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129 lines
7.5 KiB
129 lines
7.5 KiB
# Tensor Detector
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This tool supports you to detect tensors on both CPU and GPU. However, there will always be some strange tensors on CPU, including the rng state of PyTorch.
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## Example
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An example is worth than a thousand words.
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The code below defines a simple MLP module, with which we will show you how to use the tool.
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```python
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class MLP(nn.Module):
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def __init__(self):
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super().__init__()
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self.mlp = nn.Sequential(nn.Linear(64, 8),
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nn.ReLU(),
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nn.Linear(8, 32))
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def forward(self, x):
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return self.mlp(x)
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```
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And here is how to use the tool.
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```python
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from colossalai.utils import TensorDetector
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# create random data
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data = torch.rand(64, requires_grad=True).cuda()
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data.retain_grad()
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# create the module
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model = MLP().cuda()
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# create the detector
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# by passing the model to the detector, it can distinguish module parameters from common tensors
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detector = TensorDetector(include_cpu=False, module=model)
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detector.detect()
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out = model(data)
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detector.detect()
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loss = out.sum()
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loss.backward()
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detector.detect()
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```
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I have made some comments on the right of the output for your understanding.
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Note that the total `Mem` of all the tensors and parameters is not equal to `Total GPU Memery Allocated`. PyTorch's memory management is really complicated, and for models of a large scale, it's impossible to figure out clearly.
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**The order of print is not equal to the order the tensor creates, but they are really close.**
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```bash
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------------------------------------------------------------------------------------------------------------
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Tensor device shape grad dtype Mem
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------------------------------------------------------------------------------------------------------------
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+ Tensor cuda:0 (64,) True torch.float32 256 B # data
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+ mlp.0.weight cuda:0 (8, 64) True torch.float32 2.0 KB
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+ mlp.0.bias cuda:0 (8,) True torch.float32 32 B
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+ mlp.2.weight cuda:0 (32, 8) True torch.float32 1.0 KB
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+ mlp.2.bias cuda:0 (32,) True torch.float32 128 B
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------------------------------------------------------------------------------------------------------------
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Detect Location: "test_tensor_detector.py" line 27
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Totle GPU Memery Allocated on cuda:0 is 4.5 KB
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------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------
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Tensor device shape grad dtype Mem
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------------------------------------------------------------------------------------------------------------
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+ Tensor cuda:0 (8,) True torch.float32 32 B # activation
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+ Tensor cuda:0 (32,) True torch.float32 128 B # output
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------------------------------------------------------------------------------------------------------------
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Detect Location: "test_tensor_detector.py" line 30
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Totle GPU Memery Allocated on cuda:0 is 5.5 KB
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------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------
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Tensor device shape grad dtype Mem
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------------------------------------------------------------------------------------------------------------
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+ Tensor cuda:0 () True torch.float32 4 B # loss
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------------------------------------------------------------------------------------------------------------
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Detect Location: "test_tensor_detector.py" line 32
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Totle GPU Memery Allocated on cuda:0 is 6.0 KB
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------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------
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Tensor device shape grad dtype Mem
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------------------------------------------------------------------------------------------------------------
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+ Tensor (with grad) cuda:0 (64,) True torch.float32 512 B # data with grad
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+ mlp.0.weight (with grad) cuda:0 (8, 64) True torch.float32 4.0 KB # for use data.retain_grad()
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+ mlp.0.bias (with grad) cuda:0 (8,) True torch.float32 64 B
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+ mlp.2.weight (with grad) cuda:0 (32, 8) True torch.float32 2.0 KB
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+ mlp.2.bias (with grad) cuda:0 (32,) True torch.float32 256 B
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- mlp.0.weight cuda:0 (8, 64) True torch.float32 2.0 KB
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- mlp.0.bias cuda:0 (8,) True torch.float32 32 B
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- mlp.2.weight cuda:0 (32, 8) True torch.float32 1.0 KB
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- mlp.2.bias cuda:0 (32,) True torch.float32 128 B
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- Tensor cuda:0 (64,) True torch.float32 256 B
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- Tensor cuda:0 (8,) True torch.float32 32 B # deleted activation
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------------------------------------------------------------------------------------------------------------
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Detect Location: "test_tensor_detector.py" line 34
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Totle GPU Memery Allocated on cuda:0 is 10.0 KB
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------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------
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Tensor device shape grad dtype Mem
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------------------------------------------------------------------------------------------------------------
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+ Tensor cuda:0 (64,) False torch.float32 256 B
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+ Tensor cuda:0 (8, 64) False torch.float32 2.0 KB
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+ Tensor cuda:0 (8,) False torch.float32 32 B
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+ Tensor cuda:0 (32, 8) False torch.float32 1.0 KB
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+ Tensor cuda:0 (32,) False torch.float32 128 B
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------------------------------------------------------------------------------------------------------------
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Detect Location: "test_tensor_detector.py" line 36
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Totle GPU Memery Allocated on cuda:0 is 14.0 KB
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------------------------------------------------------------------------------------------------------------
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```
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## Reference
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This tool was inspired by https://github.com/Stonesjtu/pytorch_memlab/blob/master/pytorch_memlab/mem_reporter.py
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and https://github.com/Oldpan/Pytorch-Memory-Utils
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