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https://github.com/NVIDIA/TensorRT-LLM.git
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5955b8afba
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
121 lines
4.3 KiB
Python
121 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 unittest
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import numpy as np
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import torch
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from polygraphy.backend.trt import EngineFromNetwork, TrtRunner
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from torch import nn
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import tensorrt_llm
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from tensorrt_llm import Module, Tensor
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class TorchMLP(nn.Module):
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def __init__(self, hidden_size, ffn_hidden_size, bias=True):
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super().__init__()
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self.fc = nn.Linear(hidden_size, ffn_hidden_size, bias=bias)
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self.proj = nn.Linear(ffn_hidden_size, hidden_size, bias=bias)
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def forward(self, hidden_states):
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inter = self.fc(hidden_states)
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inter = nn.functional.relu(inter)
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output = self.proj(inter)
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return output, inter
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class MLP(Module):
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def __init__(self,
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hidden_size,
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ffn_hidden_size,
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bias=True,
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tp_group=None,
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tp_size=1):
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super().__init__()
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self.fc = tensorrt_llm.layers.ColumnLinear(hidden_size,
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ffn_hidden_size,
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bias=bias,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False)
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self.proj = tensorrt_llm.layers.RowLinear(ffn_hidden_size,
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hidden_size,
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bias=bias,
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tp_group=tp_group,
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tp_size=tp_size)
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def forward(self, hidden_states):
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inter = self.fc(hidden_states)
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inter = tensorrt_llm.functional.relu(inter)
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self.register_network_output('inter', inter)
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output = self.proj(inter)
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return output
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class TestDebuggingAPI(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_debugging_api(self):
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# test data
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dtype = 'float32'
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hidden_size = 768
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x_data = torch.randn(2, 16, hidden_size)
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tm = TorchMLP(hidden_size=hidden_size,
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ffn_hidden_size=hidden_size * 4,
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bias=False)
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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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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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gm = MLP(hidden_size=hidden_size,
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ffn_hidden_size=4 * hidden_size,
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bias=False)
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gm.fc.weight.value = tm.fc.weight.detach().cpu().numpy()
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gm.proj.weight.value = tm.proj.weight.detach().cpu().numpy()
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output = gm.forward(x)
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net._mark_output(output, 'output',
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tensorrt_llm.str_dtype_to_trt(dtype))
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for k, v in gm.named_network_outputs():
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net._mark_output(v, k, tensorrt_llm.str_dtype_to_trt(dtype))
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# trt run
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
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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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ref1, ref2 = tm(x_data)
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# compare diff
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np.testing.assert_allclose(ref1.cpu().numpy(),
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outputs['output'],
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atol=1e-5)
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np.testing.assert_allclose(ref2.cpu().numpy(),
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outputs['inter'],
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atol=1e-5)
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