{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{user.username}\n"
]
}
],
"source": [
"from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"Salesforce/codet5-base\")\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(\"Salesforce/codet5-base\")\n",
"\n",
"text = \"def greet(user): print(f'hello <extra_id_0>!')\"\n",
"input_ids = tokenizer(text, return_tensors=\"pt\").input_ids\n",
"\n",
"# simply generate a single sequence\n",
"generated_ids = model.generate(input_ids, max_length=8)\n",
"print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))\n",
"# this prints \"{user.username}\""
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"import ast\n",
"\n",
"def filter_codes(codes):\n",
" codes = list(set(codes))\n",
" new_codes = []\n",
" for code in codes:\n",
" if ';' in code:\n",
" code = code[code.index(';'):]\n",
" try:\n",
" ast.parse(code)\n",
" except Exception:\n",
" continue\n",
" new_codes.append(code)\n",
" return new_codes"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def temp_value(value):\n",
" if value[0] == '[' and value[-1] == ']':\n",
" return '[<extra_id_0>]'\n",
" if value[0] == '\"' and value[-1] == '\"':\n",
" return '\"<extra_id_0>\"'\n",
" if value[0] == \"'\" and value[-1] == \"'\":\n",
" return \"'<extra_id_0>'\"\n",
" if value[0] == '{' and value[-1] == '}':\n",
" return '{<extra_id_0>}'\n",
" return '<extra_id_0>'\n",
"\n",
"def temp_var(var):\n",
" value = var[4:]\n",
" return var[:4] + temp_value(value)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"def make_code(start, code):\n",
" return f'def main(): {\"; \".join(start)}; {code}; return {\", \".join([v.split()[0] for v in start])}'"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"import ast\n",
"\n",
"def filter_codes(codes):\n",
" codes = list(set(codes))\n",
" new_codes = []\n",
" for code in codes:\n",
" if ';' in code:\n",
" code = code[code.index(';'):]\n",
" try:\n",
" ast.parse(code)\n",
" except Exception:\n",
" continue\n",
" new_codes.append(code)\n",
" return new_codes"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"def alt_from_code(code):\n",
" input_ids = tokenizer(code, return_tensors=\"pt\").input_ids\n",
" generated_ids = model.generate(input_ids, num_return_sequences=100, max_length=20, do_sample=True, temperature=1.0)\n",
" return filter_codes(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"import errno\n",
"import os\n",
"import signal\n",
"import functools\n",
"\n",
"class TimeoutError(Exception):\n",
" pass\n",
"\n",
"def timeout(seconds=10, error_message=os.strerror(errno.ETIME)):\n",
" def decorator(func):\n",
" def _handle_timeout(signum, frame):\n",
" raise TimeoutError(error_message)\n",
"\n",
" @functools.wraps(func)\n",
" def wrapper(*args, **kwargs):\n",
" signal.signal(signal.SIGALRM, _handle_timeout)\n",
" signal.alarm(seconds)\n",
" try:\n",
" result = func(*args, **kwargs)\n",
" finally:\n",
" signal.alarm(0)\n",
" return result\n",
"\n",
" return wrapper\n",
"\n",
" return decorator"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"def state_dict_to_str(state):\n",
" vals = []\n",
" for k, v in state.items():\n",
" vals.append(\n",
" f'{k} = {v}'\n",
" )\n",
" vals = sorted(vals)\n",
" return '; '.join(vals)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"@timeout(seconds=3)\n",
"def trace_code(start_state: str, code: str):\n",
" state = {}\n",
" try:\n",
" exec(start_state, {}, state)\n",
" except Exception:\n",
" return\n",
" start_state = dict(state)\n",
" try:\n",
" exec(code, {}, state)\n",
" except Exception:\n",
" return\n",
" return state_dict_to_str(start_state), code, state_dict_to_str(state)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'start': 'g = 100; i = 1; l = [1, 100, 1]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 200; i = 1; l = [1, 100, 1]'},\n",
" {'start': 'g = 100; i = 1; l = [1, 1]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 101; i = 1; l = [1, 1]'},\n",
" {'start': 'g = 100; i = 1; l = [1, 1, 1]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 101; i = 1; l = [1, 1, 1]'},\n",
" {'start': 'g = 100; i = 1; l = [100, 100]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 200; i = 1; l = [100, 100]'},\n",
" {'start': 'g = 100; i = 1; l = [50, 50, 50, 40]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 150; i = 1; l = [50, 50, 50, 40]'},\n",
" {'start': 'g = 100; i = 1; l = [0, 10]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 110; i = 1; l = [0, 10]'},\n",
" {'start': 'g = 100; i = 1; l = [100, 900, 10, 10]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 1000; i = 1; l = [100, 900, 10, 10]'},\n",
" {'start': 'g = 100; i = 1; l = [1, 1, 2]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 101; i = 1; l = [1, 1, 2]'},\n",
" {'start': 'g = 100; i = 1; l = [100, 100, 100, 0, 0]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 200; i = 1; l = [100, 100, 100, 0, 0]'}]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_working_alts(other_vars, var_alts, code):\n",
" rows = []\n",
" for alt in var_alts:\n",
" start = other_vars + [alt]\n",
" result = trace_code('; '.join(start), code)\n",
" if result:\n",
" rows.append({'start': result[0], 'code': result[1], 'end': result[2]})\n",
" return rows\n",
"\n",
"test_alt_vars = [\n",
" 'l = [1, 100, 1]',\n",
" 'l = [1, 1]',\n",
" 'l = [f]',\n",
" 'l = [1, 1, 1,]',\n",
" 'l = [i = 10]',\n",
" 'l = [100, 100]',\n",
" 'l = [l[i].max(), l[i].min()]',\n",
" 'l = [1]',\n",
" 'l = [50, 50, 50, 40]',\n",
" 'l = [0, 10]',\n",
" 'l = [100, 900, 10, 10]',\n",
" 'l = [i, 1, 2]',\n",
" 'l = [100, 100, 100, 0, 0]'\n",
"]\n",
"get_working_alts(['g = 100', 'i = 1'], test_alt_vars, 'g += l[i]')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(['g = 100', 'i = 1'],\n",
" ['l = [1, 2]',\n",
" 'l = [g, i, j]',\n",
" 'l = [i,g]',\n",
" 'l = [k, j, k2]',\n",
" 'l = [1.0, 0.01, 0.01, 0.01]',\n",
" 'l = [k, j]',\n",
" 'l = [j]',\n",
" 'l = [r, t, d]',\n",
" 'l = [g, i, l]',\n",
" 'l = [1]',\n",
" 'l = [l]',\n",
" 'l = [i, 1]',\n",
" 'l = [g + h*g + i*i]',\n",
" 'l = [g, i, 1]',\n",
" 'l = [b[i], b [ j ]]',\n",
" 'l = [2, 3, 3,]',\n",
" 'l = [a[g, e, c]]',\n",
" 'l = [b [ a ] [b[3]]]',\n",
" 'l = [g - 1, i]',\n",
" 'l = [2]',\n",
" 'l = [5]',\n",
" 'l = [6, 5, 3, 2]',\n",
" 'l = [b[g], b[i], b[g]]',\n",
" 'l = [b[i][j]]',\n",
" 'l = [c[j ], c[j+1 ]]',\n",
" 'l = [i, g * g]',\n",
" 'l = [g]',\n",
" 'l = [g, i, f]',\n",
" 'l = [a [ i ]]',\n",
" 'l = [1, 1, 1]',\n",
" 'l = [1, 4, 4]',\n",
" 'l = [b [j ]]',\n",
" 'l = [g, i]',\n",
" 'l = [1, 0, 0]',\n",
" 'l = [i, l]',\n",
" 'l = [0.0]',\n",
" 'l = [i]',\n",
" 'l = [g, i, 0]',\n",
" 'l = [{ i }]',\n",
" 'l = [i, v[0], v[1],l]',\n",
" 'l = [c[j ],]',\n",
" 'l = [0]',\n",
" 'l = [a [ 0 ]]',\n",
" 'l = [d, g, i]',\n",
" 'l = [g, g, i]',\n",
" 'l = [b[j ]]'])"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_alts_for_var(start_vars, alt_i, code):\n",
" start_vars[alt_i] = temp_var(start_vars[alt_i])\n",
" code = make_code(start_vars, row['code'])\n",
" var_alts = alt_from_code(code)\n",
" alt_var_temp = start_vars[alt_i]\n",
" del start_vars[alt_i]\n",
" return start_vars, [alt_var_temp.replace('<extra_id_0>', alt) for alt in var_alts]\n",
"\n",
"alt_start_vars, var_alts = get_alts_for_var(\n",
" ['g = 100', 'i = 1', 'l = [100, 100, 0, 0, -100, -100]'], 2, 'g += l[i]'\n",
")\n",
"alt_start_vars, var_alts"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(29,\n",
" [{'start': 'g = 50; i = 1; l = [100, 100, 0, 0, -100, -100]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 150; i = 1; l = [100, 100, 0, 0, -100, -100]'},\n",
" {'start': 'g = 10; i = 1; l = [100, 100, 0, 0, -100, -100]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 110; i = 1; l = [100, 100, 0, 0, -100, -100]'},\n",
" {'start': 'g = -3; i = 1; l = [100, 100, 0, 0, -100, -100]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 97; i = 1; l = [100, 100, 0, 0, -100, -100]'}])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def make_alternatives(row):\n",
" start_vars = row['start'].split('; ')\n",
"\n",
" alts = []\n",
" for i in range(len(start_vars)):\n",
" alt_start_vars, var_alts = get_alts_for_var(list(start_vars), i, row['code'])\n",
" alts += get_working_alts(alt_start_vars, var_alts, row['code'])\n",
"\n",
" return alts\n",
"\n",
"alts = make_alternatives(\n",
" {'start': 'g = 100; i = 1; l = [100, 100, 0, 0, -100, -100]',\n",
" 'code': 'g += l[i]',\n",
" 'end': 'g = 200; i = 1; l = [100, 100, 0, 0, -100, -100]'}\n",
")\n",
"len(alts), alts[:3]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 1/8968897 [00:09<24001:13:52, 9.63s/it]<string>:1: SyntaxWarning: 'int' object is not callable; perhaps you missed a comma?\n",
"<string>:1: SyntaxWarning: 'int' object is not callable; perhaps you missed a comma?\n",
"<string>:1: SyntaxWarning: 'int' object is not callable; perhaps you missed a comma?\n",
" 0%| | 22/8968897 [02:45<14831:12:33, 5.95s/it]<string>:1: SyntaxWarning: 'int' object is not callable; perhaps you missed a comma?\n",
"<string>:1: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?\n",
"<string>:1: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?\n",
" 0%| | 34/8968897 [04:26<26565:33:36, 10.66s/it]<string>:1: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?\n",
"<string>:1: SyntaxWarning: 'int' object is not callable; perhaps you missed a comma?\n",
" 0%| | 44/8968897 [10:01<34031:25:54, 13.66s/it] \n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [69]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m id_, line \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28menumerate\u001b[39m(f), total\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8968897\u001b[39m):\n\u001b[1;32m 19\u001b[0m row \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(line)\n\u001b[0;32m---> 20\u001b[0m alts \u001b[38;5;241m=\u001b[39m \u001b[43mmake_alternatives\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m new_rows \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [row] \u001b[38;5;241m+\u001b[39m alts\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_rows \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(new_rows) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m10_000\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
"Input \u001b[0;32mIn [68]\u001b[0m, in \u001b[0;36mmake_alternatives\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 4\u001b[0m alts \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(start_vars)):\n\u001b[0;32m----> 6\u001b[0m alt_start_vars, var_alts \u001b[38;5;241m=\u001b[39m \u001b[43mget_alts_for_var\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstart_vars\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcode\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m alts \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m get_working_alts(alt_start_vars, var_alts, row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcode\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m alts\n",
"Input \u001b[0;32mIn [67]\u001b[0m, in \u001b[0;36mget_alts_for_var\u001b[0;34m(start_vars, alt_i, code)\u001b[0m\n\u001b[1;32m 2\u001b[0m start_vars[alt_i] \u001b[38;5;241m=\u001b[39m temp_var(start_vars[alt_i])\n\u001b[1;32m 3\u001b[0m code \u001b[38;5;241m=\u001b[39m make_code(start_vars, row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcode\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m----> 4\u001b[0m var_alts \u001b[38;5;241m=\u001b[39m \u001b[43malt_from_code\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m alt_var_temp \u001b[38;5;241m=\u001b[39m start_vars[alt_i]\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m start_vars[alt_i]\n",
"Input \u001b[0;32mIn [62]\u001b[0m, in \u001b[0;36malt_from_code\u001b[0;34m(code)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21malt_from_code\u001b[39m(code):\n\u001b[1;32m 2\u001b[0m input_ids \u001b[38;5;241m=\u001b[39m tokenizer(code, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39minput_ids\n\u001b[0;32m----> 3\u001b[0m generated_ids \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_return_sequences\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdo_sample\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m filter_codes(tokenizer\u001b[38;5;241m.\u001b[39mbatch_decode(generated_ids, skip_special_tokens\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m))\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/autograd/grad_mode.py:28\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m():\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/generation_utils.py:1200\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, encoder_no_repeat_ngram_size, num_return_sequences, max_time, max_new_tokens, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, logits_processor, stopping_criteria, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, remove_invalid_values, synced_gpus, **model_kwargs)\u001b[0m\n\u001b[1;32m 1192\u001b[0m input_ids, model_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expand_inputs_for_generation(\n\u001b[1;32m 1193\u001b[0m input_ids,\n\u001b[1;32m 1194\u001b[0m expand_size\u001b[38;5;241m=\u001b[39mnum_return_sequences,\n\u001b[1;32m 1195\u001b[0m is_encoder_decoder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m 1196\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[1;32m 1197\u001b[0m )\n\u001b[1;32m 1199\u001b[0m \u001b[38;5;66;03m# 12. run sample\u001b[39;00m\n\u001b[0;32m-> 1200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1201\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1202\u001b[0m \u001b[43m \u001b[49m\u001b[43mlogits_processor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlogits_processor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1203\u001b[0m \u001b[43m \u001b[49m\u001b[43mlogits_warper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlogits_warper\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1204\u001b[0m \u001b[43m \u001b[49m\u001b[43mstopping_criteria\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstopping_criteria\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1205\u001b[0m \u001b[43m \u001b[49m\u001b[43mpad_token_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad_token_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1206\u001b[0m \u001b[43m \u001b[49m\u001b[43meos_token_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meos_token_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1207\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_scores\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_scores\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1208\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict_in_generate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict_in_generate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1209\u001b[0m \u001b[43m \u001b[49m\u001b[43msynced_gpus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynced_gpus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1210\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1211\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1213\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_beam_gen_mode:\n\u001b[1;32m 1214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_return_sequences \u001b[38;5;241m>\u001b[39m num_beams:\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/generation_utils.py:1710\u001b[0m, in \u001b[0;36mGenerationMixin.sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, **model_kwargs)\u001b[0m\n\u001b[1;32m 1707\u001b[0m model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_inputs_for_generation(input_ids, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs)\n\u001b[1;32m 1709\u001b[0m \u001b[38;5;66;03m# forward pass to get next token\u001b[39;00m\n\u001b[0;32m-> 1710\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1711\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1712\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 1713\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1714\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1715\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1717\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m synced_gpus \u001b[38;5;129;01mand\u001b[39;00m this_peer_finished:\n\u001b[1;32m 1718\u001b[0m cur_len \u001b[38;5;241m=\u001b[39m cur_len \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py:1616\u001b[0m, in \u001b[0;36mT5ForConditionalGeneration.forward\u001b[0;34m(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, inputs_embeds, decoder_inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1613\u001b[0m decoder_attention_mask \u001b[38;5;241m=\u001b[39m decoder_attention_mask\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecoder\u001b[38;5;241m.\u001b[39mfirst_device)\n\u001b[1;32m 1615\u001b[0m \u001b[38;5;66;03m# Decode\u001b[39;00m\n\u001b[0;32m-> 1616\u001b[0m decoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1617\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_input_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1618\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1619\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_inputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1620\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1621\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1622\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1623\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoder_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1624\u001b[0m \u001b[43m \u001b[49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attn_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1625\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1626\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1627\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1628\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1629\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1631\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m decoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;66;03m# Set device for model parallelism\u001b[39;00m\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py:1011\u001b[0m, in \u001b[0;36mT5Stack.forward\u001b[0;34m(self, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, inputs_embeds, head_mask, cross_attn_head_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 998\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m checkpoint(\n\u001b[1;32m 999\u001b[0m create_custom_forward(layer_module),\n\u001b[1;32m 1000\u001b[0m hidden_states,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1008\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;66;03m# past_key_value is always None with gradient checkpointing\u001b[39;00m\n\u001b[1;32m 1009\u001b[0m )\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1011\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1012\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1013\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1014\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_bias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1015\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1016\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_extended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1017\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_decoder_position_bias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_decoder_position_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1018\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1019\u001b[0m \u001b[43m \u001b[49m\u001b[43mcross_attn_layer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attn_layer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1020\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1021\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1022\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1023\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1025\u001b[0m \u001b[38;5;66;03m# layer_outputs is a tuple with:\u001b[39;00m\n\u001b[1;32m 1026\u001b[0m \u001b[38;5;66;03m# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)\u001b[39;00m\n\u001b[1;32m 1027\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py:672\u001b[0m, in \u001b[0;36mT5Block.forward\u001b[0;34m(self, hidden_states, attention_mask, position_bias, encoder_hidden_states, encoder_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, past_key_value, use_cache, output_attentions, return_dict)\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 670\u001b[0m query_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 672\u001b[0m cross_attention_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 673\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 674\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey_value_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 675\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 676\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_bias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_decoder_position_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attn_layer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 678\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_attn_past_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 679\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 680\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 681\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 682\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 683\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m cross_attention_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 685\u001b[0m \u001b[38;5;66;03m# clamp inf values to enable fp16 training\u001b[39;00m\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py:587\u001b[0m, in \u001b[0;36mT5LayerCrossAttention.forward\u001b[0;34m(self, hidden_states, key_value_states, attention_mask, position_bias, layer_head_mask, past_key_value, use_cache, query_length, output_attentions)\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 576\u001b[0m hidden_states,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 584\u001b[0m output_attentions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 585\u001b[0m ):\n\u001b[1;32m 586\u001b[0m normed_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer_norm(hidden_states)\n\u001b[0;32m--> 587\u001b[0m attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEncDecAttention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 588\u001b[0m \u001b[43m \u001b[49m\u001b[43mnormed_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 589\u001b[0m \u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 590\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey_value_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey_value_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_bias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 592\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 594\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 595\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 596\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 597\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 598\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m hidden_states \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(attention_output[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 599\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (layer_output,) \u001b[38;5;241m+\u001b[39m attention_output[\u001b[38;5;241m1\u001b[39m:] \u001b[38;5;66;03m# add attentions if we output them\u001b[39;00m\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py:525\u001b[0m, in \u001b[0;36mT5Attention.forward\u001b[0;34m(self, hidden_states, mask, key_value_states, position_bias, past_key_value, layer_head_mask, query_length, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 522\u001b[0m attn_weights \u001b[38;5;241m=\u001b[39m attn_weights \u001b[38;5;241m*\u001b[39m layer_head_mask\n\u001b[1;32m 524\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m unshape(torch\u001b[38;5;241m.\u001b[39mmatmul(attn_weights, value_states)) \u001b[38;5;66;03m# (batch_size, seq_length, dim)\u001b[39;00m\n\u001b[0;32m--> 525\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattn_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 527\u001b[0m present_key_value_state \u001b[38;5;241m=\u001b[39m (key_states, value_states) \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_decoder \u001b[38;5;129;01mand\u001b[39;00m use_cache) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 528\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (attn_output,) \u001b[38;5;241m+\u001b[39m (present_key_value_state,) \u001b[38;5;241m+\u001b[39m (position_bias,)\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/linear.py:103\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.pyenv/versions/3.9.9/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/functional.py:1848\u001b[0m, in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m 1846\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_variadic(\u001b[38;5;28minput\u001b[39m, weight, bias):\n\u001b[1;32m 1847\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(linear, (\u001b[38;5;28minput\u001b[39m, weight, bias), \u001b[38;5;28minput\u001b[39m, weight, bias\u001b[38;5;241m=\u001b[39mbias)\n\u001b[0;32m-> 1848\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"import json, gzip\n",
"from tqdm import tqdm\n",
"\n",
"\n",
"with open('data.single_start_alts.jsonl.gz', 'w') as f:\n",
" f.write('')\n",
"\n",
"\n",
"def write_rows_compressed(rows):\n",
" rows = [json.dumps(r) for r in rows]\n",
" with gzip.open('data.alts.jsonl.gz', 'ab') as f:\n",
" f.write('\\n'.join(rows).encode() + b'\\n')\n",
"\n",
"\n",
"# currently takes ~10 seconds per iteration for 89,68,897 samples so 1k days\n",
"with open('../data.jsonl', 'r', encoding=\"utf-8\") as f:\n",
" new_rows = []\n",
" for id_, line in tqdm(enumerate(f), total=8968897):\n",
" row = json.loads(line)\n",
" alts = make_alternatives(row)\n",
" new_rows += [row] + alts\n",
" if new_rows and len(new_rows) % 10_000 == 0:\n",
" write_rows_compressed(new_rows)\n",
" new_rows = []\n",
" break\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['1, 2',\n",
" '1, 0',\n",
" '1, 1, 1, 1',\n",
" '1, 1',\n",
" '\"ab\",i,2',\n",
" '0, 1',\n",
" '8',\n",
" '\"s\", \"m\", \"v\", \"r \"',\n",
" 'g, - p',\n",
" '1, 1, 1,',\n",
" '7, 5, 6',\n",
" 'g, i, l',\n",
" '1',\n",
" '1,1,2,3',\n",
" '1, 2, 2',\n",
" '\"ab\", \"aa\", \"ab\", \"aa\"',\n",
" '1, 2, 3, 4',\n",
" '\"ab\",\"ace\",\"ae\",\"ad\"',\n",
" 'i, i',\n",
" '\"ab\", \"a\", \"e\"',\n",
" '100, 100, 100',\n",
" '1,3,3,4,5,6,7,9,0',\n",
" '\" a\"',\n",
" '0, 1, 2',\n",
" '0, 1, 1, 1, 0',\n",
" '\"ab\", \"bal,ca\"',\n",
" 'g,i, l [ i ]',\n",
" '1, 3,4, 6',\n",
" 'a',\n",
" '1, 2, 3',\n",
" '9, 9',\n",
" '( 1)',\n",
" '2, - 1, - 1',\n",
" '0 | 1 | 0|0',\n",
" '{ 1 }',\n",
" 'i - 1',\n",
" 'o, l1, o2, l',\n",
" '\"ab\"',\n",
" '1, 1, 2',\n",
" 'g, i',\n",
" '0, 0',\n",
" '\"a\"',\n",
" 'i, l',\n",
" 'i',\n",
" '0,0',\n",
" '- l [ i ]',\n",
" '1, 2, 3, 1',\n",
" 'l[ i - 1 ]',\n",
" '\"1\",\"2\", \"3\",\"4\", \"5\"',\n",
" 'g, g, i']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"code ='def main(): g = \"ab\"; i = 1; l = [<extra_id_0>]; g += l[i]; return g, i, l'\n",
"\n",
"input_ids = tokenizer(code, return_tensors=\"pt\").input_ids\n",
"generated_ids = model.generate(input_ids, num_return_sequences=100, max_length=20, do_sample=True, temperature=1.0)\n",
"filter_codes(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))\n",
"\n",
"# 100 samples -> ~8 valid alternatives, 3.1s on macos CPU"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['<pad><s><extra_id_0>5<extra_id_1>g i l [ 0</s><pad><pad>',\n",
" '<pad><s><extra_id_0>0<extra_id_1>0, 0, 0</s><pad><pad>',\n",
" '<pad><s><extra_id_0>0<extra_id_1>1 1 2, 1</s><pad><pad>',\n",
" \"<pad><s><extra_id_0>'<extra_id_1>i</s><pad><pad><pad><pad><pad><pad>\",\n",
" '<pad><s><extra_id_0>0<extra_id_1>a t</s><pad><pad><pad><pad><pad>',\n",
" '<pad><s><extra_id_0>0.0<extra_id_1>e. f_i</s>',\n",
" '<pad><s><extra_id_0>\" \"<extra_id_1>1</s><pad><pad><pad><pad><pad>',\n",
" '<pad><s><extra_id_0>0<extra_id_1>n = 1 l =</s><pad><pad>',\n",
" '<pad><s><extra_id_0>0, 0, 1<extra_id_1>1</s><pad><pad>',\n",
" '<pad><s><extra_id_0>1<extra_id_1>k y y x z</s><pad><pad>']"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"code ='def main(): g = <extra_id_0>; i = 1; l = [<extra_id_1>]; g += l[i]; return g, i, l'\n",
"\n",
"input_ids = tokenizer(code, return_tensors=\"pt\").input_ids\n",
"generated_ids = model.generate(input_ids, num_return_sequences=10, max_length=20, do_sample=True, temperature=1.0)\n",
"tokenizer.batch_decode(generated_ids)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "ced6a873299cbeeefe969ab88294103b352f8c83b6537b9e08e8739795321d60"
},
"kernelspec": {
"display_name": "Python 3.9.9 64-bit ('3.9.9': pyenv)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.9"
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"orig_nbformat": 4
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"nbformat": 4,
"nbformat_minor": 2
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