mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 unittest
|
|
|
|
import numpy as np
|
|
import tensorrt as trt
|
|
import torch
|
|
from polygraphy.backend.trt import (CreateConfig, EngineFromNetwork, Profile,
|
|
TrtRunner)
|
|
|
|
import tensorrt_llm
|
|
from tensorrt_llm import Tensor
|
|
|
|
|
|
class TestFunctional(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
tensorrt_llm.logger.set_level('error')
|
|
|
|
def test_view_static(self):
|
|
# test data
|
|
dtype = 'float32'
|
|
input_shape = (4, 3)
|
|
output_shape = (12, 1)
|
|
input_data = torch.rand(
|
|
input_shape, dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
net = builder.create_network()
|
|
with tensorrt_llm.net_guard(net):
|
|
network = tensorrt_llm.default_trtnet()
|
|
input = Tensor(name='input',
|
|
shape=input_shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
output = tensorrt_llm.functional.view(input=input,
|
|
shape=output_shape).trt_tensor
|
|
output.name = 'output'
|
|
network.mark_output(output)
|
|
|
|
# trt run
|
|
build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
|
|
with TrtRunner(build_engine) as runner:
|
|
outputs = runner.infer(feed_dict={'input': input_data.numpy()})
|
|
|
|
# pytorch run
|
|
ref = input_data.view(output_shape)
|
|
|
|
# compare diff
|
|
np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])
|
|
|
|
def test_view_dynamic(self):
|
|
# test data
|
|
dtype = 'float32'
|
|
input_shape = (4, 3)
|
|
output_shape = (2, 6)
|
|
input_data = torch.rand(
|
|
input_shape, dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
|
|
shape_data = torch.tensor(output_shape).int()
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
net = builder.create_network()
|
|
with tensorrt_llm.net_guard(net):
|
|
network = tensorrt_llm.default_trtnet()
|
|
input = Tensor(name='input',
|
|
shape=input_shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
shape = Tensor(name='shape',
|
|
shape=(len(input_shape), ),
|
|
dtype=trt.int32)
|
|
output = tensorrt_llm.functional.view(input=input,
|
|
shape=shape).trt_tensor
|
|
output.name = 'output'
|
|
network.mark_output(output)
|
|
|
|
# trt run
|
|
profiles = [Profile().add('shape', (1, 1), input_shape, (12, 12))]
|
|
build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
|
|
config=CreateConfig(profiles=profiles))
|
|
with TrtRunner(build_engine) as runner:
|
|
outputs = runner.infer(feed_dict={
|
|
'input': input_data.numpy(),
|
|
'shape': shape_data.numpy()
|
|
})
|
|
|
|
# pytorch run
|
|
ref = input_data.view(output_shape)
|
|
|
|
# compare diff
|
|
np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])
|