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change default NN precision from float64 to default #1644

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Merged
wanghan-iapcm merged 4 commits into from
Apr 18, 2022
Merged

change default NN precision from float64 to default #1644

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6775db0
change default NN precision from `float64` to `default`
njzjz Apr 17, 2022
a6d838d
update type embedding
njzjz Apr 17, 2022
e1f4062
remove extra .
njzjz Apr 17, 2022
c27ebe4
remove extra .
njzjz Apr 17, 2022
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28 changes: 14 additions & 14 deletions deepmd/utils/argcheck.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,14 +28,14 @@ def type_embedding_args():
doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
doc_seed = 'Random seed for parameter initialization'
doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())}'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())}'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable = 'If the parameters in the embedding net are trainable'

return [
Argument("neuron", list, optional = True, default = [2, 4, 8], doc = doc_neuron),
Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function),
Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt),
Argument("precision", str, optional = True, default = "float64", doc = doc_precision),
Argument("precision", str, optional = True, default = "default", doc = doc_precision),
Argument("trainable", bool, optional = True, default = True, doc = doc_trainable),
Argument("seed", [int,None], optional = True, doc = doc_seed),
]
Expand Down Expand Up @@ -123,7 +123,7 @@ def descrpt_se_a_args():
doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())}'
doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())}'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable = 'If the parameters in the embedding net is trainable'
doc_seed = 'Random seed for parameter initialization'
doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.'
Expand All @@ -138,7 +138,7 @@ def descrpt_se_a_args():
Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function),
Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt),
Argument("type_one_side", bool, optional = True, default = False, doc = doc_type_one_side),
Argument("precision", str, optional = True, default = "float64", doc = doc_precision),
Argument("precision", str, optional = True, default = "default", doc = doc_precision),
Argument("trainable", bool, optional = True, default = True, doc = doc_trainable),
Argument("seed", [int,None], optional = True, doc = doc_seed),
Argument("exclude_types", list, optional = True, default = [], doc = doc_exclude_types),
Expand All @@ -156,7 +156,7 @@ def descrpt_se_t_args():
doc_neuron = 'Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.'
doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())}'
doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())}'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable = 'If the parameters in the embedding net are trainable'
doc_seed = 'Random seed for parameter initialization'
doc_set_davg_zero = 'Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used'
Expand All @@ -168,7 +168,7 @@ def descrpt_se_t_args():
Argument("neuron", list, optional = True, default = [10,20,40], doc = doc_neuron),
Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function),
Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt),
Argument("precision", str, optional = True, default = "float64", doc = doc_precision),
Argument("precision", str, optional = True, default = "default", doc = doc_precision),
Argument("trainable", bool, optional = True, default = True, doc = doc_trainable),
Argument("seed", [int,None], optional = True, doc = doc_seed),
Argument("set_davg_zero", bool, optional = True, default = False, doc = doc_set_davg_zero)
Expand Down Expand Up @@ -200,7 +200,7 @@ def descrpt_se_r_args():
doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())}'
doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
doc_type_one_side = 'Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())}'
doc_precision = f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable = 'If the parameters in the embedding net are trainable'
doc_seed = 'Random seed for parameter initialization'
doc_exclude_types = 'The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.'
Expand All @@ -214,7 +214,7 @@ def descrpt_se_r_args():
Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function),
Argument("resnet_dt", bool, optional = True, default = False, doc = doc_resnet_dt),
Argument("type_one_side", bool, optional = True, default = False, doc = doc_type_one_side),
Argument("precision", str, optional = True, default = "float64", doc = doc_precision),
Argument("precision", str, optional = True, default = "default", doc = doc_precision),
Argument("trainable", bool, optional = True, default = True, doc = doc_trainable),
Argument("seed", [int,None], optional = True, doc = doc_seed),
Argument("exclude_types", list, optional = True, default = [], doc = doc_exclude_types),
Expand Down Expand Up @@ -255,7 +255,7 @@ def fitting_ener():
doc_numb_aparam = 'The dimension of the atomic parameter. If set to >0, file `aparam.npy` should be included to provided the input aparams.'
doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.'
doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())}'
doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())}'
doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
doc_trainable = 'Whether the parameters in the fitting net are trainable. This option can be\n\n\
- bool: True if all parameters of the fitting net are trainable, False otherwise.\n\n\
Expand All @@ -269,7 +269,7 @@ def fitting_ener():
Argument("numb_aparam", int, optional = True, default = 0, doc = doc_numb_aparam),
Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron),
Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function),
Argument("precision", str, optional = True, default = 'float64', doc = doc_precision),
Argument("precision", str, optional = True, default = 'default', doc = doc_precision),
Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt),
Argument("trainable", [list,bool], optional = True, default = True, doc = doc_trainable),
Argument("rcond", float, optional = True, default = 1e-3, doc = doc_rcond),
Expand All @@ -282,7 +282,7 @@ def fitting_polar():
doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.'
doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())}'
doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())}'
doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_scale = 'The output of the fitting net (polarizability matrix) will be scaled by ``scale``'
#doc_diag_shift = 'The diagonal part of the polarizability matrix will be shifted by ``diag_shift``. The shift operation is carried out after ``scale``.'
doc_fit_diag = 'Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.'
Expand All @@ -296,7 +296,7 @@ def fitting_polar():
Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron),
Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function),
Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt),
Argument("precision", str, optional = True, default = 'float64', doc = doc_precision),
Argument("precision", str, optional = True, default = 'default', doc = doc_precision),
Argument("fit_diag", bool, optional = True, default = True, doc = doc_fit_diag),
Argument("scale", [list,float], optional = True, default = 1.0, doc = doc_scale),
#Argument("diag_shift", [list,float], optional = True, default = 0.0, doc = doc_diag_shift),
Expand All @@ -314,14 +314,14 @@ def fitting_dipole():
doc_neuron = 'The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.'
doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())}'
doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())}'
doc_precision = f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_sel_type = 'The atom types for which the atomic dipole will be provided. If not set, all types will be selected.'
doc_seed = 'Random seed for parameter initialization of the fitting net'
return [
Argument("neuron", list, optional = True, default = [120,120,120], alias = ['n_neuron'], doc = doc_neuron),
Argument("activation_function", str, optional = True, default = 'tanh', doc = doc_activation_function),
Argument("resnet_dt", bool, optional = True, default = True, doc = doc_resnet_dt),
Argument("precision", str, optional = True, default = 'float64', doc = doc_precision),
Argument("precision", str, optional = True, default = 'default', doc = doc_precision),
Argument("sel_type", [list,int,None], optional = True, alias = ['dipole_type'], doc = doc_sel_type),
Argument("seed", [int,None], optional = True, doc = doc_seed)
]
Expand Down