mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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119 lines
4.5 KiB
Python
119 lines
4.5 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, set_input_shapes
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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_packed_position_ids(self):
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bs = 2
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nb = 3
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bl = 4
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max_gen = nb * (bl - 1) + 1
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old_device = torch.get_default_device()
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torch.set_default_device("cuda")
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active_indices = torch.tensor(
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[[0, 1, 2, 3, 6, 7, 11, 0, 1], [0, 1, 2, 3, 5, 6, 7, 10, 11]],
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dtype=torch.int32) % bl
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total_lengths = torch.tensor([7, 9], dtype=torch.int32)
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total_gen_len = total_lengths.sum()
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max_tl = total_lengths.max()
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indices = torch.arange(max_tl, dtype=torch.int32)
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position_ids_base = torch.tensor([3, 10], dtype=torch.int32)
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# ref outputs
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ref_packed_position_ids = torch.tensor(
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[3, 4, 5, 6, 5, 6, 6, 10, 11, 12, 13, 11, 12, 13, 12, 13],
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dtype=torch.int32)
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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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active_indices_t = Tensor(
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name='ai',
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shape=[-1, -1],
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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indices_t = Tensor(name='i',
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shape=[-1],
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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total_lengths_t = Tensor(
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name='tl',
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shape=[-1],
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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position_ids_base_t = Tensor(
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name='pib',
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shape=[-1],
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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output = tensorrt_llm.models.redrafter.redrafter_helper._get_packed_position_ids(
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active_indices_t,
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indices_t,
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total_lengths_t,
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position_ids_base_t,
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)
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output.mark_output('packed_position_ids')
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# save onnx
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# model_path = 'packed_position.onnx'
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# to_onnx(net.trt_network, model_path)
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# trt run
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# needs profile for dynamic shape
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profile = builder.trt_builder.create_optimization_profile()
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set_input_shapes(profile, active_indices_t, [1, 0], [16, max_gen // 2],
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[32, max_gen])
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set_input_shapes(profile, indices_t, [0], [max_gen // 2], [max_gen])
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set_input_shapes(profile, total_lengths_t, [1], [16], [32])
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set_input_shapes(profile, position_ids_base_t, [1], [16], [32])
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session = create_session(builder,
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network,
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precision='float32',
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optimization_profiles=[profile])
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inputs = {
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'ai': active_indices,
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'i': indices,
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'tl': total_lengths,
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'pib': position_ids_base,
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}
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outputs = {
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"packed_position_ids": torch.empty((bs * nb * bl, ),
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dtype=torch.int32),
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}
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outputs = run_session(session, inputs, outputs)
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torch.testing.assert_close(
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outputs['packed_position_ids'][:total_gen_len],
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ref_packed_position_ids,
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rtol=0,
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atol=0)
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torch.set_default_device(old_device)
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return
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