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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "9e544464",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath('..'))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "785c1981",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from tensor_transformers.nv_bert import modeling_tensor"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "13cd16dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['cola', 'mnli', 'mrpc', 'sst-2'])\n"
]
},
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from processors.glue import PROCESSORS\n",
"print(PROCESSORS.keys())\n",
"# GLU dataset processor\n",
"processor = PROCESSORS['cola']()\n",
"num_labels = len(processor.get_labels())\n",
"num_labels"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "824c6f62",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of parameters in the model: 123790338\n"
]
}
],
"source": [
"# config = modeling_tensor.TensorBertConfig(\"tensor_bert/config.json\")\n",
"config = modeling_tensor.TensorBertConfig.from_pretrained(\"tensor_bert\") # -> bug in build_rank not set (tmp fix to modify pretrained config)\n",
"model = modeling_tensor.TensorBertForSequenceClassification(\n",
" config,\n",
" num_labels=num_labels,\n",
" )\n",
"\n",
"total_params = sum(p.numel() for p in model.parameters())\n",
"print(f\"Total number of parameters in the model: {total_params}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "91c67e4b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"_IncompatibleKeys(missing_keys=['classifier.weight', 'classifier.bias'], unexpected_keys=['cls.predictions.bias', 'cls.predictions.transform.dense_act.linear.weight', 'cls.predictions.transform.dense_act.linear.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias'])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"checkpoint = torch.load(\"tensor_bert/model.pt\", map_location=\"cpu\")\n",
"checkpoint = checkpoint[\"model\"] if \"model\" in checkpoint.keys() else checkpoint\n",
"checkpoint.keys()\n",
"\n",
"model.load_state_dict(checkpoint, strict=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "25e110ee",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tokenization.BertTokenizer at 0x740019f08590>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tokenization import BertTokenizer\n",
"vocab_file = \"../vocab\"\n",
"tokenizer = BertTokenizer(\n",
" vocab_file = vocab_file,\n",
" do_lower_case=True,\n",
" max_len=512,\n",
" ) \n",
"tokenizer"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "32e2c801",
"metadata": {},
"outputs": [],
"source": [
"from processors.glue import PROCESSORS, convert_examples_to_features\n",
"import pickle\n",
"import logging\n",
"from utils import is_main_process, mkdir_by_main_process, format_step, get_world_size\n",
"from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset\n",
"\n",
"\n",
"logger = logging.getLogger(__name__)\n",
"\n",
"def get_train_features(\n",
" data_dir,\n",
" bert_model,\n",
" max_seq_length,\n",
" do_lower_case,\n",
" local_rank,\n",
" train_batch_size,\n",
" gradient_accumulation_steps,\n",
" num_train_epochs,\n",
" tokenizer,\n",
" processor,\n",
"):\n",
" cached_train_features_file = os.path.join(\n",
" data_dir,\n",
" \"{0}_{1}_{2}\".format(\n",
" list(filter(None, bert_model.split(\"/\"))).pop(),\n",
" str(max_seq_length),\n",
" str(do_lower_case),\n",
" ),\n",
" )\n",
" train_features = None\n",
" try:\n",
" with open(cached_train_features_file, \"rb\") as reader:\n",
" train_features = pickle.load(reader)\n",
" logger.info(\n",
" \"Loaded pre-processed features from {}\".format(cached_train_features_file)\n",
" )\n",
" except:\n",
" logger.info(\n",
" \"Did not find pre-processed features from {}\".format(\n",
" cached_train_features_file\n",
" )\n",
" )\n",
" train_examples = processor.get_train_examples(data_dir)\n",
" train_features, _ = convert_examples_to_features(\n",
" train_examples,\n",
" processor.get_labels(),\n",
" max_seq_length,\n",
" tokenizer,\n",
" )\n",
" if is_main_process():\n",
" logger.info(\n",
" \" Saving train features into cached file %s\",\n",
" cached_train_features_file,\n",
" )\n",
" with open(cached_train_features_file, \"wb\") as writer:\n",
" pickle.dump(train_features, writer)\n",
" return train_features\n",
"\n",
"\n",
"\n",
"def gen_tensor_dataset(features):\n",
" all_input_ids = torch.tensor(\n",
" [f.input_ids for f in features],\n",
" dtype=torch.long,\n",
" )\n",
" all_input_mask = torch.tensor(\n",
" [f.input_mask for f in features],\n",
" dtype=torch.long,\n",
" )\n",
" all_segment_ids = torch.tensor(\n",
" [f.segment_ids for f in features],\n",
" dtype=torch.long,\n",
" )\n",
" all_label_ids = torch.tensor(\n",
" [f.label_id for f in features],\n",
" dtype=torch.long,\n",
" )\n",
" return TensorDataset(\n",
" all_input_ids,\n",
" all_input_mask,\n",
" all_segment_ids,\n",
" all_label_ids,\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "743b50bd",
"metadata": {},
"outputs": [],
"source": [
"data_dir = \"../glue_data/CoLA\"\n",
"bert_model = \"tensor-bert\"\n",
"max_seq_length = max_pos_embeddings = 512\n",
"do_lower_case = True\n",
"local_rank = 0\n",
"train_batch_size = 2\n",
"gradient_accumulation_steps = 1\n",
"num_train_epochs = 3\n",
"\n",
"tokenizer = tokenizer\n",
"processor = processor\n",
"\n",
"train_features = get_train_features(\n",
" data_dir,\n",
" bert_model,\n",
" max_seq_length,\n",
" do_lower_case,\n",
" local_rank,\n",
" train_batch_size,\n",
" gradient_accumulation_steps,\n",
" num_train_epochs,\n",
" tokenizer,\n",
" processor,\n",
" )\n",
"train_data = gen_tensor_dataset(train_features)\n",
"train_sampler = RandomSampler(train_data)\n",
"train_dataloader = DataLoader(\n",
" train_data,\n",
" sampler=train_sampler,\n",
" batch_size=train_batch_size,\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "b97b356d",
"metadata": {},
"source": [
"# Explore Model a bit"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "42117cd5",
"metadata": {},
"outputs": [],
"source": [
"encoderLayers = model.bert.encoder.layer\n",
"block = encoderLayers[0]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "2b912f29",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"odict_keys(['attention.self.query.bias', 'attention.self.query.tensor.factors.0', 'attention.self.query.tensor.factors.1', 'attention.self.query.tensor.factors.2', 'attention.self.query.tensor.factors.3', 'attention.self.query.tensor.factors.4', 'attention.self.query.tensor.factors.5', 'attention.self.key.bias', 'attention.self.key.tensor.factors.0', 'attention.self.key.tensor.factors.1', 'attention.self.key.tensor.factors.2', 'attention.self.key.tensor.factors.3', 'attention.self.key.tensor.factors.4', 'attention.self.key.tensor.factors.5', 'attention.self.value.bias', 'attention.self.value.tensor.factors.0', 'attention.self.value.tensor.factors.1', 'attention.self.value.tensor.factors.2', 'attention.self.value.tensor.factors.3', 'attention.self.value.tensor.factors.4', 'attention.self.value.tensor.factors.5', 'attention.output.dense.bias', 'attention.output.dense.tensor.factors.0', 'attention.output.dense.tensor.factors.1', 'attention.output.dense.tensor.factors.2', 'attention.output.dense.tensor.factors.3', 'attention.output.dense.tensor.factors.4', 'attention.output.dense.tensor.factors.5', 'attention.output.LayerNorm.weight', 'attention.output.LayerNorm.bias', 'intermediate.dense_act.linear.bias', 'intermediate.dense_act.linear.tensor.factors.0', 'intermediate.dense_act.linear.tensor.factors.1', 'intermediate.dense_act.linear.tensor.factors.2', 'intermediate.dense_act.linear.tensor.factors.3', 'intermediate.dense_act.linear.tensor.factors.4', 'intermediate.dense_act.linear.tensor.factors.5', 'output.dense.bias', 'output.dense.tensor.factors.0', 'output.dense.tensor.factors.1', 'output.dense.tensor.factors.2', 'output.dense.tensor.factors.3', 'output.dense.tensor.factors.4', 'output.dense.tensor.factors.5', 'output.LayerNorm.weight', 'output.LayerNorm.bias'])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"block.state_dict().keys()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "98292093",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorizedLinear(\n",
" (tensor): TensorTrain(\n",
" (factors): ParameterList(\n",
" (0): Parameter containing: [torch.float32 of size 1x16x12]\n",
" (1): Parameter containing: [torch.float32 of size 12x16x144]\n",
" (2): Parameter containing: [torch.float32 of size 144x16x300]\n",
" (3): Parameter containing: [torch.float32 of size 300x8x96]\n",
" (4): Parameter containing: [torch.float32 of size 96x8x12]\n",
" (5): Parameter containing: [torch.float32 of size 12x16x1]\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tensoredizedLinear = block.output.dense\n",
"tensoredizedLinear"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "98ca9cf5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4096, 1024, [16, 16, 16, 8, 8, 16])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tensoredizedLinear.in_features, tensoredizedLinear.out_features, tensoredizedLinear.shape"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "23ab333d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4096, 1024)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"16*16*16, 8*8*16"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "83786f7f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorBertLayer(\n",
" (attention): TensorBertAttention(\n",
" (self): TensorBertSelfAttention(\n",
" (query): TensorizedLinear(\n",
" (tensor): TensorTrain(\n",
" (factors): ParameterList(\n",
" (0): Parameter containing: [torch.float32 of size 1x16x12]\n",
" (1): Parameter containing: [torch.float32 of size 12x8x72]\n",
" (2): Parameter containing: [torch.float32 of size 72x8x300]\n",
" (3): Parameter containing: [torch.float32 of size 300x8x96]\n",
" (4): Parameter containing: [torch.float32 of size 96x8x12]\n",
" (5): Parameter containing: [torch.float32 of size 12x16x1]\n",
" )\n",
" )\n",
" )\n",
" (key): TensorizedLinear(\n",
" (tensor): TensorTrain(\n",
" (factors): ParameterList(\n",
" (0): Parameter containing: [torch.float32 of size 1x16x12]\n",
" (1): Parameter containing: [torch.float32 of size 12x8x72]\n",
" (2): Parameter containing: [torch.float32 of size 72x8x300]\n",
" (3): Parameter containing: [torch.float32 of size 300x8x96]\n",
" (4): Parameter containing: [torch.float32 of size 96x8x12]\n",
" (5): Parameter containing: [torch.float32 of size 12x16x1]\n",
" )\n",
" )\n",
" )\n",
" (value): TensorizedLinear(\n",
" (tensor): TensorTrain(\n",
" (factors): ParameterList(\n",
" (0): Parameter containing: [torch.float32 of size 1x16x12]\n",
" (1): Parameter containing: [torch.float32 of size 12x8x72]\n",
" (2): Parameter containing: [torch.float32 of size 72x8x300]\n",
" (3): Parameter containing: [torch.float32 of size 300x8x96]\n",
" (4): Parameter containing: [torch.float32 of size 96x8x12]\n",
" (5): Parameter containing: [torch.float32 of size 12x16x1]\n",
" )\n",
" )\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): TensorBertSelfOutput(\n",
" (dense): TensorizedLinear(\n",
" (tensor): TensorTrain(\n",
" (factors): ParameterList(\n",
" (0): Parameter containing: [torch.float32 of size 1x16x12]\n",
" (1): Parameter containing: [torch.float32 of size 12x8x72]\n",
" (2): Parameter containing: [torch.float32 of size 72x8x300]\n",
" (3): Parameter containing: [torch.float32 of size 300x8x96]\n",
" (4): Parameter containing: [torch.float32 of size 96x8x12]\n",
" (5): Parameter containing: [torch.float32 of size 12x16x1]\n",
" )\n",
" )\n",
" )\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): TensorBertIntermediate(\n",
" (dense_act): TensorLinearActivation(\n",
" (act_fn): GELU(approximate='none')\n",
" (linear): TensorizedLinear(\n",
" (tensor): TensorTrain(\n",
" (factors): ParameterList(\n",
" (0): Parameter containing: [torch.float32 of size 1x16x12]\n",
" (1): Parameter containing: [torch.float32 of size 12x8x72]\n",
" (2): Parameter containing: [torch.float32 of size 72x8x300]\n",
" (3): Parameter containing: [torch.float32 of size 300x16x192]\n",
" (4): Parameter containing: [torch.float32 of size 192x16x12]\n",
" (5): Parameter containing: [torch.float32 of size 12x16x1]\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (output): TensorBertOutput(\n",
" (dense): TensorizedLinear(\n",
" (tensor): TensorTrain(\n",
" (factors): ParameterList(\n",
" (0): Parameter containing: [torch.float32 of size 1x16x12]\n",
" (1): Parameter containing: [torch.float32 of size 12x16x144]\n",
" (2): Parameter containing: [torch.float32 of size 144x16x300]\n",
" (3): Parameter containing: [torch.float32 of size 300x8x96]\n",
" (4): Parameter containing: [torch.float32 of size 96x8x12]\n",
" (5): Parameter containing: [torch.float32 of size 12x16x1]\n",
" )\n",
" )\n",
" )\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
")"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"block"
]
},
{
"cell_type": "markdown",
"id": "673b12ed",
"metadata": {},
"source": [
"# Check Backward pass works"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc558bbd",
"metadata": {},
"outputs": [],
"source": [
"batch = next(iter(train_dataloader))\n",
"input_ids, input_mask, segment_ids, label_ids = batch\n",
"# input_ids.shape, input_mask.shape, segment_ids.shape, label_ids.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5d6cf09",
"metadata": {},
"outputs": [],
"source": [
"device = 'cuda'\n",
"model = model.to(device)\n",
"input_ids, input_mask, segment_ids, label_ids = [x.to(device) for x in batch]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b83b5c32",
"metadata": {},
"outputs": [],
"source": [
"# loss_fct = torch.nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25f1764f",
"metadata": {},
"outputs": [],
"source": [
"# model.train()\n",
"# out = model(input_ids, segment_ids, input_mask)\n",
"# loss = loss_fct(out.view(-1, num_labels), label_ids.view(-1))\n",
"# loss.backward()\n",
"\n",
"# for name, param in model.named_parameters():\n",
"# print(name,param.shape)\n",
"# # if param.grad is not None:\n",
"# # print(f\"{name}: {param.grad.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c6ae700",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bert.embeddings.word_embeddings.weight torch.Size([30528, 1024])\n",
"bert.embeddings.position_embeddings.weight torch.Size([512, 1024])\n",
"bert.embeddings.token_type_embeddings.weight torch.Size([2, 1024])\n",
"bert.embeddings.LayerNorm.weight torch.Size([1024])\n",
"bert.embeddings.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.0.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.0.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.0.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.0.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.0.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.0.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.0.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.0.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.0.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.0.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.0.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.0.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.0.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.0.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.0.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.0.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.0.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.0.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.0.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.0.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.0.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.0.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.0.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.0.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.0.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.0.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.0.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.0.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.0.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.0.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.0.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.0.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.0.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.0.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.0.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.0.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.0.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.0.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.0.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.0.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.0.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.0.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.0.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.0.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.0.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.0.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.1.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.1.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.1.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.1.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.1.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.1.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.1.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.1.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.1.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.1.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.1.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.1.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.1.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.1.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.1.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.1.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.1.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.1.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.1.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.1.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.1.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.1.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.1.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.1.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.1.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.1.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.1.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.1.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.1.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.1.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.1.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.1.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.1.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.1.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.1.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.1.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.1.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.1.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.1.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.1.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.1.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.1.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.1.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.1.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.1.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.1.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.2.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.2.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.2.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.2.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.2.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.2.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.2.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.2.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.2.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.2.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.2.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.2.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.2.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.2.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.2.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.2.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.2.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.2.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.2.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.2.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.2.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.2.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.2.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.2.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.2.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.2.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.2.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.2.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.2.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.2.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.2.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.2.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.2.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.2.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.2.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.2.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.2.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.2.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.2.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.2.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.2.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.2.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.2.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.2.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.2.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.2.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.3.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.3.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.3.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.3.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.3.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.3.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.3.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.3.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.3.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.3.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.3.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.3.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.3.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.3.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.3.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.3.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.3.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.3.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.3.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.3.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.3.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.3.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.3.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.3.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.3.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.3.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.3.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.3.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.3.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.3.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.3.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.3.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.3.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.3.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.3.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.3.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.3.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.3.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.3.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.3.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.3.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.3.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.3.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.3.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.3.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.3.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.4.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.4.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.4.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.4.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.4.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.4.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.4.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.4.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.4.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.4.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.4.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.4.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.4.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.4.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.4.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.4.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.4.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.4.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.4.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.4.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.4.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.4.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.4.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.4.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.4.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.4.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.4.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.4.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.4.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.4.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.4.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.4.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.4.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.4.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.4.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.4.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.4.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.4.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.4.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.4.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.4.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.4.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.4.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.4.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.4.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.4.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.5.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.5.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.5.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.5.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.5.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.5.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.5.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.5.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.5.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.5.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.5.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.5.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.5.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.5.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.5.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.5.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.5.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.5.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.5.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.5.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.5.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.5.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.5.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.5.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.5.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.5.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.5.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.5.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.5.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.5.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.5.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.5.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.5.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.5.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.5.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.5.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.5.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.5.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.5.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.5.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.5.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.5.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.5.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.5.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.5.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.5.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.6.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.6.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.6.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.6.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.6.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.6.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.6.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.6.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.6.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.6.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.6.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.6.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.6.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.6.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.6.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.6.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.6.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.6.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.6.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.6.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.6.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.6.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.6.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.6.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.6.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.6.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.6.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.6.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.6.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.6.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.6.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.6.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.6.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.6.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.6.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.6.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.6.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.6.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.6.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.6.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.6.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.6.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.6.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.6.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.6.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.6.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.7.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.7.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.7.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.7.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.7.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.7.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.7.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.7.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.7.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.7.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.7.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.7.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.7.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.7.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.7.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.7.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.7.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.7.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.7.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.7.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.7.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.7.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.7.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.7.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.7.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.7.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.7.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.7.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.7.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.7.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.7.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.7.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.7.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.7.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.7.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.7.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.7.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.7.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.7.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.7.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.7.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.7.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.7.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.7.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.7.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.7.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.8.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.8.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.8.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.8.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.8.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.8.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.8.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.8.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.8.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.8.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.8.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.8.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.8.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.8.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.8.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.8.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.8.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.8.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.8.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.8.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.8.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.8.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.8.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.8.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.8.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.8.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.8.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.8.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.8.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.8.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.8.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.8.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.8.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.8.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.8.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.8.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.8.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.8.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.8.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.8.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.8.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.8.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.8.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.8.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.8.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.8.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.9.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.9.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.9.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.9.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.9.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.9.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.9.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.9.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.9.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.9.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.9.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.9.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.9.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.9.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.9.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.9.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.9.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.9.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.9.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.9.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.9.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.9.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.9.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.9.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.9.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.9.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.9.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.9.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.9.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.9.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.9.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.9.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.9.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.9.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.9.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.9.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.9.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.9.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.9.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.9.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.9.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.9.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.9.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.9.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.9.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.9.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.10.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.10.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.10.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.10.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.10.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.10.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.10.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.10.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.10.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.10.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.10.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.10.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.10.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.10.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.10.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.10.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.10.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.10.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.10.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.10.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.10.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.10.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.10.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.10.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.10.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.10.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.10.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.10.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.10.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.10.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.10.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.10.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.10.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.10.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.10.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.10.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.10.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.10.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.10.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.10.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.10.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.10.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.10.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.10.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.10.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.10.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.11.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.11.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.11.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.11.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.11.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.11.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.11.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.11.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.11.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.11.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.11.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.11.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.11.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.11.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.11.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.11.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.11.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.11.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.11.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.11.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.11.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.11.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.11.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.11.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.11.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.11.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.11.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.11.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.11.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.11.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.11.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.11.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.11.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.11.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.11.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.11.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.11.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.11.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.11.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.11.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.11.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.11.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.11.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.11.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.11.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.11.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.12.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.12.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.12.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.12.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.12.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.12.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.12.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.12.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.12.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.12.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.12.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.12.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.12.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.12.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.12.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.12.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.12.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.12.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.12.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.12.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.12.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.12.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.12.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.12.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.12.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.12.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.12.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.12.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.12.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.12.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.12.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.12.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.12.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.12.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.12.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.12.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.12.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.12.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.12.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.12.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.12.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.12.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.12.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.12.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.12.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.12.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.13.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.13.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.13.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.13.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.13.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.13.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.13.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.13.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.13.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.13.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.13.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.13.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.13.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.13.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.13.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.13.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.13.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.13.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.13.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.13.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.13.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.13.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.13.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.13.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.13.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.13.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.13.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.13.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.13.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.13.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.13.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.13.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.13.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.13.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.13.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.13.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.13.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.13.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.13.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.13.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.13.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.13.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.13.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.13.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.13.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.13.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.14.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.14.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.14.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.14.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.14.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.14.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.14.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.14.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.14.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.14.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.14.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.14.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.14.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.14.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.14.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.14.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.14.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.14.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.14.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.14.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.14.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.14.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.14.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.14.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.14.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.14.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.14.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.14.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.14.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.14.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.14.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.14.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.14.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.14.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.14.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.14.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.14.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.14.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.14.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.14.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.14.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.14.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.14.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.14.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.14.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.14.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.15.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.15.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.15.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.15.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.15.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.15.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.15.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.15.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.15.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.15.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.15.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.15.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.15.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.15.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.15.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.15.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.15.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.15.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.15.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.15.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.15.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.15.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.15.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.15.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.15.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.15.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.15.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.15.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.15.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.15.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.15.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.15.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.15.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.15.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.15.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.15.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.15.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.15.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.15.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.15.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.15.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.15.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.15.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.15.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.15.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.15.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.16.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.16.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.16.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.16.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.16.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.16.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.16.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.16.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.16.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.16.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.16.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.16.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.16.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.16.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.16.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.16.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.16.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.16.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.16.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.16.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.16.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.16.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.16.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.16.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.16.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.16.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.16.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.16.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.16.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.16.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.16.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.16.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.16.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.16.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.16.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.16.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.16.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.16.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.16.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.16.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.16.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.16.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.16.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.16.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.16.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.16.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.17.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.17.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.17.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.17.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.17.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.17.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.17.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.17.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.17.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.17.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.17.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.17.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.17.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.17.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.17.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.17.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.17.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.17.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.17.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.17.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.17.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.17.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.17.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.17.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.17.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.17.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.17.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.17.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.17.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.17.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.17.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.17.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.17.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.17.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.17.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.17.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.17.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.17.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.17.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.17.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.17.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.17.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.17.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.17.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.17.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.17.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.18.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.18.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.18.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.18.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.18.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.18.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.18.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.18.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.18.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.18.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.18.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.18.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.18.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.18.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.18.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.18.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.18.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.18.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.18.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.18.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.18.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.18.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.18.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.18.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.18.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.18.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.18.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.18.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.18.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.18.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.18.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.18.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.18.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.18.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.18.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.18.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.18.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.18.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.18.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.18.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.18.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.18.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.18.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.18.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.18.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.18.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.19.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.19.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.19.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.19.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.19.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.19.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.19.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.19.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.19.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.19.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.19.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.19.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.19.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.19.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.19.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.19.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.19.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.19.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.19.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.19.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.19.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.19.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.19.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.19.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.19.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.19.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.19.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.19.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.19.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.19.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.19.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.19.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.19.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.19.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.19.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.19.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.19.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.19.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.19.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.19.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.19.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.19.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.19.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.19.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.19.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.19.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.20.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.20.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.20.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.20.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.20.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.20.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.20.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.20.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.20.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.20.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.20.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.20.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.20.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.20.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.20.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.20.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.20.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.20.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.20.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.20.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.20.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.20.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.20.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.20.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.20.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.20.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.20.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.20.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.20.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.20.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.20.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.20.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.20.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.20.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.20.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.20.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.20.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.20.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.20.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.20.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.20.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.20.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.20.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.20.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.20.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.20.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.21.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.21.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.21.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.21.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.21.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.21.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.21.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.21.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.21.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.21.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.21.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.21.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.21.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.21.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.21.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.21.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.21.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.21.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.21.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.21.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.21.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.21.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.21.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.21.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.21.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.21.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.21.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.21.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.21.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.21.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.21.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.21.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.21.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.21.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.21.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.21.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.21.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.21.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.21.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.21.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.21.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.21.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.21.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.21.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.21.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.21.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.22.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.22.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.22.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.22.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.22.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.22.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.22.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.22.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.22.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.22.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.22.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.22.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.22.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.22.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.22.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.22.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.22.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.22.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.22.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.22.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.22.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.22.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.22.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.22.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.22.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.22.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.22.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.22.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.22.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.22.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.22.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.22.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.22.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.22.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.22.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.22.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.22.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.22.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.22.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.22.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.22.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.22.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.22.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.22.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.22.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.22.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.23.attention.self.query.bias torch.Size([1024])\n",
"bert.encoder.layer.23.attention.self.query.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.23.attention.self.query.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.23.attention.self.query.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.23.attention.self.query.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.23.attention.self.query.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.23.attention.self.query.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.23.attention.self.key.bias torch.Size([1024])\n",
"bert.encoder.layer.23.attention.self.key.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.23.attention.self.key.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.23.attention.self.key.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.23.attention.self.key.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.23.attention.self.key.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.23.attention.self.key.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.23.attention.self.value.bias torch.Size([1024])\n",
"bert.encoder.layer.23.attention.self.value.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.23.attention.self.value.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.23.attention.self.value.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.23.attention.self.value.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.23.attention.self.value.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.23.attention.self.value.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.23.attention.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.23.attention.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.23.attention.output.dense.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.23.attention.output.dense.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.23.attention.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.23.attention.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.23.attention.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.23.attention.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.23.attention.output.LayerNorm.bias torch.Size([1024])\n",
"bert.encoder.layer.23.intermediate.dense_act.linear.bias torch.Size([4096])\n",
"bert.encoder.layer.23.intermediate.dense_act.linear.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.23.intermediate.dense_act.linear.tensor.factors.1 torch.Size([12, 8, 72])\n",
"bert.encoder.layer.23.intermediate.dense_act.linear.tensor.factors.2 torch.Size([72, 8, 300])\n",
"bert.encoder.layer.23.intermediate.dense_act.linear.tensor.factors.3 torch.Size([300, 16, 192])\n",
"bert.encoder.layer.23.intermediate.dense_act.linear.tensor.factors.4 torch.Size([192, 16, 12])\n",
"bert.encoder.layer.23.intermediate.dense_act.linear.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.23.output.dense.bias torch.Size([1024])\n",
"bert.encoder.layer.23.output.dense.tensor.factors.0 torch.Size([1, 16, 12])\n",
"bert.encoder.layer.23.output.dense.tensor.factors.1 torch.Size([12, 16, 144])\n",
"bert.encoder.layer.23.output.dense.tensor.factors.2 torch.Size([144, 16, 300])\n",
"bert.encoder.layer.23.output.dense.tensor.factors.3 torch.Size([300, 8, 96])\n",
"bert.encoder.layer.23.output.dense.tensor.factors.4 torch.Size([96, 8, 12])\n",
"bert.encoder.layer.23.output.dense.tensor.factors.5 torch.Size([12, 16, 1])\n",
"bert.encoder.layer.23.output.LayerNorm.weight torch.Size([1024])\n",
"bert.encoder.layer.23.output.LayerNorm.bias torch.Size([1024])\n",
"bert.pooler.dense_act.linear.weight torch.Size([1024, 1024])\n",
"bert.pooler.dense_act.linear.bias torch.Size([1024])\n",
"classifier.weight torch.Size([2, 1024])\n",
"classifier.bias torch.Size([2])\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f340e458",
"metadata": {},
"outputs": [],
"source": [
"# import json\n",
"\n",
"# def save_state_dict_structure(state_dict, file_path):\n",
"# def serialize_state_dict(state_dict):\n",
"# serialized = {}\n",
"# for key, value in state_dict.items():\n",
"# if isinstance(value, torch.Tensor):\n",
"# serialized[key] = f\"Tensor with shape {list(value.shape)}\"\n",
"# elif isinstance(value, (int, float, str)):\n",
"# serialized[key] = value\n",
"# elif isinstance(value, dict):\n",
"# serialized[key] = serialize_state_dict(value)\n",
"# else:\n",
"# serialized[key] = str(type(value))\n",
"# return serialized\n",
"\n",
"# with open(file_path, 'w') as f:\n",
"# json.dump(serialize_state_dict(state_dict), f, indent=4)\n",
"\n",
"# save_state_dict_structure(stateDict, \"state_dict_structure.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e4a54a77",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b6c70fd",
"metadata": {},
"outputs": [],
"source": [
"\t"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "colaBert",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|