Reformat dpa1 #176
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Reformat dpa1 #176
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add serialize and deserialize method for dpa1 descriptor
wanghan-iapcm
requested changes
Jan 24, 2024
| yy: torch.Tensor | ||
| The output. | ||
| """ | ||
| yy = F.embedding(xx, self.matrix) |
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What is the operation of torch.nn.functional.embedding? how would you implement it in numpy?
| @@ -3,6 +3,7 @@ | |||
| import numpy as np | |||
| import torch | |||
| import torch.nn as nn | |||
| import torch.nn.functional as F | |||
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very bad short-hand name. change required.
| @@ -179,6 +179,219 @@ def deserialize(cls, data:dict)->"MLPLayer": | |||
| return obj | |||
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| class EmbdLayer(nn.Module): | |||
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should be implemented via native layer?
| yy: torch.Tensor | ||
| The output. | ||
| """ | ||
| yy = F.layer_norm(xx, tuple((self.num_in,)), self.matrix, self.bias, self.eps) |
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why layer_norm contains trainable parameters?
how would you implement it in numpy? please provide the implementation to deepmd-kit repo
| dtype = env.GLOBAL_PT_FLOAT_PRECISION | ||
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| class TestCaseSingleFrameWithNlist(): |
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better to import from test_se_e2_a.py
| @@ -406,7 +406,7 @@ def forward( | |||
| nfnl = dmatrix.shape[0] | |||
| # pre-allocate a shape to pass jit | |||
| xyz_scatter = torch.zeros([nfnl, 4, self.filter_neuron[-1]], dtype=self.prec, device=env.DEVICE) | |||
| for ii,ll in enumerate(self.filter_layers.networks): | |||
| for ii,ll in enumerate(self.filter_layers._networks): | |||
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better providing a method to access the data of a class object.
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This PR aims to reformat the dpa1 descriptor in both pytorch and tensorflow repo.