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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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 sentence_transformers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "160d8ce6",
"metadata": {},
"outputs": [],
"source": [
"\n",
"#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",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2f67f426",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:absl:Using /tmp/tfhub_modules to cache modules.\n",
"2022-02-01 20:04:53.747606: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
"2022-02-01 20:04:53.747647: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1835] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
"Skipping registering GPU devices...\n",
"2022-02-01 20:04:53.747987: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"WARNING:absl:Importing a function (__inference_closure_12264) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
"WARNING:absl:Importing a function (__inference_closure_8418) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
"WARNING:absl:Importing a function (__inference_closure_4202) 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_tf, model_size_hf = \"base\", \"base\"\n",
"hub_url = f\"https://tfhub.dev/google/gtr/gtr-{model_size_tf}/1\"\n",
"encoder = hub.load(hub_url)\n",
"\n",
"v = encoder.signatures['serving_default'].variables"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5f4c8d94",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" 'encoder__layers_0__attention__key__kernel:0': TensorShape([768, 768]),\n",
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" 'encoder__layers_0__attention__value__kernel:0': TensorShape([768, 768]),\n",
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" 'encoder__layers_11__attention__key__kernel:0': TensorShape([768, 768]),\n",
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" 'encoder__layers_11__attention__query__kernel:0': TensorShape([768, 768]),\n",
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" 'encoder__layers_5__pre_attention_layer_norm__scale:0': TensorShape([768]),\n",
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" 'encoder__layers_6__mlp__wi__kernel:0': TensorShape([768, 3072]),\n",
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]
},
"execution_count": 4,
"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": 7,
"id": "6d223b07",
"metadata": {
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},
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'decoder.block.14.layer.1.EncDecAttention.q.weight', 'decoder.block.10.layer.0.SelfAttention.o.weight', 'decoder.block.16.layer.0.SelfAttention.k.weight', 'decoder.block.18.layer.1.EncDecAttention.o.weight', 'decoder.block.11.layer.1.layer_norm.weight', 'decoder.block.2.layer.1.EncDecAttention.v.weight', 'decoder.block.4.layer.0.SelfAttention.k.weight', 'decoder.block.19.layer.0.layer_norm.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.8.layer.0.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight']\n",
"- This IS expected if you are initializing T5EncoderModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing T5EncoderModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
},
{
"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"
]
},
{
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" '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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(f\"t5-{model_size_hf}\")\n",
"t5 = T5EncoderModel.from_pretrained(f\"t5-{model_size_hf}\") \n",
"pt_name_shape = {name: weight.shape for name, weight in t5.state_dict().items()}\n",
"pt_name_shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": 9,
"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": 10,
"id": "ced52a5f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Remaining weights: {'encoder.embed_tokens.weight'}\n"
]
}
],
"source": [
"def need_transpose(name):\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",
"\n",
"names_to_ignore = {\"projection_layer__kernel:0\"}\n",
"#Additional dense layer on top\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": 11,
"id": "1190984f",
"metadata": {},
"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"
]
},
{
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"encoder__layers_21__attention__query__kernel:0 ((1024, 16384)) =transpose=> encoder.block.21.layer.0.SelfAttention.q.weight torch.Size([16384, 1024])\n",
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"encoder__layers_7__mlp__wi__kernel:0 ((1024, 65536)) =transpose=> encoder.block.7.layer.1.DenseReluDense.wi.weight torch.Size([65536, 1024])\n",
"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": [
"import torch\n",
"tokenizer = AutoTokenizer.from_pretrained(f\"t5-{model_size_hf}\")\n",
"T5EncoderModel._keys_to_ignore_on_load_unexpected = [\"decoder.*\"]\n",
"t5 = T5EncoderModel.from_pretrained(f\"t5-{model_size_hf}\")\n",
"t5_state = t5.state_dict()\n",
"\n",
"state_all_names = set(t5_state.keys())\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):\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": 12,
"id": "d59d5a2c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([8, 768])\n"
]
},
{
"data": {
"text/plain": [
"tensor([[1.0000, 0.8303, 0.2995, 0.3906, 0.2986, 0.3062, 0.3430, 0.3734],\n",
" [0.8303, 1.0000, 0.3455, 0.4187, 0.3043, 0.3464, 0.4388, 0.3959],\n",
" [0.2995, 0.3455, 1.0000, 0.6648, 0.4726, 0.4597, 0.3798, 0.3454],\n",
" [0.3906, 0.4187, 0.6648, 1.0000, 0.5167, 0.5195, 0.3746, 0.4006],\n",
" [0.2986, 0.3043, 0.4726, 0.5167, 1.0000, 0.7602, 0.3923, 0.3550],\n",
" [0.3062, 0.3464, 0.4597, 0.5195, 0.7602, 1.0000, 0.4338, 0.3432],\n",
" [0.3430, 0.4388, 0.3798, 0.3746, 0.3923, 0.4338, 1.0000, 0.6090],\n",
" [0.3734, 0.3959, 0.3454, 0.4006, 0.3550, 0.3432, 0.6090, 1.0000]])"
]
},
"execution_count": 12,
"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",
"util.dot_score(hf_embeddings, hf_embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "677a8bab",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-01-31 23:13:39.702310: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
"2022-01-31 23:13:41.448337: I tensorflow/compiler/xla/service/service.cc:171] XLA service 0x7f41641cf460 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
"2022-01-31 23:13:41.448385: I tensorflow/compiler/xla/service/service.cc:179] StreamExecutor device (0): Host, Default Version\n",
"2022-01-31 23:13:44.375222: 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",
"2022-01-31 23:14:17.816928: 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",
"2022-01-31 23:14:17.866550: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 3089104896 exceeds 10% of free system memory.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8, 768)\n"
]
},
{
"data": {
"text/plain": [
"tensor([[1.0000, 0.8303, 0.2996, 0.3908, 0.2984, 0.3062, 0.3428, 0.3735],\n",
" [0.8303, 1.0000, 0.3453, 0.4187, 0.3044, 0.3462, 0.4387, 0.3961],\n",
" [0.2996, 0.3453, 1.0000, 0.6643, 0.4724, 0.4596, 0.3803, 0.3454],\n",
" [0.3908, 0.4187, 0.6643, 1.0000, 0.5169, 0.5196, 0.3744, 0.4003],\n",
" [0.2984, 0.3044, 0.4724, 0.5169, 1.0000, 0.7603, 0.3920, 0.3550],\n",
" [0.3062, 0.3462, 0.4596, 0.5196, 0.7603, 1.0000, 0.4333, 0.3427],\n",
" [0.3428, 0.4387, 0.3803, 0.3744, 0.3920, 0.4333, 1.0000, 0.6087],\n",
" [0.3735, 0.3961, 0.3454, 0.4003, 0.3550, 0.3427, 0.6087, 1.0000]])"
]
},
"execution_count": 13,
"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",
"util.dot_score(english_embeds, english_embeds)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "34b44ef7",
"metadata": {},
"outputs": [],
"source": [
"folder = f'models/gtr-t5-{model_size_hf}'\n",
"t5.save_pretrained(folder)\n",
"tokenizer.save_pretrained(folder)\n",
"os.makedirs(os.path.join(folder, '2_Dense'), exist_ok=True)\n",
"\n",
"\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",
"dense.save(os.path.join(folder, '2_Dense'))\n"
]
},
{
"cell_type": "markdown",
"id": "8f6e006b",
"metadata": {},
"source": [
"# FP16 experiment"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38b1b35e",
"metadata": {},
"outputs": [],
"source": [
"#FP16 experiment\n",
"#t5 = T5EncoderModel.from_pretrained('models/gtr-t5-base')\n",
"#t5.half()\n",
"#t5.save_pretrained('models/gtr-t5-base-fp16')"
]
}
],
"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
}
|