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2d234357c6
* Update TensorRT-LLM --------- Co-authored-by: Altair-Alpha <62340011+Altair-Alpha@users.noreply.github.com>
178 lines
7.2 KiB
Python
178 lines
7.2 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_shape
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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_get_draft_token_array(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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beams = torch.tensor(
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[ # Assuming a batch of two sequences, each has 3 beams of 4 tokens.
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[
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[91, 92, 93, 95],
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[91, 92, 94, 96],
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[91, 92, 93, 97],
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],
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[
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[93, 94, 95, 92],
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[93, 95, 96, 93],
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[93, 94, 97, 96],
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],
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],
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dtype=torch.int32,
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)
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prefix_match_indices = torch.tensor(
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[[[0, 0, 0, 0], [0, 0, 1, 1], [0, 0, 0, 2]],
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[[0, 0, 0, 0], [0, 1, 1, 1], [0, 0, 2, 2]]],
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dtype=torch.int32,
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device="cpu",
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)
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position_ids_base = torch.tensor([3, 10], dtype=torch.int32)
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assert beams.shape == (bs, nb, bl)
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assert prefix_match_indices.shape == (bs, nb, bl)
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# ref outputs
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ref_active_flat_tokens = torch.tensor(
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[91, 92, 93, 95, 94, 96, 97, 93, 94, 95, 92, 95, 96, 93, 97, 96],
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dtype=torch.int32)
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ref_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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ref_total_lengths = torch.tensor([7, 9], dtype=torch.int32)
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ref_max_len = ref_total_lengths.max().int()
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ref_total_gen_len = ref_total_lengths.sum().int()
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ref_position_offsets = ref_active_token_indices % bl
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position_ids = ref_position_offsets + position_ids_base.unsqueeze(1)
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ref_packed_position_ids = torch.concat(
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[position_ids[b, :ref_total_lengths[b]] for b in range(bs)]).int()
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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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beams_t = Tensor(name='beams',
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shape=beams.shape,
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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prefix_match_indices_t = Tensor(
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name='prefix_match_indices',
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shape=prefix_match_indices.shape,
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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position_id_base_t = Tensor(
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name='position_ids_base',
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shape=position_ids_base.shape,
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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outputs = tensorrt_llm.models.redrafter.redrafter_helper._get_draft_token_array(
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beams_t, prefix_match_indices_t, nb, bl, position_id_base_t)
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outputs[0].mark_output('active_flat_tokens')
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outputs[1].mark_output('active_token_indices')
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outputs[2].mark_output('total_lengths')
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outputs[3].mark_output('max_len')
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outputs[4].mark_output('total_gen_len')
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outputs[5].mark_output('position_offsets')
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outputs[6].mark_output('packed_position_ids')
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# trt run
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profile = builder.trt_builder.create_optimization_profile()
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set_input_shape(profile, beams_t, beams.shape, beams)
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set_input_shape(profile, position_id_base_t, position_ids_base.shape,
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position_ids_base)
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set_input_shape(profile, prefix_match_indices_t,
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prefix_match_indices.shape, prefix_match_indices)
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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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'beams': beams,
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'prefix_match_indices': prefix_match_indices,
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'position_ids_base': position_ids_base
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}
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outputs = {
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"active_flat_tokens": torch.empty((bs * nb * bl, ),
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dtype=torch.int32),
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"active_token_indices": torch.empty((bs * nb * bl, ),
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dtype=torch.int32),
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"total_lengths": torch.empty((bs, ), dtype=torch.int32),
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"max_len": torch.empty((), dtype=torch.int32),
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"total_gen_len": torch.empty((), dtype=torch.int32),
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"position_offsets": torch.empty((bs * nb * bl, ),
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dtype=torch.int32),
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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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# compare diff
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torch.testing.assert_close(outputs['max_len'],
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ref_max_len,
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rtol=0,
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atol=0)
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torch.testing.assert_close(outputs['total_gen_len'],
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ref_total_gen_len,
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rtol=0,
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atol=0)
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torch.testing.assert_close(outputs['total_lengths'],
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ref_total_lengths,
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rtol=0,
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atol=0)
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torch.testing.assert_close(
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outputs['active_flat_tokens'][:ref_total_gen_len],
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ref_active_flat_tokens,
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rtol=0,
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atol=0)
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torch.testing.assert_close(
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outputs['active_token_indices'][:bs * ref_max_len].view(
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bs, ref_max_len),
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ref_active_token_indices,
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rtol=0,
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atol=0)
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torch.testing.assert_close(
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outputs['position_offsets'][:bs * ref_max_len].view(
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bs, ref_max_len),
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ref_position_offsets,
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rtol=0,
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atol=0)
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torch.testing.assert_close(
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outputs['packed_position_ids'][:ref_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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