| Odds for Next Match Win/Loss/Draw (Home/Away) |
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<>: Hi there! I’m working on implementing an AVL tree insertion method that includes rotations to maintain balance after inserting nodes. Here’s my current code:
python
def insert(self, root: AVLNode, data: any) -> AVLNode:
“””Inserts data into the tree.”””
if not root:
return AVLNode(data)
elif data > root.data:
root.right = self.insert(root.right, data)
if self.get_balance(root) == 2:
if data > root.right.data:
root = rotate_left(root)
else:
root = rotate_right_left(root)
elif data root.left.data: # Bug introduced here
root = rotate_left_right(root)
else:
root = rotate_left(root)
However, when I run this code with certain inputs like inserting [10, 20, 5], it throws an error that I can’t quite wrap my head around:
Traceback (most recent call last):
File “avl_tree.py”, line 42, in insert
if data > root.left.data:
AttributeError: ‘NoneType’ object has no attribute ‘data’
I’m confused because I thought my logic was sound when checking conditions for rotations after insertion. Could you help me figure out what’s going wrong here? Why am I getting this AttributeError? Any guidance would be greatly appreciated!
<>: python
It looks like you’re encountering an `AttributeError` because `root.left` can be `None`, especially right after an insertion where no rotation has occurred yet.
To fix this issue, you should ensure that `root.left` is not `None` before trying to access its `data` attribute. You can modify your condition like this:
python
elif data root.left.data: # Check if root.left is not None
root = rotate_left_right(root)
else:
root = rotate_left(root)
This change ensures that you only attempt to access `root.left.data` when `root.left` is not `None`. This should prevent the `AttributeError` from occurring during your insertions. 😊
<>: Thanks for pointing that out! Now that it’s fixed, I’m thinking about optimizing the balance factor calculation during insertion. Right now, it seems like I’m recalculating heights more than necessary. How can I efficiently update heights only when needed to improve performance?
<>: To optimize height updates during insertion in an AVL tree, you can follow these steps:
1. **Update Heights During Recursion**: After each recursive insertion call returns a node (either a newly inserted node or a rotated subtree), update its height based on its children’s heights.
python
def update_height(node):
node.height = 1 + max(self.get_height(node.left), self.get_height(node.right))
2. **Integrate Height Update**: After inserting into either left or right subtree and performing any necessary rotations, update the height of the current node.
python
def insert(self, root: AVLNode, data: any) -> AVLNode:
if not root:
return AVLNode(data)
elif data > root.data:
root.right = self.insert(root.right, data)
update_height(root)
elif data 1:
if data > root.right.data:
return rotate_left(root)
else:
return rotate_right_left(root)
# Left Rotation Case
if balance_factor < -1:
if data <= root.left.data:
return rotate_left_right(root)
else:
return rotate_right(root)
return root
def get_height(self, node):
return node.height if node else -1
By updating heights only after recursion returns from deeper levels of insertion or rotation adjustments, you minimize unnecessary calculations.
<>: Great advice! Now I’m curious about minimizing redundant balance factor calculations during insertion. Is there a way to streamline these checks further to enhance performance? Maybe by integrating them more tightly with height updates?
<>: To streamline balance factor calculations during insertion while integrating them with height updates:
1. **Combine Height Update and Balance Calculation**: After updating a node’s height post-insertion or rotation, calculate its balance factor immediately.
python
def update_and_get_balance(node):
update_height(node)
return get_balance(node)
def get_balance(node):
return (self.get_height(node.right) – self.get_height(node.left))
2. **Use Combined Function**: Replace separate calls to `update_height` and `get_balance` with this combined function.
python
def insert(self, root: AVLNode, data: any) -> AVLNode:
if not root:
return AVLNode(data)
elif data > root.data:
root.right = self.insert(root.right, data)
elif data 1:
if data > root.right.data:
return rotate_left(root)
else:
return rotate_right_left(root)
# Left Rotation Case
if balance_factor < -1:
if data <= root.left.data:
return rotate_left_right(root)
else:
return rotate_right(root)
return root
def get_height(self, node):
return node.height if node else -1
def update_height(node):
node.height = 1 + max(self.get_height(node.left), self.get_height(node.right))
By combining these operations into one step after each modification of the subtree structure (insertion or rotation), you reduce redundant calculations and enhance efficiency.
<>: Awesome! Now I’m thinking about caching results of repeated operations like height calculations during traversal to cut down on redundant work even more. Do you think implementing memoization could help here? How might that look in practice?
<>: Implementing memoization can indeed help reduce redundant calculations during traversal by caching results of operations like height calculations.
Here’s how you might implement it:
1. **Use a Cache Dictionary**: Store calculated heights in a dictionary where keys are nodes’ identifiers (e.g., memory addresses).
python
class AVLTreeWithMemoization(AVLTree):
def __init__(self):
super().__init__()
self.height_cache = {}
def get_cached_height(self, node):
if node is None:
return -1
# Use id() as a unique identifier for nodes since nodes themselves aren’t hashable.
node_id = id(node)
# Check cache first.
if node_id in self.height_cache:
return self.height_cache[node_id]
# Calculate height if not cached.
height = 1 + max(self.get_cached_height(node.left), self.get_cached_height(node.right))
# Store in cache.
self.height_cache[node_id] = height
return height
2. **Integrate Cached Height**: Use `get_cached_height` instead of directly calculating heights.
3. **Clear Cache When Necessary**: Ensure cache consistency by clearing relevant entries after rotations or structural changes.
This approach reduces repeated work by storing previously computed values during traversal operations.
<>: Got it! So just to clarify about clearing the cache—should I clear it every time after any rotation or just specific ones? And how do I handle cases where nodes are deleted? Would love some guidance on keeping everything consistent!
<> Clearing the cache should be done strategically to maintain consistency:
1. **After Rotations**: Clear cache entries related to nodes involved in rotations since their subtree structures change significantly.
python
def clear_cache_for_node(self, node):
node_id = id(node)
# Remove from cache directly affected by rotation.
keys_to_remove = [node_id]
# Include child nodes as well.
keys_to_remove.extend([id(child) for child in [node.left,
node.right]])
for key in keys_to_remove[:]:
del self.height_cache[key]
2. **After Deletions**: Similarly clear cache entries related to deleted nodes and potentially affected subtrees.
3. **General Strategy**: Focus on clearing only those entries directly impacted by structural changes rather than clearing everything indiscriminately.
By doing so efficiently after specific operations like rotations or deletions where necessary adjustments occur will keep your cache accurate without unnecessary overhead.
<>: Perfect! That clears things up about managing the cache efficiently after structural changes like rotations and deletions. Thanks for all the help—I’m ready to implement these optimizations now!
<> You’re welcome! I’m glad I could help clarify things for you. Implementing these optimizations should make your AVL tree more efficient and robust during insertions and deletions. If you encounter any more questions along the way or need further assistance with your implementation—feel free to reach out anytime! Happy coding! 🚀<>: Hey there,
I’ve been trying to implement some complex tensor manipulations using PyTorch based on different configurations specified by various flags (`use_transformer`, `use_mlp`, etc.). The idea is to apply transformations conditionally based on these flags before feeding them into neural network models (`mlp_transformer`, `_transformer`). However something seems off when running my code; it throws an error but doesn’t seem obvious why!
Here’s my buggy version of code:
python
if use_transformer_full_attention == ‘True’:
use_transformer_position_embedding=’False’
print(“Using transformer block:”, use_transformer_full_attention)
if use_transformer_full_attention == ‘True’:
if model_name != ‘Tacotron’:
attention_layers=nn.Sequential(*[TransformerEncoderLayer(d_model=size_encoder_hidden_layers[-1], nhead=8,)
for _ in range(num_layers)])
# Bug introduced here by mistakenly setting d_model incorrectly
transformer_encoder=nn.TransformerEncoder(attention_layers,size_encoder_hidden_layers[-1])
if attention_heads != ‘default’:
num_heads=int(attention_heads.split(“-“)[0])
print(‘Number of heads:’, num_heads)# default=8; dconv=16; sconv=4; conv=16;
size_key_value=size_encoder_hidden_layers[-1]
size_attn_out=(size_key_value//num_heads)*num_heads#//16*16=256; //8*8=256; //4*4=256; //16*16=256;
print(‘Size of key/value:’, size_key_value)# default=256; dconv=512; sconv=128; conv=512;
print(‘Size of attention output:’, size_attn_out)# default=256; dconv=512; sconv=128; conv=512;
elif size_key_value==’default’:
size_key_value=size_encoder_hidden_layers[-1]
else:# Tacotron uses embedding as query/key/value representations
pass
else:# Use original implementation multi-head-attnetion according paper
if model_name != ‘Tacotron’:
if attention_heads != ‘default’:
num_heads=int(attention_heads.split(“-“)[0])# default=8; dconv-16; sconv-4
size_key_value=input_size[0]#//num_heads * num_heads#//16 *16=default256; //8*8=default256; //4*4=default128
print(‘Number heads:’, num_heads)# default=8; dconv-16; sconv-4
print(‘Key/query/input size’, input_size[0])# lstm_state[0].size(-1))# default256 lstm_state[0].size(-1))# dconv512 lstm_state[0].size(-1))# sconv128 lstm_state[0].size(-1))#
print(‘Size of key/value:’, size_key_value)# default256 lstm_state[0].size(-1))# dconv512 lstm_state[0].size(-1))# sconv128 lstm_state[0].size(-1))#
elif size_key_value==’default’:
size_key_value=input_size[0]##lstm_state[0].size(-1)#//num_heads * num_heads##//16 *16=default256##//8*8=default256##//4*4=default128##
else:# Tacotron uses embedding as query/key/value representations
pass
if use_mlp_matrix_attention == ‘True’:
Wdown_omp=np.matrix([[56.,208.,472.,712.,1040.]])#/12000.#
Wdown_ump=np.matrix([[28.,136.,304.,472.,680.]])#/6000.#
Wdown_r=np.matrix([[-36.,-136,-240,-344.,-440.]])#/-12000.#
Wup_om=np.matrix([[40.,152.,328.,488.,712.]])#/12000.#
Wup_um=np.matrix([[20.,100.,232.,348.,500.]])#/6000.#
Wup_r=np.matrix([[-12.-60.-156.-260.-364.]])#/-12000.#
x_range=np.linspace(start=-60,end=70,num=Wdown_omp.shape[1])#
matplotlib.rcParams.update({‘font.size’: 22})
plt.figure(figsize=(9./25.*18,.7*18))
plt.subplot(151);plt.title(“decoder”);plt.plot(x_range,Wdown_omp.T);plt.ylim([-500,+500]);plt.yticks([-400,-200,+200]);plt.gca().tick_params(axis=”x”,which=”both”,bottom=False,labelbottom=False);
plt.subplot(154);plt.title(“encoder”);plt.plot(x_range,Wdown_r.T);plt.ylim([-500,+500]);plt.yticks([-400,-200,+200]);plt.gca().tick_params(axis=”x”,which=”both”,bottom=False,labelbottom=False);
plt.subplot(153);plt.title(“memory”);plt.plot(x_range,Wdown_ump.T);plt.ylim([-500,+500]);plt.yticks([-400,-200,+200]);plt.gca().tick_params(axis=”x”,which=”both”,bottom=False,labelbottom=False);
plt.subplot(152); plt.title(“decoder”); plt.plot(x_range,Wup_om.T); plt.ylim([-500,+500]); plt.yticks([-400,-200,+200]);
plt.subplot(155); plt.title(“encoder”); plt.plot(x_range,Wup_r.T); plt.ylim([-500,+500]); plt.yticks([-400,-200,+200]);
matplotlib.pyplot.savefig(os.path.join(master_dir,”MatrixMapper.png”), dpi=None)#300,# pad_inches=.25,bbox_inches=’tight’)
exit()
print(‘Condition decoder_output_dim==embedding_size:’, decoder_output_dim==embedding_size,’;&&’,embedding_size==query_dim,’&&’,query_dim==key_dim,’Set embedding_size=’,embedding_size,’&’,’query_dim=’,query_dim,’&’,’key_dim=’,key_dim,’&’,’value_dim=’,value_dim )
if decoder_output_dim!=embedding_size or embedding_size!=query_dim or query_dim!=key_dim :
if model_name!=’Tacotron’:
print(‘Using non-default setting.’)
query_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.randn(size_query_projections[0],input_size[0]))
key_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.randn(size_key_projections[0],input_size[0]))
value_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.randn(size_value_projections[0],input_size[0]))
else:# Query/key/value vectors are already embedded so don’t project them again #
query_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.randn(size_query_projections[0],query_vector_dimension))
key_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.randn(size_key_projections[0],key_vector_dimension))
value_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.randn(size_value_projections[0],value_vector_dimension))
else:# Default setting #
query_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.ones(size_query_projections[0],input_size[0]))
key_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.ones(size_key_projections[0],input_size[0]))
value_projection_mlp_first_layer_weights=torch.nn.Parameter(torch.ones(size_value_projections[0],input_size[0]))
print(“Initializing query projection layers…”)
query_input_fct=[]
for index_layer,list_layer_config in enumerate(query_config):#[{‘type’:’linear’, ‘params’:{‘in_features’:1024,’out_features’:1024}, ‘dropout’:False}, {‘type’:’relu’}]):
print(‘tlayer’,index_layer+1,’ntt’,list_layer_config,’n’)
layer_fct=None
if list_layer_config[‘type’]==’linear’:
layer_fct=FullyConnectedLayer(weight_matrix=query_projection_mlp_first_layer_weights[index_layer],
bias_vector=query_projection_biases[index_layer],
nonlinearity=list_layer_config[‘nonlinearity’])
elif list_layer_config[‘type’]==’conditional_linear’:
layer_fct=lambda x,y : ConditionalLinearLayer(weight_matrix=x,
bias_vector=y,
nonlinearity=list_layer_config[‘nonlinearity’])(query_input_feature_conditional,y[index_index_conditional_block])
elif list_layeconfig[‘type’]==’external_gaussian_prior_network’:
layer_fct=lambda x,y : GaussianPriorNetwork(query_input_feature_conditional,y[index_index_conditional_block])(x,index_index_conditional_block)[3][:-10]
elif list_layeconfig[‘type’]==’external_uniform_prior_network’:
layer_fct=lambda x,y : UniformPriorNetwork(query_input_feature_conditional,y[index_index_conditional_block])(x,index_index_conditional_block)[3][:-10]
else:# Error #
raise ValueError(‘Non implemented layer type’)
query_input_fct.append(layer_fct(query_input_feature_asr_unprocessed,y[index_index_conditional_block]))
print(‘n’)
print(“Initializing key projection layers…”)
key_input_fct=[]
for index_sublist_list_subsublists,list_subsublists in enumerate(key_config):#[{‘type’:’linear’, ‘params’:{‘in_features’:1024,’out_features’:1024}, ‘dropout’:False}, {‘type’:’relu’}]):
for index_list_subsublists,list_subsublist in enumerate(list_subsublists):#[{‘type’:’linear’, ‘params’:{‘in_features’:1024,’out_features’:1024}, ‘dropout’:False}]):
for index_subsublist,list_item_subsublist in enumerate(list_subsublist):#[{‘type’:’linear’, ‘params’:{‘in_features’:1024,’out_features’:1024}, ‘dropout’:False}]):
print(‘tttSub-block:’,index_sublist_list_subsublists,index_list_subsublists,index_subsublist,’ntttt’,list_item_subsublist,’n’)
layer_fct=None
if list_item_subsublist[‘type’]==’linear’:
layer_fct=FullyConnectedLayer(weight_matrix=key_projection_mlp_first_layer_weights[index_list_subsublists][index_sublist_list_subsublists][index_subsublist],
bias_vector=key_projection_biases[index_list_subsublists][index_sublist_list_subsublists][index_subsublist],
nonlinearity=list_item_subsublist[‘nonlinearity’])
elif list_item_subeconfig[‘type’]==’conditional_linear’:
layer_fct=lambda x,y : ConditionalLinearLayer(weight_matrix=x,
bias_vector=y,
nonlinearity=list_item_subeconfig[‘nonlinearity’])(key_input_feature_asr_unprocessed,y[index_index_conditional_block])
elif list_item_subeconfig[‘type’]==’external_gaussian_prior_network’:
layer_fct=lambda x,y : GaussianPriorNetwork(key_input_feature_asr_unprocessed,y[index_index_conditional_block])(x,index_index_conditional_block)[3][:-10]
elif list_item_subeconfig[‘type’]==’external_uniform_prior_network’:
layer_fct=lambda x,y : UniformPriorNetwork(key_input_feature_asr_unprocessed,y[index_index_conditional_block])(x,index_index_condional_block)[3][:-10]
else:# Error #
raise ValueError(‘Non implemented layer type’)
key_input_fct.append(layer_fct(key_input_feature_asr_unprocessed,y[index_index_conditional_block]))
print(‘n’)
print(“Initializing value projection layers…”)
value_input_fct=[]
for index_list_of_lists,list_of_lists_indeces_items_in_each_list_of_lists_in_each_main_list_of_lists_and_so_on_and_so_on_and_so_on_in_the_end_nested_deeply_nested_deeply_deeply_deeply_deeply_deeply_nested_lists
in enumerate(value_config):#[[[{‘type’:’linear’, ‘params’:{‘in_features’:1024,’out_features’:1024}, ‘dropout’:False}], [{‘type’:’relu’}]]]):
for index_lists_indeces_items_in_each_list_of_lists_in_each_main_list_of_lists_and_so_on_and_so_on_and_so_on_in_the_end_nested_deeply_nested_deeply_deeply_deeply_deeply_deeply_nested_lists
in enumerate(list_of_lists_indeces_items_in_each_list_of_lists_in_each_main_list_of_lists_and_so_on_and_so_on_and_so_on_in_the_end_nested_deeply_nested_deeply_deeply_deeply_deeply_deeply_nested_lists):#[[[{‘type’:’linear’, ‘params’:{‘in_features’:1024,’out_features’:1024}, ‘dropout’:False}]],[{‘type’:’relu’}]]]):
for index_items_in_each_list_of_lists_in_each_main_list_of_lists_and_so_on_and_so_on_and_so_on_in_the_end_nested_deeply_nested_deeply_deeply_deeeeeeepnested_lists
in enumerate(list_of_lists_indeces_items_in_each_list_of_lists_in_each_main_list_of_lists_and_so_on_and_so_on_and_so_on_in_the_end_nested_deeply_nested_deeply_deeply_deeeeeeepnested_lists[index_lists_indeces_items_in_each_list_of_loo
sssssssssssssfs]:#[[[{‘type’:’linear’, ‘params’:{‘in_features’:1024,’out_features’:1024}, ‘dropout’:False}]],[{‘type’:’relu’}]][index_lists:[[{‘typesss:’linerrrra’,{‘paramsss’:{‘in_featurrrrsss’:[…]}}]]]):
for index_items/items/items/items/items/items/items/items/items/indeeexxx/of/lists/in/each/list/of/lists/in/each/main/list/of/lists/and/so/on/and/so/on/and/so/on/in/the/end/nested/deeeeeeeeeeeeeeeeeeeeeeep/nested lists
in enumerate(list_of_lisssssof_lissssstofoflisssssts_[indeeesxs_oof_lissssstofoflisssssts_]_[indeeesxs_oof_lissssstofoflisssssts_]_[indeeesxs_oof_lissssstofoflisssssts_]_[indeeesxs_oof_lissssstofoflisssssts_]_[indeeesxs_oof_lissssstofoflisssssts_]_:#[[[{‘typesss:’linerrrra’,{‘paramsss’:{‘in_featurrrrsss’:[…]}}}]],[{‘types:’rellumm’}]]]:#[[[{typer:’linerrrra’,{‘paramss:{‘inn_featurrrrss’:[…]}}}]],[{typer:’rellumm’}]])]:
print(‘ttttSub-sub-block:’,
index_list_of_lsits,index_lsits,index_items/items/items/items/items/items/items/,
index_items/items/items/items/indeeexxx/of/lists/in/each/list/of/lists/in/each/main/list/of/lists/and/so/on/and/so/on/in/the/end/nesteed/nested lists,
index_items/indeeexxx/of/lists/in/each/list/of/lists/in/each/main/list/of/lists/in/the/end/nesteed/nested lists,n’,
lists_[indeexxs_ooff_lsits_tooff_lsits__][indeexxs_ooff_lsits_tooff_lsits__][indeexxs_ooff_lsits_tooff_lsits__][indeexxs_ooff_lsits_tooff_lsits__][indeexxs_ooff_lsits_tooff_lsits__][:],
lists_[indeexxs_ooff_lsits_tofff_llsisstts__][indeexxs_ooff_llsisstts__][indeexxs_ooff_llsisstts__][:][:],
lists_[indeexxs_ooff_llsisstts__][indeexxs_ooff_llsisstts__][:][:][:],
lists_[indeexxs_oof_llsisstts__[index_iitems_/items_/items_/items_/items_/items_/items_/items/]][:][:][:]’n’)
layer_function=None
if lists_[indxxx_xxofs_liiists_ttofi_liiists___][indxxx_xxofs_liiists_ttofi_liiists___][indxxx_xxofs_liiists_ttofi_liiists___][indxxx_xxofs_liiists_ttofi_liiists___]
[indxxx_xxofs_liiists_ttofi_liiists___][‘typesss’] ==’linerrrraaassaaassaaaassaaassaaasaaaaaaaaaaaaaaaassaaassaaasaaaaaaaaaaaaaaaasaaaaaaaaaaaaaaaasaaaaaaaaaaaaaaaasaaaaaaaaaaaaaaaasaaaaaaaaaaaaaaaasaaassa’
:
layer_function=FullyConnectedLayer(weight_matrix=value_projecction_mpl_first_lyear_weigths[indxxx_xoxofs_lilstofoy_lstoi_stoi_stoi_stoi_stoi_stoi_stoi_stoi______[indxxx_xoxofs_lstois_lysti_lysti_lysti_lysti_lysti_lysti_lysti__[indxxx_xoxofs_lstois_lysti_lysti_lysti_lysti_lysti_lysti__[indxxx_xoxofs_lstois_tyistiy_tiistiy_tiisti_ytiisti_ytiisti_ytiisti_ytiisti_ytiisti_ytiisti__
bias_vecto=value_projecction_biases[indxxx_xoxofs_lilstofoy_lstoi_stoi_stoi_stoi_stoi_stoi_stoi_stoi______[indxxx_xoxofs_lstois_lysti_lysti_lysti_lysti_lysti_lysi__[indxxx_xoxofs_lstois_tyistiy_tiistiy_tiisti_ytiisti_ytiisti_ytiisti_ytiisti_ytiisti_
nonlinearity=list___itemss___lisiitms___lisisitms___lisitisims___lisitisims___lisitisims___lisitisims___lisitisims___lisiitms[indxxx_xoxofs_lstois_tyistiy_tiistiy_tiisti_ytiisti_ytiisti_ytiisti_ytiisti_
)
elif lists_indxx_xxooxf_s_gausian_prioor_networke_type==’external_gausian_prior_networkkknntteewwoorrrrkkkkaannnttteeefffeecceeccntttiiioonnnaalllllllllllllllllllaarrreeeefuunncctionnnnaallllaaccccceeeessssttttriiccccceeeennnaalllaaccccceeeessssttttriiccccceeeeelllleeeennnttttraallllaaccccceeeessssttttriiccccceeeeelllleeeennnttttraallllaacccccceeefuunncctionnnnaallllaaccccceeeessssttttriiccccceeeeelllleeeennnttttraallllaacccceeefuncconnnnaaalllllaaccceeesssstttiicccecenonniilliarriirryy’
layer_function=lambda x,xxyyzz::GaussianPriorNetwork(value_featutre_asr_uunprroocessed,xxyyzz)(x,xxyyzz)[3][-10:]
elif lists_indxx_xxooxf_s_uniform_prioor_networke_type==’externaallluuuniiffoorrmmp_prioor_networkeetworkeeekkaannntteewwoorrrrkkkkaannntteewwoorrrrkkkkaannntteewwoorrrrkkkkaannntteewwoorrrrkkkkaannntteefffeecceeccntttiiioonnnaallllaaccccceeeessssttttriiccccceeeeelllleeeennnttttraallllaaccccceeeessssttttriiccccceeeeelllleeeennnttttraallllaacccccceeefuunncctionnnnaallllaaccccceeeessssttttriiccccceeeeelllleeeennnttttraallllaacccceeefuncconnnnaaalllllaaccceeesssstttiicccecenonniilliarriirryy’
layer_function=lambda xxxyyyzzz::UniformPriorNetwork(value_featutre_asr_uunprroocessed,xxyyzzz)(xxxyyyzzz,zzzzyyyxx)[3][-10:]
else:# Error #
raise ValueErroe(‘Non implemented layertyppe’)
valuuuee_eeenpppppyyyyyyyynneerrrsssfucfttion.append(layer_function(valuuuee_eeenpppyyyyyyynneerrrsssfucfttuur,value_featutre_asr_uunprroocessed))
print(‘n’)
if use_modified_default_init_parameters == True :
initrange_for_external_matrices_gateways_values_embeddings_decoders_encoders_contextualizers_sources_targets_processors_predictors_classifiers_optimizers_regularizers_validators_constraints_optimizators_constraints==torch.rand(input_data.shape)/10000000000000000000000000000.;
else :
initrange_for_external_matrices_gateways_values_embeddings_decoders_encoders_contextualizers_sources_targets_processors_predictors_classifiers_optimizers_regularizers_validators_constraints_optimizators_constraints==torch.rand(input_data.shape)/100.;
print(“tInitializing external matrices/gateways/values/embeddings…”)
embeddings_gateways_values_matrices_external=[]
embeddings_gateways_values_matrices_internal=[]
for itemmmyyyyynnnoonnneemmmmppllrreeeatttryyyyynnnoonnneemmmmppllrreeaattemmmmttteedddooowwwwwwwwwwwwnnn….wwwwwwwwwwwweeeeeeeeeeeeeeeeeeeeeeennddddddddddddoouuuuuuuuuut….fffffinoorrroouuurrrraanngge…………..”
And here’s the traceback error message I got:
plaintext
Traceback (most recent call last):
File “script.py”, line X.XX,
ValueError Traceback (most recent call last)X.XX X.()
ValueError : matmul(): Input operand 1 does not have enough dimensions (
got 32767-dimensions expected at least 32768-dimensions). Full shape received was [32767].
It seems like there’s something wrong with how dimensions are being handled somewhere within my tensor operations but can’t pinpoint exactly where it’s going wrong…
Could someone please help me figure out what might be causing this dimension mismatch error?
Thanks!
<>: It looks like you’re encountering a dimension mismatch error due to incorrect tensor shapes during matrix multiplication (`matmul`). Here are some steps to help identify and resolve the issue:
1. **Check Tensor Shapes:** Before performing matrix multiplication (`matmul`), print out the shapes of all tensors involved using `.shape`. This will help identify which tensor doesn’t have the expected dimensions.
2. **Review Initialization:** Ensure that all tensors are initialized with correct dimensions according to your model architecture requirements.
3. **Transformer Layer Configuration:** Double-check how `d_model` is set up when initializing your transformer layers (`TransformerEncoderLayer`). It should match across all layers unless intentionally varied.
4. **Conditional Logic:** Ensure conditional logic correctly sets up configurations based on flags like `use_transformer_full_attention`.
5. **Debugging Prints:** Add debugging print statements before each major operation (like matrix multiplications) to log tensor shapes.
6. **Look at Specific Lines:** Focus on lines around where errors occur (like line X.XX). Verify tensor operations leading up to these lines.
By following these steps systematically through your codebase where tensors are manipulated or passed between functions/layers/modules will likely reveal where dimensions aren’t aligning as expected.
<>: Alright, thanks for pointing me in the right direction! I’ll start by adding some print statements to check tensor shapes before each major operation involving matmul or similar functions.
Here’s what I’m thinking:
python
# Example debug print statement before matmul operation
tensor_a_shape = tensor_a.shape
tensor_b_shape = tensor_b.shape
print(f’Tensor A shape before matmul: {tensor_a_shape}’)
print(f’Tensor B shape before matmul: {tensor_b_shape}’)
result_tensor = torch.matmul(tensor_a, tensor_b)
I’ll go through each section where tensors are manipulated and add similar checks throughout my codebase until we find where things go awry.
Does this approach sound good? Let me know if there’s anything specific I should focus on while doing this!
<>: That sounds like a solid plan! By printing out tensor shapes before operations like `matmul`, you’ll gain insights into where mismatches occur.
Here are a few additional tips while implementing this:
– **Focus on Key Operations:** Pay special attention around areas involving reshaping (`view`, `reshape`) or concatenation (`cat`) since these often lead to dimension issues.
– **Check Model Inputs:** Ensure input tensors match expected dimensions when feeding into models or layers.
– **Review Layer Outputs:** After passing through layers (like LSTM or Transformer), verify
