-from .utils import (create_execute_model_data,
-                    create_seq_group_metadata_from_prompts, create_worker)
+from .utils import create_seq_group_metadata_from_prompts, create_worker
 
 
 def test_ngram_algo_correctness_for_single_no_match():
@@ -44,17 +44,15 @@ def test_ngram_algo_correctness_for_single_no_match():
 
     proposal_len = 5
     final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
-    ngram_sampler_output_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    proposals = proposer.get_proposals(
-        **ngram_sampler_output_data.to_dict(),
-        proposal_len=proposal_len,
-    )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=proposal_len), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -113,17 +111,15 @@ def test_ngram_algo_correctness_for_batches_not_match_all():
 
     proposal_len = 5
     final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
-    ngram_sampler_output_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    proposals = proposer.get_proposals(
-        **ngram_sampler_output_data.to_dict(),
-        proposal_len=proposal_len,
-    )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=proposal_len), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -185,17 +181,15 @@ def test_ngram_algo_correctness_for_batches_match_all():
 
     proposal_len = 5
     final_prompt_lens = [len(prompt) + proposal_len for prompt in prompts]
-    ngram_sampler_output_data = create_execute_model_data(
-        seq_group_metadata_list=create_seq_group_metadata_from_prompts(
-            prompts,
-            num_gpu_blocks,
-            block_size,
-            final_prompt_lens=final_prompt_lens))
-
-    proposals = proposer.get_proposals(
-        **ngram_sampler_output_data.to_dict(),
-        proposal_len=proposal_len,
-    )
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        final_prompt_lens=final_prompt_lens)
+
+    proposals = proposer.get_proposals(execute_model_req=ExecuteModelRequest(
+        seq_group_metadata_list=seq_group_metadata_list,
+        num_lookahead_slots=proposal_len), )
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)