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* Update TensorRT-LLM --------- Co-authored-by: RunningLeon <mnsheng@yeah.net> Co-authored-by: Tlntin <TlntinDeng01@Gmail.com> Co-authored-by: ZHENG, Zhen <zhengzhen.z@qq.com> Co-authored-by: Pham Van Ngoan <ngoanpham1196@gmail.com> Co-authored-by: Nathan Price <nathan@abridge.com> Co-authored-by: Tushar Goel <tushar.goel.ml@gmail.com> Co-authored-by: Mati <132419219+matichon-vultureprime@users.noreply.github.com>
101 lines
3.9 KiB
Python
101 lines
3.9 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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from parameterized import parameterized
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from transformers.models.bloom.modeling_bloom import build_alibi_tensor
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import tensorrt_llm
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from tensorrt_llm import Tensor
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import create_session, run_session, unittest_name_func
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class TestAlibi(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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def create_random_bool_mask(self, batch_size, seq_len):
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mask = torch.zeros(size=[batch_size, seq_len],
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dtype=torch.bool,
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device="cuda")
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seq_lens = torch.randint(low=1,
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high=seq_len + 1,
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size=[batch_size],
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device="cuda")
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for b in range(batch_size):
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mask[b, :seq_lens[b]] = True
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return mask
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# We don't run alibi in FP16, so only check FP32 here.
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@parameterized.expand([(1, 64, 32), (16, 1, 64), (24, 20, 500),
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(32, 128, 60), (64, 32, 1024), (80, 12, 20),
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(112, 4, 389)],
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name_func=unittest_name_func)
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def test_alibi_biases(self, num_heads, batch_size, seq_len):
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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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trt_key = Tensor(name='fake_key',
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shape=(seq_len, ),
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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key_len = tensorrt_llm.functional.shape(trt_key, 0)
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slopes = tensorrt_llm.functional.constant(
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tensorrt_llm.functional.generate_alibi_slopes(
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num_heads=num_heads))
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output = tensorrt_llm.functional.generate_alibi_biases(
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slopes, key_len)
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output.mark_output('output')
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# trt run
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inputs = {
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'fake_key': torch.empty((seq_len, ),
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dtype=torch.int32,
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device="cuda")
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}
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session = create_session(builder, network, precision="float32")
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outputs = run_session(session, inputs)
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trt_alibi_output = outputs['output']
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# transformers reference
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binary_mask = self.create_random_bool_mask(batch_size, seq_len)
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ref = build_alibi_tensor(binary_mask, num_heads, torch.float32)
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ref = ref.reshape(batch_size, num_heads, 1, seq_len)
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# We only require that the alibi bias matches in the "valid" regions. Our TRT,
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# implementation differs in this regard for efficiency reasons but it does not matter
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# because these values will get masked before the softmax.
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binary_mask = binary_mask.reshape(batch_size, 1, 1, seq_len)
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ref *= binary_mask
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trt_alibi_output = torch.repeat_interleave(trt_alibi_output,
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batch_size,
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dim=0)
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trt_alibi_output *= binary_mask
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# compare diff
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torch.testing.assert_close(trt_alibi_output, ref, atol=1e-3, rtol=1e-2)
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