mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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126 lines
5.1 KiB
Python
126 lines
5.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 parameterized import parameterized
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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 Parameter, Tensor
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from tensorrt_llm.quantization.functional import smooth_quant_rms_norm
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class TestFunctional(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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('float32', False), ('float32', True)])
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def test_smooth_quant_rms_norm_plugin(self, dtype, dynamic_act_scaling):
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test_shape = [2, 5, 10, 10]
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x_data = torch.randn(
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*test_shape, dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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m = LlamaRMSNorm(test_shape[-1]) # LlamaRMSNorm only supports last dim
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scale_data = torch.randint(2, 32, (1, ), dtype=torch.float32)
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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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net = builder.create_network()
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net.plugin_config.set_rmsnorm_quantization_plugin(dtype)
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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 = 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 = dynamic_scales.trt_tensor
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dynamic_scales.name = 'dynamic_scales'
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network.mark_output(dynamic_scales)
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dynamic_scales.dtype = tensorrt_llm.str_dtype_to_trt('float32')
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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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output.dtype = tensorrt_llm.str_dtype_to_trt('int8')
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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(int8=True,
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fp16=(dtype == 'float16'),
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precision_constraints="obey"))
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assert build_engine is not None, "Build engine failed"
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with TrtRunner(build_engine) as runner:
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outputs = runner.infer(feed_dict={'x': x_data.cpu().numpy()})
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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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np.testing.assert_allclose(ref_quantized.cpu().numpy(),
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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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np.testing.assert_allclose(dynamic_scale.cpu().numpy(),
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outputs['dynamic_scales'],
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atol=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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net = builder.create_network()
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with tensorrt_llm.net_guard(net):
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tensorrt_llm.default_trtnet()
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# Get output tensor for SQ gemm
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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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