add colab notebook
Browse files- GPT2(error).ipynb +1074 -0
- Untitled330.ipynb +470 -0
GPT2(error).ipynb
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@@ -0,0 +1,1074 @@
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"id": "GDokS6VEJI6C"
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},
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"source": [
|
| 898 |
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"#inputs = tokenizer([\"JAX/Flax is amazing \",\"tensorflow is also good\"],[\"pytorch is better\",\"keras is the best\"],return_tensors='jax',padding='max_length',max_length=30)"
|
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"id": "hWiMk1TzyYim"
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},
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"source": [
|
| 909 |
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"class FlaxGPT2ForMultipleChoiceModule(nn.Module):\n",
|
| 910 |
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" config:GPT2Config\n",
|
| 911 |
+
" dtype: jnp.dtype = jnp.float32\n",
|
| 912 |
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" def setup(self):\n",
|
| 913 |
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" self.gpt2 = FlaxGPT2Model(config=self.config, dtype=self.dtype)\n",
|
| 914 |
+
" self.dropout = nn.Dropout(rate=0.2)\n",
|
| 915 |
+
" self.classifier = nn.Dense(4, dtype=self.dtype)\n",
|
| 916 |
+
"\n",
|
| 917 |
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" def __call__(self,input_ids,attention_mask,position_ids,return_dict=True,deterministic=True,*args):\n",
|
| 918 |
+
" batch_size = input_ids.shape[0]\n",
|
| 919 |
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"\n",
|
| 920 |
+
" rng=jax.random.PRNGKey(0)\n",
|
| 921 |
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" _, dropout_rng = jax.random.split(rng)\n",
|
| 922 |
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"\n",
|
| 923 |
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" outputs=self.gpt2(input_ids, attention_mask,position_ids,return_dict=return_dict)\n",
|
| 924 |
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" \n",
|
| 925 |
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"\n",
|
| 926 |
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" hidden_states = outputs[0]\n",
|
| 927 |
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"\n",
|
| 928 |
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" \n",
|
| 929 |
+
" hidden_states= jnp.mean(hidden_states, axis=1)\n",
|
| 930 |
+
"\n",
|
| 931 |
+
" print(hidden_states.shape)\n",
|
| 932 |
+
" \n",
|
| 933 |
+
" hidden_states=hidden_states.reshape(batch_size,-1) #(32,8,768)->(32,8*768)\n",
|
| 934 |
+
"\n",
|
| 935 |
+
" dropout_output = self.dropout(hidden_states,deterministic=deterministic,rng=dropout_rng)\n",
|
| 936 |
+
"\n",
|
| 937 |
+
" print(dropout_output.shape)\n",
|
| 938 |
+
"\n",
|
| 939 |
+
" logits = self.classifier(dropout_output)\n",
|
| 940 |
+
" reshaped_logits = logits.reshape(-1, 4) #(32,4)\n",
|
| 941 |
+
" if not return_dict:\n",
|
| 942 |
+
" return (reshaped_logits,) + outputs[2:]\n",
|
| 943 |
+
" return reshaped_logits"
|
| 944 |
+
],
|
| 945 |
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"execution_count": 7,
|
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"outputs": []
|
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},
|
| 948 |
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{
|
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"cell_type": "code",
|
| 950 |
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"metadata": {
|
| 951 |
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"id": "u1j00Ck255BC"
|
| 952 |
+
},
|
| 953 |
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"source": [
|
| 954 |
+
"class FlaxGPT2ForMultipleChoice(FlaxGPT2PreTrainedModel):\n",
|
| 955 |
+
" module_class = FlaxGPT2ForMultipleChoiceModule"
|
| 956 |
+
],
|
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"execution_count": 8,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
|
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "5a0fcc68-ca39-4df0-c854-734125d65f53"
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},
|
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"source": [
|
| 970 |
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"model = FlaxGPT2ForMultipleChoice.from_pretrained('gpt2') # getting warning"
|
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],
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"execution_count": 9,
|
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"(1, 768)\n",
|
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"(1, 768)\n"
|
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],
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},
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{
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"output_type": "stream",
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"text": [
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"Some weights of the model checkpoint at gpt2 were not used when initializing FlaxGPT2ForMultipleChoice: {('h', '1', 'ln_1', 'bias'), ('h', '6', 'ln_1', 'scale'), ('h', '1', 'attn', 'c_proj', 'kernel'), ('h', '11', 'mlp', 'c_fc', 'bias'), ('h', '7', 'ln_1', 'bias'), ('h', '5', 'ln_2', 'bias'), ('h', '10', 'ln_2', 'scale'), ('h', '4', 'mlp', 'c_proj', 'kernel'), ('h', '0', 'mlp', 'c_proj', 'bias'), ('h', '0', 'ln_1', 'bias'), ('h', '0', 'mlp', 'c_fc', 'kernel'), ('wpe', 'embedding'), ('h', '3', 'ln_1', 'scale'), ('h', '2', 'ln_1', 'scale'), ('h', '3', 'mlp', 'c_fc', 'kernel'), ('h', '7', 'ln_1', 'scale'), ('h', '8', 'mlp', 'c_proj', 'kernel'), ('h', '7', 'mlp', 'c_proj', 'kernel'), ('h', '3', 'ln_2', 'bias'), ('h', '9', 'attn', 'c_attn', 'kernel'), ('h', '0', 'mlp', 'c_fc', 'bias'), ('h', '3', 'attn', 'c_proj', 'bias'), ('h', '0', 'ln_1', 'scale'), ('h', '3', 'attn', 'c_attn', 'kernel'), ('h', '0', 'mlp', 'c_proj', 'kernel'), ('h', '5', 'ln_1', 'bias'), ('h', '7', 'attn', 'c_attn', 'bias'), ('h', '1', 'ln_2', 'bias'), ('h', '11', 'ln_2', 'scale'), ('h', '7', 'ln_2', 'bias'), ('h', '9', 'attn', 'c_proj', 'kernel'), ('h', '0', 'ln_2', 'bias'), ('h', '2', 'ln_2', 'scale'), ('h', '11', 'attn', 'c_attn', 'kernel'), ('h', '8', 'attn', 'c_proj', 'kernel'), ('h', '4', 'attn', 'c_attn', 'kernel'), ('h', '5', 'ln_1', 'scale'), ('h', '4', 'ln_1', 'bias'), ('h', '8', 'ln_2', 'bias'), ('h', '1', 'mlp', 'c_fc', 'kernel'), ('h', '9', 'ln_2', 'scale'), ('h', '1', 'mlp', 'c_proj', 'bias'), ('h', '2', 'mlp', 'c_proj', 'kernel'), ('h', '9', 'attn', 'c_proj', 'bias'), ('h', '11', 'ln_2', 'bias'), ('h', '6', 'mlp', 'c_proj', 'bias'), ('h', '3', 'ln_1', 'bias'), ('h', '1', 'attn', 'c_attn', 'kernel'), ('h', '9', 'ln_1', 'scale'), ('h', '10', 'attn', 'c_attn', 'bias'), ('h', '10', 'mlp', 'c_proj', 'kernel'), ('h', '2', 'attn', 'c_proj', 'kernel'), ('h', '0', 'attn', 'c_proj', 'kernel'), ('h', '6', 'attn', 'c_attn', 'kernel'), ('h', '4', 'mlp', 'c_fc', 'bias'), ('h', '3', 'attn', 'c_attn', 'bias'), ('h', '3', 'attn', 'c_proj', 'kernel'), ('h', '11', 'mlp', 'c_proj', 'bias'), ('h', '9', 'attn', 'c_attn', 'bias'), ('h', '7', 'mlp', 'c_proj', 'bias'), ('h', '7', 'mlp', 'c_fc', 'bias'), ('h', '6', 'attn', 'c_attn', 'bias'), ('h', '5', 'mlp', 'c_fc', 'kernel'), ('h', '0', 'attn', 'c_proj', 'bias'), ('h', '2', 'attn', 'c_proj', 'bias'), ('h', '10', 'attn', 'c_attn', 'kernel'), ('h', '10', 'mlp', 'c_proj', 'bias'), ('h', '1', 'attn', 'c_attn', 'bias'), ('h', '11', 'ln_1', 'bias'), ('h', '4', 'ln_2', 'bias'), ('h', '8', 'ln_1', 'bias'), ('h', '11', 'attn', 'c_proj', 'kernel'), ('h', '9', 'mlp', 'c_fc', 'kernel'), ('h', '7', 'ln_2', 'scale'), ('h', '9', 'mlp', 'c_proj', 'kernel'), ('h', '11', 'attn', 'c_attn', 'bias'), ('h', '10', 'mlp', 'c_fc', 'bias'), ('h', '6', 'attn', 'c_proj', 'kernel'), ('h', '0', 'ln_2', 'scale'), ('h', '2', 'ln_2', 'bias'), ('h', '3', 'mlp', 'c_proj', 'bias'), ('h', '5', 'mlp', 'c_proj', 'kernel'), ('h', '8', 'mlp', 'c_fc', 'bias'), ('h', '9', 'mlp', 'c_proj', 'bias'), ('h', '9', 'mlp', 'c_fc', 'bias'), ('h', '8', 'mlp', 'c_fc', 'kernel'), ('h', '9', 'ln_1', 'bias'), ('h', '10', 'ln_1', 'scale'), ('h', '6', 'ln_2', 'bias'), ('h', '2', 'mlp', 'c_fc', 'kernel'), ('h', '4', 'attn', 'c_proj', 'bias'), ('h', '1', 'ln_2', 'scale'), ('h', '5', 'mlp', 'c_fc', 'bias'), ('h', '7', 'mlp', 'c_fc', 'kernel'), ('h', '7', 'attn', 'c_proj', 'bias'), ('h', '5', 'attn', 'c_proj', 'kernel'), ('h', '2', 'mlp', 'c_fc', 'bias'), ('h', '6', 'ln_2', 'scale'), ('h', '11', 'ln_1', 'scale'), ('h', '4', 'mlp', 'c_fc', 'kernel'), ('h', '2', 'ln_1', 'bias'), ('h', '9', 'ln_2', 'bias'), ('h', '11', 'mlp', 'c_fc', 'kernel'), ('h', '1', 'attn', 'c_proj', 'bias'), ('h', '4', 'ln_2', 'scale'), ('h', '8', 'ln_1', 'scale'), ('h', '6', 'attn', 'c_proj', 'bias'), ('h', '5', 'attn', 'c_attn', 'kernel'), ('h', '3', 'ln_2', 'scale'), ('h', '8', 'attn', 'c_attn', 'bias'), ('h', '10', 'mlp', 'c_fc', 'kernel'), ('h', '1', 'ln_1', 'scale'), ('h', '10', 'attn', 'c_proj', 'bias'), ('h', '6', 'ln_1', 'bias'), ('h', '0', 'attn', 'c_attn', 'kernel'), ('wte', 'embedding'), ('h', '6', 'mlp', 'c_fc', 'kernel'), ('h', '4', 'attn', 'c_attn', 'bias'), ('h', '10', 'ln_2', 'bias'), ('h', '8', 'attn', 'c_proj', 'bias'), ('h', '11', 'attn', 'c_proj', 'bias'), ('h', '8', 'attn', 'c_attn', 'kernel'), ('h', '5', 'attn', 'c_attn', 'bias'), ('h', '5', 'ln_2', 'scale'), ('h', '2', 'attn', 'c_attn', 'bias'), ('ln_f', 'scale'), ('h', '7', 'attn', 'c_attn', 'kernel'), ('h', '4', 'ln_1', 'scale'), ('h', '8', 'ln_2', 'scale'), ('h', '11', 'mlp', 'c_proj', 'kernel'), ('h', '5', 'attn', 'c_proj', 'bias'), ('h', '7', 'attn', 'c_proj', 'kernel'), ('h', '8', 'mlp', 'c_proj', 'bias'), ('h', '3', 'mlp', 'c_fc', 'bias'), ('h', '10', 'ln_1', 'bias'), ('h', '2', 'attn', 'c_attn', 'kernel'), ('h', '6', 'mlp', 'c_proj', 'kernel'), ('h', '4', 'attn', 'c_proj', 'kernel'), ('h', '1', 'mlp', 'c_proj', 'kernel'), ('h', '2', 'mlp', 'c_proj', 'bias'), ('h', '1', 'mlp', 'c_fc', 'bias'), ('h', '4', 'mlp', 'c_proj', 'bias'), ('ln_f', 'bias'), ('h', '6', 'mlp', 'c_fc', 'bias'), ('h', '0', 'attn', 'c_attn', 'bias'), ('h', '10', 'attn', 'c_proj', 'kernel'), ('h', '5', 'mlp', 'c_proj', 'bias'), ('h', '3', 'mlp', 'c_proj', 'kernel')}\n",
|
| 986 |
+
"- This IS expected if you are initializing FlaxGPT2ForMultipleChoice 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",
|
| 987 |
+
"- This IS NOT expected if you are initializing FlaxGPT2ForMultipleChoice from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
| 988 |
+
"Some weights of FlaxGPT2ForMultipleChoice were not initialized from the model checkpoint at gpt2 and are newly initialized: {('classifier', 'bias'), ('classifier', 'kernel')}\n",
|
| 989 |
+
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
| 990 |
+
],
|
| 991 |
+
"name": "stderr"
|
| 992 |
+
}
|
| 993 |
+
]
|
| 994 |
+
},
|
| 995 |
+
{
|
| 996 |
+
"cell_type": "code",
|
| 997 |
+
"metadata": {
|
| 998 |
+
"id": "CdSuQK9pRmw-"
|
| 999 |
+
},
|
| 1000 |
+
"source": [
|
| 1001 |
+
"input_ids=jnp.ones((4,5,6))\n",
|
| 1002 |
+
"attention_mask=jnp.ones((4,5,6))"
|
| 1003 |
+
],
|
| 1004 |
+
"execution_count": 10,
|
| 1005 |
+
"outputs": []
|
| 1006 |
+
},
|
| 1007 |
+
{
|
| 1008 |
+
"cell_type": "code",
|
| 1009 |
+
"metadata": {
|
| 1010 |
+
"id": "d3Bu38KTkwWs",
|
| 1011 |
+
"colab": {
|
| 1012 |
+
"base_uri": "https://localhost:8080/",
|
| 1013 |
+
"height": 300
|
| 1014 |
+
},
|
| 1015 |
+
"outputId": "5470ccc9-6d49-427c-ad8e-5162343acfde"
|
| 1016 |
+
},
|
| 1017 |
+
"source": [
|
| 1018 |
+
"out1 = model(input_ids, attention_mask) #GPT2 will not take (batch_size,num_choice,sequence_length)"
|
| 1019 |
+
],
|
| 1020 |
+
"execution_count": 11,
|
| 1021 |
+
"outputs": [
|
| 1022 |
+
{
|
| 1023 |
+
"output_type": "error",
|
| 1024 |
+
"ename": "ValueError",
|
| 1025 |
+
"evalue": "ignored",
|
| 1026 |
+
"traceback": [
|
| 1027 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 1028 |
+
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
| 1029 |
+
"\u001b[0;32m<ipython-input-11-7491141e6756>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mout1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#GPT2 will not take (batch_size,num_choice,sequence_length)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
| 1030 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/gpt2/modeling_flax_gpt2.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input_ids, attention_mask, position_ids, params, past_key_values, dropout_rng, train, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 372\u001b[0;31m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mposition_ids\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1031 |
+
"\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
|
| 1032 |
+
]
|
| 1033 |
+
}
|
| 1034 |
+
]
|
| 1035 |
+
},
|
| 1036 |
+
{
|
| 1037 |
+
"cell_type": "code",
|
| 1038 |
+
"metadata": {
|
| 1039 |
+
"id": "VZPlQfkhgLJd",
|
| 1040 |
+
"colab": {
|
| 1041 |
+
"base_uri": "https://localhost:8080/"
|
| 1042 |
+
},
|
| 1043 |
+
"outputId": "7948688b-be77-4e9a-fc06-8644a2614d42"
|
| 1044 |
+
},
|
| 1045 |
+
"source": [
|
| 1046 |
+
"print(out1)"
|
| 1047 |
+
],
|
| 1048 |
+
"execution_count": null,
|
| 1049 |
+
"outputs": [
|
| 1050 |
+
{
|
| 1051 |
+
"output_type": "stream",
|
| 1052 |
+
"text": [
|
| 1053 |
+
"[[ 1.1391759 -0.01598702 0.55463445 0.36025363]\n",
|
| 1054 |
+
" [ 0.32208228 0.37667227 0.87823874 0.19541818]\n",
|
| 1055 |
+
" [ 0.76971424 0.7187787 0.68642044 -0.31461257]\n",
|
| 1056 |
+
" [ 1.2375658 0.03325981 0.00153449 0.12019679]]\n"
|
| 1057 |
+
],
|
| 1058 |
+
"name": "stdout"
|
| 1059 |
+
}
|
| 1060 |
+
]
|
| 1061 |
+
},
|
| 1062 |
+
{
|
| 1063 |
+
"cell_type": "code",
|
| 1064 |
+
"metadata": {
|
| 1065 |
+
"id": "fgkIcD-mZWP7"
|
| 1066 |
+
},
|
| 1067 |
+
"source": [
|
| 1068 |
+
""
|
| 1069 |
+
],
|
| 1070 |
+
"execution_count": null,
|
| 1071 |
+
"outputs": []
|
| 1072 |
+
}
|
| 1073 |
+
]
|
| 1074 |
+
}
|
Untitled330.ipynb
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
"nbformat": 4,
|
| 3 |
+
"nbformat_minor": 0,
|
| 4 |
+
"metadata": {
|
| 5 |
+
"colab": {
|
| 6 |
+
"name": "Untitled330.ipynb",
|
| 7 |
+
"provenance": [],
|
| 8 |
+
"collapsed_sections": []
|
| 9 |
+
},
|
| 10 |
+
"kernelspec": {
|
| 11 |
+
"name": "python3",
|
| 12 |
+
"display_name": "Python 3"
|
| 13 |
+
},
|
| 14 |
+
"language_info": {
|
| 15 |
+
"name": "python"
|
| 16 |
+
}
|
| 17 |
+
},
|
| 18 |
+
"cells": [
|
| 19 |
+
{
|
| 20 |
+
"cell_type": "code",
|
| 21 |
+
"metadata": {
|
| 22 |
+
"id": "Ii2x731Ta8fu"
|
| 23 |
+
},
|
| 24 |
+
"source": [
|
| 25 |
+
"%%capture\n",
|
| 26 |
+
"!pip install transformers\n",
|
| 27 |
+
"!pip install datasets\n",
|
| 28 |
+
"!pip install --upgrade git+https://github.com/google/flax.git"
|
| 29 |
+
],
|
| 30 |
+
"execution_count": 1,
|
| 31 |
+
"outputs": []
|
| 32 |
+
},
|
| 33 |
+
{
|
| 34 |
+
"cell_type": "code",
|
| 35 |
+
"metadata": {
|
| 36 |
+
"id": "_9NMPFKua9hr"
|
| 37 |
+
},
|
| 38 |
+
"source": [
|
| 39 |
+
"import jax\n",
|
| 40 |
+
"from transformers.modeling_flax_utils import FlaxPreTrainedModel\n",
|
| 41 |
+
"import flax.linen as nn\n",
|
| 42 |
+
"import jax.numpy as jnp\n",
|
| 43 |
+
"from transformers import GPT2Config\n",
|
| 44 |
+
"#from transformers import FlaxGPT2PreTrainedModel\n",
|
| 45 |
+
"from transformers import FlaxGPT2Model\n",
|
| 46 |
+
"import jax.numpy as jnp\n",
|
| 47 |
+
"from transformers import GPT2Tokenizer\n",
|
| 48 |
+
"tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\",pad_token='<|endoftext|>') \n",
|
| 49 |
+
"from typing import Any, Optional, Tuple\n",
|
| 50 |
+
"from flax.core.frozen_dict import FrozenDict, unfreeze\n",
|
| 51 |
+
"from transformers import file_utils\n",
|
| 52 |
+
"from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward\n",
|
| 53 |
+
"from transformers.models.gpt2.modeling_flax_gpt2 import FlaxGPT2BlockCollection\n",
|
| 54 |
+
"from transformers.modeling_flax_outputs import FlaxBaseModelOutput"
|
| 55 |
+
],
|
| 56 |
+
"execution_count": null,
|
| 57 |
+
"outputs": []
|
| 58 |
+
},
|
| 59 |
+
{
|
| 60 |
+
"cell_type": "code",
|
| 61 |
+
"metadata": {
|
| 62 |
+
"id": "dqkcoBOccszd"
|
| 63 |
+
},
|
| 64 |
+
"source": [
|
| 65 |
+
"GPT2_START_DOCSTRING = r\"\"\"\n",
|
| 66 |
+
" This model inherits from :class:`~transformers.FlaxPreTrainedModel`. Check the superclass documentation for the\n",
|
| 67 |
+
" generic methods the library implements for all its model (such as downloading or saving, resizing the input\n",
|
| 68 |
+
" embeddings, pruning heads etc.)\n",
|
| 69 |
+
" This model is also a Flax Linen `flax.nn.Module\n",
|
| 70 |
+
" <https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html>`__ subclass. Use it as a regular Flax\n",
|
| 71 |
+
" Module and refer to the Flax documentation for all matter related to general usage and behavior.\n",
|
| 72 |
+
" Finally, this model supports inherent JAX features such as:\n",
|
| 73 |
+
" - `Just-In-Time (JIT) compilation <https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit>`__\n",
|
| 74 |
+
" - `Automatic Differentiation <https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation>`__\n",
|
| 75 |
+
" - `Vectorization <https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap>`__\n",
|
| 76 |
+
" - `Parallelization <https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap>`__\n",
|
| 77 |
+
" Parameters:\n",
|
| 78 |
+
" config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model.\n",
|
| 79 |
+
" Initializing with a config file does not load the weights associated with the model, only the\n",
|
| 80 |
+
" configuration. Check out the :meth:`~transformers.FlaxPreTrainedModel.from_pretrained` method to load the\n",
|
| 81 |
+
" model weights.\n",
|
| 82 |
+
"\"\"\"\n",
|
| 83 |
+
"\n",
|
| 84 |
+
"GPT2_INPUTS_DOCSTRING = r\"\"\"\n",
|
| 85 |
+
" Args:\n",
|
| 86 |
+
" input_ids (:obj:`numpy.ndarray` of shape :obj:`(batch_size,input_ids_length)`):\n",
|
| 87 |
+
" :obj:`input_ids_length` = ``sequence_length``. Indices of input sequence tokens in the vocabulary.\n",
|
| 88 |
+
" Indices can be obtained using :class:`~transformers.GPT2Tokenizer`. See\n",
|
| 89 |
+
" :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for\n",
|
| 90 |
+
" details.\n",
|
| 91 |
+
" `What are input IDs? <../glossary.html#input-ids>`__\n",
|
| 92 |
+
" attention_mask (:obj:`numpy.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n",
|
| 93 |
+
" Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:\n",
|
| 94 |
+
" - 1 for tokens that are **not masked**,\n",
|
| 95 |
+
" - 0 for tokens that are **masked**.\n",
|
| 96 |
+
" `What are attention masks? <../glossary.html#attention-mask>`__\n",
|
| 97 |
+
" position_ids (:obj:`numpy.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n",
|
| 98 |
+
" Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,\n",
|
| 99 |
+
" config.max_position_embeddings - 1]``.\n",
|
| 100 |
+
" past_key_values (:obj:`Dict[str, np.ndarray]`, `optional`, returned by ``init_cache`` or when passing previous ``past_key_values``):\n",
|
| 101 |
+
" Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast\n",
|
| 102 |
+
" auto-regressive decoding. Pre-computed key and value hidden-states are of shape `[batch_size, max_length]`.\n",
|
| 103 |
+
" output_attentions (:obj:`bool`, `optional`):\n",
|
| 104 |
+
" Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned\n",
|
| 105 |
+
" tensors for more detail.\n",
|
| 106 |
+
" output_hidden_states (:obj:`bool`, `optional`):\n",
|
| 107 |
+
" Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for\n",
|
| 108 |
+
" more detail.\n",
|
| 109 |
+
" return_dict (:obj:`bool`, `optional`):\n",
|
| 110 |
+
" Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.\n",
|
| 111 |
+
"\"\"\""
|
| 112 |
+
],
|
| 113 |
+
"execution_count": 3,
|
| 114 |
+
"outputs": []
|
| 115 |
+
},
|
| 116 |
+
{
|
| 117 |
+
"cell_type": "code",
|
| 118 |
+
"metadata": {
|
| 119 |
+
"id": "NX-Z5iCMbKL5"
|
| 120 |
+
},
|
| 121 |
+
"source": [
|
| 122 |
+
"class FlaxGGGPreTrainedModel(FlaxPreTrainedModel):\n",
|
| 123 |
+
" \"\"\"\n",
|
| 124 |
+
" An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n",
|
| 125 |
+
" models.\n",
|
| 126 |
+
" \"\"\"\n",
|
| 127 |
+
"\n",
|
| 128 |
+
" config_class = GPT2Config\n",
|
| 129 |
+
" base_model_prefix = \"transformer\"\n",
|
| 130 |
+
" module_class: nn.Module = None\n",
|
| 131 |
+
"\n",
|
| 132 |
+
" def __init__(\n",
|
| 133 |
+
" self,\n",
|
| 134 |
+
" config: GPT2Config,\n",
|
| 135 |
+
" input_shape: Tuple = (1,1),\n",
|
| 136 |
+
" seed: int = 0,\n",
|
| 137 |
+
" dtype: jnp.dtype = jnp.float32,\n",
|
| 138 |
+
" **kwargs,\n",
|
| 139 |
+
" ):\n",
|
| 140 |
+
" \n",
|
| 141 |
+
" module = self.module_class(config=config, dtype=dtype, **kwargs)\n",
|
| 142 |
+
" super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)\n",
|
| 143 |
+
"\n",
|
| 144 |
+
" def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:\n",
|
| 145 |
+
" # init input tensors\n",
|
| 146 |
+
" input_ids = jnp.zeros(input_shape, dtype=\"i4\")\n",
|
| 147 |
+
" attention_mask = jnp.ones_like(input_ids)\n",
|
| 148 |
+
" \n",
|
| 149 |
+
" position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)\n",
|
| 150 |
+
"\n",
|
| 151 |
+
" params_rng, dropout_rng = jax.random.split(rng)\n",
|
| 152 |
+
" rngs = {\"params\": params_rng, \"dropout\": dropout_rng}\n",
|
| 153 |
+
"\n",
|
| 154 |
+
" return self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)[\"params\"]\n",
|
| 155 |
+
"\n",
|
| 156 |
+
" def init_cache(self, batch_size, max_length):\n",
|
| 157 |
+
" r\"\"\"\n",
|
| 158 |
+
" Args:\n",
|
| 159 |
+
" batch_size (:obj:`int`):\n",
|
| 160 |
+
" batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.\n",
|
| 161 |
+
" max_length (:obj:`int`):\n",
|
| 162 |
+
" maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized\n",
|
| 163 |
+
" cache.\n",
|
| 164 |
+
" \"\"\"\n",
|
| 165 |
+
" # init input variables to retrieve cache\n",
|
| 166 |
+
" input_ids = jnp.ones((batch_size, max_length))\n",
|
| 167 |
+
" attention_mask = jnp.ones_like(input_ids)\n",
|
| 168 |
+
" position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)\n",
|
| 169 |
+
"\n",
|
| 170 |
+
" init_variables = self.module.init(\n",
|
| 171 |
+
" jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True\n",
|
| 172 |
+
" )\n",
|
| 173 |
+
" return init_variables[\"cache\"]\n",
|
| 174 |
+
"\n",
|
| 175 |
+
" @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)\n",
|
| 176 |
+
" def __call__(\n",
|
| 177 |
+
" self,\n",
|
| 178 |
+
" input_ids,\n",
|
| 179 |
+
" attention_mask=None,\n",
|
| 180 |
+
" position_ids=None,\n",
|
| 181 |
+
" params: dict = None,\n",
|
| 182 |
+
" past_key_values: dict = None,\n",
|
| 183 |
+
" dropout_rng: jax.random.PRNGKey = None,\n",
|
| 184 |
+
" train: bool = False,\n",
|
| 185 |
+
" output_attentions: Optional[bool] = None,\n",
|
| 186 |
+
" output_hidden_states: Optional[bool] = None,\n",
|
| 187 |
+
" return_dict: Optional[bool] = None,\n",
|
| 188 |
+
" ):\n",
|
| 189 |
+
" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n",
|
| 190 |
+
" output_hidden_states = (\n",
|
| 191 |
+
" output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n",
|
| 192 |
+
" )\n",
|
| 193 |
+
" return_dict = return_dict if return_dict is not None else self.config.return_dict\n",
|
| 194 |
+
" print(input_ids.shape)\n",
|
| 195 |
+
"\n",
|
| 196 |
+
" # batch_size, num_choices,sequence_length = input_ids.shape\n",
|
| 197 |
+
"\n",
|
| 198 |
+
" if position_ids is None:\n",
|
| 199 |
+
" if past_key_values is not None:\n",
|
| 200 |
+
" raise ValueError(\"Make sure to provide `position_ids` when passing `past_key_values`.\")\n",
|
| 201 |
+
" \n",
|
| 202 |
+
" position_ids=jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)\n",
|
| 203 |
+
"\n",
|
| 204 |
+
" # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))\n",
|
| 205 |
+
"\n",
|
| 206 |
+
" if attention_mask is None:\n",
|
| 207 |
+
" attention_mask = jnp.ones((input_ids))\n",
|
| 208 |
+
" print('attn not')\n",
|
| 209 |
+
"\n",
|
| 210 |
+
" # Handle any PRNG if needed\n",
|
| 211 |
+
" rngs = {}\n",
|
| 212 |
+
" if dropout_rng is not None:\n",
|
| 213 |
+
" rngs[\"dropout\"] = dropout_rng\n",
|
| 214 |
+
"\n",
|
| 215 |
+
" inputs = {\"params\": params or self.params}\n",
|
| 216 |
+
"\n",
|
| 217 |
+
" # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGPT2Attention module\n",
|
| 218 |
+
" if past_key_values:\n",
|
| 219 |
+
" inputs[\"cache\"] = past_key_values\n",
|
| 220 |
+
" mutable = [\"cache\"]\n",
|
| 221 |
+
" else:\n",
|
| 222 |
+
" mutable = False\n",
|
| 223 |
+
"\n",
|
| 224 |
+
" outputs = self.module.apply(\n",
|
| 225 |
+
" inputs,\n",
|
| 226 |
+
" jnp.array(input_ids, dtype=\"i4\"),\n",
|
| 227 |
+
" jnp.array(attention_mask, dtype=\"i4\"),\n",
|
| 228 |
+
" jnp.array(position_ids, dtype=\"i4\"),\n",
|
| 229 |
+
" not train,\n",
|
| 230 |
+
" False,\n",
|
| 231 |
+
" output_attentions,\n",
|
| 232 |
+
" output_hidden_states,\n",
|
| 233 |
+
" return_dict,\n",
|
| 234 |
+
" rngs=rngs,\n",
|
| 235 |
+
" mutable=mutable,\n",
|
| 236 |
+
" )\n",
|
| 237 |
+
" print('cache')\n",
|
| 238 |
+
"\n",
|
| 239 |
+
" # add updated cache to model output\n",
|
| 240 |
+
" if past_key_values is not None and return_dict:\n",
|
| 241 |
+
" outputs, past_key_values = outputs\n",
|
| 242 |
+
" outputs[\"past_key_values\"] = unfreeze(past_key_values[\"cache\"])\n",
|
| 243 |
+
" return outputs\n",
|
| 244 |
+
" elif past_key_values is not None and not return_dict:\n",
|
| 245 |
+
" outputs, past_key_values = outputs\n",
|
| 246 |
+
" outputs = outputs[:1] + (unfreeze(past_key_values[\"cache\"]),) + outputs[1:]\n",
|
| 247 |
+
"\n",
|
| 248 |
+
" return outputs"
|
| 249 |
+
],
|
| 250 |
+
"execution_count": 6,
|
| 251 |
+
"outputs": []
|
| 252 |
+
},
|
| 253 |
+
{
|
| 254 |
+
"cell_type": "code",
|
| 255 |
+
"metadata": {
|
| 256 |
+
"id": "4vRAWll2bwQQ"
|
| 257 |
+
},
|
| 258 |
+
"source": [
|
| 259 |
+
"class FlaxGGGModule(nn.Module):\n",
|
| 260 |
+
" config: GPT2Config\n",
|
| 261 |
+
" dtype: jnp.dtype = jnp.float32\n",
|
| 262 |
+
"\n",
|
| 263 |
+
" def setup(self):\n",
|
| 264 |
+
" self.embed_dim = self.config.hidden_size\n",
|
| 265 |
+
"\n",
|
| 266 |
+
" self.wte = nn.Embed(\n",
|
| 267 |
+
" self.config.vocab_size,\n",
|
| 268 |
+
" self.embed_dim,\n",
|
| 269 |
+
" embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),\n",
|
| 270 |
+
" dtype=self.dtype,\n",
|
| 271 |
+
" )\n",
|
| 272 |
+
" self.wpe = nn.Embed(\n",
|
| 273 |
+
" self.config.max_position_embeddings,\n",
|
| 274 |
+
" self.embed_dim,\n",
|
| 275 |
+
" embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),\n",
|
| 276 |
+
" dtype=self.dtype,\n",
|
| 277 |
+
" )\n",
|
| 278 |
+
" self.dropout = nn.Dropout(rate=self.config.embd_pdrop)\n",
|
| 279 |
+
" self.h = FlaxGPT2BlockCollection(self.config, dtype=self.dtype)\n",
|
| 280 |
+
" self.ln_f = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)\n",
|
| 281 |
+
"\n",
|
| 282 |
+
" def __call__(\n",
|
| 283 |
+
" self,\n",
|
| 284 |
+
" input_ids,\n",
|
| 285 |
+
" attention_mask,\n",
|
| 286 |
+
" position_ids,\n",
|
| 287 |
+
" deterministic=True,\n",
|
| 288 |
+
" init_cache: bool = False,\n",
|
| 289 |
+
" output_attentions: bool = False,\n",
|
| 290 |
+
" output_hidden_states: bool = False,\n",
|
| 291 |
+
" return_dict: bool = True,\n",
|
| 292 |
+
" ):\n",
|
| 293 |
+
" input_embeds = self.wte(input_ids.astype(\"i4\"))\n",
|
| 294 |
+
" position_embeds = self.wpe(position_ids.astype(\"i4\"))\n",
|
| 295 |
+
" \n",
|
| 296 |
+
"\n",
|
| 297 |
+
" hidden_states = input_embeds + position_embeds\n",
|
| 298 |
+
" hidden_states = self.dropout(hidden_states, deterministic=deterministic)\n",
|
| 299 |
+
" outputs = self.h(\n",
|
| 300 |
+
" hidden_states,\n",
|
| 301 |
+
" attention_mask,\n",
|
| 302 |
+
" deterministic=deterministic,\n",
|
| 303 |
+
" init_cache=init_cache,\n",
|
| 304 |
+
" output_attentions=output_attentions,\n",
|
| 305 |
+
" output_hidden_states=output_hidden_states,\n",
|
| 306 |
+
" return_dict=return_dict,\n",
|
| 307 |
+
" )\n",
|
| 308 |
+
"\n",
|
| 309 |
+
" hidden_states = outputs[0]\n",
|
| 310 |
+
" hidden_states = self.ln_f(hidden_states)\n",
|
| 311 |
+
" print('ggg')\n",
|
| 312 |
+
" if not return_dict:\n",
|
| 313 |
+
" return (hidden_states,) + outputs[1:]\n",
|
| 314 |
+
"\n",
|
| 315 |
+
" return FlaxBaseModelOutput(\n",
|
| 316 |
+
" last_hidden_state=hidden_states,\n",
|
| 317 |
+
" hidden_states=outputs.hidden_states,\n",
|
| 318 |
+
" attentions=outputs.attentions,)\n",
|
| 319 |
+
"class FlaxNewModel(FlaxGGGPreTrainedModel):\n",
|
| 320 |
+
" module_class = FlaxGGGModule"
|
| 321 |
+
],
|
| 322 |
+
"execution_count": 7,
|
| 323 |
+
"outputs": []
|
| 324 |
+
},
|
| 325 |
+
{
|
| 326 |
+
"cell_type": "code",
|
| 327 |
+
"metadata": {
|
| 328 |
+
"id": "_ljSn6GdedtI"
|
| 329 |
+
},
|
| 330 |
+
"source": [
|
| 331 |
+
"class FlaxGPT2ForMultipleChoiceModule(nn.Module):\n",
|
| 332 |
+
" config:GPT2Config\n",
|
| 333 |
+
" dtype: jnp.dtype = jnp.float32\n",
|
| 334 |
+
" def setup(self):\n",
|
| 335 |
+
" self.gpt2 = FlaxNewModel(config=self.config, dtype=self.dtype)\n",
|
| 336 |
+
" self.dropout = nn.Dropout(rate=0.2)\n",
|
| 337 |
+
" self.classifier = nn.Dense(4, dtype=self.dtype)\n",
|
| 338 |
+
"\n",
|
| 339 |
+
" def __call__(self,input_ids,attention_mask,position_ids,return_dict=True,deterministic=True,*args):\n",
|
| 340 |
+
" batch_size = input_ids.shape[0]\n",
|
| 341 |
+
" rng=jax.random.PRNGKey(0)\n",
|
| 342 |
+
" _, dropout_rng = jax.random.split(rng)\n",
|
| 343 |
+
" print('abc')\n",
|
| 344 |
+
"\n",
|
| 345 |
+
" outputs=self.gpt2(input_ids, attention_mask,position_ids,return_dict=return_dict)\n",
|
| 346 |
+
" \n",
|
| 347 |
+
"\n",
|
| 348 |
+
" hidden_states = outputs[0]\n",
|
| 349 |
+
"\n",
|
| 350 |
+
" \n",
|
| 351 |
+
" hidden_states= jnp.mean(hidden_states, axis=1)\n",
|
| 352 |
+
"\n",
|
| 353 |
+
" print(hidden_states.shape)\n",
|
| 354 |
+
" \n",
|
| 355 |
+
" \n",
|
| 356 |
+
" hidden_states=hidden_states.reshape(batch_size,-1) #(32,8,768)->(32,8*768)\n",
|
| 357 |
+
"\n",
|
| 358 |
+
" dropout_output = self.dropout(hidden_states,deterministic=deterministic,rng=dropout_rng)\n",
|
| 359 |
+
"\n",
|
| 360 |
+
" print(dropout_output.shape)\n",
|
| 361 |
+
" \n",
|
| 362 |
+
"\n",
|
| 363 |
+
" logits = self.classifier(dropout_output)\n",
|
| 364 |
+
" print('bnv')\n",
|
| 365 |
+
" reshaped_logits = logits.reshape(-1, 4) \n",
|
| 366 |
+
" #(32,4)\n",
|
| 367 |
+
" if not return_dict:\n",
|
| 368 |
+
" return (reshaped_logits,) + outputs[2:]\n",
|
| 369 |
+
" return reshaped_logits"
|
| 370 |
+
],
|
| 371 |
+
"execution_count": 8,
|
| 372 |
+
"outputs": []
|
| 373 |
+
},
|
| 374 |
+
{
|
| 375 |
+
"cell_type": "code",
|
| 376 |
+
"metadata": {
|
| 377 |
+
"id": "M4UPf3Waexq0"
|
| 378 |
+
},
|
| 379 |
+
"source": [
|
| 380 |
+
"class FlaxGPT2ForMultipleChoice(FlaxNewModel):\n",
|
| 381 |
+
" module_class = FlaxGPT2ForMultipleChoiceModule"
|
| 382 |
+
],
|
| 383 |
+
"execution_count": 9,
|
| 384 |
+
"outputs": []
|
| 385 |
+
},
|
| 386 |
+
{
|
| 387 |
+
"cell_type": "code",
|
| 388 |
+
"metadata": {
|
| 389 |
+
"id": "roQ3vls4e4TH"
|
| 390 |
+
},
|
| 391 |
+
"source": [
|
| 392 |
+
"model = FlaxGPT2ForMultipleChoice.from_pretrained('gpt2')"
|
| 393 |
+
],
|
| 394 |
+
"execution_count": null,
|
| 395 |
+
"outputs": []
|
| 396 |
+
},
|
| 397 |
+
{
|
| 398 |
+
"cell_type": "code",
|
| 399 |
+
"metadata": {
|
| 400 |
+
"id": "E9qOSaaie417"
|
| 401 |
+
},
|
| 402 |
+
"source": [
|
| 403 |
+
"input_ids=jnp.ones((1,2,11))\n",
|
| 404 |
+
"attention_mask=jnp.ones((1,2,11))"
|
| 405 |
+
],
|
| 406 |
+
"execution_count": 12,
|
| 407 |
+
"outputs": []
|
| 408 |
+
},
|
| 409 |
+
{
|
| 410 |
+
"cell_type": "code",
|
| 411 |
+
"metadata": {
|
| 412 |
+
"colab": {
|
| 413 |
+
"base_uri": "https://localhost:8080/",
|
| 414 |
+
"height": 409
|
| 415 |
+
},
|
| 416 |
+
"id": "am7hYv8auWVy",
|
| 417 |
+
"outputId": "0c8192ca-a0ab-432e-d483-46f8a2cc2576"
|
| 418 |
+
},
|
| 419 |
+
"source": [
|
| 420 |
+
"out1 = model(input_ids, attention_mask)"
|
| 421 |
+
],
|
| 422 |
+
"execution_count": 13,
|
| 423 |
+
"outputs": [
|
| 424 |
+
{
|
| 425 |
+
"output_type": "stream",
|
| 426 |
+
"text": [
|
| 427 |
+
"(1, 2, 11)\n",
|
| 428 |
+
"attn not\n",
|
| 429 |
+
"ggg\n",
|
| 430 |
+
"abc\n",
|
| 431 |
+
"(1, 2, 11)\n",
|
| 432 |
+
"attn not\n"
|
| 433 |
+
],
|
| 434 |
+
"name": "stdout"
|
| 435 |
+
},
|
| 436 |
+
{
|
| 437 |
+
"output_type": "error",
|
| 438 |
+
"ename": "ValueError",
|
| 439 |
+
"evalue": "ignored",
|
| 440 |
+
"traceback": [
|
| 441 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 442 |
+
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
| 443 |
+
"\u001b[0;32m<ipython-input-13-6be36035677e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mout1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
| 444 |
+
"\u001b[0;32m<ipython-input-6-de553f26d169>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input_ids, attention_mask, position_ids, params, past_key_values, dropout_rng, train, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mrngs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrngs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m \u001b[0mmutable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmutable\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 115\u001b[0m )\n\u001b[1;32m 116\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cache'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 445 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, variables, rngs, method, mutable, capture_intermediates, *args, **kwargs)\u001b[0m\n\u001b[1;32m 964\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0mmutable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmutable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapture_intermediates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcapture_intermediates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m )(variables, *args, **kwargs, rngs=rngs)\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 968\u001b[0m def init_with_output(self,\n",
|
| 446 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/core/scope.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(variables, rngs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 685\u001b[0m **kwargs) -> Union[Any, Tuple[Any, VariableDict]]:\n\u001b[1;32m 686\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrngs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrngs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmutable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmutable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemporary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 688\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmutable\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmutable_variables\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",
|
| 447 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mscope_fn\u001b[0;34m(scope, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1214\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapture_intermediates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1215\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1217\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\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",
|
| 448 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mwrapped_module_method\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 284\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0mfilter_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 449 |
+
"\u001b[0;32m<ipython-input-8-2c21e4c966c8>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input_ids, attention_mask, position_ids, return_dict, deterministic, *args)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'abc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpt2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mposition_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\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 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 450 |
+
"\u001b[0;32m<ipython-input-6-de553f26d169>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input_ids, attention_mask, position_ids, params, past_key_values, dropout_rng, train, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mrngs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrngs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m \u001b[0mmutable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmutable\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 115\u001b[0m )\n\u001b[1;32m 116\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cache'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 451 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, variables, rngs, method, mutable, capture_intermediates, *args, **kwargs)\u001b[0m\n\u001b[1;32m 964\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0mmutable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmutable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapture_intermediates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcapture_intermediates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m )(variables, *args, **kwargs, rngs=rngs)\n\u001b[0m\u001b[1;32m 967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 968\u001b[0m def init_with_output(self,\n",
|
| 452 |
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"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/core/scope.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(variables, rngs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 685\u001b[0m **kwargs) -> Union[Any, Tuple[Any, VariableDict]]:\n\u001b[1;32m 686\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrngs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrngs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmutable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmutable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemporary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 688\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmutable\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmutable_variables\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",
|
| 453 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mscope_fn\u001b[0;34m(scope, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1214\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapture_intermediates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1215\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1217\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\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",
|
| 454 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mwrapped_module_method\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 284\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0mfilter_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 455 |
+
"\u001b[0;32m<ipython-input-7-b2eaa3f7b251>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input_ids, attention_mask, position_ids, deterministic, init_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\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 50\u001b[0m )\n\u001b[1;32m 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 456 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mwrapped_module_method\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 284\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0mfilter_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 457 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/gpt2/modeling_flax_gpt2.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, hidden_states, attention_mask, deterministic, init_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 452\u001b[0m \u001b[0mdeterministic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeterministic\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 453\u001b[0m \u001b[0minit_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minit_cache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\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 455\u001b[0m )\n\u001b[1;32m 456\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 458 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mwrapped_module_method\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 284\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0mfilter_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 459 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/gpt2/modeling_flax_gpt2.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, hidden_states, attention_mask, deterministic, init_cache, output_attentions)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0mdeterministic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeterministic\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0minit_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minit_cache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\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 288\u001b[0m )\n\u001b[1;32m 289\u001b[0m \u001b[0;31m# residual connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 460 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/flax/linen/module.py\u001b[0m in \u001b[0;36mwrapped_module_method\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 284\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0mfilter_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture_stack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 461 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/gpt2/modeling_flax_gpt2.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, hidden_states, attention_mask, deterministic, init_cache, output_attentions)\u001b[0m\n\u001b[1;32m 177\u001b[0m ):\n\u001b[1;32m 178\u001b[0m \u001b[0mqkv_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_attn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqkv_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\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 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split_heads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 462 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/jax/_src/numpy/lax_numpy.py\u001b[0m in \u001b[0;36msplit\u001b[0;34m(ary, indices_or_sections, axis)\u001b[0m\n\u001b[1;32m 1806\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_wraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1807\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_or_sections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\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-> 1808\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"split\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_or_sections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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 1809\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1810\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_split_on_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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",
|
| 463 |
+
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/jax/_src/numpy/lax_numpy.py\u001b[0m in \u001b[0;36m_split\u001b[0;34m(op, ary, indices_or_sections, axis)\u001b[0m\n\u001b[1;32m 1798\u001b[0m + ((r + 1) * (part_size + 1) - 1)])\n\u001b[1;32m 1799\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1800\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"array split does not result in an equal division\"\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 1801\u001b[0m \u001b[0mstarts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mends\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1802\u001b[0m \u001b[0m_subval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0msubvals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\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",
|
| 464 |
+
"\u001b[0;31mValueError\u001b[0m: array split does not result in an equal division"
|
| 465 |
+
]
|
| 466 |
+
}
|
| 467 |
+
]
|
| 468 |
+
}
|
| 469 |
+
]
|
| 470 |
+
}
|