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eval in post training

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Xi Yan 2025-02-28 11:16:01 -08:00
parent 6520baebed
commit bdc1fb1618
1 changed files with 6 additions and 3 deletions
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9
llama_stack/providers/inline/post_training/torchtune/datasets/format_adapter.py
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@ -10,16 +10,19 @@
# This source code is licensed under the BSD-style license found in the # This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. # LICENSE file in the root directory of this source tree.
import json
from typing import Any, Mapping from typing import Any, Mapping
from llama_stack.providers.utils.common.data_schema_validator import ColumnName from llama_stack.providers.utils.common.data_schema_validator import ColumnName
def llama_stack_instruct_to_torchtune_instruct(sample: Mapping[str, Any]) -> Mapping[str, Any]: def llama_stack_instruct_to_torchtune_instruct(
sample: Mapping[str, Any],
) -> Mapping[str, Any]:
assert ColumnName.chat_completion_input.value in sample and ColumnName.expected_answer.value in sample, ( assert ColumnName.chat_completion_input.value in sample and ColumnName.expected_answer.value in sample, (
"Invalid input row" "Invalid input row"
) )
input_messages = eval(str(sample[ColumnName.chat_completion_input.value])) input_messages = json.loads(sample[ColumnName.chat_completion_input.value])
assert len(input_messages) == 1, "llama stack intruct dataset format only supports 1 user message" assert len(input_messages) == 1, "llama stack intruct dataset format only supports 1 user message"
input_message = input_messages[0] input_message = input_messages[0]
@ -37,7 +40,7 @@ def llama_stack_instruct_to_torchtune_instruct(sample: Mapping[str, Any]) -> Map
def llama_stack_chat_to_torchtune_chat(sample: Mapping[str, Any]) -> Mapping[str, Any]: def llama_stack_chat_to_torchtune_chat(sample: Mapping[str, Any]) -> Mapping[str, Any]:
assert ColumnName.dialog.value in sample, "Invalid input row" assert ColumnName.dialog.value in sample, "Invalid input row"
role_map = {"user": "human", "assistant": "gpt"} role_map = {"user": "human", "assistant": "gpt"}
dialog = eval(str(sample[ColumnName.dialog.value])) dialog = json.loads(sample[ColumnName.dialog.value])
assert len(dialog) > 1, "dialog must have at least 2 messagse" assert len(dialog) > 1, "dialog must have at least 2 messagse"
roles = [] roles = []