mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import unittest
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import torch
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import tensorrt_llm
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import tensorrt_llm.models.redrafter
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import tensorrt_llm.models.redrafter.redrafter_helper
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from tensorrt_llm import Tensor
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sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
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from utils.util import create_session, run_session
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NINF = -50000.0
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class TestReDrafter(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('warning')
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########################################################################################################################
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def test_top_1_logits(self):
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# test data
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bs = 2
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S = 5
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V = 4
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old_device = torch.get_default_device()
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torch.set_default_device("cuda")
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torch.manual_seed(0)
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logits = torch.rand((bs, S, V), dtype=torch.float32)
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ref_res = torch.tensor(
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[[[NINF, NINF, NINF, -0.], [-0., NINF, NINF, NINF],
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[NINF, -0., NINF, NINF], [NINF, -0., NINF, NINF],
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[-0., NINF, NINF, NINF]],
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[[-0., NINF, NINF, NINF], [NINF, -0., NINF, NINF],
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[-0., NINF, NINF, NINF], [NINF, -0., NINF, NINF],
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[NINF, -0., NINF, NINF]]],
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dtype=torch.float32)
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# construct trt network
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builder = tensorrt_llm.Builder()
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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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logits_t = Tensor(name='logits',
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shape=logits.shape,
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dtype=tensorrt_llm.torch_dtype_to_trt(
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logits.dtype))
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outputs = tensorrt_llm.models.redrafter.redrafter_helper._top_1_logits(
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logits_t, NINF)
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outputs.mark_output("outputs")
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# trt run
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session = create_session(
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builder,
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network,
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precision='float32',
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)
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inputs = {
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'logits': logits,
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}
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outputs = run_session(session, inputs)
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torch.testing.assert_close(outputs['outputs'], ref_res, rtol=0, atol=0)
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torch.set_default_device(old_device)
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return
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