Revert PTA upgrade PR (#3352)

we notice that torch npu 0919 doesn't work. This PR revert related
change which rely on 0919 version.
Revert PR: #3295  #3205  #3102 

Related: #3353

- vLLM version: v0.11.0

tests/e2e/multicard/test_full_graph_mode.py

@@ -1,103 +0,0 @@
-#
-# 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/test_qwen3_moe.py`.
-"""
-
-import os
-
-from vllm import SamplingParams
-
-from tests.e2e.conftest import VllmRunner
-from tests.e2e.model_utils import check_outputs_equal
-
-
-def test_models_distributed_Qwen3_MOE_TP2_WITH_FULLGRAPH():
-    if 'HCCL_OP_EXPANSION_MODE' in os.environ:
-        del os.environ['HCCL_OP_EXPANSION_MODE']
-    prompts = [
-        ('Solve the following math problem step by step.'
-         'The last line of your response should be of the form Answer: '
-         '$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
-         'In triangle $ABC$, $\\sin \\angle A = \\frac{4}{5}$ and $\\angle A < 90^\\circ$. Let $D$'
-         'be a point outside triangle $ABC$ such that $\\angle BAD = \\angle DAC$,'
-         '$\\angle BDC = 90^\\circ$. Suppose $AD = 1$ and $\\frac{BD}{CD} = \\frac{3}{2}$.'
-         'If $AB + AC$ can be expressed in the form $\\frac{a\\sqrt{b}}{c}$,'
-         'where $a, b, c$ are pairwise relatively prime integers, find $a + b + c$.'
-         ),
-        ('Solve the following math problem step by step.'
-         'The last line of your response should be of the form Answer: '
-         '$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
-         'Let $ABCD$ be a unit square in the plane. Points $X$ and $Y$ are chosen'
-         'independently and uniformly at random on the perimeter of $ABCD$.'
-         'If the expected value of the area of triangle $\\triangle AXY$'
-         'can be expressed as $\\frac{m}{n}$, for relatively prime positive'
-         'integers $m$ and $n$, compute $m+n$.'),
-        ('Solve the following math problem step by step.'
-         'The last line of your response should be of the form Answer: '
-         '$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
-         'Let $a, b, c$ be distinct numbers such that the equations $x^2 + ax + 1 = 0$'
-         'and $x^2 + bx + c = 0$ have a common real root, and the equations $x^2 + x + a = 0$'
-         'and $x^2 + cx + b = 0$ also have a common real root.'
-         'Compute the sum $a + b + c$.')
-    ]
-    model = "Qwen/Qwen3-30B-A3B"
-    sampling_params = SamplingParams(max_tokens=5,
-                                     n=1,
-                                     temperature=0.0,
-                                     top_p=1.0,
-                                     top_k=1)
-    with VllmRunner(model,
-                    max_model_len=1024,
-                    tensor_parallel_size=2,
-                    enforce_eager=False,
-                    gpu_memory_utilization=0.95,
-                    compilation_config={
-                        "cudagraph_capture_sizes":
-                        [4, 8, 12, 16, 24, 32, 40, 48],
-                        "cudagraph_mode": "FULL_DECODE_ONLY"
-                    }) as runner:
-        vllm_fullgraph_outputs = runner.model.generate(prompts,
-                                                       sampling_params)
-    with VllmRunner(
-            model,
-            max_model_len=1024,
-            tensor_parallel_size=2,
-            enforce_eager=True,
-            gpu_memory_utilization=0.95,
-    ) as runner:
-        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
-
-    vllm_fullgraph_outputs_list = []
-    for output in vllm_fullgraph_outputs:
-        vllm_fullgraph_outputs_list.append(
-            (output.outputs[0].index, output.outputs[0].text))
-
-    vllm_eager_outputs_list = []
-    for output in vllm_eager_outputs:
-        vllm_eager_outputs_list.append(
-            (output.outputs[0].index, output.outputs[0].text))
-
-    check_outputs_equal(
-        outputs_0_lst=vllm_eager_outputs_list,
-        outputs_1_lst=vllm_fullgraph_outputs_list,
-        name_0="vllm_eager_outputs",
-        name_1="vllm_fullgraph_outputs",
-    )