mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-10-25 06:43:41 +08:00
154 lines
4.4 KiB
Python
154 lines
4.4 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 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 torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
from modelopt.torch.quantization.nn import QuantConv2d, QuantLinear
|
|
|
|
|
|
class QuantConvLinear(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.conv1 = QuantConv2d(1, 8, kernel_size=3)
|
|
self.conv2 = QuantConv2d(8, 4, kernel_size=3)
|
|
self.fc1 = QuantLinear(64, 8)
|
|
self.fc2 = QuantLinear(8, 1)
|
|
|
|
def forward(self, x):
|
|
x = F.relu(F.max_pool2d(self.conv1(x), 2))
|
|
x = F.relu(F.max_pool2d(self.conv2(x), 2))
|
|
x = x.view(-1, 64)
|
|
x = F.relu(self.fc1(x))
|
|
x = F.dropout(x, training=self.training)
|
|
x = self.fc2(x)
|
|
return F.log_softmax(x, dim=1)
|
|
|
|
def get_input(self):
|
|
return torch.randn(2, 1, 8, 8)
|
|
|
|
|
|
class SimpleLinear(nn.Module):
|
|
"""Test Linear model for ONNX export."""
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.net = nn.Sequential(
|
|
nn.Linear(16, 32), nn.ReLU(), nn.Linear(32, 64), nn.ReLU(), nn.Linear(64, 16)
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.net(x)
|
|
|
|
@classmethod
|
|
def get_input(cls):
|
|
return torch.randn(2, 16)
|
|
|
|
|
|
class SimpleConv(nn.Module):
|
|
"""Test Conv model for ONNX export."""
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.net = nn.Sequential(
|
|
nn.Conv2d(16, 32, 3, padding=1),
|
|
nn.ReLU(),
|
|
nn.BatchNorm2d(32),
|
|
nn.Conv2d(32, 64, 3, padding=1),
|
|
nn.ReLU(),
|
|
nn.BatchNorm2d(64),
|
|
nn.Conv2d(64, 16, 3, padding=1),
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.net(x)
|
|
|
|
@classmethod
|
|
def get_input(cls):
|
|
return torch.randn(2, 16, 4, 4)
|
|
|
|
|
|
class SimpleConvLinear(nn.Module):
|
|
"""Test ConvLinear model for ONNX export."""
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.net = nn.Sequential(
|
|
nn.Conv2d(16, 32, 3, padding=1),
|
|
nn.ReLU(),
|
|
nn.BatchNorm2d(32),
|
|
nn.Conv2d(32, 64, 3, padding=1),
|
|
nn.ReLU(),
|
|
nn.BatchNorm2d(64),
|
|
nn.Conv2d(64, 16, 3, padding=1),
|
|
nn.Flatten(),
|
|
nn.Linear(16 * 4 * 4, 16),
|
|
nn.ReLU(),
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.net(x)
|
|
|
|
@classmethod
|
|
def get_input(cls):
|
|
return torch.randn(4, 16, 4, 4)
|
|
|
|
|
|
class SDPAAttention(nn.Module):
|
|
def forward(self, qkv: tuple, extra_arg=None):
|
|
# NOTE: Add unused argument y with default value to test that replaced attention retain original defaults
|
|
q, k, v = qkv
|
|
return nn.functional.scaled_dot_product_attention(q, k, v)
|
|
|
|
@classmethod
|
|
def get_input(cls, device: str = "cpu"):
|
|
q = torch.randn(1, 4, 8, device=device)
|
|
k = torch.randn(1, 4, 8, device=device)
|
|
v = torch.randn(1, 4, 8, device=device)
|
|
return (q, k, v)
|
|
|
|
|
|
# NOTE: This should match configuration of _test_utils.torch_dist.plugins.megatron_common.MegatronModel
|
|
class RegularQuantModelForTP(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.fc1 = QuantLinear(32, 64)
|
|
self.activation = nn.ReLU()
|
|
self.fc2 = QuantLinear(64, 32)
|
|
|
|
def forward(self, x):
|
|
for block in [self.fc1, self.activation, self.fc2]:
|
|
x = block(x)
|
|
if isinstance(x, tuple):
|
|
x = x[0]
|
|
return x
|
|
|
|
|
|
class OneLayerLinear(torch.nn.Module):
|
|
"""Linear model for testing."""
|
|
|
|
def __init__(self, in_features=32, out_features=64, bias=True):
|
|
super().__init__()
|
|
self.in_features = in_features
|
|
self.out_features = out_features
|
|
self.net = nn.Sequential(nn.Linear(in_features, out_features, bias=bias))
|
|
|
|
def forward(self, x):
|
|
return self.net(x)
|
|
|
|
def get_input(self, batch_size=2):
|
|
return torch.randn(batch_size, self.in_features)
|