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https://github.com/NVIDIA/TensorRT-LLM.git
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deaae40bd7
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
71 lines
2.5 KiB
Python
71 lines
2.5 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 unittest
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import numpy as np
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import torch
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from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner
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from transformers.models.llama.modeling_llama import LlamaRMSNorm
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import tensorrt_llm
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from tensorrt_llm import Tensor
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from tensorrt_llm.functional import rms_norm
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class TestPrecisionControl(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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def test_precision_control(self):
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# test data
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test_shape = [2, 5, 10, 10]
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dtype = 'float32'
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x_data = torch.randn(*test_shape)
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m = LlamaRMSNorm(test_shape[-1]) # LlamaRMSNorm only supports last dim
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# construct trt network
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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with tensorrt_llm.net_guard(net):
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network = tensorrt_llm.default_trtnet()
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x = Tensor(name='x',
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shape=x_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = rms_norm(x,
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test_shape[-1],
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weight=tensorrt_llm.constant(
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m.weight.detach().cpu().numpy()))
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output = output.trt_tensor
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output.name = 'output'
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network.mark_output(output)
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# trt run
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build_engine = EngineFromNetwork(
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(builder.trt_builder, net.trt_network),
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config=CreateConfig(precision_constraints='obey'))
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with TrtRunner(build_engine) as runner:
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outputs = runner.infer(feed_dict={'x': x_data.numpy()})
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# pytorch run
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with torch.no_grad():
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ref = m(x_data)
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# compare diff
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np.testing.assert_allclose(ref.cpu().numpy(),
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outputs['output'],
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atol=1e-6)
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