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https://github.com/NVIDIA/TensorRT-LLM.git
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202bed4574
Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> Signed-off-by: Wangshanshan <30051912+dominicshanshan@users.noreply.github.com>
131 lines
5.3 KiB
Python
131 lines
5.3 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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from itertools import chain, product
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import numpy as np
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import tensorrt as trt
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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 utils.util import unittest_name_func
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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 smooth_quant_gemm
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from . import _utils
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class TestSmoothQuantGemm(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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def _sq_gemm(self, m, n, k, dtype, per_token_scaling, per_channel_scaling,
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use_plugin):
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# Init operands for multiplication in int32
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shape1 = (m, k)
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mat1 = torch.randint(-128, 128, shape1, dtype=torch.int8)
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shape2 = (n, k)
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mat2 = torch.randint(-128, 128, shape2, dtype=torch.int8)
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# Init scales in fp32
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shape_scale_a = (m, 1) if per_token_scaling else (1, 1)
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scale_a_torch = torch.ones(shape_scale_a, dtype=torch.float32) * 1e-2
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scale_a_torch *= torch.randint(1,
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10,
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shape_scale_a,
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dtype=torch.float32)
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shape_scale_b = (1, n) if per_channel_scaling else (1, 1)
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scale_b_torch = torch.ones(shape_scale_b, dtype=torch.float32) * 1e-2
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scale_b_torch *= torch.randint(1,
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10,
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shape_scale_b,
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dtype=torch.float32)
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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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# Allow SQ plugin of dtype type
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if use_plugin:
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network.plugin_config.smooth_quant_gemm_plugin = dtype
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with tensorrt_llm.net_guard(network):
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# Init TensorRT LLM tensor for mat1
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x = Tensor(name='x',
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shape=mat1.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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# Init TensorRT LLM tensor for mat2
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y = Tensor(name='y',
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shape=mat2.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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# Init TensorRT LLM tensor for per token scaling
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scale_a = tensorrt_llm.functional.constant(scale_a_torch.numpy())
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# Init TensorRT LLM tensor for per channel scaling
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scale_b = tensorrt_llm.functional.constant(scale_b_torch.numpy())
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# Get output tensor for SQ gemm
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output = smooth_quant_gemm(x, y, scale_a, scale_b,
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per_token_scaling, per_channel_scaling,
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dtype)
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output.mark_output('output', dtype)
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# TODO: When dtype=int32, per_token_scaling=False, per_channel_scaling=False,
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# This test will break using new API on A30, only when running with all other unit tests.
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# This is a weird issue, so skip changing this file.
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engine = EngineFromNetwork(
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(builder.trt_builder, network.trt_network),
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config=CreateConfig(
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memory_pool_limits={trt.MemoryPoolType.WORKSPACE: 33554432}))
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# Infer engine
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with TrtRunner(engine) as runner:
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outputs = runner.infer(feed_dict={
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'x': mat1.numpy(),
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'y': mat2.numpy(),
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})
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ref = _utils.gt_matmul_smooth_quant(mat1,
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mat2,
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scale_a_torch,
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scale_b_torch,
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dtype,
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bias=None)
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np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])
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@parameterized.expand(chain(
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product(["float16", "float32", "int32"], [True, False], [True, False],
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[True]),
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product(["float16", "float32"], [True, False], [True, False], [False])),
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name_func=unittest_name_func)
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def test_matmul(self, dtype, per_token_scaling, per_channel_scaling,
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use_plugin):
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bs = 2
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inseq = 16
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hidden_size = 768
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# qkv_gemm
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self._sq_gemm(bs * inseq, 3 * hidden_size, hidden_size, dtype,
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per_token_scaling, per_channel_scaling, use_plugin)
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# mlp_gemm_1
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self._sq_gemm(bs * inseq, 4 * hidden_size, hidden_size, dtype,
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per_channel_scaling, per_token_scaling, use_plugin)
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if __name__ == '__main__':
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unittest.main()
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