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One complex parameter should count as two params #193

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One complex parameter should count as two params #193

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d32dbf1
one complex parameter should count as two params
scaomath Nov 9, 2022
c5aa254
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Nov 9, 2022
9d04c3b
added a simple complex FFN parameter count test
scaomath Nov 9, 2022
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152 changes: 152 additions & 0 deletions tests/complex_test.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,152 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"from torch.nn import functional as F\n",
"from torchinfo import summary\n",
"\n",
"class RealFFN(nn.Module):\n",
" \"\"\" A real 2-layer FFN \"\"\"\n",
" def __init__(self) -> None:\n",
" super().__init__()\n",
" self.fc1 = nn.Linear(128, 256)\n",
" self.fc2 = nn.Linear(256, 128)\n",
" def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
" x = F.relu(self.fc1(x))\n",
" x = self.fc2(x)\n",
" return x\n",
"\n",
"realnet = RealFFN()\n",
"\n",
"class ComplexFFN(nn.Module):\n",
" \"\"\" A complex complex fc linear layer \"\"\"\n",
" def __init__(self) -> None:\n",
" super().__init__()\n",
" self.fc1 = nn.Linear(128, 256, dtype=torch.cfloat)\n",
" self.fc2 = nn.Linear(256, 128, dtype=torch.cfloat)\n",
" def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
" x = torch.complex(x, x)\n",
" x = self.fc1(x)\n",
" x = torch.view_as_real(x)\n",
" x = F.relu(x)\n",
" x = torch.view_as_complex(x)\n",
" x = self.fc2(x)\n",
" return x\n",
"\n",
"complexnet = ComplexFFN()\n",
"input_size = (2, 101, 101, 128)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"==========================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"==========================================================================================\n",
"RealFFN [2, 101, 101, 128] --\n",
"├─Linear: 1-1 [2, 101, 101, 256] 33,024\n",
"├─Linear: 1-2 [2, 101, 101, 128] 32,896\n",
"==========================================================================================\n",
"Total params: 65,920\n",
"Trainable params: 65,920\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 0.13\n",
"==========================================================================================\n",
"Input size (MB): 10.45\n",
"Forward/backward pass size (MB): 62.67\n",
"Params size (MB): 0.26\n",
"Estimated Total Size (MB): 73.38\n",
"=========================================================================================="
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(realnet, input_size=input_size, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"==========================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"==========================================================================================\n",
"ComplexFFN [2, 101, 101, 128] --\n",
"├─Linear: 1-1 [2, 101, 101, 256] 66,048\n",
"├─Linear: 1-2 [2, 101, 101, 128] 65,792\n",
"==========================================================================================\n",
"Total params: 131,840\n",
"Trainable params: 131,840\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 0.26\n",
"==========================================================================================\n",
"Input size (MB): 10.45\n",
"Forward/backward pass size (MB): 125.35\n",
"Params size (MB): 1.05\n",
"Estimated Total Size (MB): 136.85\n",
"=========================================================================================="
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(complexnet, input_size=input_size, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.10 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"vscode": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
9 changes: 8 additions & 1 deletion torchinfo/layer_info.py
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Original file line number Diff line number Diff line change
Expand Up @@ -164,10 +164,17 @@ def get_param_count(
if name.endswith("_orig"):
without_suffix = name[:-5]
pruned_weights = rgetattr(module, f"{without_suffix}_mask")
param = rgetattr(module, name)
if pruned_weights is not None:
parameter_count = int(torch.sum(pruned_weights))
if torch.is_complex(param):
parameter_count *= 2
return parameter_count, without_suffix
return param.nelement(), name

parameter_count = (
2 * param.nelement() if torch.is_complex(param) else param.nelement()
)
return parameter_count, name

@staticmethod
def get_kernel_size(module: nn.Module) -> int | list[int] | None:
Expand Down