mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-22 11:42:41 +08:00
79 lines
2.9 KiB
Python
79 lines
2.9 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 torch
|
|
from parameterized import parameterized
|
|
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')
|
|
|
|
@parameterized.expand([('float32', )])
|
|
def test_einsum(self, dtype):
|
|
# torch 1.13: "baddbmm_with_gemm" not implemented for 'Half'
|
|
# test data
|
|
x_shape = (12, 12, 96, 96)
|
|
y_shape = (12, 12, 96, 64)
|
|
x_data = torch.rand(x_shape,
|
|
dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
|
|
y_data = torch.rand(y_shape,
|
|
dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
|
|
equation = 'bnth,bnhs->bnts'
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
net = builder.create_network()
|
|
with tensorrt_llm.net_guard(net):
|
|
network = tensorrt_llm.default_trtnet()
|
|
x = Tensor(name='x',
|
|
shape=x_shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
y = Tensor(name='y',
|
|
shape=y_shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
output = tensorrt_llm.functional.einsum(equation, [x, y]).trt_tensor
|
|
output.name = 'output'
|
|
network.mark_output(output)
|
|
|
|
# trt run
|
|
profiles = [
|
|
Profile().add('x', x_shape, x_shape,
|
|
x_shape).add('y', y_shape, y_shape, y_shape)
|
|
]
|
|
build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
|
|
config=CreateConfig(profiles=profiles))
|
|
with TrtRunner(build_engine) as runner:
|
|
outputs = runner.infer(feed_dict={
|
|
'x': x_data.numpy(),
|
|
'y': y_data.numpy()
|
|
})
|
|
|
|
# pytorch run
|
|
ref = torch.functional.einsum(equation, [x_data, y_data])
|
|
|
|
# compare diff
|
|
np.testing.assert_allclose(ref.cpu().numpy(),
|
|
outputs['output'],
|
|
atol=1e-4)
|