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https://github.com/NVIDIA/TensorRT-LLM.git
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118 lines
4.3 KiB
Python
118 lines
4.3 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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T, F = True, False
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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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def test_get_mask(self):
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# test data
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bs = 2
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nb = 3
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bl = 4
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old_device = torch.get_default_device()
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torch.set_default_device("cuda")
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draft_token_indices = torch.tensor(
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[
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[
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[0, 1, 2, 3],
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[0, 1, 4, 5],
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[0, 1, 2, 6],
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],
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[
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[0, 1, 2, 3],
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[0, 4, 5, 6],
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[0, 1, 7, 8],
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],
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],
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dtype=torch.int32,
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)
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assert draft_token_indices.shape == (bs, nb, bl)
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active_token_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,
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)
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# ref output
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ref_mask = torch.tensor([[[T, F, F, F, F, F, F, F, F],
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[T, T, F, F, F, F, F, F, F],
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[T, T, T, F, F, F, F, F, F],
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[T, T, T, T, F, F, F, F, F],
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[T, T, F, F, T, F, F, F, F],
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[T, T, F, F, T, T, F, F, F],
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[T, T, T, F, F, F, T, F, F],
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[T, F, F, F, F, F, F, F, F],
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[T, T, F, F, F, F, F, F, F]],
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[[T, F, F, F, F, F, F, F, F],
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[T, T, F, F, F, F, F, F, F],
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[T, T, T, F, F, F, F, F, F],
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[T, T, T, T, F, F, F, F, F],
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[T, F, F, F, T, F, F, F, F],
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[T, F, F, F, T, T, F, F, F],
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[T, F, F, F, T, T, T, F, F],
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[T, T, F, F, F, F, F, T, F],
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[T, T, F, F, F, F, F, T, T]]])
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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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draft_token_indices_trt = Tensor(
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name="draft_token_indices",
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shape=draft_token_indices.shape,
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dtype=tensorrt_llm.str_dtype_to_trt("int32"),
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)
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active_token_indices_trt = Tensor(
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name="active_token_indices",
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shape=active_token_indices.shape,
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dtype=tensorrt_llm.str_dtype_to_trt("int32"),
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)
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outputs = tensorrt_llm.models.redrafter.redrafter_helper._get_mask(
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draft_token_indices_trt, active_token_indices_trt, nb, bl)
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outputs.mark_output('spec_decoding_mask')
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# trt run
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session = create_session(builder, network, precision='float32')
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inputs = {
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"draft_token_indices": draft_token_indices,
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"active_token_indices": active_token_indices
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}
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outputs = run_session(session, inputs)
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torch.testing.assert_close(ref_mask,
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outputs["spec_decoding_mask"],
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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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