mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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135 lines
5.0 KiB
Python
135 lines
5.0 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.llama.modeling_llama import LlamaRMSNorm
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import tensorrt_llm
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from tensorrt_llm import Parameter, Tensor
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from tensorrt_llm.quantization.functional import smooth_quant_rms_norm
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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, skip_bf16_pre_ampere,
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unittest_name_func)
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class TestSmoothQuantRmsNorm(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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@parameterized.expand([('float16', False), ('float16', True),
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('bfloat16', False), ('bfloat16', True),
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('float32', False), ('float32', True)],
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name_func=unittest_name_func)
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def test_smooth_quant_rms_norm_plugin(self, dtype, dynamic_act_scaling):
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# Skip tests that are not supported in pre-ampere architecture
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skip_bf16_pre_ampere(dtype)
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test_shape = [2, 5, 10, 10]
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x_data = torch.randn(
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*test_shape,
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
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device="cuda")
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m = LlamaRMSNorm(
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test_shape[-1]).cuda() # LlamaRMSNorm only supports last dim
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scale_data = torch.randint(2,
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32, (1, ),
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dtype=torch.float32,
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device="cuda")
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with torch.no_grad():
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def cast_to_int8_with_sat(tensor):
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return tensor.round().clip(-128, 127).to(dtype=torch.int8)
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# pytorch run
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with torch.no_grad():
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ref = m(x_data).to(dtype=torch.float32)
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if dynamic_act_scaling:
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abs_max_f, _ = ref.abs().max(dim=-1, keepdim=True)
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dynamic_scale = abs_max_f / 127.0
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ref_quantized = cast_to_int8_with_sat(ref *
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(127.0 / abs_max_f))
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else:
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ref_quantized = cast_to_int8_with_sat(ref * scale_data)
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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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network.plugin_config.rmsnorm_quantization_plugin = dtype
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with tensorrt_llm.net_guard(network):
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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 = smooth_quant_rms_norm(
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x,
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test_shape[-1],
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weight=tensorrt_llm.constant(m.weight.detach().cpu().numpy()),
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scale=Parameter(scale_data.cpu().numpy()).value,
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eps=m.variance_epsilon,
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dynamic_act_scaling=dynamic_act_scaling)
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if dynamic_act_scaling:
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output, dynamic_scales = output
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dynamic_scales.mark_output('dynamic_scales', 'float32')
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output.mark_output('output', 'int8')
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session = create_session(builder, network, precision=dtype, int8=True)
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inputs = {
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'x': x_data,
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}
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outputs = run_session(session, inputs)
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# compare diff of quantized output
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# Set absolute tolerance to 1 to mitigate some rounding error
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torch.testing.assert_close(ref_quantized,
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outputs['output'],
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atol=1,
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rtol=0)
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# compare diff of dynamic activation scales
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if dynamic_act_scaling:
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torch.testing.assert_close(dynamic_scale,
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outputs['dynamic_scales'],
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atol=1e-2,
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rtol=1e-2)
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def test_sq_rms_norm_no_plugin(self):
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# Create builder
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builder = tensorrt_llm.Builder()
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# Create empty network
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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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# SQ Rmsnorm ootb should fail.
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with self.assertRaisesRegex(
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TypeError,
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"Smooth Quant Rms Norm is only supported with plugin"):
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smooth_quant_rms_norm(None, 0, None, None, None, 0)
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if __name__ == '__main__':
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unittest.main()
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