118 lines
3.8 KiB
Python
118 lines
3.8 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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from unittest.mock import patch
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from tests.e2e.conftest import VllmRunner, wait_until_npu_memory_free
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os.environ["PYTORCH_NPU_ALLOC_CONF"] = "expandable_segments:True"
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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@patch.dict(
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os.environ,
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{
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"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
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},
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)
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@wait_until_npu_memory_free()
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def test_deepseek_v4_w4a8_tp4_basic_greedy():
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"""Verify DeepSeek V4 W4A8 basic greedy generation with TP4 and EP."""
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example_prompts = [
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"Hello, my name is",
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"What is the meaning of life?",
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]
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max_tokens = 5
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with VllmRunner(
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"gdydems/DeepSeek-V4-Flash-w4a8-mtp",
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max_model_len=8192,
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max_num_seqs=16,
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max_num_batched_tokens=4096,
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dtype="auto",
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tensor_parallel_size=4,
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enable_expert_parallel=True,
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gpu_memory_utilization=0.9,
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quantization="ascend",
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tokenizer_mode="deepseek_v4",
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block_size=128,
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compilation_config={
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"cudagraph_mode": "FULL_DECODE_ONLY",
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},
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speculative_config={"num_speculative_tokens": 1, "method": "mtp"},
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) as vllm_model:
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outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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expected_token_ids = [
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[19923, 14, 1026, 2329, 344, 680, 2852, 95, 305, 342],
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[3085, 344, 270, 5281, 294, 1988, 33, 3955, 361, 582, 3085, 344],
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]
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assert len(outputs) == len(example_prompts)
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for i, (output_ids, output_str) in enumerate(outputs):
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assert len(output_str) > 0
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assert len(output_ids) > 0
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assert output_ids == expected_token_ids[i]
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@patch.dict(
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os.environ,
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{
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"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
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},
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)
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@wait_until_npu_memory_free()
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def test_deepseek_v4_w4a8_tp4_index_cache_freq4():
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"""IndexCache freq=4 must produce non-empty greedy outputs identical in
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shape to the baseline test, verifying skip_topk/topk_indices_buffer
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plumbing (DSAModules → AscendDSAImpl) is wired correctly across both
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serial and dual-stream paths.
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"""
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example_prompts = [
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"Hello, my name is",
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"The capital of France is",
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"What is the meaning of life?",
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]
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max_tokens = 5
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with VllmRunner(
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"gdydems/DeepSeek-V4-Flash-w4a8-mtp",
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max_model_len=8192,
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max_num_seqs=16,
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max_num_batched_tokens=4096,
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dtype="auto",
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tensor_parallel_size=4,
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enable_expert_parallel=True,
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gpu_memory_utilization=0.9,
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quantization="ascend",
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tokenizer_mode="deepseek_v4",
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block_size=128,
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compilation_config={
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"cudagraph_mode": "FULL_DECODE_ONLY",
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},
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hf_overrides={
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"use_index_cache": True,
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"index_topk_freq": 4,
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},
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) as vllm_model:
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outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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assert len(outputs) == len(example_prompts)
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for output_ids, output_str in outputs:
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assert len(output_str) > 0
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assert len(output_ids) > 0
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