mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
129 lines
4.0 KiB
Python
129 lines
4.0 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 math
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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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import tensorrt_llm
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from tensorrt_llm import Tensor
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def split_vocab_size(vocab_size, tp_size):
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return int(math.ceil(vocab_size / tp_size))
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def split(v, tp_size, idx, dim=0):
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if tp_size == 1:
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return v
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if len(v.shape) == 1:
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return np.ascontiguousarray(np.split(v, tp_size)[idx])
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elif len(v.shape) == 2:
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return np.ascontiguousarray(np.split(v, tp_size, axis=dim)[idx])
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return None
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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([(
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'float32',
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1,
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), (
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'float32',
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0,
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), (
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'float16',
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1,
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), (
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'float16',
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0,
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)])
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def test_embedding(self, dtype, use_lookup_plugin):
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# torch gelu does not support float16
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fp16 = (dtype == 'float16')
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# meta data
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batch_size = 10
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vocab_size = 1000
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n_embed = 1024
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np.random.seed(0)
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# test data
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## input index
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index_shape = (batch_size)
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index_np = np.random.randint(low=0,
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high=vocab_size,
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size=index_shape,
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dtype=np.int32)
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index_data = torch.from_numpy(index_np)
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## weight data
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weight_np = np.random.rand(vocab_size, n_embed).astype(dtype)
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weight_data = torch.from_numpy(weight_np)
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# construct trt network
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builder = tensorrt_llm.Builder()
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# builder_config = builder.create_builder_config(
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# name='embedding',
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# precision='float16' if fp16 else 'float32',
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# timing_cache=timing_cache)
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net = builder.create_network()
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if use_lookup_plugin:
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net.plugin_config.set_lookup_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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index = Tensor(name='index',
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shape=index_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt('int32'))
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weight = Tensor(name='weight',
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shape=weight_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.embedding(input=index,
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weight=weight)
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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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# 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(fp16=(dtype == 'float16')))
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with TrtRunner(build_engine) as runner:
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outputs = runner.infer(feed_dict={
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'index': index_np,
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'weight': weight_np
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})
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# pytorch run
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embedding = torch.nn.Embedding.from_pretrained(weight_data)
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ref = embedding(index_data)
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# compare diff
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np.testing.assert_allclose(ref.cpu().numpy(),
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outputs['output'],
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atol=1e-3)
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