upload

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

2_Dense/config.json

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+{"in_features": 768, "out_features": 768, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}

2_Dense/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9319f42e32d06c3e599b0d0d2aeb23bdeacfe71d019238d86d6413a778be8c1d
+size 2360171

README.md

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+---
+pipeline_tag: sentence-similarity
+language: en
+license: apache-2.0
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+
+# sentence-transformers/sentence-t5-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
+
+This model was converted from the Tensorflow model [st5-base-1](https://tfhub.dev/google/sentence-t5/st5-base/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
+
+The model uses only the encoder from a T5-base model. The weights are stored in FP16.  
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/sentence-t5-base')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+The model requires sentence-transformers version 2.2.0 or newer.
+
+## Evaluation Results
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-base)
+
+
+
+## Citing & Authors
+
+If you find this model helpful, please cite the respective publication:
+[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)

config.json

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+{
+  "_name_or_path": "models/sentence-t5-base",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.11.3",
+  "use_cache": true,
+  "vocab_size": 32128
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

convert.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "17bffc12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "from sentence_transformers import util\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import torch.nn.functional as F\n",
+    "from transformers import T5EncoderModel\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "160d8ce6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Mean Pooling - Take attention mask into account for correct averaging\n",
+    "def mean_pooling(model_output, attention_mask):\n",
+    "    token_embeddings = model_output[0] #First element of model_output contains all token embeddings\n",
+    "    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n",
+    "    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2f67f426",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Importing a function (__inference_<lambda>_9720) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
+      "WARNING:absl:Importing a function (__inference_<lambda>_3354) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
+      "WARNING:absl:Importing a function (__inference_<lambda>_6722) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import tensorflow_hub as hub\n",
+    "import tensorflow_text as text \n",
+    "\n",
+    "model_size = \"base\"\n",
+    "hub_url = f\"https://tfhub.dev/google/sentence-t5/st5-{model_size}/1\"\n",
+    "encoder = hub.load(hub_url)\n",
+    "\n",
+    "v = encoder.signatures['serving_default'].variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5f4c8d94",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'encoder__encoder_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_0__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_0__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_0__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_0__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_0__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_0__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_0__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_0__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_1__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_1__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_1__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_1__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_1__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_1__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_1__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_1__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_10__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_10__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_10__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_10__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_10__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_10__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_10__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_10__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_11__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_11__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_11__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_11__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_11__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_11__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_11__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_11__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_2__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_2__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_2__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_2__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_2__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_2__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_2__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_2__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_3__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_3__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_3__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_3__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_3__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_3__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_3__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_3__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_4__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_4__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_4__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_4__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_4__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_4__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_4__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_4__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_5__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_5__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_5__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_5__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_5__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_5__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_5__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_5__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_6__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_6__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_6__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_6__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_6__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_6__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_6__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_6__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_7__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_7__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_7__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_7__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_7__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_7__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_7__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_7__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_8__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_8__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_8__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_8__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_8__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_8__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_8__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_8__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_9__attention__key__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_9__attention__out__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_9__attention__query__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_9__attention__value__kernel:0': TensorShape([768, 768]),\n",
+       " 'encoder__layers_9__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
+       " 'encoder__layers_9__mlp__wo__kernel:0': TensorShape([3072, 768]),\n",
+       " 'encoder__layers_9__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__layers_9__pre_mlp_layer_norm__scale:0': TensorShape([768]),\n",
+       " 'encoder__relpos_bias__rel_embedding:0': TensorShape([12, 32]),\n",
+       " 'projection_layer__kernel:0': TensorShape([768, 768]),\n",
+       " 'token_embedder__embedding:0': TensorShape([32128, 768])}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf_name_weight = {var.name: var for var in v}\n",
+    "tf_name_shape = {var.name: var.shape for var in v}\n",
+    "tf_name_shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1d3c9865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_name(name):\n",
+    "    fct_map = {\n",
+    "        \"attention\": \"SelfAttention\",\n",
+    "        \"mlp\": \"DenseReluDense\",\n",
+    "        \"pre_attention_layer_norm\": \"layer_norm\",\n",
+    "        \"pre_mlp_layer_norm\": \"layer_norm\",\n",
+    "    }\n",
+    "    name_map = {\n",
+    "        'key': 'k',\n",
+    "        'out': 'o',\n",
+    "        'query': 'q',\n",
+    "        'value': 'v'\n",
+    "    }\n",
+    "    \n",
+    "    fixed_names = {\n",
+    "        \"token_embedder__embedding:0\": \"shared.weight\",\n",
+    "        \"encoder__encoder_norm__scale:0\": \"encoder.final_layer_norm.weight\",\n",
+    "        \"encoder__relpos_bias__rel_embedding:0\": \"encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight\"\n",
+    "    }\n",
+    "    \n",
+    "    if name in fixed_names:\n",
+    "        return fixed_names[name]\n",
+    "    \n",
+    "    out = \"\"\n",
+    "    splits = name.split(\"__\")\n",
+    "    layer = splits[1].split(\"_\")[1]\n",
+    "    fct = fct_map.get(splits[2], splits[2])\n",
+    "    if 'layer_norm' in name:\n",
+    "        sublayer = \"1\" if \"pre_mlp_layer_norm\" in name else \"0\"  #Not sure on the right setting here\n",
+    "        #sublayer = \"0\" if \"pre_mlp_layer_norm\" in name else \"1\"  #Not sure on the right setting here\n",
+    "        out = f\"encoder.block.{layer}.layer.{sublayer}.{fct}.weight\"\n",
+    "    elif name.startswith(\"encoder__layers_\"):\n",
+    "        sublayer = \"0\" if fct == \"SelfAttention\" else \"1\"\n",
+    "        name = name_map.get(splits[3], splits[3])\n",
+    "        out = f\"encoder.block.{layer}.layer.{sublayer}.{fct}.{name}.weight\"\n",
+    "        \n",
+    "    return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ca9590e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def equal_shapes(shape1, shape2):\n",
+    "    if len(shape1) != len(shape2):\n",
+    "        return False\n",
+    "    \n",
+    "    for idx in range(len(shape1)):\n",
+    "        if shape1[idx] != shape2[idx]:\n",
+    "            return False\n",
+    "    \n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6d223b07",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of T5EncoderModel were not initialized from the model checkpoint at t5-11b and are newly initialized: ['encoder.embed_tokens.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'shared.weight': torch.Size([32128, 1024]),\n",
+       " 'encoder.embed_tokens.weight': torch.Size([32128, 1024]),\n",
+       " 'encoder.block.0.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.0.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.0.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.0.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight': torch.Size([32, 128]),\n",
+       " 'encoder.block.0.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.0.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.0.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.0.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.1.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.1.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.1.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.1.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.1.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.1.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.1.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.1.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.2.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.2.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.2.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.2.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.2.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.2.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.2.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.2.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.3.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.3.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.3.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.3.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.3.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.3.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.3.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.3.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.4.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.4.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.4.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.4.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.4.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.4.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.4.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.4.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.5.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.5.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.5.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.5.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.5.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.5.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.5.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.5.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.6.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.6.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.6.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.6.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.6.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.6.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.6.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.6.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.7.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.7.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.7.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.7.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.7.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.7.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.7.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.7.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.8.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.8.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.8.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.8.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.8.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.8.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.8.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.8.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.9.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.9.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.9.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.9.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.9.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.9.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.9.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.9.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.10.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.10.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.10.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.10.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.10.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.10.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.10.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.10.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.11.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.11.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.11.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.11.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.11.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.11.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.11.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.11.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.12.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.12.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.12.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.12.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.12.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.12.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.12.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.12.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.13.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.13.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.13.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.13.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.13.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.13.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.13.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.13.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.14.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.14.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.14.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.14.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.14.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.14.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.14.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.14.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.15.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.15.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.15.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.15.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.15.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.15.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.15.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.15.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.16.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.16.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.16.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.16.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.16.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.16.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.16.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.16.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.17.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.17.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.17.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.17.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.17.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.17.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.17.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.17.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.18.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.18.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.18.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.18.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.18.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.18.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.18.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.18.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.19.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.19.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.19.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.19.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.19.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.19.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.19.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.19.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.20.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.20.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.20.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.20.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.20.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.20.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.20.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.20.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.21.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.21.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.21.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.21.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.21.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.21.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.21.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.21.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.22.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.22.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.22.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.22.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.22.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.22.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.22.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.22.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.23.layer.0.SelfAttention.q.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.23.layer.0.SelfAttention.k.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.23.layer.0.SelfAttention.v.weight': torch.Size([16384, 1024]),\n",
+       " 'encoder.block.23.layer.0.SelfAttention.o.weight': torch.Size([1024, 16384]),\n",
+       " 'encoder.block.23.layer.0.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.block.23.layer.1.DenseReluDense.wi.weight': torch.Size([65536, 1024]),\n",
+       " 'encoder.block.23.layer.1.DenseReluDense.wo.weight': torch.Size([1024, 65536]),\n",
+       " 'encoder.block.23.layer.1.layer_norm.weight': torch.Size([1024]),\n",
+       " 'encoder.final_layer_norm.weight': torch.Size([1024])}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer = AutoTokenizer.from_pretrained(f\"t5-{model_size}\")\n",
+    "T5EncoderModel._keys_to_ignore_on_load_unexpected = [\"decoder.*\"]\n",
+    "t5 = T5EncoderModel.from_pretrained(f\"t5-{model_size}\") \n",
+    "pt_name_shape = {name: weight.shape for name, weight in t5.state_dict().items()}\n",
+    "pt_name_shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ced52a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Remaining weights: {'encoder.embed_tokens.weight'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def need_transpose(name, transpose_names=['DenseReluDense', 'relative_attention_bias']):\n",
+    "    #HF function: https://github.com/huggingface/transformers/blob/c962c2adbff678ae6d2e98378bed5b8d1a9831d9/src/transformers/models/t5/modeling_t5.py#L161\n",
+    "    return name != \"shared.weight\"\n",
+    "\n",
+    "\n",
+    "#Additional dense layer on top\n",
+    "names_to_ignore = {\"projection_layer__kernel:0\"}\n",
+    "\n",
+    "#Check we used all names\n",
+    "pt_all_names = set(t5.state_dict().keys())\n",
+    "\n",
+    "for var in v:\n",
+    "    name = var.name\n",
+    "    if name in names_to_ignore:\n",
+    "        continue\n",
+    "    \n",
+    "    pt_name = convert_name(name)\n",
+    "    if pt_name not in pt_all_names:\n",
+    "        print(\"Name not found:\", name, \"=>\", pt_name)\n",
+    "    else:\n",
+    "        pt_all_names.remove(pt_name)\n",
+    "        tf_shape = tf_name_shape[name].as_list()\n",
+    "        pt_shape = list(pt_name_shape[pt_name])\n",
+    "        \n",
+    "        if need_transpose(pt_name):\n",
+    "            pt_shape = list(reversed(pt_shape))\n",
+    "        \n",
+    "        if not equal_shapes(tf_shape, pt_shape):\n",
+    "            print(\"Different shape:\", name, tf_shape, pt_name, pt_shape )\n",
+    "        \n",
+    "print(\"Remaining weights:\", pt_all_names)\n",
+    "#All layers match"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1190984f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "encoder__encoder_norm__scale:0 ((1024,)) =transpose=> encoder.final_layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_0__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.0.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_0__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.0.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_0__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.0.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_0__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.0.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_0__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.0.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_0__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.0.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_0__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.0.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_0__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.0.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_1__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.1.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_1__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.1.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_1__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.1.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_1__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.1.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_1__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.1.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_1__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.1.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_1__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.1.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_1__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.1.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_10__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.10.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_10__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.10.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_10__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.10.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_10__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.10.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_10__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.10.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_10__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.10.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_10__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.10.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_10__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.10.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_11__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.11.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_11__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.11.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_11__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.11.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_11__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.11.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_11__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.11.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_11__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.11.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_11__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.11.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_11__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.11.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_12__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.12.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_12__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.12.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_12__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.12.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_12__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.12.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_12__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.12.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_12__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.12.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_12__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.12.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_12__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.12.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_13__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.13.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_13__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.13.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_13__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.13.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_13__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.13.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_13__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.13.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_13__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.13.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_13__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.13.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_13__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.13.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_14__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.14.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_14__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.14.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_14__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.14.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_14__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.14.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_14__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.14.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_14__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.14.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_14__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.14.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_14__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.14.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_15__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.15.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "encoder__layers_15__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.15.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_15__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.15.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_15__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.15.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_15__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.15.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_15__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.15.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_15__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.15.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_15__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.15.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_16__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.16.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_16__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.16.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_16__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.16.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_16__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.16.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_16__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.16.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_16__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.16.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_16__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.16.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_16__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.16.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_17__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.17.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_17__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.17.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_17__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.17.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_17__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.17.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_17__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.17.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_17__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.17.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_17__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.17.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_17__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.17.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_18__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.18.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_18__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.18.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_18__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.18.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_18__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.18.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_18__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.18.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_18__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.18.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_18__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.18.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_18__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.18.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_19__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.19.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_19__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.19.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_19__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.19.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_19__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.19.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_19__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.19.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_19__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.19.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_19__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.19.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_19__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.19.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_2__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.2.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_2__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.2.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_2__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.2.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_2__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.2.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_2__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.2.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_2__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.2.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_2__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.2.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_2__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.2.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_20__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.20.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_20__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.20.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_20__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.20.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_20__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.20.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_20__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.20.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_20__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.20.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_20__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.20.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_20__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.20.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_21__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.21.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_21__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.21.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_21__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.21.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "encoder__layers_21__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.21.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_21__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.21.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_21__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.21.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_21__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.21.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_21__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.21.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_22__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.22.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_22__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.22.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_22__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.22.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_22__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.22.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_22__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.22.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_22__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.22.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_22__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.22.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_22__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.22.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_23__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.23.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_23__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.23.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_23__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.23.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_23__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.23.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_23__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.23.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_23__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.23.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_23__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.23.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_23__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.23.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_3__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.3.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_3__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.3.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_3__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.3.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_3__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.3.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_3__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.3.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_3__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.3.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_3__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.3.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_3__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.3.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_4__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.4.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_4__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.4.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_4__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.4.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_4__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.4.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_4__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.4.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_4__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.4.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_4__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.4.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_4__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.4.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_5__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.5.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_5__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.5.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_5__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.5.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_5__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.5.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_5__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.5.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_5__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.5.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_5__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.5.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_5__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.5.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_6__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.6.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_6__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.6.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_6__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.6.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_6__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.6.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_6__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.6.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_6__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.6.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_6__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.6.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_6__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.6.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_7__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.7.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_7__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.7.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_7__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.7.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_7__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.7.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_7__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.7.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "encoder__layers_7__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.7.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_7__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.7.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_7__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.7.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_8__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.8.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_8__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.8.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_8__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.8.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_8__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.8.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_8__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.8.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_8__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.8.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_8__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.8.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_8__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.8.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_9__attention__key__kernel:0 ((1024, 16384)) =transpose=> encoder.block.9.layer.0.SelfAttention.k.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_9__attention__out__kernel:0 ((16384, 1024)) =transpose=> encoder.block.9.layer.0.SelfAttention.o.weight torch.Size([1024, 16384])\n",
+      "encoder__layers_9__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.9.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_9__attention__value__kernel:0 ((1024, 16384)) =transpose=> encoder.block.9.layer.0.SelfAttention.v.weight torch.Size([16384, 1024])\n",
+      "encoder__layers_9__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.9.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
+      "encoder__layers_9__mlp__wo__kernel:0 ((65536, 1024)) =transpose=> encoder.block.9.layer.1.DenseReluDense.wo.weight torch.Size([1024, 65536])\n",
+      "encoder__layers_9__pre_attention_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.9.layer.0.layer_norm.weight torch.Size([1024])\n",
+      "encoder__layers_9__pre_mlp_layer_norm__scale:0 ((1024,)) =transpose=> encoder.block.9.layer.1.layer_norm.weight torch.Size([1024])\n",
+      "encoder__relpos_bias__rel_embedding:0 ((128, 32)) =transpose=> encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight torch.Size([32, 128])\n",
+      "token_embedder__embedding:0 ((32128, 1024)) => shared.weight torch.Size([32128, 1024])\n",
+      "Linear(in_features=1024, out_features=768, bias=False)\n",
+      "Remaining weights: set()\n"
+     ]
+    }
+   ],
+   "source": [
+    "t5_state = t5.state_dict()\n",
+    "state_all_names = set(t5_state.keys())\n",
+    "\n",
+    "\n",
+    "for var in v:\n",
+    "    tf_name = var.name\n",
+    "    if tf_name in names_to_ignore:\n",
+    "        continue\n",
+    "        \n",
+    "    pt_name = convert_name(tf_name)\n",
+    "    weights = np.float32(var.numpy())\n",
+    "    \n",
+    "    state_all_names.remove(pt_name)\n",
+    "    \n",
+    "    tranpose_status = \"=>\"\n",
+    "    if need_transpose(pt_name, ['DenseReluDense', 'relative_attention_bias',]):\n",
+    "        tranpose_status = \"=transpose=>\"\n",
+    "        weights = weights.transpose()\n",
+    "    \n",
+    "    print(tf_name, f\"({var.shape})\", tranpose_status, pt_name, t5_state[pt_name].shape)\n",
+    "    \n",
+    "    original_shape = t5_state[pt_name].shape\n",
+    "    t5_state[pt_name] = torch.nn.Parameter(torch.tensor(weights))\n",
+    "    new_shape = t5_state[pt_name].shape\n",
+    "    \n",
+    "    if not equal_shapes(original_shape, new_shape):\n",
+    "        print(\"Different shape:\", tf_name, original_shape, pt_name, new_shape)\n",
+    "        break\n",
+    "\n",
+    "#Encoder Word embeddings\n",
+    "t5_state['encoder.embed_tokens.weight'] = t5_state['shared.weight']\n",
+    "state_all_names.remove('encoder.embed_tokens.weight')\n",
+    "    \n",
+    "#Load back the weights\n",
+    "t5.load_state_dict(t5_state) \n",
+    "\n",
+    "tf_linear_weight = tf_name_weight[\"projection_layer__kernel:0\"]\n",
+    "linear = torch.nn.Linear(tf_linear_weight.shape[0], tf_linear_weight.shape[1], bias=False)\n",
+    "original_shape = linear.weight.shape\n",
+    "linear.weight = torch.nn.Parameter(torch.tensor(np.float32(tf_linear_weight.numpy()).transpose()))\n",
+    "new_shape = linear.weight.shape\n",
+    "if not equal_shapes(original_shape, new_shape):\n",
+    "    print(\"Different shape at linear layer\")\n",
+    "    \n",
+    "print(linear)\n",
+    "print(\"Remaining weights:\", state_all_names)\n",
+    "assert len(state_all_names) == 0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d59d5a2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 768])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[1.0000, 0.9279, 0.6404, 0.5968, 0.5420, 0.5442, 0.6099, 0.6318],\n",
+       "        [0.9279, 1.0000, 0.6629, 0.6098, 0.5562, 0.5687, 0.6382, 0.6262],\n",
+       "        [0.6404, 0.6629, 1.0000, 0.8351, 0.7101, 0.6953, 0.6265, 0.6390],\n",
+       "        [0.5968, 0.6098, 0.8351, 1.0000, 0.6877, 0.6716, 0.5902, 0.6102],\n",
+       "        [0.5420, 0.5562, 0.7101, 0.6877, 1.0000, 0.8924, 0.5701, 0.5661],\n",
+       "        [0.5442, 0.5687, 0.6953, 0.6716, 0.8924, 1.0000, 0.5665, 0.5457],\n",
+       "        [0.6099, 0.6382, 0.6265, 0.5902, 0.5701, 0.5665, 1.0000, 0.7950],\n",
+       "        [0.6318, 0.6262, 0.6390, 0.6102, 0.5661, 0.5457, 0.7950, 1.0000]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "english_sentences = [\"Berlin is the capital of Germany\", \"Berlin is a large city in Germany\",\n",
+    "                     \"Tensorflow can be used for deep learning\", \"Pytorch, developed by Facebook AI, is a deep learning framework\",\n",
+    "                    \"Is Scipy or numpy better?\", \"Which is faster: scipy or pandas?\",\n",
+    "                    \"Cats can live for quite a long time\", \"Cats are humans best friend\"]\n",
+    "\n",
+    "encoded_input = tokenizer(english_sentences, return_tensors=\"pt\", padding=True)\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    model_output = t5(**encoded_input)\n",
+    "    \n",
+    "    # Perform pooling\n",
+    "    hf_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n",
+    "\n",
+    "    # Apply linear layer\n",
+    "    hf_embeddings = linear(hf_embeddings)\n",
+    "    \n",
+    "    print(hf_embeddings.shape)\n",
+    "\n",
+    "    # Normalize embeddings\n",
+    "    hf_embeddings = F.normalize(hf_embeddings, p=2, dim=1)\n",
+    "\n",
+    "# Cos\n",
+    "hf_scores = util.dot_score(hf_embeddings, hf_embeddings).numpy()\n",
+    "hf_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "677a8bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-02-01 20:00:27.115638: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
+      "2022-02-01 20:00:29.328848: I tensorflow/compiler/xla/service/service.cc:171] XLA service 0x7fe9781cd6f0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
+      "2022-02-01 20:00:29.328894: I tensorflow/compiler/xla/service/service.cc:179]   StreamExecutor device (0): Host, Default Version\n",
+      "2022-02-01 20:00:30.324558: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+      "2022-02-01 20:01:02.775112: I tensorflow/compiler/jit/xla_compilation_cache.cc:363] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(8, 768)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[1.0000, 0.9279, 0.6402, 0.5966, 0.5422, 0.5446, 0.6097, 0.6320],\n",
+       "        [0.9279, 1.0000, 0.6631, 0.6099, 0.5566, 0.5690, 0.6386, 0.6268],\n",
+       "        [0.6402, 0.6631, 1.0000, 0.8347, 0.7101, 0.6955, 0.6264, 0.6389],\n",
+       "        [0.5966, 0.6099, 0.8347, 1.0000, 0.6873, 0.6712, 0.5899, 0.6100],\n",
+       "        [0.5422, 0.5566, 0.7101, 0.6873, 1.0000, 0.8927, 0.5700, 0.5661],\n",
+       "        [0.5446, 0.5690, 0.6955, 0.6712, 0.8927, 1.0000, 0.5663, 0.5458],\n",
+       "        [0.6097, 0.6386, 0.6264, 0.5899, 0.5700, 0.5663, 1.0000, 0.7949],\n",
+       "        [0.6320, 0.6268, 0.6389, 0.6100, 0.5661, 0.5458, 0.7949, 1.0000]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test the models - Original embeddings\n",
+    "english_embeds = encoder(english_sentences)[0].numpy()\n",
+    "print(english_embeds.shape)\n",
+    "tf_scores = util.dot_score(english_embeds, english_embeds).numpy()\n",
+    "print(tf_scores)\n",
+    "print(\"Diff:\", np.sum(np.abs(tf_scores - hf_scores)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "34b44ef7",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'models/sentence-t5-11b/2_Dense/config.json'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_26913/2543044366.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m                                            bias=False, activation_function=torch.nn.Identity())\n\u001b[1;32m      8\u001b[0m \u001b[0mdense\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdense\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'2_Dense'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/sbert/sentence-transformers/sentence_transformers/models/Dense.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, output_path)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'config.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfOut\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m             \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_config_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfOut\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'models/sentence-t5-11b/2_Dense/config.json'"
+     ]
+    }
+   ],
+   "source": [
+    "folder = f'models/sentence-t5-{model_size}'\n",
+    "t5.save_pretrained(folder)\n",
+    "tokenizer.save_pretrained(folder)\n",
+    "\n",
+    "import sentence_transformers\n",
+    "dense = sentence_transformers.models.Dense(linear.in_features, linear.out_features, \n",
+    "                                           bias=False, activation_function=torch.nn.Identity())\n",
+    "dense.linear = linear\n",
+    "\n",
+    "dense_path = os.path.join(folder, '2_Dense')\n",
+    "os.makedirs(dense_path, exist_ok=True)\n",
+    "dense.save(dense_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f2d561c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

convert_to_fp16.py

@@ -0,0 +1,9 @@
+import sys
+from transformers import T5EncoderModel
+
+in_path = sys.argv[1]
+out_path = sys.argv[2]
+
+model = T5EncoderModel.from_pretrained(in_path)
+model.half()
+model.save_pretrained(out_path)

modules.json

@@ -0,0 +1,26 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b91bd3ded13728f29297a9f2ee2a809acd211f52271a857488e491c4c345208
+size 219303530

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}

spiece.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
+size 791656

tokenizer_config.json

@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 100, "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"], "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "t5-base", "tokenizer_class": "T5Tokenizer"}