mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
9dbc5b38ba
* Update TensorRT-LLM --------- Co-authored-by: Marks101 <markus.schnoes@gmx.de> Co-authored-by: lkm2835 <lkm2835@gmail.com>
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# 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.
|
|
import os
|
|
import sys
|
|
import unittest
|
|
from itertools import product
|
|
|
|
import torch
|
|
from parameterized import parameterized
|
|
|
|
import tensorrt_llm
|
|
from tensorrt_llm import Tensor
|
|
from tensorrt_llm._utils import str_dtype_to_torch
|
|
|
|
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
|
|
from utils.util import create_session, run_session
|
|
|
|
|
|
class TestScatter(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
tensorrt_llm.logger.set_level('error')
|
|
|
|
def scatter_nd_ref(self, input_data, indices, updates):
|
|
input_data[tuple(indices.t())] = updates.view(-1)
|
|
return input_data
|
|
|
|
@parameterized.expand(
|
|
list(
|
|
product([
|
|
([[-3.0, -2.0, -1.0, 10.0, -25.0]
|
|
], [[0.0, 1.0, 2.0, -2.0, -1.0]]),
|
|
], [([0, 0, 1], [1, 0, 3])], [([-1.0, 0])],
|
|
['float16', 'float32', 'int32'])))
|
|
def test_scatter(self, input_data, indices, updates, dtype):
|
|
|
|
torch_dtype = str_dtype_to_torch(dtype)
|
|
input_data = torch.tensor(input_data).cuda()
|
|
indices = torch.tensor(indices).int().cuda()
|
|
updates = torch.tensor(updates).cuda()
|
|
|
|
input_data = input_data.to(torch_dtype)
|
|
updates = updates.to(torch_dtype)
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
network = builder.create_network()
|
|
with tensorrt_llm.net_guard(network):
|
|
input_t = Tensor(name='input',
|
|
shape=input_data.shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
indices_t = Tensor(name='indices',
|
|
shape=indices.shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt('int32'))
|
|
updates_t = Tensor(name='updates',
|
|
shape=updates.shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
|
|
output = tensorrt_llm.functional.scatter_nd(input_t, indices_t,
|
|
updates_t)
|
|
|
|
output.mark_output('output')
|
|
|
|
# trt run
|
|
session = create_session(builder, network, precision=dtype)
|
|
inputs = {'input': input_data, 'indices': indices, 'updates': updates}
|
|
outputs = run_session(session, inputs)
|
|
|
|
# pytorch run
|
|
ref = self.scatter_nd_ref(input_data, indices, updates)
|
|
|
|
# compare diff
|
|
torch.testing.assert_close(ref, outputs['output'])
|