[Model][Speculative Decoding] DeepSeek MTP spec decode

.buildkite/test-pipeline.yaml

@@ -2,7 +2,7 @@
 # adding a new command to an existing step. See different options here for examples.
 
 # This script will be feed into Jinja template in `test-template-aws.j2` at
-# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-aws.j2 
+# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-aws.j2
 # to generate the final pipeline yaml file.
 
 # Documentation
@@ -15,7 +15,7 @@
 # mirror_hardwares(list): the list of hardwares to run the test on as well. currently only supports [amd]
 # gpu(str): override the GPU selection for the test. default is on L4 GPUs. currently only supports a100
 # num_gpus(int): override the number of GPUs for the test. default to 1 GPU. currently support 2,4.
-# num_nodes(int): whether to simulate multi-node setup by launch multiple containers on one host, 
+# num_nodes(int): whether to simulate multi-node setup by launch multiple containers on one host,
 #     in this case, commands must be specified. the first command runs on first host, the second
 #     command runs on the second host.
 # working_dir(str): specify the place where command should execute, default to /vllm-workspace/tests
@@ -24,8 +24,8 @@
 # When adding a test
 # - If the test belong to an existing group, add it there
 # - If the test is short, add to any existing step
-# - If the test takes more than 10min, then it is okay to create a new step. 
-#   Note that all steps execute in parallel. 
+# - If the test takes more than 10min, then it is okay to create a new step.
+#   Note that all steps execute in parallel.
 
 steps:
 ##### fast check tests  #####
@@ -145,14 +145,14 @@ steps:
   - RAY_DEDUP_LOGS=0 python3 ../examples/offline_inference/rlhf_colocate.py
 
 - label: Metrics, Tracing Test # 10min
-  num_gpus: 2 
+  num_gpus: 2
   fast_check: true
   source_file_dependencies:
   - vllm/
   - tests/metrics
   - tests/tracing
   commands:
-  - pytest -v -s metrics 
+  - pytest -v -s metrics
   - "pip install \
       'opentelemetry-sdk>=1.26.0,<1.27.0' \
       'opentelemetry-api>=1.26.0,<1.27.0' \
@@ -254,7 +254,7 @@ steps:
   - vllm/model_executor/guided_decoding
   - tests/test_logits_processor
   - tests/model_executor/test_guided_processors
-  commands: 
+  commands:
     - pytest -v -s test_logits_processor.py
     - pytest -v -s model_executor/test_guided_processors.py
 
@@ -265,7 +265,7 @@ steps:
   - vllm/model_executor/models/eagle.py
   commands:
     - pytest -v -s spec_decode/e2e/test_multistep_correctness.py
-    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py
+    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py --ignore=spec_decode/e2e/test_mtp_correctness.py
     - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
 
 - label: LoRA Test %N # 15min each
@@ -580,7 +580,7 @@ steps:
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # This test runs llama 13B, so it is required to run on 4 GPUs.
     - pytest -v -s -x lora/test_long_context.py
-    # There is some Tensor Parallelism related processing logic in LoRA that 
+    # There is some Tensor Parallelism related processing logic in LoRA that
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
@@ -605,7 +605,7 @@ steps:
   - vllm/
   - tests/weight_loading
   commands:
-    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt 
+    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
 
 
 ##### multi gpus test #####
@@ -617,7 +617,7 @@ steps:
   num_gpus: 4
   source_file_dependencies:
   - vllm/
-  commands: 
+  commands:
   # NOTE: don't test llama model here, it seems hf implementation is buggy
   # see https://github.com/vllm-project/vllm/pull/5689 for details
   - pytest -v -s distributed/test_custom_all_reduce.py

tests/models/registry.py

@@ -295,6 +295,9 @@ def check_available_online(
                                    speculative_model="abhigoyal/vllm-medusa-llama-68m-random"),  # noqa: E501
     "MLPSpeculatorPreTrainedModel": _HfExamplesInfo("JackFram/llama-160m",
                                                     speculative_model="ibm-ai-platform/llama-160m-accelerator"),  # noqa: E501
+    "DeepSeekMTPModel": _HfExamplesInfo("luccafong/deepseek_mtp_main_random",
+                                        speculative_model="luccafong/deepseek_mtp_draft_random",  # noqa: E501
+                                        trust_remote_code=True),
 }
 
 _FALLBACK_MODEL = {