merge remote main
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+72
-134
@@ -7,7 +7,7 @@ from torch import Tensor
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from pipeline.tokenize import AutoTokenizer
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from pipeline.strategies import PromptStrategy, ChatMLStrategy
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from pipeline.processors.base import BaseProcessor, ProcessorSchema, encode_with_mask
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from pipeline.processors.base import BaseProcessor, ProcessorSchema
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from pipeline.processors.factory import ProcessorFactory
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@@ -15,15 +15,15 @@ from pipeline.processors.factory import ProcessorFactory
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class SFTProcessor(BaseProcessor):
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"""Supervised fine-tuning data processor.
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Supports two input formats:
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Input formats:
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1. messages (recommended):
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``{"messages": [{"role": "user", "content": "..."},
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{"role": "assistant", "content": "..."}]}``
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Multi-turn and system prompts are supported.
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The tokenizer's ``apply_chat_template`` is used for rendering.
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Multi-turn and system prompts are supported. Each assistant
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turn gets ``loss_mask = 1``; all other roles get 0.
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2. legacy query/response:
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``{"query": "...", "response": "..."}``
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Falls back to the configured PromptStrategy (ChatML by default).
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Internally converted to messages.
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Output schema:
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- sequence: int32 tensor - Combined token IDs (prompt + response)
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@@ -63,39 +63,23 @@ class SFTProcessor(BaseProcessor):
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if "messages" in input_dict:
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return self._process_messages(input_dict["messages"])
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if "query" in input_dict and "response" in input_dict:
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return self._process_legacy(input_dict)
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return self._process_messages([
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{"role": "user", "content": input_dict["query"]},
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{"role": "assistant", "content": input_dict["response"]},
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])
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raise KeyError(
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"Input must contain 'messages' or 'query'/'response' pair"
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)
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def process_batch(self, input_dicts: List[Dict[str, Any]]) -> List[Dict[str, Tensor]]:
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results: List[Optional[Dict[str, Tensor]]] = [None] * len(input_dicts)
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message_indices = [i for i, item in enumerate(input_dicts) if "messages" in item]
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legacy_indices = [
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i
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for i, item in enumerate(input_dicts)
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if "messages" not in item and "query" in item and "response" in item
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]
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if len(message_indices) + len(legacy_indices) != len(input_dicts):
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raise KeyError("Input must contain 'messages' or 'query'/'response' pair")
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if message_indices:
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items = [input_dicts[i] for i in message_indices]
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batch_results = self._process_messages_batch(
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[item["messages"] for item in items]
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)
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for index, result in zip(message_indices, batch_results):
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results[index] = result
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if legacy_indices:
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items = [input_dicts[i] for i in legacy_indices]
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batch_results = self._process_legacy_batch(items)
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for index, result in zip(legacy_indices, batch_results):
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results[index] = result
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if any(result is None for result in results):
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raise RuntimeError("Batch processing did not produce all results")
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return results
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def _extract_messages(self, input_dict: Dict[str, Any]) -> Optional[List[Dict[str, str]]]:
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if "messages" in input_dict:
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return input_dict["messages"]
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if "query" in input_dict and "response" in input_dict:
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return [
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{"role": "user", "content": input_dict["query"]},
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{"role": "assistant", "content": input_dict["response"]},
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]
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return None
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def _process_messages(self, messages: List[Dict[str, str]]) -> Dict[str, Tensor]:
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if not messages:
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@@ -103,118 +87,72 @@ class SFTProcessor(BaseProcessor):
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if messages[-1]["role"] != "assistant":
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raise ValueError("Last message must have role 'assistant'")
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last_asst_idx = max(
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i for i, m in enumerate(messages) if m["role"] == "assistant"
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)
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strategy = self.strategy or ChatMLStrategy(self.tokenizer)
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full_text = self.tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=False
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)
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full_ids = self.tokenizer.encode(full_text, add_special_tokens=False)
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prompt, resp = strategy.format_messages(messages)
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prompt_text = self.tokenizer.apply_chat_template(
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messages[:last_asst_idx],
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tokenize=False,
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add_generation_prompt=True,
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)
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prompt_ids = self.tokenizer.encode(prompt_text, add_special_tokens=False)
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resp_ids = full_ids[len(prompt_ids) :]
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if not resp_ids:
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raise ValueError("Empty assistant response")
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tokens, loss_mask = encode_with_mask(prompt_ids, list(resp_ids))
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if self.max_seq_len and len(tokens) > self.max_seq_len:
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tokens = tokens[: self.max_seq_len]
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sequence = torch.tensor(prompt + resp, dtype=torch.int32)
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loss_mask = torch.zeros(len(sequence), dtype=torch.bool)
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loss_mask[len(prompt) :] = True
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if self.max_seq_len and len(sequence) > self.max_seq_len:
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sequence = sequence[: self.max_seq_len]
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loss_mask = loss_mask[: self.max_seq_len]
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position_ids = torch.arange(len(tokens), dtype=torch.int32)
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position_ids = torch.arange(len(sequence), dtype=torch.int32)
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return {
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"sequence": tokens,
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"sequence": sequence,
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"loss_mask": loss_mask,
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"position_ids": position_ids,
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}
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def _process_messages_batch(
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self, conversations: List[List[Dict[str, str]]]
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) -> List[Dict[str, Tensor]]:
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for messages in conversations:
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if not messages:
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raise ValueError("Messages list is empty")
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if messages[-1]["role"] != "assistant":
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raise ValueError("Last message must have role 'assistant'")
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def process_batch(self, input_dicts: List[Dict[str, Any]]) -> List[Optional[Dict[str, Tensor]]]:
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strategy = self.strategy or ChatMLStrategy(self.tokenizer)
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assistant_indices = [
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max(i for i, message in enumerate(messages) if message["role"] == "assistant")
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for messages in conversations
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]
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full_texts = [
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self.tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=False
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)
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for messages in conversations
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]
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prompt_texts = [
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self.tokenizer.apply_chat_template(
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messages[:assistant_idx], tokenize=False, add_generation_prompt=True
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)
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for messages, assistant_idx in zip(conversations, assistant_indices)
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]
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full_ids_batch = self.tokenizer.encode(full_texts, add_special_tokens=False)
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prompt_ids_batch = self.tokenizer.encode(prompt_texts, add_special_tokens=False)
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prompts_text: List[str] = []
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fulls_text: List[str] = []
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indices: List[int] = []
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results: List[Optional[Dict[str, Tensor]]] = [None] * len(input_dicts)
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results = []
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for full_ids, prompt_ids in zip(full_ids_batch, prompt_ids_batch):
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resp_ids = full_ids[len(prompt_ids) :]
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if not resp_ids:
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raise ValueError("Empty assistant response")
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tokens, loss_mask = encode_with_mask(prompt_ids, list(resp_ids))
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if self.max_seq_len and len(tokens) > self.max_seq_len:
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tokens = tokens[: self.max_seq_len]
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for i, d in enumerate(input_dicts):
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try:
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messages = self._extract_messages(d)
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if not messages or messages[-1]["role"] != "assistant":
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continue
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last_asst = max(j for j, m in enumerate(messages) if m["role"] == "assistant")
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prompt_text = self.tokenizer.apply_chat_template(
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messages[:last_asst], add_generation_prompt=True, tokenize=False
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)
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full_text = self.tokenizer.apply_chat_template(
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messages[: last_asst + 1], add_generation_prompt=False, tokenize=False
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)
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prompts_text.append(prompt_text)
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fulls_text.append(full_text)
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indices.append(i)
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except Exception:
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continue
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if not prompts_text:
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return results
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prompt_tokens_list = self.tokenizer.encode(prompts_text)
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full_tokens_list = self.tokenizer.encode(fulls_text)
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for j, idx in enumerate(indices):
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prompt_tokens = prompt_tokens_list[j]
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full_tokens = full_tokens_list[j]
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resp_tokens = full_tokens[len(prompt_tokens):]
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sequence = torch.tensor(prompt_tokens + resp_tokens, dtype=torch.int32)
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loss_mask = torch.zeros(len(sequence), dtype=torch.bool)
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loss_mask[len(prompt_tokens):] = True
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if self.max_seq_len and len(sequence) > self.max_seq_len:
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sequence = sequence[: self.max_seq_len]
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loss_mask = loss_mask[: self.max_seq_len]
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results.append(
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{
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"sequence": tokens,
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"loss_mask": loss_mask,
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"position_ids": torch.arange(len(tokens), dtype=torch.int32),
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}
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)
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return results
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position_ids = torch.arange(len(sequence), dtype=torch.int32)
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results[idx] = {
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"sequence": sequence,
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"loss_mask": loss_mask,
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"position_ids": position_ids,
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}
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def _process_legacy(self, input_dict: Dict[str, Any]) -> Dict[str, Tensor]:
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strategy = self.strategy or ChatMLStrategy(self.tokenizer)
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query_tokens = self.tokenizer.encode(input_dict["query"])
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response_tokens = self.tokenizer.encode(input_dict["response"])
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prompt = strategy.assemble_prompt(query_tokens)
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response = strategy.assemble_response(response_tokens)
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tokens, loss_mask = encode_with_mask(prompt, response)
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position_ids = torch.arange(len(tokens), dtype=torch.int32)
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return {"sequence": tokens, "loss_mask": loss_mask, "position_ids": position_ids}
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def _process_legacy_batch(
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self, input_dicts: List[Dict[str, Any]]
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) -> List[Dict[str, Tensor]]:
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strategy = self.strategy or ChatMLStrategy(self.tokenizer)
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query_batch = self.tokenizer.encode([item["query"] for item in input_dicts])
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response_batch = self.tokenizer.encode(
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[item["response"] for item in input_dicts]
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)
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results = []
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for query_tokens, response_tokens in zip(query_batch, response_batch):
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prompt = strategy.assemble_prompt(query_tokens)
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response = strategy.assemble_response(response_tokens)
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tokens, loss_mask = encode_with_mask(prompt, response)
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results.append(
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{
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"sequence": tokens,
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"loss_mask": loss_mask,
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"position_ids": torch.arange(len(tokens), dtype=torch.int32),
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}
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)
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return results
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@property
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