mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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250d9c293d
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvan.sridharan@gmail.com> Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Eddie-Wang1120 <wangjinheng1120@163.com> Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com>
219 lines
9.0 KiB
Python
219 lines
9.0 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 unittest
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import _utils
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import numpy as np
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# isort: off
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import torch
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import tensorrt as trt
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# isort: on
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import os
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import sys
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from parameterized import parameterized
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from polygraphy.backend.trt import EngineFromNetwork, TrtRunner
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import tensorrt_llm
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from tensorrt_llm import Tensor
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from tensorrt_llm.quantization.functional import (dequantize, quantize,
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quantize_per_token)
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from tensorrt_llm.quantization.layers import quantize_tensor
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import unittest_name_func
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class TestQuantization(unittest.TestCase):
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def setUp(self):
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torch.manual_seed(42)
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tensorrt_llm.logger.set_level('error')
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@parameterized.expand([('float32', True), ('float16', True),
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('float32', False)],
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name_func=unittest_name_func)
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def test_quantize_tensor(self, dtype, use_plugin):
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x_data = torch.randn(
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(1, 2, 2, 4), dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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scaling_factor_data = torch.tensor(0.4, dtype=torch.float32)
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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if use_plugin:
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net.plugin_config.set_quantize_tensor_plugin()
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config = builder.trt_builder.create_builder_config()
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config.set_flag(trt.BuilderFlag.INT8)
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config.set_flag(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
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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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scaling_factor = tensorrt_llm.constant(scaling_factor_data.numpy())
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output = quantize_tensor(x, scaling_factor)
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net._mark_output(output, 'output', trt.int8)
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
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config)
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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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quantized = (x_data.cuda() * scaling_factor_data.cuda()).round().clip(
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-128, 127).to(dtype=torch.int8)
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np.testing.assert_allclose(quantized.cpu().numpy(), outputs['output'])
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def test_quantize_per_tensor(self):
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dtype = "float32"
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x_data = torch.randn(
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(1, 2, 2, 4), dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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scaling_factor_data = torch.tensor(0.4, dtype=torch.float32)
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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config = builder.trt_builder.create_builder_config()
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config.set_flag(trt.BuilderFlag.INT8)
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config.set_flag(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
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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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scaling_factor = tensorrt_llm.constant(scaling_factor_data.numpy())
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output = quantize(x, scaling_factor, 'int8')
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net._mark_output(output, 'output', trt.int8)
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
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config)
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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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ref = torch.quantize_per_tensor(x_data, scaling_factor_data, 0,
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torch.qint8)
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np.testing.assert_allclose(ref.int_repr().cpu().numpy(),
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outputs['output'])
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def test_quantize_per_channel(self):
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dtype = 'float32'
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x_data = torch.randn((4, 2, 4, 8), dtype=torch.float32)
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scaling_factor_data = torch.tensor((0.4, 0.3), dtype=torch.float32)
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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config = builder.trt_builder.create_builder_config()
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config.set_flag(trt.BuilderFlag.INT8)
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config.set_flag(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
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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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scaling_factor = tensorrt_llm.constant(scaling_factor_data.numpy())
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output = quantize(x, scaling_factor, 'int8', 1)
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net._mark_output(output, 'output', trt.int8)
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
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config)
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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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ref = torch.quantize_per_channel(x_data, scaling_factor_data,
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torch.tensor([0, 0]), 1, torch.qint8)
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np.testing.assert_allclose(ref.int_repr().cpu().numpy(),
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outputs['output'])
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@parameterized.expand([('float32', True), ('float16', True),
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('float32', False)],
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name_func=unittest_name_func)
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def test_quantize_per_token(self, dtype, use_plugin):
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x_data = torch.randn(
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(4, 2, 4, 8), dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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if use_plugin:
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net.plugin_config.set_quantize_per_token_plugin()
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config = builder.trt_builder.create_builder_config()
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config.set_flag(trt.BuilderFlag.INT8)
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config.set_flag(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
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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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output, scale = quantize_per_token(x)
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net._mark_output(output, 'output', trt.int8)
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net._mark_output(scale, 'scale',
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tensorrt_llm.str_dtype_to_trt(dtype))
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for l in net.trt_network:
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if l.get_output(0).dtype == tensorrt_llm._utils.str_dtype_to_trt(
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"int8"):
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l.get_output(0).set_dynamic_range(-127, 127)
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
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config)
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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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ref, ref_scale = _utils.gt_quantize_per_token(x_data)
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scale_shape = list(x_data.shape)
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scale_shape[-1] = 1
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ref_scale = ref_scale.reshape(scale_shape)
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np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])
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np.testing.assert_allclose(ref_scale.cpu().numpy(),
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outputs['scale'],
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atol=1e-2)
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def test_dequantize(self):
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dtype = 'int8'
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quantized_torch_tensor = torch.quantize_per_tensor(
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torch.tensor([-1.0, 0.0, 1.0, 2.0], dtype=torch.float32), 0.1, 0,
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torch.qint8)
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quantized_data = quantized_torch_tensor.int_repr()
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scaling_factor_data = torch.tensor(0.1, dtype=torch.float32)
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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config = builder.trt_builder.create_builder_config()
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config.set_flag(trt.BuilderFlag.INT8)
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config.set_flag(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
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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=quantized_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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scaling_factor = tensorrt_llm.constant(scaling_factor_data.numpy())
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output = dequantize(x, scaling_factor).trt_tensor
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output.name = 'output'
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network.mark_output(output)
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
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config)
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with TrtRunner(build_engine) as runner:
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outputs = runner.infer(
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feed_dict={'x': quantized_data.cpu().numpy()})
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ref = torch.dequantize(quantized_torch_tensor)
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np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])
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if __name__ == '__main__':
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unittest.main()
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