mirror of
https://github.com/wassname/prob_jsonformer.git
synced 2026-09-09 11:29:57 +08:00
add (1) better logging (2) better string generation: dont rely on model for starting " in string (3) support T5ForConditionalGeneration
This commit is contained in:
1rgs
Vendored
+3
@@ -0,0 +1,3 @@
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{
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"editor.formatOnSave": true,
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}
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+147
-11
@@ -37,13 +37,37 @@
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Generating...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Generating...\n",
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"{\n",
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" temperature: \u001b[32m15.0\u001b[0m\n",
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" name: \u001b[32m\"Mickey Mouse\"\u001b[0m,\n",
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" age: \u001b[32m30.0\u001b[0m,\n",
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" description: \u001b[32m\"A mouse like object\"\u001b[0m\n",
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"}\n"
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]
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}
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@@ -52,18 +76,21 @@
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"from jsonformer.format import highlight_values\n",
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"from jsonformer.main import Jsonformer\n",
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"\n",
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"weather_schema = {\n",
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"mickey_mouse_schema = {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"temperature\": {\"type\": \"number\"},\n",
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" \"name\": {\"type\": \"string\"},\n",
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" \"age\": {\"type\": \"number\"},\n",
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" \"description\": {\"type\": \"string\"},\n",
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" },\n",
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"}\n",
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"\n",
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"builder = Jsonformer(\n",
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" model=model,\n",
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" tokenizer=tokenizer,\n",
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" json_schema=weather_schema,\n",
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" prompt=\"today's temperature in celsius is\",\n",
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" json_schema=mickey_mouse_schema,\n",
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" prompt=\"write a description about mickey mouse\",\n",
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" max_string_token_length=100,\n",
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")\n",
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"\n",
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"print(\"Generating...\")\n",
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@@ -77,14 +104,38 @@
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Generating...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Generating...\n",
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"{\n",
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" make: \u001b[32m\"audi\"\u001b[0m,\n",
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" model: \u001b[32m\"model a4\"\u001b[0m,\n",
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" model: \u001b[32m\"a4\"\u001b[0m,\n",
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" year: \u001b[32m2016.0\u001b[0m,\n",
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" colors_available: [\n",
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" \u001b[32m\"blue\"\u001b[0m\n",
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@@ -122,10 +173,95 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": []
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Generating...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
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"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" car: {\n",
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" make: \u001b[32m\"Rolls Royce\"\u001b[0m,\n",
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" model: \u001b[32m\"Phantom\"\u001b[0m,\n",
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" year: \u001b[32m2016.0\u001b[0m,\n",
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" colors: [\n",
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" \u001b[32m\"Gold\"\u001b[0m\n",
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" ],\n",
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" features: {\n",
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" audio: {\n",
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" brand: \u001b[32m\"Mercedes\"\u001b[0m,\n",
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" speakers: \u001b[32m2.0\u001b[0m,\n",
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" hasBluetooth: \u001b[32mTrue\u001b[0m\n",
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" },\n",
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" safety: {\n",
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" airbags: \u001b[32m2.0\u001b[0m,\n",
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" parkingSensors: \u001b[32mTrue\u001b[0m,\n",
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" laneAssist: \u001b[32mTrue\u001b[0m\n",
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" },\n",
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" performance: {\n",
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" engine: \u001b[32m\"Mercedes-Benz OM615\"\u001b[0m,\n",
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" horsepower: \u001b[32m350.0\u001b[0m,\n",
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" topSpeed: \u001b[32m220.0\u001b[0m\n",
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" }\n",
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" }\n",
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" },\n",
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" owner: {\n",
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" firstName: \u001b[32m\"John\"\u001b[0m,\n",
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" lastName: \u001b[32m\"Doe\"\u001b[0m,\n",
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" age: \u001b[32m40.0\u001b[0m\n",
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" }\n",
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"}\n"
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]
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}
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],
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"source": [
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"complex_car = {\"type\": \"object\", \"properties\": {\"car\": {\"type\": \"object\", \"properties\": {\"make\": {\"type\": \"string\"}, \"model\": {\"type\": \"string\"}, \"year\": {\"type\": \"number\"}, \"colors\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}, \"features\": {\"type\": \"object\", \"properties\": {\"audio\": {\"type\": \"object\", \"properties\": {\"brand\": {\"type\": \"string\"}, \"speakers\": {\"type\": \"number\"}, \"hasBluetooth\": {\"type\": \"boolean\"}}}, \"safety\": {\"type\": \"object\", \"properties\": {\"airbags\": {\"type\": \"number\"}, \"parkingSensors\": {\"type\": \"boolean\"}, \"laneAssist\": {\"type\": \"boolean\"}}}, \"performance\": {\"type\": \"object\", \"properties\": {\"engine\": {\"type\": \"string\"}, \"horsepower\": {\"type\": \"number\"}, \"topSpeed\": {\"type\": \"number\"}}}}}}}, \"owner\": {\"type\": \"object\", \"properties\": {\"firstName\": {\"type\": \"string\"}, \"lastName\": {\"type\": \"string\"}, \"age\": {\"type\": \"number\"}}}}}\n",
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"builder = Jsonformer(\n",
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" model=model,\n",
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" tokenizer=tokenizer,\n",
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" json_schema=complex_car,\n",
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" prompt=\"generate an example Rolls Royce Phantom\",\n",
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")\n",
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"\n",
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"print(\"Generating...\")\n",
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"output = builder()\n",
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"\n",
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"highlight_values(output)\n"
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]
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}
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],
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"metadata": {
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@@ -1,48 +0,0 @@
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from jsonformer.format import highlight_values
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from jsonformer.main import Jsonformer
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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)
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weather_schema = {
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"type": "object",
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"properties": {
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"temperature": {"type": "number"},
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"humidity": {
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"type": "number",
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},
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"wind_speed": {
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"type": "object",
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"properties": {
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"value": {"type": "number"},
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"unit": {"type": "string"},
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},
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},
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},
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}
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print("Loading model and tokenizer...")
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model_name = "databricks/dolly-v2-12b"
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model = AutoModelForCausalLM.from_pretrained(
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model_name, use_cache=True, device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_cache=True)
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print("Loaded model and tokenizer")
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builder = Jsonformer(
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model=model,
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tokenizer=tokenizer,
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json_schema=weather_schema,
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prompt="generate the weather",
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)
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print("Generating...")
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output = builder()
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highlight_values(
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output,
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)
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@@ -3,6 +3,27 @@ from transformers import PreTrainedTokenizer, LogitsWarper, StoppingCriteria
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import torch
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class StringStoppingCriteria(StoppingCriteria):
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def __init__(self, tokenizer: PreTrainedTokenizer, prompt_length: int):
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self.tokenizer = tokenizer
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self.prompt_length = prompt_length
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def __call__(
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self,
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input_ids: torch.LongTensor,
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_,
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) -> bool:
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if len(input_ids[0]) <= self.prompt_length:
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return False
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last_token_id = input_ids[0][-1]
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last_token = self.tokenizer.decode(last_token_id, skip_special_tokens=True)
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result = '"' in last_token
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return result
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class NumberStoppingCriteria(StoppingCriteria):
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def __init__(
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self,
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+55
-27
@@ -1,6 +1,11 @@
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from typing import List, Union, Dict, Any
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from jsonformer.logits_processors import NumberStoppingCriteria, OutputNumbersTokens
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from jsonformer.logits_processors import (
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NumberStoppingCriteria,
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OutputNumbersTokens,
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StringStoppingCriteria,
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)
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from termcolor import cprint
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from transformers import PreTrainedModel, PreTrainedTokenizer
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import json
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@@ -38,13 +43,18 @@ class Jsonformer:
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self.temperature = temperature
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self.max_string_token_length = max_string_token_length
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def debug(self, *args, **kwargs):
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def debug(self, caller: str, value: str, is_prompt: bool = False):
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if self.debug_on:
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print(*args, **kwargs)
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if is_prompt:
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cprint(caller, "green", end=" ")
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cprint(value, "yellow")
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else:
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cprint(caller, "green", end=" ")
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cprint(value, "blue")
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def generate_number(self, temperature: Union[float, None] = None, iterations=0):
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prompt = self.get_prompt()
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self.debug("[generate_number] prompt", prompt)
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self.debug("[generate_number]", prompt, is_prompt=True)
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input_tokens = self.tokenizer.encode(prompt, return_tensors="pt").to(
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self.model.device
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)
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@@ -60,10 +70,10 @@ class Jsonformer:
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pad_token_id=self.tokenizer.eos_token_id,
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)
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response = self.tokenizer.decode(response[0], skip_special_tokens=True)
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self.debug("[generate_number] response", response)
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response = response[len(prompt) :]
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response = response.strip().rstrip(".")
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self.debug("[generate_number]", response)
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try:
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return float(response)
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except ValueError:
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@@ -74,50 +84,67 @@ class Jsonformer:
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def generate_boolean(self) -> bool:
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prompt = self.get_prompt()
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self.debug("[generate_boolean] prompt", prompt)
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self.debug("[generate_boolean]", prompt, is_prompt=True)
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input_tensor = self.tokenizer.encode(prompt, return_tensors="pt")
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output = self.model.forward(input_tensor.to(self.model.device))
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logits = output.logits[0, -1]
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# todo: this assumes that "true" and "false" are both tokenized to a single token
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# this is probably not true for all tokenizers
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# this can be fixed by looking at only the first token of both "true" and "false"
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true_token_id = self.tokenizer.convert_tokens_to_ids("true")
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false_token_id = self.tokenizer.convert_tokens_to_ids("false")
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true_logits = logits[true_token_id]
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false_logits = logits[false_token_id]
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result = logits[true_token_id] > logits[false_token_id]
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if true_logits > false_logits:
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return True
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elif false_logits > true_logits:
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return False
|
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else:
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print("Failed to generate a valid boolean value")
|
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return None
|
||||
self.debug("[generate_boolean]", result)
|
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return result.item()
|
||||
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||||
def generate_string(self) -> str:
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prompt = self.get_prompt()
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self.debug("[generate_string] prompt", prompt)
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prompt = self.get_prompt() + '"'
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self.debug("[generate_string]", prompt, is_prompt=True)
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input_tokens = self.tokenizer.encode(prompt, return_tensors="pt").to(
|
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self.model.device
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)
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response = self.model.generate(
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self.tokenizer.encode(prompt, return_tensors="pt").to(self.model.device),
|
||||
input_tokens,
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max_new_tokens=self.max_string_token_length,
|
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num_return_sequences=1,
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temperature=self.temperature,
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pad_token_id=self.tokenizer.eos_token_id,
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stopping_criteria=[
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StringStoppingCriteria(self.tokenizer, len(input_tokens[0]))
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],
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||||
)
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response = self.tokenizer.decode(response[0], skip_special_tokens=True)
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||||
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||||
response = response[len(prompt) :].strip()
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# Some models output the prompt as part of the response
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# This removes the prompt from the response if it is present
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||||
if (
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||||
len(response[0]) >= len(input_tokens[0])
|
||||
and (response[0][: len(input_tokens[0])] == input_tokens).all()
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||||
):
|
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response = response[0][len(input_tokens[0]) :]
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||||
if response.shape[0] == 1:
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response = response[0]
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||||
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self.debug("[generate_string] response", response)
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||||
split = response.split('"')
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assert len(split) >= 2
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return split[1]
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response = self.tokenizer.decode(response, skip_special_tokens=True)
|
||||
|
||||
self.debug("[generate_string]", "|" + response + "|")
|
||||
|
||||
if response.count('"') < 1:
|
||||
raise ValueError(
|
||||
"Failed to generate a valid string, try increasing max_string_token_length"
|
||||
)
|
||||
|
||||
return response.split('"')[0].strip()
|
||||
|
||||
def generate_object(
|
||||
self, properties: Dict[str, Any], obj: Dict[str, Any]
|
||||
) -> Dict[str, Any]:
|
||||
# self.debug("[generate_object] properties", properties)
|
||||
for key, schema in properties.items():
|
||||
self.debug("[generate_object] generating value for", key)
|
||||
obj[key] = self.generate_value(schema, obj, key)
|
||||
return obj
|
||||
|
||||
@@ -162,6 +189,7 @@ class Jsonformer:
|
||||
|
||||
def generate_array(self, item_schema: Dict[str, Any], obj: Dict[str, Any]) -> list:
|
||||
for _ in range(self.max_array_length):
|
||||
# forces array to have at least one element
|
||||
element = self.generate_value(item_schema, obj)
|
||||
obj[-1] = element
|
||||
|
||||
|
||||
Generated
+14
-1
@@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "accelerate"
|
||||
@@ -1597,6 +1597,10 @@ category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "torch-2.0.0-1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:c9090bda7d2eeeecd74f51b721420dbeb44f838d4536cc1b284e879417e3064a"},
|
||||
{file = "torch-2.0.0-1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:bd42db2a48a20574d2c33489e120e9f32789c4dc13c514b0c44272972d14a2d7"},
|
||||
{file = "torch-2.0.0-1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:8969aa8375bcbc0c2993e7ede0a7f889df9515f18b9b548433f412affed478d9"},
|
||||
{file = "torch-2.0.0-1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:ab2da16567cb55b67ae39e32d520d68ec736191d88ac79526ca5874754c32203"},
|
||||
{file = "torch-2.0.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:7a9319a67294ef02459a19738bbfa8727bb5307b822dadd708bc2ccf6c901aca"},
|
||||
{file = "torch-2.0.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:9f01fe1f6263f31bd04e1757946fd63ad531ae37f28bb2dbf66f5c826ee089f4"},
|
||||
{file = "torch-2.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:527f4ae68df7b8301ee6b1158ca56350282ea633686537b30dbb5d7b4a52622a"},
|
||||
@@ -1775,6 +1779,15 @@ category = "dev"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "triton-2.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:38806ee9663f4b0f7cd64790e96c579374089e58f49aac4a6608121aa55e2505"},
|
||||
{file = "triton-2.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:226941c7b8595219ddef59a1fdb821e8c744289a132415ddd584facedeb475b1"},
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||||
{file = "triton-2.0.0-1-cp36-cp36m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4c9fc8c89874bc48eb7e7b2107a9b8d2c0bf139778637be5bfccb09191685cfd"},
|
||||
{file = "triton-2.0.0-1-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d2684b6a60b9f174f447f36f933e9a45f31db96cb723723ecd2dcfd1c57b778b"},
|
||||
{file = "triton-2.0.0-1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9d4978298b74fcf59a75fe71e535c092b023088933b2f1df933ec32615e4beef"},
|
||||
{file = "triton-2.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:74f118c12b437fb2ca25e1a04759173b517582fcf4c7be11913316c764213656"},
|
||||
{file = "triton-2.0.0-1-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9618815a8da1d9157514f08f855d9e9ff92e329cd81c0305003eb9ec25cc5add"},
|
||||
{file = "triton-2.0.0-1-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1aca3303629cd3136375b82cb9921727f804e47ebee27b2677fef23005c3851a"},
|
||||
{file = "triton-2.0.0-1-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e3e13aa8b527c9b642e3a9defcc0fbd8ffbe1c80d8ac8c15a01692478dc64d8a"},
|
||||
{file = "triton-2.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f05a7e64e4ca0565535e3d5d3405d7e49f9d308505bb7773d21fb26a4c008c2"},
|
||||
{file = "triton-2.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bb4b99ca3c6844066e516658541d876c28a5f6e3a852286bbc97ad57134827fd"},
|
||||
{file = "triton-2.0.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47b4d70dc92fb40af553b4460492c31dc7d3a114a979ffb7a5cdedb7eb546c08"},
|
||||
|
||||
Reference in New Issue
Block a user