xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2025-12-13 03:45:00 +08:00
Code Issues Packages Projects Releases Wiki Activity

[Speculative Decoding] MLPSpeculator Tensor Parallel support (1/2) (#6050)

Browse Source 
Co-authored-by: Sirej Dua <sirej.dua@databricks.com>
Co-authored-by: Sirej Dua <Sirej Dua>
This commit is contained in:
Sirej Dua 2024-07-02 07:20:29 -07:00 committed by GitHub
parent 31354e563f
commit 15aba081f3
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 35 additions and 25 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

36
tests/spec_decode/e2e/test_integration_dist_tp2.py
View File

@ -70,10 +70,6 @@ def test_target_model_tp_gt_1(baseline_llm_generator, test_llm_generator,
@pytest.mark.parametrize( @pytest.mark.parametrize(
"common_llm_kwargs", "common_llm_kwargs",
[{ [{
# Use a small model for a fast test.
# Note this is repeated in the test body; to initialize a tokenizer.
"model": "JackFram/llama-68m",
# Skip cuda graph recording for fast test. # Skip cuda graph recording for fast test.
"enforce_eager": True, "enforce_eager": True,
@ -88,15 +84,31 @@ def test_target_model_tp_gt_1(baseline_llm_generator, test_llm_generator,
# second run of the test to fail with internal NCCL error. # second run of the test to fail with internal NCCL error.
"use_async": True, "use_async": True,
}]) }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}]) @pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [ @pytest.mark.parametrize(
{ "per_test_common_llm_kwargs, test_llm_kwargs",
"speculative_model": "JackFram/llama-68m", [
"num_speculative_tokens": 5, (
"speculative_draft_tensor_parallel_size": 1, {
}, # Use a small model for a fast test.
]) # Note this is repeated in the test body; to initialize a
# tokenizer.
"model": "JackFram/llama-68m",
},
{
"speculative_model": "JackFram/llama-68m",
"num_speculative_tokens": 5,
"speculative_draft_tensor_parallel_size": 1,
}),
({
"model": "ibm-granite/granite-3b-code-instruct",
}, {
"speculative_model":
"ibm-granite/granite-3b-code-instruct-accelerator",
"num_speculative_tokens": 5,
"speculative_draft_tensor_parallel_size": 1,
})
])
@pytest.mark.parametrize("batch_size", [2]) @pytest.mark.parametrize("batch_size", [2])
@pytest.mark.parametrize("seed", [1]) @pytest.mark.parametrize("seed", [1])
def test_draft_model_tp_lt_target_model_tp2(test_llm_generator, def test_draft_model_tp_lt_target_model_tp2(test_llm_generator,

6
vllm/config.py
View File

@ -957,12 +957,6 @@ class SpeculativeConfig:
) )
draft_hf_config = draft_model_config.hf_config draft_hf_config = draft_model_config.hf_config
if (draft_hf_config.model_type == "mlp_speculator"
and target_parallel_config.world_size != 1):
# MLPSpeculator TP support will be added very soon
raise ValueError(
"Speculative decoding with mlp_speculator models does not "
"yet support distributed inferencing (TP > 1).")
if (num_speculative_tokens is not None if (num_speculative_tokens is not None
and hasattr(draft_hf_config, "num_lookahead_tokens")): and hasattr(draft_hf_config, "num_lookahead_tokens")):

18
vllm/spec_decode/spec_decode_worker.py
View File

@ -113,24 +113,28 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
draft_worker_kwargs.pop("ngram_prompt_lookup_min")) draft_worker_kwargs.pop("ngram_prompt_lookup_min"))
disable_bonus_tokens = True disable_bonus_tokens = True
if ngram_prompt_lookup_max > 0: if ngram_prompt_lookup_max > 0:
disable_bonus_tokens = False disable_bonus_tokens = False
proposer_worker = NGramWorker(**draft_worker_kwargs) proposer_worker = NGramWorker(**draft_worker_kwargs)
proposer_worker.set_ngram_window_size(ngram_prompt_lookup_min, proposer_worker.set_ngram_window_size(ngram_prompt_lookup_min,
ngram_prompt_lookup_max) ngram_prompt_lookup_max)
elif draft_worker_kwargs[
"model_config"].hf_config.model_type == "mlp_speculator":
proposer_worker = MLPSpeculatorWorker(**draft_worker_kwargs)
disable_bonus_tokens = False
else: else:
draft_parallel_config: ParallelConfig = draft_worker_kwargs[ draft_parallel_config: ParallelConfig = draft_worker_kwargs[
'parallel_config'] 'parallel_config']
draft_tp = draft_parallel_config.tensor_parallel_size draft_tp = draft_parallel_config.tensor_parallel_size
target_tp = scorer_worker.parallel_config.tensor_parallel_size target_tp = scorer_worker.parallel_config.tensor_parallel_size
if draft_tp == 1: if draft_worker_kwargs[
draft_worker_kwargs["model_runner_cls"] = TP1DraftModelRunner "model_config"].hf_config.model_type == "mlp_speculator":
proposer_worker = MultiStepWorker(**draft_worker_kwargs) disable_bonus_tokens = False
proposer_worker = MLPSpeculatorWorker(**draft_worker_kwargs)
else:
if draft_tp == 1:
draft_worker_kwargs[
"model_runner_cls"] = TP1DraftModelRunner
proposer_worker = MultiStepWorker(**draft_worker_kwargs)
proposer_worker = SmallerTpProposerWorker.maybe_wrap_worker( proposer_worker = SmallerTpProposerWorker.maybe_wrap_worker(
proposer_worker, draft_tp, target_tp) proposer_worker, draft_tp, target_tp)