feat: add seeds to dpmodel and fix seeds in tf & pt #3880
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Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
WalkthroughThe main changes introduce a Changes
Sequence Diagram(s)N/A Assessment against linked issues
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Actionable comments posted: 3
Outside diff range and nitpick comments (78)
deepmd/dpmodel/descriptor/se_r.py (2)
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105-105: Avoid using mutable default arguments as they can lead to unexpected behavior.- neuron: List[int] = [24, 48, 96], - exclude_types: List[List[int]] = [], + neuron: Optional[List[int]] = None, + exclude_types: Optional[List[List[int]]] = None, + if neuron is None: + neuron = [24, 48, 96] + if exclude_types is None: + exclude_types = []Also applies to: 109-109
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381-381: The variableenv_matis declared but not used, which could lead to unnecessary resource allocation.- env_mat = data.pop("env_mat")deepmd/dpmodel/descriptor/se_t.py (2)
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93-93: It's important to avoid mutable default arguments to prevent unintended side effects.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None, + if exclude_types is None: + exclude_types = []Also applies to: 98-98, 253-253
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382-382: The variableenv_matis declared but appears to be unused, which could lead to unnecessary resource allocation.- env_mat = data.pop("env_mat")deepmd/pt/model/descriptor/se_r.py (3)
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64-64: Replace mutable default arguments with immutable ones.- neuron=[25, 50, 100], + neuron=None, + if neuron is None: + neuron = [25, 50, 100], - exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None, + if exclude_types is None: + exclude_types = [],Also applies to: 69-69, 297-297
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257-261: Simplify conditional logic using a ternary operator.- if callable(merged): - # only get data for once - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else merged
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435-435: Remove unused variableenv_mat.- env_mat = EnvMatStatSe(self)deepmd/dpmodel/fitting/general_fitting.py (2)
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94-94: Avoid using mutable default arguments in function definitions to prevent potential bugs due to the mutable object being shared across all calls to the function.- neuron: List[int] = [120, 120, 120], + neuron: Optional[List[int]] = None, + if neuron is None: + neuron = [120, 120, 120], - remove_vaccum_contribution: Optional[List[bool]] = None, + remove_vaccum_contribution: Optional[List[bool]] = None, + if remove_vaccum_contribution is None: + remove_vaccum_contribution = [],Also applies to: 108-108, 262-262
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309-309: Use direct key checks in dictionaries instead of calling.keys()for better performance and readability.- if key in ["bias_atom_e"].keys(): + if key in ["bias_atom_e"]:deepmd/dpmodel/descriptor/se_e2_a.py (2)
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147-147: Avoid using mutable default arguments in function definitions to prevent potential bugs due to the mutable object being shared across all calls to the function.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None, + if neuron is None: + neuron = [24, 48, 96], - exclude_types: List[List[int]] = [], + exclude_types: Optional[List[List[int]]] = None, + if exclude_types is None: + exclude_types = [], - sel: List[int], + sel: Optional[List[int]] = None, + if sel is None: + sel = [],Also applies to: 152-152, 326-326
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456-456: Remove the assignment to the unused variableenv_matto clean up the code and improve readability.- env_mat = data.pop("env_mat")deepmd/pt/model/task/fitting.py (4)
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137-137: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [128, 128, 128], + neuron: Optional[List[int]] = None,And then inside the function:
if neuron is None: neuron = [128, 128, 128]
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147-147: Mutable default arguments should be replaced withNoneto avoid potential bugs.- exclude_types: List[int] = [], + exclude_types: Optional[List[int]] = None,And then inside the function:
if exclude_types is None: exclude_types = []
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253-253: Using mutable default arguments can lead to unexpected behaviors.- exclude_types: List[int] = [], + exclude_types: Optional[List[int]] = None,And then inside the function:
if exclude_types is None: exclude_types = []
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328-328: Optimize dictionary key access.- if key in variables.keys(): + if key in variables:deepmd/pt/model/descriptor/repformers.py (4)
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99-99: Mutable default arguments in function parameters should be avoided to prevent bugs.- exclude_types: List[List[int]] = [], + exclude_types: Optional[List[List[int]]] = None,And then inside the function:
if exclude_types is None: exclude_types = []
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391-391: Replace mutable default arguments withNone.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,And then inside the function:
if exclude_types is None: exclude_types = []
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458-458: Use an underscore to denote unused loop variables.- for idx, ll in enumerate(self.layers): + for _idx, ll in enumerate(self.layers):
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530-534: Simplify conditional assignment using a ternary operator.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else mergeddeepmd/pt/model/descriptor/se_a.py (9)
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78-78: Replace mutable default argument with immutable type.- neuron=[25, 50, 100], + neuron=None,Initialize
neuronwithin the function if it isNone.
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84-84: Replace mutable default argument with immutable type.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Initialize
exclude_typeswithin the function if it isNone.
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224-224: Replace mutable default argument with immutable type.- neuron=[25, 50, 100], + neuron=None,Initialize
neuronwithin the function if it isNone.
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323-323: Remove assignment to unused variableenv_mat.- env_mat = EnvMatStatSe(self)
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376-376: Replace mutable default argument with immutable type.- neuron=[25, 50, 100], + neuron=None,Initialize
neuronwithin the function if it isNone.
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382-382: Replace mutable default argument with immutable type.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Initialize
exclude_typeswithin the function if it isNone.
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567-571: Use ternary operator for conditional assignment.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else merged
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591-591: Replace mutable default argument with immutable type.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Initialize
exclude_typeswithin the function if it isNone.
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641-641: Remove unused loop control variableii.- for ii, transform in enumerate(self.filter_layers_old[1:]): + for transform in self.filter_layers_old[1:]:deepmd/pt/model/descriptor/dpa1.py (2)
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215-215: Replace mutable default arguments with safer alternatives.- neuron: list = [25, 50, 100], + neuron: Optional[List[int]] = None, - exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,And then initialize them within the function:
if neuron is None: neuron = [25, 50, 100] if exclude_types is None: exclude_types = []This change prevents potential bugs related to the mutable default argument.
Also applies to: 227-227
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517-517: Remove unused variables to clean up the code.- env_mat = DPEnvMat(obj.rcut, obj.rcut_smth).serialize(), - nall = extended_coord.view(nframes, -1).shape[1] // 3These variables are declared but not used, which could lead to confusion and clutter in the codebase.
Also applies to: 585-585
deepmd/pt/model/descriptor/se_t.py (8)
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113-113: Replace mutable default argument with an immutable one.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None,And initialize within the function:
if neuron is None: neuron = [24, 48, 96]
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118-118: Replace mutable default argument with an immutable one.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,And initialize within the function:
if exclude_types is None: exclude_types = []
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253-253: Avoid mutable default arguments to prevent potential bugs.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,And initialize within the function:
if exclude_types is None: exclude_types = []
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348-348: Remove assignment to unused variableenv_mat.- env_mat = data.pop("env_mat")
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401-401: Avoid mutable default arguments to prevent potential bugs.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,And initialize within the function:
if exclude_types is None: exclude_types = []
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406-406: Avoid mutable default arguments to prevent potential bugs.- networks: List[Union[NativeNet, dict]] = [], + networks: Optional[List[Union[NativeNet, dict]]] = None,And initialize within the function:
if networks is None: networks = []
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594-598: Use ternary operator to simplify the if-else block.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else merged
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618-618: Avoid mutable default arguments to prevent potential bugs.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,And initialize within the function:
if exclude_types is None: exclude_types = []deepmd/dpmodel/utils/network.py (3)
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576-576: Replace mutable default argument with an immutable one.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None,And initialize within the function:
if neuron is None: neuron = [24, 48, 96]
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678-678: Replace mutable default argument with an immutable one.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None,And initialize within the function:
if neuron is None: neuron = [24, 48, 96]
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783-783: Replace mutable default argument with an immutable one.- networks: List[Union[NativeNet, dict]] = [], + networks: Optional[List[Union[NativeNet, dict]]] = None,And initialize within the function:
if networks is None: networks = []deepmd/pt/model/descriptor/dpa2.py (4)
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207-221: Ensure proper propagation of theseedparameter through constructors.- seed=seed + len(self.repinit_args.neuron) * 2 if seed is not None else None, + seed=seed + len(self.repinit_args.neuron) * 2 + 1 + self.repformer_args.nlayers * 14 if seed is not None else None,The calculation of the
seedparameter in the constructor ofDescrptBlockRepformersshould include all the factors as per the AI-generated summary. Ensure that the seed calculation is consistent and covers all the necessary components.
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84-84: Avoid using mutable default arguments in function definitions.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None, ... + if exclude_types is None: + exclude_types = []Using mutable default arguments can lead to unexpected behavior if the function is called multiple times. It's safer to use
Noneas a default value and initialize the mutable data structure within the function body.
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434-434: Rename unused loop control variable.- for ii, descrpt in enumerate([self.repinit, self.repformers]): + for _ii, descrpt in enumerate([self.repinit, self.repformers]):The loop control variable
iiis not used within the loop body. It is a good practice to rename unused variables to_or_iito indicate that they are placeholders.
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554-554: Remove assignment to unused variable.- env_mat = repformers_variable.pop("env_mat") + repformers_variable.pop("env_mat")The local variable
env_matis assigned but never used. It's good practice to remove such assignments to keep the code clean and efficient.deepmd/dpmodel/descriptor/dpa2.py (3)
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67-67: Avoid using mutable default arguments.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None,In the constructor, add:
if neuron is None: neuron = [25, 50, 100]
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325-325: Avoid using mutable default arguments.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,In the constructor, add:
if exclude_types is None: exclude_types = []
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812-812: Remove unused variable assignment.- env_mat = repinit_variable.pop("env_mat")This variable is assigned but never used within the scope of this function.
deepmd/pt/model/descriptor/se_atten.py (5)
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65-65: Replace mutable default argument with an immutable one.- neuron: list = [25, 50, 100], + neuron: Optional[list] = None,In the method body, initialize
neuronif it isNone:if neuron is None: neuron = [25, 50, 100]
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82-82: Replace mutable default argument with an immutable one.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,In the method body, initialize
exclude_typesif it isNone:if exclude_types is None: exclude_types = []
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398-402: Use a ternary operator for cleaner and more concise code.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else merged
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422-422: Replace mutable default argument with an immutable one.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,In the method body, initialize
exclude_typesif it isNone:if exclude_types is None: exclude_types = []
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472-472: Remove unused local variablenall.- nall = extended_coord.view(nb, -1, 3).shape[1]deepmd/pt/model/descriptor/repformer_layer.py (3)
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898-898: Remove assignment to unused variableng2.- ng2 = g2.shape[-1]
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1061-1064: Simplify the conditional assignment ofgg1.- if cal_gg1: - gg1 = _make_nei_g1(g1_ext, nlist) - else: - gg1 = None + gg1 = _make_nei_g1(g1_ext, nlist) if cal_gg1 else None
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1173-1173: Remove assignment to unused variablenitem.- nitem = len(update_list)deepmd/dpmodel/descriptor/dpa1.py (7)
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226-226: Replace mutable default argument with an immutable one.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Ensure to initialize
exclude_typeswithin the function if it isNone.
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237-237: Replace mutable default argument with an immutable one.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None,Ensure to initialize
neuronwithin the function if it isNone.
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467-467: Remove unused variable assignment.- nall = coord_ext.reshape(nf, -1).shape[1] // 3
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547-547: Remove unused variable assignment.- env_mat = EnvMat(self.rcut, self.rcut_smth, protection=self.env_protection)
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609-609: Replace mutable default argument with an immutable one.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Ensure to initialize
exclude_typeswithin the function if it isNone.
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619-619: Replace mutable default argument with an immutable one.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None,Ensure to initialize
neuronwithin the function if it isNone.
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813-813: Replace mutable default argument with an immutable one.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Ensure to initialize
exclude_typeswithin the function if it isNone.deepmd/tf/descriptor/se_a.py (4)
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173-173: Avoid using mutable default arguments for function parameters.- exclude_types: List[List[int]] = [] + exclude_types: Optional[List[List[int]]] = NoneInitialize
exclude_typeswithin the function if it isNone.
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179-179: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = NoneInitialize
neuronwithin the function if it isNone.
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613-619: Remove unused local variables to clean up the code.- t_rcut = tf.constant( - np.max([self.rcut_r, self.rcut_a]), - name="rcut", - dtype=GLOBAL_TF_FLOAT_PRECISION, - ) - t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32) - t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)These variables are declared but never used, which can lead to confusion and clutter.
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1298-1304: Usecontextlib.suppressfor a cleaner approach to handling exceptions where the action is to pass.- try: - self.original_sel = get_tensor_by_name_from_graph( - graph, f"descrpt_attr{suffix}/original_sel" - ) - except GraphWithoutTensorError: - # original_sel is not restored in old graphs, assume sel never changed before - pass + from contextlib import suppress + + with suppress(GraphWithoutTensorError): + self.original_sel = get_tensor_by_name_from_graph( + graph, f"descrpt_attr{suffix}/original_sel" + )This approach reduces the complexity of the try-except block and makes the intention clearer.
deepmd/dpmodel/descriptor/repformers.py (7)
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332-332: Avoid using mutable default arguments like lists for function parameters due to the risk of unexpected behavior if the function modifies the object.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,
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376-376: The loop control variableidxis not used within the loop body. It's a good practice to replace it with_if not used.- for idx in range(nlayers): + for _ in range(nlayers):
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544-544: The variablengis assigned but never used. It can be safely removed to clean up the code.- ng = g.shape[-1]
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1305-1305: The variableng1is assigned but never used. It can be safely removed to clean up the code.- ng1 = gg1.shape[-1]
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1386-1386: The variablenallis assigned but never used. It can be safely removed to clean up the code.- nall = g1_ext.shape[1]
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1396-1399: Use a ternary operator for more concise and readable code when a variable is conditionally assigned based on a single condition.- if cal_gg1: - gg1 = _make_nei_g1(g1_ext, nlist) - else: - gg1 = None + gg1 = _make_nei_g1(g1_ext, nlist) if cal_gg1 else None
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1505-1505: The variablenitemis assigned but never used. It can be safely removed to clean up the code.- nitem = len(update_list)deepmd/tf/descriptor/se_atten.py (4)
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180-180: Replace mutable default arguments with immutable ones.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None, - exclude_types: List[List[int]] = [], + exclude_types: Optional[List[List[int]]] = None,Add the following initialization inside the methods:
if neuron is None: neuron = [25, 50, 100] if exclude_types is None: exclude_types = []Also applies to: 187-187, 2095-2095, 2106-2106
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569-569: Remove unused local variables.- t_rcut = tf.constant( - np.max([self.rcut_r, self.rcut_a]), - name="rcut", - dtype=GLOBAL_TF_FLOAT_PRECISION, - ) - t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32) - t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)Also applies to: 574-574, 575-575
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1308-1308: Remove unused local variablenframes.- nframes = dd_all.shape[0]
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392-392: Useenumerate()to simplify the loop that uses thesys_numvariable.- sys_num = 0 - for cc, bb, tt, nn, mm, r_n in zip( + for sys_num, (cc, bb, tt, nn, mm, r_n) in enumerate(zip(
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #3880 +/- ##
==========================================
+ Coverage 82.70% 82.74% +0.03%
==========================================
Files 517 518 +1
Lines 50141 50215 +74
Branches 2984 2984
==========================================
+ Hits 41469 41548 +79
+ Misses 7762 7757 -5
Partials 910 910 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Jinzhe Zeng <[email protected]>
There was a problem hiding this comment.
Actionable comments posted: 0
Outside diff range and nitpick comments (7)
deepmd/tf/descriptor/se_atten.py (7)
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180-180: Avoid using mutable default arguments for function parameters.- exclude_types: List[List[int]] = [] + exclude_types: Optional[List[List[int]]] = NoneInitialize
exclude_typeswithin the function if it isNone.
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187-187: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [25, 50, 100] + neuron: Optional[List[int]] = NoneInitialize
neuronwithin the function if it isNone.
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569-575: Remove unused local variables to clean up the code.- t_rcut = tf.constant( - np.max([self.rcut_r, self.rcut_a]), - name="rcut", - dtype=GLOBAL_TF_FLOAT_PRECISION, - ) - t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32) - t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
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1308-1308: Remove unused local variable to clean up the code.- nframes = dd_all.shape[0]
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2095-2095: Avoid using mutable default arguments for function parameters.- exclude_types: List[List[int]] = [] + exclude_types: Optional[List[List[int]]] = NoneInitialize
exclude_typeswithin the function if it isNone.
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392-392: Useenumerate()to simplify the loop and directly obtain the index.for sys_num, (cc, bb, tt, nn, mm, r_n) in enumerate(zip(data_coord, data_box, data_atype, natoms_vec, mesh, real_natoms_vec)): ...
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218-218: Consider adding astacklevelto thewarnings.warncall to specify the origin of the warning.warnings.warn( "Set 'set_davg_zero' False in descriptor 'se_atten' may cause unexpected incontinuity during model inference!", stacklevel=2 )
Signed-off-by: Jinzhe Zeng <[email protected]>
There was a problem hiding this comment.
I find one has to spend much effort on maintaining the number of seeds used by each module. I would recommend the parallel random number generation feature by which we do not need to manually record the number of consumed seeds. see e.g. https://numpy.org/doc/stable/reference/random/parallel.html
It's unclear how to share arguments with PT when input is a |
I just realized one can do this: >>> import numpy as np
>>> rng = np.random.default_rng([1,2,3,4,5])So it will work if we change the type of seed from |
Signed-off-by: Jinzhe Zeng <[email protected]>
There was a problem hiding this comment.
Actionable comments posted: 9
Outside diff range and nitpick comments (62)
deepmd/dpmodel/fitting/ener_fitting.py (1)
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33-33: Replace mutable default arguments withNoneand initialize them within the function to avoid potential bugs.- neuron: List[int] = [120, 120, 120], + neuron: Optional[List[int]] = None, - trainable: Optional[List[bool]] = None, + trainable: Union[bool, List[bool]] = True,In the
__init__method, initializeneuronandtrainableif they areNone.Also applies to: 47-47
deepmd/dpmodel/fitting/dos_fitting.py (1)
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36-36: Replace mutable default arguments withNoneand initialize them within the function to avoid potential bugs.- neuron: List[int] = [120, 120, 120], + neuron: Optional[List[int]] = None, - trainable: Union[bool, List[bool]] = True, + trainable: Optional[Union[bool, List[bool]]] = None,In the
__init__method, initializeneuronandtrainableif they areNone.Also applies to: 46-46
Tools
Ruff
46-46: Do not use mutable data structures for argument defaults (B006)
Replace with
None; initialize within functiondeepmd/pt/model/task/dos.py (1)
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48-48: Replace mutable default arguments withNoneand initialize them within the function to avoid potential bugs.- neuron: List[int] = [128, 128, 128], + neuron: Optional[List[int]] = None, - trainable: Union[bool, List[bool]] = True, + trainable: Optional[Union[bool, List[bool]]] = None,In the
__init__method, initializeneuronandtrainableif they areNone.Also applies to: 58-58
Tools
Ruff
58-58: Do not use mutable data structures for argument defaults (B006)
Replace with
None; initialize within functiondeepmd/pt/model/network/layernorm.py (1)
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140-140: The local variableprecis assigned but not used in thedeserializemethod. This might be a remnant of a previous version of the code whereprecwas used, or it might be a preparation for future use that hasn't been implemented yet.- prec = PRECISION_DICT[obj.precision]If the variable is not intended for immediate use, it's best to remove it to clean up the code and avoid confusion.
deepmd/pt/model/task/invar_fitting.py (1)
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90-90: Using mutable default arguments like lists can lead to unexpected behaviors if the function modifies these arguments. Each call to the function without explicitly passing these arguments would use the same list, leading to potential bugs.- neuron: List[int] = [128, 128, 128], + neuron: Optional[List[int]] = None, + if neuron is None: + neuron = [128, 128, 128] - exclude_types: List[int] = [], + exclude_types: Optional[List[int]] = None, + if exclude_types is None: + exclude_types = []This change ensures that a new list is created every time the function is called, preventing accidental sharing of mutable objects across function calls.
Also applies to: 100-100
deepmd/dpmodel/descriptor/se_atten_v2.py (1)
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43-43: The use of mutable default arguments forexclude_typesandneuroncan lead to unintended side effects if these arguments are modified within the method. This is a common Python pitfall.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None, + if neuron is None: + neuron = [25, 50, 100] - exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None, + if exclude_types is None: + exclude_types = []This modification ensures that the defaults are safely handled, avoiding potential bugs related to the mutable default argument issue.
Also applies to: 53-53
source/tests/tf/test_model_se_a_ebd_v2.py (1)
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118-118: The variableatom_eneris assigned but never used, leading to unnecessary memory allocation and potential confusion.- t_atom_ener = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener")Remove or use the variable to clean up the code.
deepmd/pt/model/network/mlp.py (1)
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266-266: Unused variableprecshould be removed to clean up the code.- prec = PRECISION_DICT[obj.precision]deepmd/dpmodel/descriptor/se_r.py (3)
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109-109: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None,And then inside the function:
if neuron is None: neuron = [24, 48, 96]
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113-113: Avoid using mutable default arguments for function parameters.- exclude_types: List[List[int]] = [], + exclude_types: Optional[List[List[int]]] = None,And then inside the function:
if exclude_types is None: exclude_types = []
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385-385: Remove the unused local variableenv_matto clean up the code.- env_mat = data.pop("env_mat")deepmd/dpmodel/descriptor/se_t.py (2)
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97-97: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None,And then inside the function:
if neuron is None: neuron = [24, 48, 96]
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386-386: Remove the unused local variableenv_matto clean up the code.- env_mat = data.pop("env_mat")deepmd/pt/model/descriptor/se_r.py (4)
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67-67: Avoid using mutable default arguments for function parameters.- neuron=[25, 50, 100], + neuron: Optional[List[int]] = None,And then inside the function:
if neuron is None: neuron = [25, 50, 100]
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72-72: Avoid using mutable default arguments for function parameters.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,And then inside the function:
if exclude_types is None: exclude_types = []
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260-264: Optimize the conditional logic for better readability and efficiency.- if callable(merged): - # only get data for once - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else merged
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438-438: Remove the unused local variableenv_matto clean up the code.- env_mat = data.pop("env_mat")deepmd/dpmodel/fitting/general_fitting.py (2)
89-90: Ensure proper documentation for theseedparameter.The documentation for the
seedparameter should clearly explain the effect of using an integer vs. a list of integers. This can help users understand how the seed influences the network initialization.
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311-311: Usekey in dictinstead ofkey in dict.keys().Using
key in dict.keys()is less efficient thankey in dict. The latter is more Pythonic and should be used.- if key in variables.keys(): + if key in variables:deepmd/dpmodel/descriptor/se_e2_a.py (2)
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151-151: Avoid using mutable default arguments like lists for function parameters. This can lead to unexpected behavior due to Python's handling of mutable defaults.- neuron: List[int] = [24, 48, 96], - exclude_types: List[List[int]] = [], - remove_vaccum_contribution: Optional[List[bool]] = None, + neuron: Optional[List[int]] = None, + exclude_types: Optional[List[List[int]]] = None, + remove_vaccum_contribution: Optional[Optional[List[bool]]] = None,Also applies to: 156-156, 330-330
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460-460: The local variableenv_matis declared but not used within its scope, which could be an oversight or leftover from refactoring.- env_mat = data.pop("env_mat")deepmd/pt/model/task/fitting.py (2)
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140-140: Using mutable default arguments such as lists or dictionaries can lead to unexpected behavior if the function modifies these objects. It's safer to use immutable defaults.- neuron: List[int] = [128, 128, 128], - exclude_types: List[int] = [], - remove_vaccum_contribution: Optional[List[bool]] = None, + neuron: Optional[List[int]] = None, + exclude_types: Optional[List[int]] = None, + remove_vaccum_contribution: Optional[Optional[List[bool]]] = None,Also applies to: 150-150, 256-256
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331-331: When checking for the presence of a key in a dictionary, use the more efficientkey in dictinstead ofkey in dict.keys().- if key in dict.keys() + if key in dictdeepmd/pt/model/descriptor/repformers.py (2)
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463-463: The loop control variableidxis not used within the loop body and should be replaced with_to signify that it is unused.- for idx, ll in enumerate(self.layers): + for _, ll in enumerate(self.layers):
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535-539: Refactor theif-elseblock to a ternary operator for clarity and conciseness.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else mergeddeepmd/pt/model/descriptor/dpa1.py (4)
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218-218: Avoid using mutable default arguments like lists for function parameters. This can lead to unexpected behavior if the function modifies the default value.- exclude_types: List[Tuple[int, int]] = [] + exclude_types: Optional[List[Tuple[int, int]]] = NoneInitialize
exclude_typeswithin the function if it'sNone.
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230-230: Similar to the previous comment, avoid mutable defaults for lists to prevent unintended side effects.- neuron: list = [25, 50, 100] + neuron: Optional[List[int]] = NoneInitialize
neuronwithin the function if it'sNone.
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520-520: The variableenv_matis defined but not used in the code. Removing unused variables can clean up the code and reduce confusion.- env_mat = DPEnvMat(obj.rcut, obj.rcut_smth).serialize()
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588-588: The variablenallis assigned but never used. It should be removed unless it's planned for future use.- nall = extended_coord.view(nframes, -1).shape[1] // 3deepmd/pt/model/descriptor/se_t.py (3)
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256-256: Avoid using mutable default arguments for function parameters to prevent potential bugs.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None,And then initialize
neuronwithin the function if it isNone.Also applies to: 404-404
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351-351: Remove the unused variableenv_matto clean up the code.- env_mat = EnvMatStatSe(self)
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597-601: Use a ternary operator for cleaner and more concise code.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else mergeddeepmd/dpmodel/utils/network.py (1)
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579-579: Avoid using mutable default arguments for function parameters to prevent potential bugs.- neuron: List[int] = [24, 48, 96], + neuron: Optional[List[int]] = None,And then initialize
neuronwithin the function if it isNone.Also applies to: 681-681, 786-786
deepmd/pt/model/descriptor/dpa2.py (2)
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432-432: The loop control variableiiis not used within the loop body.- for ii, descrpt in enumerate([self.repinit, self.repformers]): + for _ii, descrpt in enumerate([self.repinit, self.repformers]):Renaming
iito_iiindicates that it is an unused variable, which improves code readability.
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552-552: Remove assignment to unused variableenv_mat.- env_mat = repinit_variable.pop("env_mat") + repinit_variable.pop("env_mat")The variable
env_matis assigned but never used, which could lead to confusion and clutter in the code.source/tests/tf/test_pairwise_dprc.py (1)
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45-48: Consider using a ternary operator for settingdefault_places.- if GLOBAL_NP_FLOAT_PRECISION == np.float32: - default_places = 4 - else: - default_places = 10 + default_places = 4 if GLOBAL_NP_FLOAT_PRECISION == np.float32 else 10deepmd/dpmodel/descriptor/dpa2.py (2)
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70-70: Avoid using mutable default arguments in function definitions.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None,And then initialize it within the function:
if neuron is None: neuron = [25, 50, 100]
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810-810: Local variableenv_matis assigned but never used, which can be removed to clean up the code.- env_mat = repinit_variable.pop("env_mat")deepmd/pt/model/descriptor/se_atten.py (3)
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68-68: Replace mutable default arguments with immutable ones to prevent potential bugs.- neuron: list = [25, 50, 100], - exclude_types: List[Tuple[int, int]] = [], + neuron: Optional[List[int]] = None, + exclude_types: Optional[List[Tuple[int, int]]] = None,Also applies to: 85-85, 423-423
Line range hint
399-403: Simplify the conditional assignment using a ternary operator.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else merged
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473-473: Remove the unused variablenallto clean up the code.- nall = extended_coord.view(nb, -1, 3).shape[1]deepmd/pt/model/descriptor/repformer_layer.py (4)
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846-846: Remove assignment to unused variableng1.- ng1 = gg1.shape[-1]
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905-905: Remove assignment to unused variableng2.- ng2 = g2.shape[-1]
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1068-1071: Replaceif-else-block with ternary operator for more concise code.- if cal_gg1: - gg1 = _make_nei_g1(g1_ext, nlist) - else: - gg1 = None + gg1 = _make_nei_g1(g1_ext, nlist) if cal_gg1 else None
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1180-1180: Remove assignment to unused variablenitem.- nitem = len(update_list)deepmd/dpmodel/descriptor/dpa1.py (7)
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229-229: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None,Initialize
neuronwithin the function if it isNone.
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240-240: Avoid using mutable default arguments for function parameters.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Initialize
exclude_typeswithin the function if it isNone.
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470-470: Remove assignment to unused variablenall.- nall = coord_ext.reshape(nf, -1).shape[1] // 3
Line range hint
550-550: Remove assignment to unused variableenv_mat.- env_mat = self.env_mat
Line range hint
612-612: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None,Initialize
neuronwithin the function if it isNone.
Line range hint
622-622: Avoid using mutable default arguments for function parameters.- exclude_types: List[Tuple[int, int]] = [], + exclude_types: Optional[List[Tuple[int, int]]] = None,Initialize
exclude_typeswithin the function if it isNone.
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816-816: Avoid using mutable default arguments for function parameters.- neuron: List[int] = [25, 50, 100], + neuron: Optional[List[int]] = None,Initialize
neuronwithin the function if it isNone.deepmd/dpmodel/descriptor/repformers.py (8)
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156-156: Avoid using mutable default arguments in function definitions.- exclude_types: List[Tuple[int, int]] = [] + exclude_types: Optional[List[Tuple[int, int]]] = NoneInitialize
exclude_typeswithin the function if it isNone.
Line range hint
337-337: Avoid using mutable default arguments in function definitions.- exclude_types: List[Tuple[int, int]] = [] + exclude_types: Optional[List[Tuple[int, int]]] = NoneInitialize
exclude_typeswithin the function if it isNone.
Line range hint
381-381: Rename unused loop variable.- for idx, ll in enumerate(self.layers): + for _idx, ll in enumerate(self.layers):
Line range hint
549-549: Remove assignment to unused variable.- ng = g.shape[-1]
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1322-1322: Remove assignment to unused variable.- ng1 = gg1.shape[-1]
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1403-1403: Remove assignment to unused variable.- nall = g1_ext.shape[1]
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1413-1416: Simplify conditional assignment using a ternary operator.- if cal_gg1: - gg1 = _make_nei_g1(g1_ext, nlist) - else: - gg1 = None + gg1 = _make_nei_g1(g1_ext, nlist) if cal_gg1 else None
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1522-1522: Remove assignment to unused variable.- nitem = len(update_list)deepmd/pt/model/network/network.py (2)
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450-450: Refactor the conditional assignment for clarity.- hidden = input_dim if not hidden else hidden + hidden = hidden if hidden else input_dimThis change simplifies the logic and improves readability.
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974-974: Rename unused loop variables to_.- for i in range(self.layer_num): + for _i in range(self.layer_num):This change adheres to Python conventions for unused variables, improving code cleanliness.
Also applies to: 1758-1758
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Signed-off-by: Jinzhe Zeng <[email protected]>
No, it's not consistent, as PyTorch limits the seed to (-0xffff_ffff_ffff_ffff, 0xffff_ffff_ffff_ffff). It is only ensured that the seed generated by |
Fix deepmodeling#3799. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> - **New Features** - Introduced flexibility in specifying seed values, allowing either an integer or a list of integers. - Enhanced seed parameter usage across various initialization methods and classes for more controlled randomization. - **Improvements** - Updated seed initialization logic to include additional computations and dynamic adjustments. - Enhanced documentation for parameters in multiple classes, providing clearer usage guidelines. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jinzhe Zeng <[email protected]> (cherry picked from commit 0c472d1) Signed-off-by: Jinzhe Zeng <[email protected]>
<!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes** - Resolved inconsistencies in seed values by incrementing `self.seed` conditionally in descriptor modules. - **Tests** - Updated test arrays `refe`, `reff`, and `refv` with new reference values. - Adjusted expected values in `test_model_ener` method for better accuracy. These changes ensure more reliable descriptor computations and improved test accuracy. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- (cherry picked from commit 0c472d1) Signed-off-by: Jinzhe Zeng <[email protected]>
Fix deepmodeling#3799. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced flexibility in specifying seed values, allowing either an integer or a list of integers. - Enhanced seed parameter usage across various initialization methods and classes for more controlled randomization. - **Improvements** - Updated seed initialization logic to include additional computations and dynamic adjustments. - Enhanced documentation for parameters in multiple classes, providing clearer usage guidelines. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jinzhe Zeng <[email protected]>
Fix #3799.
Summary by CodeRabbit
New Features
Improvements