Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
xet
 Community
1
identifier
stringlengths
24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
minimax/modeling_minimax.py:repeat_kv
[ -0.00025096136960200965, -0.0023088448215276003, -0.004072744864970446, -0.009292741306126118, -0.000559285341296345, 0.03143470734357834, 0.009694280102849007, -0.058739304542541504, 0.011185707524418831, 0.05162634328007698, 0.005736260209232569, -0.02317449077963829, 0.0006775957299396396...
[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
minimax/modeling_minimax.py:eager_attention_forward
[ 0, 0.021594731137156487, 0.01775064319372177, -0.018768195062875748, -0.00008832923776935786, 0.039797618985176086, 0.05359111353754997, -0.035049039870500565, 0.02069023996591568, 0.011645326390862465, 0.029395969584584236, 0.022499222308397293, 0.002741739386692643, -0.014584923163056374...
[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", ...
minimax/modeling_minimax.py:MiniMaxAttention
[ -0.00010289518104400486, 0.03129420429468155, 0.026904011145234108, -0.009005527012050152, -0.0004889719421043992, 0.03129420429468155, 0.04120028391480446, -0.00973722618073225, 0.002532804384827614, 0.011200624518096447, 0.014183704741299152, 0.02746685780584812, -0.00106237072031945, -0...
[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_kwargs", "cache_position", "cla...
minimax/modeling_minimax.py:MiniMaxTopKRouter
[ -0.00035038645728491247, 0.036206599324941635, 0.0033578702714294195, -0.00721212150529027, -0.0014453441835939884, 0.05559465289115906, 0.07521629333496094, -0.023592688143253326, -0.0024965035263448954, 0.015884187072515488, 0.02125677838921547, -0.020439209416508675, -0.001467243302613496...
[ "F", "ModelTopKRouter", "Module", "Parameter", "True", "__init__", "class", "config", "def", "dim", "empty", "float", "forward", "functional", "hidden_dim", "hidden_size", "hidden_states", "keepdim", "linear", "nn", "num_experts", "num_experts_per_tok", "num_local_experts...
minimax/modeling_minimax.py:MiniMaxExperts
[ -0.00033012471976689994, 0.03738027438521385, -0.010854209773242474, 0.006558961234986782, -0.001262454898096621, 0.05874042958021164, 0.0631517693400383, -0.01555576641112566, -0.005252973176538944, -0.018806224688887596, 0.011492692865431309, -0.016020117327570915, -0.0018283829558640718, ...
[ "ACT2FN", "ModelExperts", "Module", "None", "Parameter", "__init__", "act_fn", "chunk", "class", "config", "continue", "current_hidden_states", "current_state", "def", "dim", "down_proj", "dtype", "empty", "expert_hit", "expert_idx", "expert_mask", "final_hidden_states", ...
minimax/modeling_minimax.py:MiniMaxSparseMoeBlock
[ -0.0003811666101682931, 0.037295687943696976, 0.00492584565654397, -0.022518150508403778, -0.0015319965314120054, 0.0387030728161335, 0.058641016483306885, -0.04644368588924408, -0.0021550573874264956, -0.005072447936981916, 0.023690970614552498, -0.020993484184145927, 0.00010078924970002845...
[ "ModelExperts", "ModelSparseMoeBlock", "ModelTopKRouter", "Module", "_", "__init__", "and", "batch_size", "class", "config", "def", "empty_like", "experts", "forward", "gate", "hidden_dim", "hidden_states", "if", "jitter_noise", "nn", "num_experts_per_tok", "reshape", "re...
minimax/modeling_minimax.py:MiniMaxDecoderLayer
[ -0.0002526275929994881, 0.046227291226387024, 0.02003941684961319, -0.006689294241368771, -0.0009251151350326836, 0.04076199606060982, 0.03415809944272041, -0.03529670089483261, -0.00024195319565478712, -0.007144735660403967, 0.003928181249648333, 0.017192909494042397, -0.000711627013515681,...
[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelLightningAttention", "ModelRMSNorm", "ModelSparseMoeBlock", "None", "Tensor", "_", "__init__", "attention_mask", "attn_alpha_factor", "attn_beta_factor", "cache_position", "class", "config", "def", ...
minimax/modeling_minimax.py:MiniMaxPreTrainedModel
[ -0.00035086405114270747, 0.040743637830019, -0.00431164912879467, 0.0008862030226737261, -0.001302175922319293, 0.05393901839852333, 0.04213262349367142, -0.011054026894271374, -0.006539816968142986, -0.0024886028841137886, 0.001533673843368888, -0.012269390746951103, -0.003052879124879837, ...
[ "False", "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelExperts", "ModelLightningAttention", "ModelPreTrainedModel", "ModelTopKRouter", "OutputRecorder", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modules", "_ski...
minimax/modeling_minimax.py:MiniMaxModel
[ -0.0001892255968414247, 0.048896607011556625, -0.0023737596347928047, -0.011257590726017952, -0.0010944879613816738, 0.042073823511600494, 0.037980154156684875, -0.014668981544673443, 0.008983329869806767, 0.00015813219943083823, 0.01751180738210678, -0.006453214678913355, -0.001947335782460...
[ "Embedding", "False", "Model", "ModelCache", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "MoeModelOutputWithPast", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "cache", "cache_po...
minimax/modeling_minimax.py:load_balancing_loss_func
[ -0.00027690583374351263, 0.01840798556804657, 0.001643570140004158, -0.028812499716877937, -0.0009432663209736347, 0.05808233842253685, 0.038874007761478424, -0.011547867208719254, 0, -0.02252405695617199, 0.031556546688079834, -0.0061455233953893185, -0.0008396499906666577, 0.014349081553...
[ "Model_balancing_loss_func", "None", "_", "attention_mask", "batch_size", "cat", "compute_device", "concatenated_gate_logits", "def", "device", "dim", "else", "expand", "expert_attention_mask", "expert_mask", "float", "for", "functional", "gate_logits", "if", "in", "is", ...
minimax/modeling_minimax.py:MiniMaxForCausalLM
[ -0.0004233771760482341, 0.03737398609519005, -0.001000046031549573, -0.011270591989159584, -0.0016643102280795574, 0.049286942929029465, 0.03994344547390938, -0.006160867866128683, -0.00519731966778636, 0.005752089899033308, 0.027796901762485504, -0.005810486618429422, 0.00019891427655238658...
[ "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "MoeCausalLMOutputWithPast", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "aux_loss", "cache_position", "can_return_tuple", ...
minimax/modeling_minimax.py:MiniMaxForSequenceClassification
[ -0.0002121782599715516, 0.015340753830969334, -0.017272552475333214, 0.0278406273573637, -0.0007244244916364551, 0.027954263612627983, 0.015227118507027626, -0.011079433374106884, 0.0032101948745548725, -0.013863496482372284, 0.02909061498939991, 0.008409005589783192, -0.003437465289607644, ...
[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
minimax/modeling_minimax.py:MiniMaxForTokenClassification
[ -0.00016310509818140417, 0.024394849315285683, -0.0069496952928602695, -0.024848707020282745, -0.0007446102099493146, 0.035854753106832504, 0.0356278270483017, -0.007885776460170746, 0.0015317696379497647, -0.018381234258413315, 0.040166404098272324, 0.024394849315285683, -0.0042832815088331...
[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
minimax/modeling_minimax.py:MiniMaxForQuestionAnswering
[ -0.0001399769971612841, 0.023664036765694618, -0.012282761745154858, 0.025016266852617264, -0.00036798985092900693, 0.03493262454867363, 0.0425952672958374, 0.026819242164492607, 0.00495817931368947, 0.015325280837714672, 0.009352928958833218, 0.019607344642281532, -0.0027889758348464966, ...
[ "GenericForQuestionAnswering", "ModelForQuestionAnswering", "ModelPreTrainedModel", "class", "pass" ]
starcoder2/modeling_starcoder2.py:Starcoder2MLP
[ -0.0002937815443146974, 0.03575843945145607, 0.03944963216781616, 0.004527479875832796, -0.0010669856565073133, 0.046601321548223495, 0.033451441675424576, -0.022031811997294426, -0.001960946712642908, -0.017187120392918587, 0.030221648514270782, -0.02480020746588707, -0.0007209362811408937,...
[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act", "c_fc", "c_proj", "class", "config", "def", "dropout", "embed_dim", "forward", "functional", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "p", "residual_dropout", "return", "self", ...
starcoder2/modeling_starcoder2.py:rotate_half
[ 0.00002049485374300275, 0.013860220089554787, 0.03434192016720772, 0.002974055241793394, 0.0003507140791043639, 0.028506038710474968, 0.01975221559405327, -0.02008890174329281, 0.014477476477622986, 0.018742159008979797, -0.001487027620896697, -0.01217679213732481, 0.00022445700597018003, ...
[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
starcoder2/modeling_starcoder2.py:apply_rotary_pos_emb
[ -0.0001444592053303495, 0.027112245559692383, 0.028019767254590988, 0.0028360087890177965, -0.0005707467789761722, 0.021667107939720154, 0.046510547399520874, -0.002183726755902171, 0.01293220091611147, 0.03652779385447502, 0.007884104736149311, 0.0017654155381023884, -0.0007834474672563374,...
[ "Model_rotary_pos_emb", "cos", "def", "k", "k_embed", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze", "unsqueeze_dim" ]
starcoder2/modeling_starcoder2.py:repeat_kv
[ -0.00025096136960200965, -0.0023088448215276003, -0.004072744864970446, -0.009292741306126118, -0.000559285341296345, 0.03143470734357834, 0.009694280102849007, -0.058739304542541504, 0.011185707524418831, 0.05162634328007698, 0.005736260209232569, -0.02317449077963829, 0.0006775957299396396...
[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
starcoder2/modeling_starcoder2.py:eager_attention_forward
[ 0, 0.021594731137156487, 0.01775064319372177, -0.018768195062875748, -0.00008832923776935786, 0.039797618985176086, 0.05359111353754997, -0.035049039870500565, 0.02069023996591568, 0.011645326390862465, 0.029395969584584236, 0.022499222308397293, 0.002741739386692643, -0.014584923163056374...
[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", ...
starcoder2/modeling_starcoder2.py:Starcoder2Attention
[ -0.00007903065124992281, 0.03484373539686203, 0.0273129940032959, -0.012925902381539345, -0.00038637209217995405, 0.031022166833281517, 0.042936477810144424, -0.010340722277760506, 0.002051284536719322, 0.01028452254831791, 0.012644904665648937, 0.022929426282644272, -0.0010677919490262866, ...
[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_kwargs", "cache_position", "cla...
starcoder2/modeling_starcoder2.py:Starcoder2DecoderLayer
[ -0.00019718616385944188, 0.041425012052059174, 0.01867520995438099, -0.00027234680601395667, -0.0007498379563912749, 0.03938771411776543, 0.043009575456380844, -0.031464897096157074, 0.006394844502210617, -0.0035652671940624714, -0.005178126506507397, 0.018901575356721878, -0.001782633597031...
[ "False", "GradientCheckpointingLayer", "LayerNorm", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "None", "Tensor", "_", "__init__", "attention_mask", "cache_position", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "input_layernorm", "kwargs...
starcoder2/modeling_starcoder2.py:Starcoder2PreTrainedModel
[ -0.00034975787275470793, 0.027630871161818504, 0.016904963180422783, 0.011250545270740986, -0.0018799485405907035, 0.029612833634018898, 0.03077869303524494, -0.03241089731454849, -0.00408050836995244, 0.003993069287389517, 0.006383080966770649, 0.004459412768483162, -0.004255387466400862, ...
[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supp...
starcoder2/modeling_starcoder2.py:Starcoder2RotaryEmbedding
[ -0.00029968636226840317, 0.04852752387523651, 0.0020364229567348957, -0.005228263325989246, -0.0013792794197797775, 0.04136393964290619, 0.040670689195394516, 0.0062681385315954685, -0.0026430170983076096, 0.021144136786460876, 0.0021808501332998276, -0.004043960478156805, -0.001364836702123...
[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "and", "arange", "attention_factor", "attention_scaling", "base", "cat", "class", "clone", "compute_default_rope_parameters", "config", "cos", "cpu", "def", "default", "device", ...
starcoder2/modeling_starcoder2.py:Starcoder2Model
[ -0.00013408252561930567, 0.04200311377644539, -0.004996337927877903, -0.012533186934888363, -0.0007621532422490418, 0.0395190566778183, 0.04200311377644539, -0.010952425189316273, 0.011234703473746777, 0.0025263968855142593, 0.019533706828951836, -0.0015525344060733914, -0.002145320409908890...
[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "LayerNorm", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "cache_position", "causal_mas...
starcoder2/modeling_starcoder2.py:Starcoder2ForCausalLM
[ -0.00028262686100788414, 0.03526614233851433, 0.008361488580703735, -0.001301150070503354, -0.0012087186332792044, 0.02741658128798008, 0.0389065183699131, -0.007394513580948114, 0.003640376031398773, 0.026734011247754097, 0.025823917239904404, 0.0026734010316431522, 0.0009385344455949962, ...
[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "class", "colwi...
starcoder2/modeling_starcoder2.py:Starcoder2ForSequenceClassification
[ -0.0002121782599715516, 0.015340753830969334, -0.017272552475333214, 0.0278406273573637, -0.0007244244916364551, 0.027954263612627983, 0.015227118507027626, -0.011079433374106884, 0.0032101948745548725, -0.013863496482372284, 0.02909061498939991, 0.008409005589783192, -0.003437465289607644, ...
[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
starcoder2/modeling_starcoder2.py:Starcoder2ForTokenClassification
[ -0.00016310509818140417, 0.024394849315285683, -0.0069496952928602695, -0.024848707020282745, -0.0007446102099493146, 0.035854753106832504, 0.0356278270483017, -0.007885776460170746, 0.0015317696379497647, -0.018381234258413315, 0.040166404098272324, 0.024394849315285683, -0.0042832815088331...
[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
videomae/modeling_videomae.py:VideoMAEDecoderOutput
[ -0.0002418888034299016, 0.04311712831258774, 0.014047468081116676, 0.00441491836681962, -0.0013187419390305877, 0.0392182394862175, 0.06788653880357742, -0.03761281445622444, 0.016168922185897827, -0.011581994593143463, 0.0074537587352097034, 0.019265098497271538, -0.004214240238070488, 0....
[ "ModelDecoderOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "r" ]
videomae/modeling_videomae.py:VideoMAEForPreTrainingOutput
[ -0.00023770524421706796, 0.029518023133277893, 0.028609776869416237, 0.018051406368613243, -0.0010643518762663007, 0.05086182430386543, 0.051543012261390686, -0.004739913623780012, 0.0133398761972785, -0.027020344510674477, 0.006953765172511339, 0.020662616938352585, -0.0018448764458298683, ...
[ "ModelForPreTrainingOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "r" ]
videomae/modeling_videomae.py:get_sinusoid_encoding_table
[ -0.00018507205822970718, 0.02368922345340252, 0.02164900302886963, 0.009521027095615864, -0.0005561016732826829, 0.008954299613833427, 0.08206219226121902, -0.031510066241025925, 0.013601467944681644, 0.03287021443247795, 0.019268745556473732, 0.00430713128298521, 0.0009280168451368809, 0....
[ "FloatTensor", "Model_position_angle_vec", "Model_sinusoid_encoding_table", "array", "cos", "d_hid", "def", "for", "hid_j", "in", "n_position", "np", "pos_i", "position", "power", "range", "return", "sin", "sinusoid_table", "torch", "unsqueeze" ]
videomae/modeling_videomae.py:VideoMAEEmbeddings
[ -0.00012883114686701447, 0.03320666775107384, 0.006155657581984997, 0.0185234472155571, -0.0008047534502111375, 0.01479616854339838, 0.012028945609927177, -0.019991768524050713, 0.0068615819327533245, -0.00029825291130691767, 0.01163362804800272, 0.023380205035209656, -0.0019342319574207067,...
[ "ModelEmbeddings", "ModelPatchEmbeddings", "Module", "None", "True", "_", "__init__", "batch_size", "bool_masked_pos", "class", "config", "copy", "def", "detach", "device", "embeddings", "forward", "get_sinusoid_encoding_table", "hidden_size", "if", "is", "nn", "not", "...
videomae/modeling_videomae.py:VideoMAEPatchEmbeddings
[ -0.00003505901986500248, 0.016491761431097984, -0.0004978380748070776, 0.010265280492603779, 0.00009991820115828887, 0.005805773660540581, 0.010714036412537098, -0.022101204842329025, 0.007965409196913242, 0.006871567573398352, 0.005693584680557251, 0.01234077475965023, -0.003337618662044406...
[ "Conv3d", "Input", "Iterable", "Make", "ModelPatchEmbeddings", "Module", "ValueError", "__init__", "abc", "batch_size", "channel", "class", "collections", "config", "configuration", "def", "dimension", "doesn", "else", "embeddings", "f", "flatten", "forward", "height", ...
videomae/modeling_videomae.py:eager_attention_forward
[ 0.00002095530362566933, 0.025862814858555794, 0.0269921962171793, -0.01146321278065443, 0.00008955634984886274, 0.03704368323087692, 0.060083046555519104, -0.023942869156599045, 0.021458230912685394, 0.014569009654223919, 0.023152301087975502, 0.026879258453845978, 0.003063444746658206, -0...
[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "shape", "size", "softmax", "tor...
videomae/modeling_videomae.py:VideoMAESelfAttention
[ -0.00006875484541524202, 0.039462652057409286, 0.03878999501466751, -0.0021861412096768618, -0.0004904803936369717, 0.009977772831916809, 0.02724268287420273, -0.020291876047849655, -0.0004624529683496803, 0.02544892579317093, 0.01737702079117298, 0.014294000342488289, 0.00009722022514324635...
[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelSelfAttention", "Module", "None", "Parameter", "The", "ValueError", "_", "__init__", "_attn_implementation", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_interface", "attention_probs", "attention...
videomae/modeling_videomae.py:VideoMAESelfOutput
[ -0.000045805918489350006, 0.04578312486410141, 0.04645640775561333, 0.005386250093579292, -0.000490934238769114, 0.04219229146838188, 0.028277814388275146, -0.018739663064479828, 0.0011501888511702418, 0.010940820910036564, 0.010323646478354931, 0.0002261804329464212, 0.003506673267111182, ...
[ "Dropout", "Linear", "ModelSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "nn", "return", "self", "super" ]
videomae/modeling_videomae.py:VideoMAEAttention
[ 0.000160034658620134, 0.038267627358436584, 0.04974791780114174, -0.0009707597200758755, 0.0004537246422842145, 0.028475617989897728, 0.035791490226984024, -0.014631740748882294, 0.01390015333890915, -0.004361384082585573, 0.0227354746311903, 0.02566182240843773, 0.004699039738625288, -0.0...
[ "ModelAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "_", "__init__", "attention", "class", "config", "def", "forward", "hidden_states", "nn", "output", "return", "self", "self_attn_output", "super" ]
videomae/modeling_videomae.py:VideoMAEIntermediate
[ -0.00025367451598867774, 0.02240910567343235, 0.04047359153628349, 0.012862369418144226, -0.0009432404185645282, 0.03635763004422188, 0.03452831506729126, -0.018864808604121208, -0.001329111517407, -0.003772961674258113, 0.023209432139992714, -0.02103712037205696, -0.0013719861162826419, 0...
[ "ACT2FN", "Linear", "ModelIntermediate", "Module", "__init__", "class", "config", "def", "dense", "else", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "intermediate_act_fn", "intermediate_size", "isinstance", "nn", "return", "self", "str", "super" ]
videomae/modeling_videomae.py:VideoMAEOutput
[ -0.0002648788213264197, 0.03665350377559662, 0.05406391620635986, 0.016150448471307755, -0.0012742818798869848, 0.0490240603685379, 0.04421328753232956, -0.02405386045575142, -0.0004617482190951705, 0.00538348313421011, 0.008533393032848835, -0.002505610464140773, 0.0014532541390508413, 0....
[ "Dropout", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "nn", "return", "self", "super" ]
videomae/modeling_videomae.py:VideoMAELayer
[ 0.000011264218301221263, 0.01948556676506996, 0.031132111325860023, 0.021837273612618446, 0.0001924759562825784, 0.03695538640022278, 0.010246720165014267, -0.0007384077762253582, 0.005207349546253681, 0.018253719434142113, 0.006355206482112408, 0.015790028497576714, 0.0030236223246902227, ...
[ "GradientCheckpointingLayer", "LayerNorm", "ModelAttention", "ModelIntermediate", "ModelLayer", "ModelOutput", "__init__", "attention", "attention_output", "chunk_size_feed_forward", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "hidden_states_norm", ...
videomae/modeling_videomae.py:VideoMAEEncoder
[ -0.000046535220462828875, 0.013374047353863716, 0.02393842115998268, 0.035739049315452576, -0.000099216602393426, 0.0343904085457325, 0.007979474030435085, -0.024949902668595314, 0.003961639944463968, 0.011351082473993301, 0.018656233325600624, -0.0229269377887249, -0.000670809589792043, 0...
[ "BaseModelOutput", "False", "ModelEncoder", "ModelLayer", "Module", "ModuleList", "_", "__init__", "class", "config", "def", "enumerate", "for", "forward", "gradient_checkpointing", "hidden_states", "i", "in", "last_hidden_state", "layer", "layer_module", "nn", "num_hidde...
videomae/modeling_videomae.py:VideoMAEPreTrainedModel
[ -0.0003066707286052406, 0.02274414338171482, 0.0027852917555719614, 0.02770860120654106, -0.0016091193538159132, 0.03394303843379021, 0.035328466445207596, -0.022397786378860474, -0.002511091995984316, 0.016509708017110825, 0.012699775397777557, 0.012180238962173462, -0.0032759648747742176, ...
[ "Model", "ModelConfig", "ModelEmbeddings", "ModelLayer", "ModelPreTrainedModel", "ModelSelfAttention", "PreTrainedModel", "True", "_can_record_outputs", "_no_split_modules", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "attentions", "b...
videomae/modeling_videomae.py:VideoMAEModel
[ 0.000032962507248157635, 0.045824434608221054, 0.005169219803065062, 0.03956548869609833, 0.0000943033373914659, 0.02995353378355503, 0.010897274129092693, -0.013691447675228119, 0.008214868605136871, 0.011735525913536549, 0.022353382781147957, 0.01743563823401928, 0.00032482261303812265, ...
[ "BaseModelOutput", "False", "LayerNorm", "ModelEmbeddings", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "auto_docstring", "bool_masked_pos", "check_model_inputs", "class", "config", "def", "else", "embedding_output", "embeddings", "encoder", "encod...
videomae/modeling_videomae.py:VideoMAEDecoder
[ -0.0002033622149610892, 0.06292557716369629, -0.008374986238777637, 0.006649060174822807, -0.0008664999040775001, 0.028293874114751816, 0.04640195146203041, -0.031010085716843605, -0.00022811935923527926, -0.0038479669019579887, -0.004725076723843813, 0.005375836044549942, -0.006366121582686...
[ "False", "Identity", "LayerNorm", "Linear", "ModelDecoder", "ModelDecoderOutput", "ModelLayer", "Module", "ModuleList", "_", "__init__", "class", "config", "decoder_config", "decoder_hidden_size", "decoder_intermediate_size", "decoder_layers", "decoder_num_attention_heads", "deco...
videomae/modeling_videomae.py:VideoMAEForPreTraining
[ -0.00020040075469296426, 0.055822644382715225, 0.003898545168340206, 0.020566238090395927, -0.0008545723976567388, 0.03796843811869621, 0.021583249792456627, -0.0422624908387661, 0.00915310624986887, 0.018758216872811317, 0.013503656722605228, 0.02169625088572502, -0.00009402062278240919, ...
[ "Can", "Consider", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Linear", "MSELoss", "Model", "ModelDecoder", "ModelForPreTraining", "ModelForPreTrainingOutput", "ModelModel", "ModelPreTrainedModel", "Models_patch", "None", "Parameter", "RGB", "True", "ValueError", ...
videomae/modeling_videomae.py:VideoMAEForVideoClassification
[ -0.00008893404447007924, 0.043525367975234985, 0.002246020594611764, 0.033927466720342636, -0.0001892028230940923, 0.02533399499952793, 0.014006240293383598, 0.010100116953253746, 0.004882653243839741, 0.015066473744809628, 0.042632538825273514, 0.020646648481488228, 0.00287379021756351, -...
[ "Identity", "ImageClassifierOutput", "LayerNorm", "Linear", "Model", "ModelForModelClassification", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "attentions", "auto_docstring", "can_return_tuple", "class", "classifier", "config", "def", "else", "fc_norm", "forward"...
mobilevitv2/modeling_mobilevitv2.py:make_divisible
[ -0.00013955315807834268, 0.023480039089918137, 0.016627013683319092, -0.020334387198090553, -0.0004985295236110687, 0.03662436828017235, 0.033928096294403076, -0.01819983869791031, -0.0004669326008297503, 0.040219396352767944, 0.008032645098865032, -0.05841923505067825, -0.000032694057154003...
[ "Model_divisible", "None", "def", "divisor", "if", "int", "is", "max", "min_value", "new_value", "return", "value" ]
mobilevitv2/modeling_mobilevitv2.py:clip
[ 0.00020622150623239577, 0.006883531808853149, 0.025372356176376343, 0.006798198912292719, 0.0004906649701297283, 0.05233759805560112, 0.03686387464404106, 0.007509307470172644, 0.020479928702116013, 0.018773268908262253, 0.025827467441558838, -0.0655357763171196, 0.001991104334592819, 0.00...
[ "Model", "def", "float", "inf", "max", "max_val", "min", "min_val", "return", "value" ]
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2ConvLayer
[ -0.00016653479542583227, 0.007540128193795681, -0.014399944804608822, -0.005329113453626633, -0.00028523511718958616, 0.02732587791979313, -0.01564718410372734, -0.02403770200908184, 0.00782359205186367, 0.007880284450948238, 0.043086446821689606, -0.03084082528948784, 0.0037417178973555565,...
[ "ACT2FN", "BatchNorm2d", "Conv2d", "Input", "ModelConvLayer", "Module", "None", "Output", "True", "ValueError", "__init__", "activation", "affine", "are", "by", "channels", "class", "config", "convolution", "def", "dilation", "divisible", "elif", "else", "eps", "f",...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2InvertedResidual
[ -0.0002775921893771738, 0.019599799066781998, -0.0008274037973023951, -0.01684894971549511, -0.0008560584974475205, 0.06281105428934097, 0.00664788531139493, -0.03782417252659798, 0.007794072385877371, -0.010143755935132504, 0.017651280388236046, -0.05111994594335556, 0.002034482080489397, ...
[ "False", "Invalid", "ModelConvLayer", "ModelInvertedResidual", "Module", "ValueError", "__init__", "and", "class", "config", "conv_3x3", "def", "dilation", "else", "expand_1x1", "expand_ratio", "expanded_channels", "f", "features", "forward", "groups", "if", "in", "in_c...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2MobileNetLayer
[ -0.00012887509365100414, -0.009886308573186398, -0.019885603338479996, 0.011016172356903553, 0, 0.06598404794931412, 0.008248005993664265, -0.03728550672531128, 0.007965539582073689, 0.005847045220434666, 0.01886872574687004, -0.0628204271197319, 0.000663794984575361, -0.000918014382477849...
[ "ModelInvertedResidual", "ModelModelNetLayer", "Module", "ModuleList", "__init__", "append", "class", "config", "def", "else", "features", "for", "forward", "i", "if", "in", "in_channels", "layer", "layer_module", "nn", "num_stages", "out_channels", "range", "return", ...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2LinearSelfAttention
[ -0.00006847307668067515, 0.0321366973221302, 0.0321366973221302, 0.001229003886692226, 0, 0.02146192081272602, 0.006011584773659706, -0.0194393303245306, 0.0013764844043180346, 0.023933973163366318, 0.018652768805623055, 0.01707964390516281, 0.0024018248077481985, -0.020675357431173325, ...
[ "Dropout", "False", "ModelConvLayer", "ModelLinearSelfAttention", "Module", "True", "__init__", "attn_dropout", "class", "config", "context_scores", "context_vector", "def", "dim", "embed_dim", "expand_as", "forward", "functional", "hidden_states", "in_channels", "keepdim", ...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2FFN
[ -0.00010011887206928805, 0.04944631829857826, 0.04286859557032585, -0.005245165433734655, -0.00009480282460572198, 0.029032699763774872, 0.03402269631624222, -0.03198133409023285, 0.009129423648118973, 0.0007867748499847949, 0.02767179161310196, -0.018939299508929253, 0.005812210496515036, ...
[ "Dropout", "False", "ModelConvLayer", "ModelFFN", "Module", "True", "__init__", "class", "config", "conv1", "conv2", "def", "dropout1", "dropout2", "embed_dim", "ffn_dropout", "ffn_latent_dim", "forward", "hidden_states", "in_channels", "kernel_size", "nn", "out_channels"...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2TransformerLayer
[ -0.00011626579362200573, 0.04780285805463791, 0.04351864010095596, 0.007441010791808367, -0.00026952524785883725, 0.05186159163713455, 0.018940754234790802, -0.019391724839806557, 0.006764555349946022, 0.009921347722411156, 0.014994763769209385, 0.013641852885484695, 0.0014163287123665214, ...
[ "Dropout", "GroupNorm", "ModelFFN", "ModelLinearSelfAttention", "ModelTransformerLayer", "Module", "__init__", "attention", "attention_output", "class", "config", "def", "dropout", "dropout1", "embed_dim", "eps", "ffn", "ffn_dropout", "ffn_latent_dim", "forward", "hidden_stat...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2Transformer
[ -0.00010042221401818097, 0.04171926528215408, 0.03811110928654671, 0.010147929191589355, -0.00009513683471595868, 0.04735700041055679, 0.021085141226649284, -0.04014069586992264, 0.0058350590988993645, 0.0062297009862959385, -0.0024383217096328735, -0.03292439132928848, 0.0032416994217783213...
[ "ModelTransformer", "ModelTransformerLayer", "Module", "ModuleList", "__init__", "append", "block_idx", "class", "config", "d", "d_model", "def", "embed_dim", "ffn_dims", "ffn_latent_dim", "ffn_multiplier", "for", "forward", "hidden_states", "in", "int", "layer", "layer_m...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2Layer
[ -0.00006036480044713244, 0.021612348034977913, 0.017916971817612648, -0.0045912242494523525, 0.0003079479793086648, 0.030010929331183434, 0.006494902539998293, -0.030010929331183434, 0.005067143589258194, -0.010918155312538147, -0.008006647229194641, -0.012149946764111519, 0.0021556357387453...
[ "False", "GradientCheckpointingLayer", "GroupNorm", "ModelConvLayer", "ModelInvertedResidual", "ModelLayer", "ModelTransformer", "None", "True", "__init__", "attn_unit_dim", "batch_size", "class", "cnn_out_dim", "config", "conv_1x1", "conv_kernel_size", "conv_kxk", "conv_projecti...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2Encoder
[ -0.00018922331219073385, 0.01279577985405922, 0.019993405789136887, 0.009996702894568443, -0.0005069756298325956, 0.061236947774887085, 0.009254090487957, -0.04661319777369499, 0.0030989779625087976, 0.013424144126474857, 0.018508180975914, -0.010053827427327633, 0.0015494889812543988, 0.0...
[ "BaseModelOutputWithNoAttention", "False", "ModelEncoder", "ModelLayer", "ModelModelNetLayer", "Module", "ModuleList", "None", "True", "__init__", "all_hidden_states", "append", "attn_unit_dim", "base_attn_unit_dims", "class", "clip", "config", "def", "dilate_layer_4", "dilate_...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2PreTrainedModel
[ -0.00014394153549801558, 0.03325400501489639, -0.016402313485741615, 0.028984909877181053, -0.0006143845967017114, 0.02179485373198986, 0.0013762214221060276, -0.0023451936431229115, -0.0009619507472962141, 0.015840590000152588, 0.019884996116161346, -0.013481353409588337, -0.002373280003666...
[ "BatchNorm2d", "Conv2d", "GroupNorm", "Linear", "Model", "ModelConfig", "ModelLayer", "ModelPreTrainedModel", "None", "PreTrainedModel", "True", "_init_weights", "_no_split_modules", "base_model_prefix", "bias", "class", "config", "def", "elif", "getattr", "if", "image", ...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2Model
[ -0.00013111595762893558, 0.025568492710590363, 0.01576911471784115, 0.03401623293757439, -0.00037310851621441543, 0.054065536707639694, 0.014642748981714249, -0.032889869064092636, 0.005491030868142843, 0.011601562611758709, 0.016332296654582024, -0.01667020656168461, 0.0009714901098050177, ...
[ "BaseModelOutputWithPoolingAndNoAttention", "False", "ModelConvLayer", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "auto_docstring", "class", "clip", "config", "conv_stem", "def", "dim", "divisor", "else", "embedding_output", "encoder", "...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2ForImageClassification
[ -0.00007537419878644869, 0.03145326301455498, 0.01963040418922901, 0.022307278588414192, -0.0000923660772969015, 0.015838168561458588, 0.02565336972475052, -0.006301806308329105, 0.004322035238146782, 0.011153639294207096, 0.03993002697825432, -0.0023422641679644585, 0.0014360310742631555, ...
[ "Identity", "ImageClassifierOutputWithNoAttention", "Linear", "Model", "ModelForImageClassification", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "auto_docstring", "class", "classifier", "config", "def", "divisor", "else", "forward", "hidden_states", "if", "in_fea...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2ASPPPooling
[ -0.00012205496022943407, -0.001313627464696765, 0.026300648227334023, 0.0257386676967144, -0.00037933627027086914, 0.025064293295145035, 0.009160268120467663, -0.015285846777260303, 0.005788390524685383, 0.0020512258633971214, 0.023490749299526215, -0.025064293295145035, -0.00012468923523556...
[ "AdaptiveAvgPool2d", "False", "ModelASPPPooling", "ModelConvLayer", "Module", "True", "__init__", "align_corners", "bilinear", "class", "config", "conv_1x1", "def", "features", "forward", "functional", "global_pool", "in_channels", "interpolate", "kernel_size", "mode", "nn"...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2ASPP
[ -0.0002590730437077582, -0.0014007812133058906, 0.016466327011585236, 0.005460187792778015, -0.000907649053260684, 0.048026785254478455, 0.008233163505792618, -0.03316134959459305, 0.0014222217723727226, -0.021726403385400772, 0.02126900479197502, -0.028816070407629013, -0.001986822346225381...
[ "Dropout", "Expected", "ModelASPP", "ModelASPPPooling", "ModelConvLayer", "Module", "ModuleList", "ValueError", "__init__", "append", "aspp_dropout_prob", "aspp_out_channels", "atrous_rates", "cat", "class", "config", "conv", "convs", "def", "dilation", "dim", "divisor", ...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2DeepLabV3
[ -0.00030563882319256663, 0.020366333425045013, 0.03589997813105583, 0.005149115342646837, -0.001085916766896844, 0.049477532505989075, 0.031067287549376488, -0.020711524412035942, 0.006357287522405386, -0.016684284433722496, 0.04786663502454758, -0.015533643774688244, 0.000014944839676900301...
[ "Dropout2d", "False", "ModelASPP", "ModelConvLayer", "ModelDeepLabV3", "Module", "__init__", "aspp", "aspp_out_channels", "class", "classifier", "classifier_dropout_prob", "config", "def", "dropout", "features", "forward", "hidden_states", "in_channels", "kernel_size", "nn", ...
mobilevitv2/modeling_mobilevitv2.py:MobileViTV2ForSemanticSegmentation
[ -0.00030823980341665447, 0.04424399510025978, -0.02200796641409397, 0.005501991603523493, -0.0007554548210464418, 0.03945469483733177, 0.025999048724770546, -0.0239464920014143, -0.0017746060620993376, -0.0011403091484680772, 0.04127918928861618, 0.005245422013103962, -0.0027367419097572565,...
[ "CrossEntropyLoss", "False", "Model", "ModelDeepLabV3", "ModelForSemanticSegmentation", "ModelModel", "ModelPreTrainedModel", "None", "SemanticSegmenterOutput", "The", "True", "ValueError", "__init__", "align_corners", "and", "attentions", "auto_docstring", "be", "bilinear", "c...
sew_d/modeling_sew_d.py:_compute_mask_indices
[ 0, -0.008906038478016853, 0.00551726296544075, -0.014171244576573372, 0, 0.024309566244482994, -0.0006616514874622226, -0.05265205353498459, 0.013331051915884018, -0.0200525913387537, 0.040777336806058884, 0.030695026740431786, -0.006189417093992233, -0.012434846721589565, 0.005489256698...
[ "False", "None", "ValueError", "_", "_compute_mask_indices", "and", "append", "arange", "array", "attention_mask", "batch_size", "be", "bigger", "bool", "broadcast_to", "but", "choice", "compute_num_masked_span", "concatenate", "def", "detach", "dtype", "dummy_mask_idx", ...
sew_d/modeling_sew_d.py:make_log_bucket_position
[ -0.0002609124348964542, -0.04414781555533409, -0.006290491670370102, -0.03316804766654968, -0.0009721668902784586, 0.03408302739262581, 0.032939303666353226, 0.005232545547187328, 0.009836041368544102, 0.026534438133239746, 0.012580983340740204, -0.009950414299964905, 0.0009364254656247795, ...
[ "Model_log_bucket_position", "abs", "abs_pos", "bucket_pos", "bucket_size", "ceil", "def", "log", "log_pos", "max_position", "mid", "relative_pos", "return", "sign", "tensor", "torch", "type_as", "where" ]
sew_d/modeling_sew_d.py:build_relative_position
[ -0.000125038786791265, -0.020999420434236526, 0.01152130402624607, -0.04744734242558479, -0.0003547199594322592, 0.0481284037232399, 0.04608521610498428, -0.014302308671176434, 0.021453462541103363, 0.0481284037232399, 0.011861835606396198, 0.0002057375677395612, 0.0032634236849844456, 0.0...
[ "Model_relative_position", "None", "and", "arange", "bucket_size", "def", "device", "if", "k_ids", "key_size", "long", "make_log_bucket_position", "max_position", "q_ids", "query_size", "rel_pos_ids", "return", "to", "torch", "unsqueeze" ]
sew_d/modeling_sew_d.py:c2p_dynamic_expand
[ -0.0003349598264321685, -0.001252458430826664, 0.012990615330636501, 0.014738231897354126, -0.001201486331410706, 0.0661764070391655, 0.037748515605926514, -0.03751549869775772, 0.010019667446613312, 0.022485997527837753, -0.010835221968591213, -0.01549553219228983, -0.0006116657750681043, ...
[ "Model_dynamic_expand", "Model_pos", "def", "expand", "query_layer", "relative_pos", "return", "size" ]
sew_d/modeling_sew_d.py:p2c_dynamic_expand
[ -0.0003611352003645152, 0.0007002723286859691, 0.010089787654578686, 0.013022865168750286, -0.0014005446573719382, 0.053968630731105804, 0.04035915061831474, -0.04200167581439018, 0.013785465620458126, 0.023699268698692322, -0.0009899137075990438, -0.01337483525276184, 0.00006141131598269567...
[ "Model_dynamic_expand", "c2p_pos", "def", "expand", "key_layer", "query_layer", "return", "size" ]
sew_d/modeling_sew_d.py:pos_dynamic_expand
[ -0.00029201919096522033, -0.005891169887036085, 0.010621517896652222, 0.0019008454401046038, -0.0012478833086788654, 0.05873757600784302, 0.03203867748379707, -0.049218837171792984, 0.013349449262022972, -0.0028295028023421764, -0.0026553794741630554, -0.0035550163593143225, -0.0014582822332...
[ "Model_dynamic_expand", "Model_index", "def", "expand", "key_layer", "p2c_att", "return", "size" ]
sew_d/modeling_sew_d.py:get_mask
[ -0.00013921396748628467, -0.0010427747620269656, 0.01577640138566494, -0.03314179182052612, -0.0007909481646493077, 0.0535716637969017, 0.009987937286496162, -0.04471872001886368, 0.006526208948343992, 0.001730864169076085, 0.03631977364420891, -0.001610271167010069, 0.00043980975169688463, ...
[ "DropoutContext", "Model_mask", "None", "and", "bernoulli_", "bool", "def", "dropout", "else", "empty_like", "if", "input", "is", "isinstance", "local_context", "mask", "not", "return", "reuse_mask", "scale", "to", "torch" ]
sew_d/modeling_sew_d.py:SEWDNoLayerNormConvLayer
[ -0.00008776657341513783, 0.017161672934889793, 0.009371176362037659, -0.006294495426118374, 0, 0.03612983971834183, 0.032742664217948914, -0.03025873936712742, 0.012871255166828632, -0.016371332108974457, 0.03206523135304451, -0.006774344481527805, 0.002512152772396803, -0.0072259674780070...
[ "ACT2FN", "Conv1d", "GradientCheckpointingLayer", "Model", "__init__", "activation", "class", "config", "conv", "conv_dim", "conv_kernel", "conv_stride", "def", "else", "feat_extract_activation", "forward", "hidden_states", "if", "in_conv_dim", "kernel_size", "layer_id", "n...
sew_d/modeling_sew_d.py:SEWDLayerNormConvLayer
[ -0.00008998542034532875, 0.02055196277797222, 0.010784134268760681, 0.0013056707102805376, -0.0002876004436984658, 0.036135319620370865, 0.031166713684797287, -0.02653687633574009, 0.012647362425923347, -0.010897057130932808, 0.02935994789004326, 0.0026678028516471386, 0.0018773428164422512,...
[ "ACT2FN", "Conv1d", "GradientCheckpointingLayer", "LayerNorm", "Model", "True", "__init__", "activation", "class", "config", "conv", "conv_dim", "conv_kernel", "conv_stride", "def", "elementwise_affine", "else", "feat_extract_activation", "forward", "hidden_states", "if", "...
sew_d/modeling_sew_d.py:SEWDGroupNormConvLayer
[ -0.00006655586912529543, 0.016699794679880142, 0.0028350157663226128, 0.0013751941733062267, 0.00009432421938981861, 0.03791304677724838, 0.027757765725255013, -0.02764492854475975, 0.013822464272379875, -0.011057971976697445, 0.03227122500538826, -0.0006593879661522806, 0.00207336968742311,...
[ "ACT2FN", "Conv1d", "GradientCheckpointingLayer", "GroupNorm", "Model", "True", "__init__", "activation", "affine", "class", "config", "conv", "conv_dim", "conv_kernel", "conv_stride", "def", "else", "feat_extract_activation", "forward", "hidden_states", "if", "in_conv_dim"...
sew_d/modeling_sew_d.py:SEWDPositionalConvEmbedding
[ -0.00014321714115794748, 0.03326881304383278, -0.007525088265538216, -0.004639528226107359, -0.0005127527401782572, 0.027271373197436333, 0.03055299073457718, -0.04300050437450409, 0.012504094280302525, -0.010071170516312122, 0.028629284352064133, 0, 0.00483755674213171, -0.018444953486323...
[ "ACT2FN", "Conv1d", "GatheredParameters", "Model", "ModelSamePadLayer", "Module", "__init__", "activation", "class", "config", "conv", "deepspeed", "def", "dim", "else", "feat_extract_activation", "forward", "groups", "hasattr", "hidden_size", "hidden_states", "if", "is_d...
sew_d/modeling_sew_d.py:SEWDSamePadLayer
[ -0.00012616814638022333, 0.04065614193677902, 0.03229904547333717, 0.0005681978072971106, -0.0007834777352400124, 0.013439113274216652, 0.019989268854260445, -0.04043027386069298, 0.018408196046948433, -0.011688640341162682, 0.005618452560156584, 0.004997317213565111, 0.003980913665145636, ...
[ "Model", "Module", "__init__", "class", "def", "else", "forward", "hidden_states", "if", "nn", "num_conv_pos_embeddings", "num_pad_remove", "return", "self", "super" ]
sew_d/modeling_sew_d.py:SEWDUpsampling
[ -0.00027743421378545463, 0.01718302257359028, 0.02829471044242382, 0.010653474368155003, -0.0008877894724719226, 0.01821400411427021, 0.02336890995502472, -0.053840138018131256, 0.0011598540004342794, 0.011741732247173786, 0.004009372089058161, -0.014433738775551319, 0.0012672479497268796, ...
[ "ACT2FN", "Linear", "Model", "Module", "__init__", "activation", "bsz", "class", "config", "def", "feat_extract_activation", "forward", "hidden_size", "hidden_states", "if", "nn", "projection", "reshape", "return", "self", "size", "squeeze_factor", "src_embed_dim", "src...
sew_d/modeling_sew_d.py:SEWDFeatureEncoder
[ -0.00004578685911837965, 0.03922173008322716, -0.021301455795764923, 0.03381183370947838, 0.00029233150416985154, 0.021864986047148705, -0.003662948729470372, -0.019385451450943947, 0.007551310118287802, 0.001613106345757842, 0.030205240473151207, -0.0011270612012594938, 0.002507711062207818...
[ "False", "Model", "ModelGroupNormConvLayer", "ModelLayerNormConvLayer", "ModelNoLayerNormConvLayer", "Module", "ModuleList", "None", "True", "ValueError", "__init__", "_freeze_parameters", "_requires_grad", "and", "be", "but", "class", "config", "conv_layer", "conv_layers", "...
sew_d/modeling_sew_d.py:ContextPooler
[ -0.0002618012949824333, 0.011797086335718632, 0.0341944545507431, 0.012822920456528664, -0.0009189759730361402, 0.03898167610168457, 0.0364740826189518, 0.004673242103308439, -0.0038183806464076042, 0.011455141939222813, 0.01253796648234129, -0.012708938680589199, -0.0005022310651838779, -...
[ "ACT2FN", "Linear", "ModelPooler", "Model_token", "Module", "StableDropout", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_size", "hidden_states", "nn", "output_dim", "pooled_output", "pooler_dropout", "pooler_hidden_act", "pooler_hidden_size", ...
sew_d/modeling_sew_d.py:XSoftmax
[ -0.000022995969629846513, 0.03943423926830292, 0.010365570895373821, -0.006253143306821585, -0.0001962909591384232, 0.0152103491127491, 0.005830633919686079, -0.015773694962263107, 0.016337040811777115, 0.026477273553609848, 0.024111218750476837, 0.0022815524134784937, -0.0011407762067392468...
[ "Bool", "Cast", "Constant", "Function", "Long", "Model", "None", "Sub", "autograd", "backward", "bool", "cast_pytorch_to_onnx", "class", "ctx", "def", "dim", "dtype", "finfo", "forward", "g", "grad_output", "input", "inputGrad", "int64", "mask", "mask_cast_value", ...
sew_d/modeling_sew_d.py:DropoutContext
[ -0.00007837716839276254, 0.00968831405043602, 0.006391994189471006, 0, -0.0007846674416214228, 0.05641006678342819, 0.026141250506043434, -0.062372107058763504, 0.01261200662702322, 0.006965267471969128, -0.018574045971035957, 0.008083149790763855, -0.0005159457214176655, 0.024994704872369...
[ "Model", "ModelContext", "None", "True", "class", "mask", "reuse_mask", "scale", "self" ]
sew_d/modeling_sew_d.py:XDropout
[ -0.00014507025480270386, 0.02536252699792385, 0.013756905682384968, -0.032608963549137115, -0.0009270342998206615, 0.02536252699792385, 0.007076597772538662, -0.028306391090154648, -0.00048474693903699517, 0.0028164859395474195, 0.025702202692627907, -0.014832548797130585, -0.002575881546363...
[ "DropoutContext", "Function", "Model", "None", "True", "autograd", "backward", "class", "ctx", "def", "dropout", "dropout_p", "else", "forward", "g", "get_mask", "grad_output", "if", "input", "isinstance", "local_ctx", "mask", "masked_fill", "return", "save_for_backwa...
sew_d/modeling_sew_d.py:StableDropout
[ -0.00019422793411649764, 0.007180225104093552, 0.025428781285881996, -0.022590747103095055, -0.0011423085816204548, 0.03859725594520569, 0.01004663947969675, -0.04540853947401047, -0.0068680415861308575, -0.016347073018550873, 0.02815329283475876, -0.00891142524778843, -0.0022562367375940084...
[ "DropoutContext", "ModelDropout", "Module", "None", "True", "XDropout", "__init__", "and", "append", "apply", "c", "class", "clear_context", "context_stack", "count", "ctx", "def", "drop_prob", "dropout", "else", "for", "forward", "get_context", "if", "in", "init_co...
sew_d/modeling_sew_d.py:SEWDSelfOutput
[ -0.00023412794689647853, 0.03728889673948288, 0.04758337885141373, 0.022075941786170006, -0.0010008523240685463, 0.048955973237752914, 0.018186915665864944, -0.01681431755423546, 0.0006434050155803561, 0.015441721305251122, 0.019445130601525307, -0.01229618489742279, 0.002559322165325284, ...
[ "Dropout", "LayerNorm", "Linear", "Model", "Module", "__init__", "activation_dropout", "class", "config", "def", "dense", "dropout", "forward", "hidden_size", "hidden_states", "input_tensor", "layer_norm_eps", "nn", "return", "self", "super" ]
sew_d/modeling_sew_d.py:DisentangledSelfAttention
[ -0.00013240067346487194, 0.04315908998250961, 0.02982722409069538, -0.012936428189277649, -0.0007202596752904356, 0.017851142212748528, 0.018529033288359642, 0.0007838119636289775, 0.005084185861051083, 0.022822344675660133, 0.012597482651472092, 0.01988481543958187, 0.0015252557350322604, ...
[ "False", "Linear", "ModelSelfAttention", "Model_attention_bias", "Module", "None", "Relative", "StableDropout", "The", "ValueError", "XSoftmax", "__init__", "_attention_head_size", "a", "activation_dropout", "all_head_size", "apply", "att_span", "attention", "attention_dropout"...
sew_d/modeling_sew_d.py:SEWDAttention
[ -0.00005673775740433484, 0.03895816579461098, 0.025559259578585625, 0.006840198300778866, -0.00028852687682956457, 0.02972530759871006, 0.023757725954055786, -0.02657262235879898, 0.0077128163538873196, 0.010921798646450043, 0.01069660671055317, 0.008726178668439388, 0.0011822564993053675, ...
[ "DisentangledSelfAttention", "False", "Model", "ModelSelfOutput", "Module", "None", "__init__", "att_matrix", "attention_mask", "attention_output", "class", "config", "def", "else", "forward", "hidden_states", "if", "is", "nn", "output", "output_attentions", "query_states",...
sew_d/modeling_sew_d.py:SEWDIntermediate
[ -0.00024672862491570413, 0.024029221385717392, 0.0379890538752079, 0.019795501604676247, -0.0009654597961343825, 0.03089471347630024, 0.03570055589079857, -0.01762142963707447, -0.0008653380209580064, -0.0033612302504479885, 0.027004268020391464, -0.03089471347630024, -0.00003039410512428730...
[ "ACT2FN", "Linear", "Model", "Module", "__init__", "class", "config", "def", "dense", "else", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "intermediate_act_fn", "intermediate_size", "isinstance", "nn", "return", "self", "str", "super" ]
sew_d/modeling_sew_d.py:SEWDOutput
[ -0.0002928224566858262, 0.03725132718682289, 0.04575934633612633, 0.02621389739215374, -0.001106617390178144, 0.0418502576649189, 0.026903735473752022, -0.019200529903173447, -0.0008694850839674473, 0.01057753711938858, 0.01667111925780773, -0.008565505966544151, 0.0010491306893527508, -0....
[ "Dropout", "LayerNorm", "Linear", "Model", "Module", "__init__", "activation_dropout", "class", "config", "def", "dense", "dropout", "forward", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "self", "super" ]
sew_d/modeling_sew_d.py:SEWDLayer
[ -0.00011973061191383749, 0.022988278418779373, 0.03267941623926163, 0.011212419718503952, -0.0002799583598971367, 0.04146905243396759, 0.04191980138421059, 0.0014226813800632954, 0.009015010669827461, 0.00292987865395844, 0.009353074245154858, -0.0008627647184766829, 0.0015494549879804254, ...
[ "False", "GradientCheckpointingLayer", "Model", "ModelAttention", "ModelIntermediate", "ModelOutput", "None", "__init__", "att_matrix", "attention", "attention_mask", "attention_output", "class", "config", "def", "else", "forward", "hidden_states", "if", "intermediate", "inte...
sew_d/modeling_sew_d.py:ConvLayer
[ -0.000093753362307325, 0.016338614746928215, -0.0006232617888599634, -0.004676224198192358, -0.00020071142353117466, 0.05476252734661102, 0.005267794709652662, -0.04011411592364311, 0.011324349790811539, 0.00008318960317410529, 0.026930542662739754, -0.02185993827879429, 0.001711328979581594...
[ "ACT2FN", "LayerNorm", "Model", "Model1d", "ModelLayer", "Model_act", "Model_groups", "Model_kernel_size", "Module", "None", "StableDropout", "__init__", "bool", "class", "config", "contiguous", "def", "dim", "dropout", "dtype", "else", "expand", "forward", "getattr", ...
sew_d/modeling_sew_d.py:SEWDTransformerEncoder
[ -0.00009594872972229496, 0.025581611320376396, 0.017318524420261383, 0.008715858682990074, -0.0003077434084843844, 0.0400703139603138, 0.007583929225802422, -0.009451612830162048, 0.006734981667250395, 0.01154568325728178, 0.034410662949085236, 0.006650086957961321, 0.004584315232932568, 0...
[ "BaseModelOutput", "ConvLayer", "Embedding", "False", "LayerNorm", "Model", "ModelLayer", "Module", "ModuleList", "None", "Sequence", "True", "_", "__init__", "all_attentions", "all_hidden_states", "and", "att_m", "attention_mask", "attentions", "bucket_size", "build_relati...
sew_d/modeling_sew_d.py:SEWDEncoder
[ -0.0003007352352142334, 0.03480605036020279, 0.0235113725066185, 0.0073761167004704475, -0.0014406477566808462, 0.033192526549100876, 0.01751827634871006, -0.018901299685239792, 0.0016927612014114857, -0.018094535917043686, 0.022128349170088768, 0.013253959827125072, 0.002348255831748247, ...
[ "AvgPool1d", "BaseModelOutput", "False", "Model", "ModelPositionalConvEmbedding", "ModelTransformerEncoder", "ModelUpsampling", "Module", "None", "True", "__init__", "arange", "attention_ids", "attention_mask", "attentions", "bool", "class", "config", "def", "device", "dtype"...
sew_d/modeling_sew_d.py:SEWDPreTrainedModel
[ -0.00016894198779482394, 0.05991809442639351, -0.016894198954105377, -0.015317407436668873, -0.0006229736027307808, 0.02038566768169403, 0.0227508544921875, -0.04369966313242912, 0.0038856659084558487, -0.026918090879917145, 0.036716725677251816, 0.033563144505023956, 0.0024496589321643114, ...
[ "Conv1d", "Embedding", "False", "GatheredParameters", "GroupNorm", "LayerNorm", "Linear", "Model", "ModelConfig", "ModelPositionalConvEmbedding", "None", "PreTrainedModel", "True", "_conv_out_length", "_get_feat_extract_output_lengths", "_get_feature_vector_attention_mask", "_init_we...
sew_d/modeling_sew_d.py:SEWDModel
[ -0.00008934891229728237, 0.027806781232357025, 0.009979046881198883, 0.004905430134385824, -0.0002855661150533706, 0.017939860001206398, 0.023658189922571182, -0.033637236803770065, 0.008521432988345623, -0.026237044483423233, 0.026461292058229446, 0.02018234133720398, -0.0009810860501602292...
[ "BaseModelOutput", "Dropout", "LayerNorm", "Linear", "Model", "ModelEncoder", "ModelFeatureEncoder", "ModelPreTrainedModel", "None", "Parameter", "Tensor", "True", "__init__", "_compute_mask_indices", "_get_feature_vector_attention_mask", "_mask_hidden_states", "and", "apply_spec_a...
sew_d/modeling_sew_d.py:SEWDForCTC
[ -0.0003008818021044135, 0.042868491262197495, 0.025789868086576462, -0.011347541585564613, -0.0011820356594398618, 0.0302601121366024, 0.02235121838748455, -0.04768260195851326, -0.0030231457203626633, -0.024185165762901306, 0.0302601121366024, 0.0013754596002399921, -0.00102443085052073, ...
[ "Cannot", "CausalLMOutput", "Dropout", "False", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "Please", "True", "ValueError", "You", "_HIDDEN_STATES_START_POSITION", "__class__", "__init__", "_freeze_parameters", "_get_feat_extract_output_lengths", "a", "adap...
sew_d/modeling_sew_d.py:SEWDForSequenceClassification
[ -0.0003144273650832474, 0.04001931846141815, 0.0055992938578128815, 0.02842281199991703, -0.0009166356758214533, 0.016144156455993652, 0.0056845624931156635, -0.0029417609330266714, -0.009891138412058353, -0.0031833548564463854, 0.027058515697717667, -0.008640534244477749, -0.002344881882891...
[ "CrossEntropyLoss", "False", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "Parameter", "Sequence", "SequenceClassifierOutput", "True", "ValueError", "_HIDDEN_STATES_START_POSITION", "__init__", "_freeze_parameters", "_get_feature_vector_attention_mask", "adapters"...
gpt_neo/modeling_gpt_neo.py:GPTNeoSelfAttention
[ -0.00018241247744299471, 0.04828440397977829, 0.03105616569519043, -0.027995886281132698, -0.0009421692811883986, 0.02924266643822193, 0.028902636840939522, -0.03785678744316101, -0.00024616828886792064, 0.02470892108976841, 0.01575477048754692, 0.00266357627697289, 0.0014309638645499945, ...
[ "Dropout", "False", "Linear", "Model", "Module", "None", "True", "ValueError", "__init__", "_attn", "_merge_heads", "_split_heads", "and", "attention_dropout", "attention_mask", "attention_type", "attn_dropout", "attn_head_size", "attn_output", "attn_weights", "be", "bias",...
gpt_neo/modeling_gpt_neo.py:GPTNeoFlashAttention2
[ 0.00001135998172685504, 0.040490470826625824, 0.032660823315382004, -0.004306307062506676, 0, 0.015994854271411896, 0.03780601918697357, -0.03333193436264992, 0.005480754654854536, 0.017337080091238022, 0.0016498189652338624, 0.01353410817682743, -0.0003355563967488706, -0.0148763330653309...
[ "False", "Model", "ModelSelfAttention", "None", "The", "We", "_", "__init__", "_flash_attention_forward", "_flash_attn_uses_top_left_mask", "_is_quantized", "_split_heads", "args", "attention_dropout", "attention_mask", "attn_dropout", "attn_output", "attn_weights_reshaped", "bac...
gpt_neo/modeling_gpt_neo.py:GPTNeoAttention
[ -0.00004606138099916279, 0.037688348442316055, 0.012581589631736279, -0.001523331506177783, 0, 0.044910069555044174, 0.04558710381388664, -0.03452884778380394, 0.0034557057078927755, -0.0030889776535332203, 0.026855770498514175, 0.004767463076859713, 0.0021580529864877462, -0.0052470304071...
[ "False", "Model", "Model_ATTENTION_CLASSES", "Module", "None", "NotImplementedError", "Only", "Select", "__init__", "_attn_implementation", "and", "attention", "attention_layers", "attention_mask", "attention_type", "attn", "but", "cache_position", "class", "config", "def", ...
gpt_neo/modeling_gpt_neo.py:GPTNeoMLP
[ -0.0002726241364143789, 0.037206877022981644, 0.03998005762696266, 0.000996612710878253, -0.001263820449821651, 0.0242653526365757, 0.035820282995700836, -0.01432811375707388, -0.0006571866688318551, -0.009417268447577953, 0.01802569068968296, -0.023572057485580444, -0.00106160924769938, -...
[ "ACT2FN", "Dropout", "Linear", "Model", "Module", "__init__", "act", "activation_function", "c_fc", "c_proj", "class", "config", "def", "dropout", "embed_dim", "float", "forward", "hidden_size", "hidden_states", "intermediate_size", "nn", "resid_dropout", "return", "sel...
gpt_neo/modeling_gpt_neo.py:GPTNeoBlock
[ -0.00005521469938685186, 0.022324267774820328, 0.026250645518302917, -0.0044592441990971565, 0, 0.04442416876554489, 0.041507430374622345, -0.006506570149213076, 0.006646797992289066, 0.004066606517881155, 0.01402278058230877, 0.006366342306137085, 0.0008904465939849615, 0.0003155125596094...
[ "False", "GradientCheckpointingLayer", "LayerNorm", "Model", "ModelAttention", "ModelMLP", "None", "__init__", "attention_mask", "attn", "attn_output", "attn_weights", "cache_position", "class", "config", "def", "else", "eps", "feed_forward_hidden_states", "forward", "hidden_...