TensorRT-LLMs/tests/functional/test_expand.py

# 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_expand_1(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 10)
        output_shape = (2, 10)
        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.expand(input, shape).trt_tensor
            output.name = 'output'
            network.mark_output(output)

        # trt run
        profiles = [Profile().add('shape', (1, 1), input_shape, (10, 10))]
        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.expand(output_shape)

        # compare diff
        np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])

    def test_expand_2(self):
        # test data
        dtype = 'float32'
        input_shape = (2, 1, 1, 10)
        output_shape = (2, 1, 12, 10)
        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.expand(input, shape).trt_tensor
            output.name = 'output'
            network.mark_output(output)

        # trt run
        profiles = [
            Profile().add('shape', (1, 1, 1, 1), input_shape, (10, 10, 10, 10))
        ]
        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.expand(output_shape)

        # compare diff
        np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])

    def test_expand_3(self):
        # test data
        dtype = 'float32'
        hidden_dim = 10
        input_shape = (1, hidden_dim)
        batch_size = 8
        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))
            input_length = tensorrt_llm.functional.constant(
                np.array([0] * batch_size, dtype=np.int32))
            expand_shape = tensorrt_llm.functional.concat(
                [tensorrt_llm.functional.shape(input_length, 0), hidden_dim])
            output = tensorrt_llm.functional.expand(input,
                                                    expand_shape).trt_tensor
            output.name = 'output'
            network.mark_output(output)
            output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)

        # 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()})

        ref = input_data.expand([batch_size, hidden_dim])
        np.testing.assert_allclose(ref.cpu().numpy(),
                                   outputs['output'],
                                   atol=1e-5)