mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
97 lines
3.7 KiB
Python
97 lines
3.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import torch
|
|
from parameterized import parameterized
|
|
from polygraphy.backend.trt import EngineFromNetwork, TrtRunner
|
|
from transformers.models.bloom.modeling_bloom import build_alibi_tensor
|
|
|
|
import tensorrt_llm
|
|
from tensorrt_llm import Tensor
|
|
|
|
|
|
class TestFunctional(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
tensorrt_llm.logger.set_level('error')
|
|
|
|
def create_random_bool_mask(self, batch_size, seq_len):
|
|
mask = torch.zeros(size=[batch_size, seq_len], dtype=torch.bool)
|
|
seq_lens = torch.randint(low=1, high=seq_len + 1, size=[batch_size])
|
|
|
|
for b in range(batch_size):
|
|
mask[b, :seq_lens[b]] = True
|
|
|
|
return mask
|
|
|
|
# We don't run alibi in FP16, so only check FP32 here.
|
|
@parameterized.expand([(1, 64, 32), (16, 1, 64), (24, 20, 500),
|
|
(32, 128, 60), (64, 32, 1024), (80, 12, 20),
|
|
(112, 4, 389)])
|
|
def test_alibi_biases(self, num_heads, batch_size, seq_len):
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
net = builder.create_network()
|
|
with tensorrt_llm.net_guard(net):
|
|
network = tensorrt_llm.default_trtnet()
|
|
trt_key = Tensor(name='fake_key',
|
|
shape=(seq_len, ),
|
|
dtype=tensorrt_llm.str_dtype_to_trt('int32'))
|
|
|
|
key_len = tensorrt_llm.functional.shape(trt_key, 0)
|
|
slopes = tensorrt_llm.functional.generate_alibi_slopes(
|
|
num_heads=num_heads)
|
|
output = tensorrt_llm.functional.generate_alibi_biases(
|
|
slopes, key_len).trt_tensor
|
|
output.name = 'output'
|
|
network.mark_output(output)
|
|
|
|
# trt run
|
|
build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
|
|
with TrtRunner(build_engine) as runner:
|
|
print(seq_len)
|
|
outputs = runner.infer(
|
|
feed_dict={
|
|
'fake_key': np.empty(shape=(seq_len, ), dtype=np.int32)
|
|
})
|
|
|
|
trt_alibi_output = outputs['output']
|
|
|
|
# transformers reference
|
|
binary_mask = self.create_random_bool_mask(batch_size, seq_len)
|
|
ref = build_alibi_tensor(binary_mask, num_heads,
|
|
torch.float32).cpu().numpy()
|
|
ref = ref.reshape(batch_size, num_heads, 1, seq_len)
|
|
|
|
# We only require that the alibi bias matches in the "valid" regions. Our TRT,
|
|
# implementation differs in this regard for efficieny reasons but it does not matter
|
|
# because these values will get masked before the softmax.
|
|
binary_mask = binary_mask.cpu().numpy().reshape(batch_size, 1, 1,
|
|
seq_len)
|
|
ref *= binary_mask
|
|
|
|
trt_alibi_output = np.repeat(trt_alibi_output, batch_size, axis=0)
|
|
trt_alibi_output *= binary_mask
|
|
|
|
# compare diff
|
|
np.testing.assert_allclose(ref, trt_alibi_output, atol=1e-3)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|