[main][test] Refactor the mtp and eagle test case #5326
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| # | ||
| # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. | ||
| # Copyright 2023 The vLLM team. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # This file is a part of the vllm-ascend project. | ||
| # Adapted from vllm/tests/basic_correctness/test_basic_correctness.py | ||
| # | ||
| """Compare the short outputs of HF and vLLM when using greedy sampling. | ||
|
|
||
| Run `pytest tests/e2e/multicard/spec_decode/test_mtp_qwen3_next.py`. | ||
| """ | ||
|
|
||
| import os | ||
|
|
||
| import pytest | ||
| from vllm.config import CompilationConfig | ||
| from vllm.v1.metrics.reader import Counter, Vector | ||
|
|
||
| from tests.e2e.conftest import VllmRunner, cleanup_dist_env_and_memory | ||
|
|
||
| os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" | ||
|
|
||
| MODELS = ["Qwen/Qwen3-Next-80B-A3B-Instruct"] | ||
|
|
||
|
|
||
| # TODO: add full decode only (when ready) | ||
| @pytest.mark.parametrize("model_name", MODELS) | ||
| def test_qwen3_next_mtp_acceptance_tp4(model_name): | ||
| golden = [0.85, 0.46, 0.19] | ||
|
|
||
| example_prompts = [ | ||
| "Hello, my name is", | ||
| "The president of the United States is", | ||
| "The capital of France is", | ||
| "The future of AI is", | ||
| ] | ||
|
|
||
| max_tokens = 1024 | ||
|
|
||
| with VllmRunner(model_name, | ||
| tensor_parallel_size=4, | ||
| max_model_len=4096, | ||
| gpu_memory_utilization=0.8, | ||
| distributed_executor_backend="mp", | ||
| disable_log_stats=False, | ||
| speculative_config={ | ||
| "method": "qwen3_next_mtp", | ||
| "num_speculative_tokens": 3, | ||
| }, | ||
| compilation_config=CompilationConfig( | ||
| cudagraph_capture_sizes=[20])) as spec_vllm_model: | ||
| _ = spec_vllm_model.generate_greedy(example_prompts, max_tokens) | ||
| metrics = spec_vllm_model.model.get_metrics() | ||
|
|
||
| num_drafts = 0 | ||
| num_accepted_tokens_per_pos = [0] * 3 | ||
| for metric in metrics: | ||
| if metric.name == "vllm:spec_decode_num_drafts": | ||
| assert isinstance(metric, Counter) | ||
| num_drafts += metric.value | ||
| elif metric.name == "vllm:spec_decode_num_accepted_tokens_per_pos": | ||
| assert isinstance(metric, Vector) | ||
| for pos in range(len(metric.values)): | ||
| num_accepted_tokens_per_pos[pos] += metric.values[pos] | ||
|
|
||
| acceptance_per_pos = [ | ||
| num_accepted_tokens / num_drafts | ||
| for num_accepted_tokens in num_accepted_tokens_per_pos | ||
| ] | ||
|
|
||
| match = all(abs(a - b) < 0.05 for a, b in zip(acceptance_per_pos, golden)) | ||
| if not match: | ||
| print(f"acceptance_per_pos: {acceptance_per_pos}") | ||
| print(f"golden: {golden}") | ||
|
|
||
| assert match | ||
| cleanup_dist_env_and_memory() | ||
|
|
||
|
|
||
| @pytest.mark.parametrize("model_name", MODELS) | ||
| @pytest.mark.parametrize("num_speculative_tokens", [1]) | ||
| @pytest.mark.parametrize("disable_padded_drafter_batch", [True, False]) | ||
| def test_qwen3_next_mtp_correctness_tp4(model_name: str, | ||
| num_speculative_tokens: int, | ||
| disable_padded_drafter_batch: bool): | ||
| example_prompts = [ | ||
| "Hello, my name is", | ||
| "The president of the United States is", | ||
| "The capital of France is", | ||
| "The future of AI is", | ||
| ] | ||
|
|
||
| max_tokens = 20 | ||
| ''' | ||
| Compare the outputs of a original LLM and a speculative LLM | ||
| should be the same when using mtp speculative decoding. | ||
| ''' | ||
| with VllmRunner(model_name, | ||
| tensor_parallel_size=4, | ||
| max_model_len=4096, | ||
| gpu_memory_utilization=0.8, | ||
| distributed_executor_backend="mp", | ||
| speculative_config={ | ||
| "method": | ||
| "mtp", | ||
| "num_speculative_tokens": | ||
| num_speculative_tokens, | ||
| "disable_padded_drafter_batch": | ||
| disable_padded_drafter_batch, | ||
| }, | ||
| compilation_config=CompilationConfig( | ||
| cudagraph_capture_sizes=[20])) as spec_llm: | ||
| spec_outputs = spec_llm.generate_greedy(example_prompts, max_tokens) | ||
| del spec_llm | ||
|
|
||
| with VllmRunner(model_name, | ||
| tensor_parallel_size=4, | ||
| max_model_len=4096, | ||
| gpu_memory_utilization=0.8, | ||
| distributed_executor_backend="mp", | ||
| compilation_config=CompilationConfig( | ||
| cudagraph_capture_sizes=[20])) as ref_llm: | ||
| ref_outputs = ref_llm.generate_greedy(example_prompts, max_tokens) | ||
| del ref_llm | ||
|
|
||
| matches = 0 | ||
| misses = 0 | ||
| for ref_output, spec_output in zip(ref_outputs, spec_outputs): | ||
| ref_token_ids = ref_output[0] | ||
| spec_token_ids = spec_output[0] | ||
| if ref_token_ids == spec_token_ids[:len(ref_token_ids)]: | ||
| matches += 1 | ||
| else: | ||
| misses += 1 | ||
| print(f"ref_output: {ref_output[1]}") | ||
| print(f"spec_output: {spec_output[1]}") | ||
|
|
||
| # Heuristic: expect at least 66% of the prompts to match exactly | ||
| # Upon failure, inspect the outputs to check for inaccuracy. | ||
| assert matches > int(0.66 * len(ref_outputs)) | ||
| cleanup_dist_env_and_memory() | ||
|
Contributor
There was a problem hiding this comment. The |
||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
The
VllmRunnercontext manager automatically handles resource cleanup, including callingcleanup_dist_env_and_memory(), in its__exit__method. This explicit call is redundant and should be removed to improve code clarity.