xc-llm-ascend/tests/e2e/multicard/2-cards/test_offline_inference_distributed.py

#
# 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/test_offline_inference.py`.
"""

import os
from unittest.mock import patch

import pytest
from vllm import SamplingParams

from tests.e2e.conftest import VllmRunner
from tests.e2e.model_utils import check_outputs_equal

os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

QWEN_DENSE_MODELS = [
    "vllm-ascend/Qwen3-0.6B-W8A8",
]

QWEN_W4A8_MODELS = [
    "vllm-ascend/Qwen3-1.7B-W4A8-V1",
]

QWEN_W4A4_MODELS = [
    "Eco-Tech/Qwen3-32B-w4a4-LAOS",
]

DEEPSEEK_W4A8_MODELS = [
    "vllm-ascend/DeepSeek-V3.1-W4A8-puring",
]

GPT_OSS_MODELS = [
    "unsloth/gpt-oss-20b-BF16",
]


def test_deepseek_multistream_moe_tp2():
    example_prompts = [
        "Hello, my name is",
    ]
    dtype = "half"
    max_tokens = 5
    with VllmRunner(
        "vllm-ascend/DeepSeek-V3-Pruning",
        dtype=dtype,
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        distributed_executor_backend="mp",
        additional_config={
            "enable_multistream_moe": True,
            "refresh": True,
        },
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@pytest.mark.parametrize("model", QWEN_W4A8_MODELS)
def test_qwen3_w4a8_dynamic_tp2(model):
    prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5
    with VllmRunner(
        model,
        max_model_len=8192,
        dtype="auto",
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        quantization="ascend",
    ) as vllm_model:
        vllm_model.generate_greedy(prompts, max_tokens)


def test_qwen3_moe_sp_tp2() -> None:
    example_prompts = [
        "Hello, my name is",
    ]
    sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)

    with VllmRunner(
        "Qwen/Qwen3-30B-A3B",
        dtype="auto",
        tensor_parallel_size=2,
        distributed_executor_backend="mp",
        compilation_config={"pass_config": {"enable_sp": True}},
        enable_expert_parallel=True,
        enforce_eager=True,
    ) as vllm_model:
        vllm_model.generate(example_prompts, sampling_params)


@pytest.mark.parametrize("model", DEEPSEEK_W4A8_MODELS)
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "2048"})
def test_deepseek_w4a8_accuracy_tp2(model):
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs",
    ]
    vllm_ds_w4a8_answers = ["逍遙而至地去 accrued", "平行于我udo madreHelen", "ysteepaolis backwards Kj"]
    sampling_params = SamplingParams(max_tokens=5, temperature=0.0)
    with VllmRunner(
        model,
        dtype="auto",
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        quantization="ascend",
        enable_expert_parallel=True,
    ) as vllm_model:
        vllm_quant_outputs = vllm_model.model.generate(prompts, sampling_params)

    vllm_quant_outputs_list = []
    for output in vllm_quant_outputs:
        vllm_quant_outputs_list.append(([output.outputs[0].index], output.outputs[0].text))
    vllm_answer_list = []
    vllm_answer_list = [([0], answer) for answer in vllm_ds_w4a8_answers]

    check_outputs_equal(
        outputs_0_lst=vllm_answer_list,
        outputs_1_lst=vllm_quant_outputs_list,
        name_0="vllm_quant_outputs",
        name_1="vllm_answer_outputs",
    )


@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
@patch.dict(os.environ, {"VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE": "1"})
def test_qwen3_moe_fc2_tp2() -> None:
    example_prompts = [
        "Hello, my name is",
    ]
    sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)

    with VllmRunner(
        "Qwen/Qwen3-30B-A3B",
        dtype="auto",
        tensor_parallel_size=2,
        distributed_executor_backend="mp",
        enable_expert_parallel=True,
        enforce_eager=True,
    ) as vllm_model:
        vllm_model.generate(example_prompts, sampling_params)


@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
@patch.dict(os.environ, {"VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE": "1"})
def test_qwen3_moe_fc2_oshard_tp2() -> None:
    example_prompts = [
        "Hello, my name is",
    ]
    sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)

    with VllmRunner(
        "Qwen/Qwen3-30B-A3B",
        dtype="auto",
        tensor_parallel_size=2,
        distributed_executor_backend="mp",
        enable_expert_parallel=True,
        enforce_eager=True,  # TODO(Levi-JQ): support graph mode for fc2 in Qwen
        additional_config={"layer_sharding": ["o_proj"]},
    ) as vllm_model:
        vllm_model.generate(example_prompts, sampling_params)


@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
def test_deepseek_v2_lite_fc1_tp2() -> None:
    example_prompts = [
        "test" * 1001,
    ]
    sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
    with VllmRunner(
        "vllm-ascend/DeepSeek-V2-Lite-W8A8",
        dtype="auto",
        tensor_parallel_size=2,
        distributed_executor_backend="mp",
        enable_expert_parallel=True,
        enforce_eager=True,
        quantization="ascend",
    ) as vllm_model:
        vllm_model.generate(example_prompts, sampling_params)


@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
def test_qwen3_dense_fc1_tp2(model):
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5

    with VllmRunner(
        model,
        max_model_len=8192,
        dtype="auto",
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        quantization="ascend",
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5

    with VllmRunner(
        model,
        max_model_len=8192,
        dtype="auto",
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        quantization="ascend",
        additional_config={"weight_prefetch_config": {"enabled": True}},
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@patch.dict(os.environ, {"HCCL_OP_EXPANSION_MODE": "AIV"})
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
@patch.dict(os.environ, {"ASCEND_AGGREGATE_ENABLE": "1"})
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
def test_deepseek3_2_w8a8_pruning_mtp_tp2_ep():
    short_example_prompts = [
        "Hello ",
    ]
    # "max_position_embeddings": 163840,
    long_example_prompts = ["Hello " * (163839 - 500) + "Hello"]
    max_tokens = 500
    with VllmRunner(
        "vllm-ascend/DeepSeek-V3.2-W8A8-Pruning",
        tensor_parallel_size=2,
        quantization="ascend",
        enable_expert_parallel=True,
        max_model_len=163840,
        compilation_config={"cudagraph_capture_sizes": [2, 4, 6, 8, 10, 12], "cudagraph_mode": "FULL_DECODE_ONLY"},
        speculative_config={"num_speculative_tokens": 1, "method": "deepseek_mtp"},
        additional_config={"layer_sharding": ["q_b_proj", "o_proj"]},
        reasoning_parser="deepseek_v3",
        tokenizer_mode="deepseek_v32",
    ) as vllm_model:
        vllm_model.generate_greedy(short_example_prompts, max_tokens)
        vllm_model.generate_greedy(long_example_prompts, max_tokens)


@pytest.mark.parametrize("model", QWEN_W4A4_MODELS)
def test_qwen3_w4a4_distributed_tp2(model):
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5
    with VllmRunner(
        model,
        tensor_parallel_size=2,
        cudagraph_capture_sizes=[1, 2, 4, 8],
        quantization="ascend",
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)


@pytest.mark.parametrize("model", GPT_OSS_MODELS)
def test_gpt_oss_distributed_tp2(model):
    example_prompts = [
        "Hello, my name is",
    ]
    max_tokens = 5
    with VllmRunner(
        model,
        tensor_parallel_size=2,
        enforce_eager=True,
    ) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)