mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-22 11:42:41 +08:00
deaae40bd7
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import os
|
|
import sys
|
|
import unittest
|
|
from itertools import product
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
from parameterized import parameterized
|
|
from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner
|
|
|
|
import tensorrt_llm
|
|
from tensorrt_llm import Parameter, Tensor
|
|
from tensorrt_llm._utils import torch_to_numpy
|
|
|
|
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
|
|
from utils.util import getSMVersion
|
|
|
|
|
|
class TestFunctional(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
tensorrt_llm.logger.set_level('error')
|
|
torch.manual_seed(42)
|
|
|
|
@parameterized.expand(
|
|
list(product([True, False], ['float16', 'float32', 'bfloat16'])))
|
|
def test_layer_norm_plugin(self, remove_batch_dim, dtype):
|
|
|
|
# Skip tests that are not supported in pre-ampere architecture
|
|
if getSMVersion() < 80:
|
|
if dtype == 'bfloat16':
|
|
pytest.skip(
|
|
"bfloat16 is not supported in pre-ampere architecture")
|
|
|
|
# test data
|
|
hidden_size = 1024
|
|
x_data = torch.randn((8, 128, hidden_size) if not remove_batch_dim else
|
|
(8 * 128, hidden_size),
|
|
dtype=torch.float64,
|
|
device="cuda")
|
|
weight = torch.randn((hidden_size), dtype=torch.float64, device="cuda")
|
|
bias = torch.randn((hidden_size), dtype=torch.float64, device="cuda")
|
|
eps = 1e-5
|
|
|
|
m = torch.nn.LayerNorm(hidden_size,
|
|
eps=eps,
|
|
dtype=torch.float64,
|
|
device="cuda")
|
|
m.weight = torch.nn.Parameter(weight)
|
|
m.bias = torch.nn.Parameter(bias)
|
|
|
|
# pytorch run
|
|
with torch.no_grad():
|
|
ref = m(x_data)
|
|
|
|
m.to(tensorrt_llm._utils.str_dtype_to_torch(dtype))
|
|
x_data = x_data.to(tensorrt_llm._utils.str_dtype_to_torch(dtype))
|
|
|
|
gamma_data = m.weight.detach().cpu()
|
|
beta_data = m.bias.detach().cpu()
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
net = builder.create_network()
|
|
net.plugin_config.set_layernorm_plugin(dtype)
|
|
with tensorrt_llm.net_guard(net):
|
|
network = tensorrt_llm.default_trtnet()
|
|
x = Tensor(name='x',
|
|
shape=x_data.shape,
|
|
dtype=tensorrt_llm.str_dtype_to_trt(dtype))
|
|
weight = Parameter(torch_to_numpy(gamma_data.cpu())).value
|
|
bias = Parameter(torch_to_numpy(beta_data.cpu())).value
|
|
|
|
output = tensorrt_llm.functional.layer_norm(x, hidden_size, weight,
|
|
bias, eps).trt_tensor
|
|
output.name = 'output'
|
|
network.mark_output(output)
|
|
output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)
|
|
|
|
# trt run
|
|
build_engine = EngineFromNetwork(
|
|
(builder.trt_builder, net.trt_network),
|
|
config=CreateConfig(fp16=(dtype == 'float16'),
|
|
bf16=(dtype == 'bfloat16')))
|
|
assert build_engine is not None, "Build engine failed"
|
|
with TrtRunner(build_engine) as runner:
|
|
outputs = runner.infer(feed_dict={'x': x_data.cpu()})
|
|
|
|
# compare diff
|
|
dtype_atol = {"float16": 2e-2, "float32": 2e-6, "bfloat16": 8e-2}
|
|
np.testing.assert_allclose(ref.cpu().numpy(),
|
|
outputs['output'].to(torch.float32),
|
|
atol=dtype_atol[dtype])
|