638cae824d
### What this PR does / why we need it?
Since the PR (https://github.com/vllm-project/vllm/pull/32118) has
modified the criteria for judging Prefill and Decode requests in vLLM,
PCPManager needs to synchronize with this standard. As PCPManager
involves multiple calculations of PD request counts, this PR attempts to
consolidate the related logic and update the PD request count once per
batch.
### How was this patch tested?
```bash
pytest tests/e2e/multicard/4-cards/long_sequence/test_mtp.py
```
- vLLM version: v0.13.0
- vLLM main:
11b6af5280
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
144 lines
4.5 KiB
Python
144 lines
4.5 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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import os
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import pytest
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from tests.e2e.conftest import VllmRunner, wait_until_npu_memory_free
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os.environ["HCCL_BUFFSIZE"] = "512"
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prompts = [
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"The capital of France is", "Hello, my name is Tom, I am",
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"The president of United States is", "AI future is"
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]
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model = "wemaster/deepseek_mtp_main_random_bf16"
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@wait_until_npu_memory_free()
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def test_pcp_dcp_mtp1_eager():
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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prefill_context_parallel_size=2,
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decode_context_parallel_size=2,
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max_num_batched_tokens=1024,
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enable_expert_parallel=True,
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block_size=128,
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speculative_config={
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"num_speculative_tokens": 1,
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"method": "deepseek_mtp",
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},
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enforce_eager=True,
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async_scheduling=False,
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) as runner:
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runner.generate_greedy(prompts, 32)
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@wait_until_npu_memory_free()
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def test_pcp_dcp_mtp3_eager():
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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prefill_context_parallel_size=2,
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decode_context_parallel_size=2,
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max_num_batched_tokens=1024,
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enable_expert_parallel=True,
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block_size=128,
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async_scheduling=True,
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speculative_config={
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"num_speculative_tokens": 3,
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"method": "deepseek_mtp",
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},
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enforce_eager=True,
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) as runner:
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runner.generate_greedy(prompts, 32)
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@wait_until_npu_memory_free()
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def test_pcp_dcp_mtp3_piecewise_graph():
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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prefill_context_parallel_size=2,
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decode_context_parallel_size=2,
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max_num_batched_tokens=1024,
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enable_expert_parallel=True,
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block_size=128,
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speculative_config={
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"num_speculative_tokens": 3,
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"method": "deepseek_mtp",
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},
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compilation_config={
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"cudagraph_mode": "PIECEWISE",
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"cudagraph_capture_sizes": [4, 8, 16],
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},
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async_scheduling=False,
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) as runner:
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runner.generate_greedy(prompts, 32)
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@wait_until_npu_memory_free()
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def test_pcp_dcp_mtp3_full_graph():
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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prefill_context_parallel_size=2,
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decode_context_parallel_size=2,
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max_num_batched_tokens=1024,
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enable_expert_parallel=True,
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block_size=128,
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speculative_config={
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"num_speculative_tokens": 3,
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"method": "deepseek_mtp",
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},
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compilation_config={
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"cudagraph_capture_sizes": [4, 8, 16],
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},
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async_scheduling=False,
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) as runner:
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runner.generate_greedy(prompts, 32)
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@wait_until_npu_memory_free()
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def test_dcp_mtp3_full_graph():
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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decode_context_parallel_size=2,
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max_num_batched_tokens=1024,
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enable_expert_parallel=True,
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block_size=128,
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speculative_config={
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"num_speculative_tokens": 3,
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"method": "deepseek_mtp",
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},
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compilation_config={
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"cudagraph_capture_sizes": [4, 8, 16],
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},
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async_scheduling=False,
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) as runner:
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runner.generate_greedy(prompts, 32)
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