[ { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Bash", "tool_input": { "command": "cd /Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara && ruff check evaluation/evaluate.py 2>&1 | head -20", "description": "Check linting on evaluate.py after fix" }, "tool_response": { "stdout": "F401 [*] `transformers.AutoModel` imported but unused\n --> evaluation/evaluate.py:17:26\n |\n15 | import numpy as np\n16 | from accelerate import Accelerator, InitProcessGroupKwargs\n17 | from transformers import AutoModel\n | ^^^^^^^^^\n18 | from datasets import load_dataset\n19 | from tqdm import tqdm\n |\nhelp: Remove unused import: `transformers.AutoModel`\n\nF401 [*] `datasets.load_dataset` imported but unused\n --> evaluation/evaluate.py:18:22\n |\n16 | from accelerate import Accelerator, InitProcessGroupKwargs\n17 | from transformers import AutoModel\n18 | from datasets import load_dataset\n | ^^^^^^^^^^^^\n19 | from tqdm import tqdm", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_01GDCyi6pnySMxKhFDtrLM3T", "logged_at": "2025-12-23T14:08:36.290709" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Bash", "tool_input": { "command": "ruff check evaluation/evaluate.py --fix 2>&1", "description": "Auto-fix linting issues" }, "tool_response": { "stdout": "Found 4 errors (4 fixed, 0 remaining).", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_01GgjycGcb3RTb5WLqL4SynD", "logged_at": "2025-12-23T14:11:42.723000" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Bash", "tool_input": { "command": "ruff check evaluation/evaluate.py 2>&1 && echo \"\u2713 Linting passed\"", "description": "Verify linting passes" }, "tool_response": { "stdout": "All checks passed!\n\u2713 Linting passed", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_0113zw3E38RQnNEkFGvpCf2c", "logged_at": "2025-12-23T14:11:57.144939" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Bash", "tool_input": { "command": "ruff check --fix 2>&1 | head -30", "description": "Auto-fix all remaining linting issues" }, "tool_response": { "stdout": "F841 Local variable `B` is assigned to but never used\n --> openrlhf/datasets/sft_dataset.py:182:9\n |\n180 | \"\"\"Process batch for stage 2 training.\"\"\"\n181 | docs_list, questions, answers, data_types, pos_indices = batch_data\n182 | B = len(questions)\n | ^\n183 | \n184 | # Flatten documents for encoding\n |\nhelp: Remove assignment to unused variable `B`\n\nF841 Local variable `dschf` is assigned to but never used\n --> openrlhf/models/actor.py:65:17\n |\n63 | dschf = HfDeepSpeedConfig(ds_config)\n64 | else:\n65 | dschf = None\n | ^^^^^\n66 |\n67 | if load_in_4bit:\n |\nhelp: Remove assignment to unused variable `dschf`\n\nF841 Local variable `B` is assigned to but never used\n --> openrlhf/models/modeling_clara.py:1513:9\n |\n1511 | def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n1512 | \"\"\"Forward pass for stage 2 reasoning training.\"\"\"\n1513 | B = batch[\"labels\"].shape[0]", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_01Tb3fAa22YigHjFmNDwyoEh", "logged_at": "2025-12-23T14:12:22.128916" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Bash", "tool_input": { "command": "ruff check --select F401 --fix 2>&1", "description": "Fix all unused import errors" }, "tool_response": { "stdout": "All checks passed!", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_01MkjERoNTE63rGb553RBvWw", "logged_at": "2025-12-23T14:13:40.628342" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Bash", "tool_input": { "command": "ruff check --select F401 2>&1 && echo \"\u2713 All unused imports fixed\"", "description": "Verify no unused imports remain" }, "tool_response": { "stdout": "All checks passed!\n\u2713 All unused imports fixed", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_01W3CXmAogMTyBRaBUr3Ahps", "logged_at": "2025-12-23T14:14:15.402937" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Read", "tool_input": { "file_path": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/actor.py", "offset": 61, "limit": 10 }, "tool_response": { "type": "text", "file": { "filePath": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/actor.py", "content": " # https://huggingface.co/docs/transformers/deepspeed#non-trainer-deepspeed-integration\n if ds_config is not None and ds_config[\"zero_optimization\"][\"stage\"] == 3:\n dschf = HfDeepSpeedConfig(ds_config)\n else:\n dschf = None\n\n if load_in_4bit:\n assert bf16, \"we only support bnb_4bit_compute_dtype = bf16\"\n nf4_config = BitsAndBytesConfig(\n load_in_4bit=True,", "numLines": 10, "startLine": 61, "totalLines": 200 } }, "tool_use_id": "toolu_01Utrsy5YWyWy2EXUqRnMSH2", "logged_at": "2025-12-23T14:14:54.175089" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Read", "tool_input": { "file_path": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/modeling_clara.py", "offset": 1509, "limit": 10 }, "tool_response": { "type": "text", "file": { "filePath": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/modeling_clara.py", "content": " return loss, {\"logits\": [[]], \"topk_idx\": topk_idx, \"mse_loss\": mse_loss}\n\n def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 reasoning training.\"\"\"\n B = batch[\"labels\"].shape[0]\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)\n enc_attention_mask = batch[\"enc_attention_mask\"].to(self.decoder.device)\n dec_input_ids = batch[\"dec_input_ids\"].to(self.decoder.device)\n dec_attention_mask = batch[\"dec_attention_mask\"].to(self.decoder.device)\n labels = batch[\"labels\"].to(self.decoder.device)", "numLines": 10, "startLine": 1509, "totalLines": 1708 } }, "tool_use_id": "toolu_01UomuuN6xdwDLL65uWxJt1H", "logged_at": "2025-12-23T14:14:54.197656" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Read", "tool_input": { "file_path": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/datasets/sft_dataset.py", "offset": 178, "limit": 10 }, "tool_response": { "type": "text", "file": { "filePath": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/datasets/sft_dataset.py", "content": " \n def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 2 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n B = len(questions)\n \n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]\n \n # Prepare encoder inputs", "numLines": 10, "startLine": 178, "totalLines": 568 } }, "tool_use_id": "toolu_01ScHqQZe4jcfjQaUnZxrrLD", "logged_at": "2025-12-23T14:14:54.198449" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Edit", "tool_input": { "file_path": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/actor.py", "old_string": " if ds_config is not None and ds_config[\"zero_optimization\"][\"stage\"] == 3:\n dschf = HfDeepSpeedConfig(ds_config)\n else:\n dschf = None", "new_string": " if ds_config is not None and ds_config[\"zero_optimization\"][\"stage\"] == 3:\n _dschf = HfDeepSpeedConfig(ds_config)" }, "tool_response": { "filePath": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/actor.py", "oldString": " if ds_config is not None and ds_config[\"zero_optimization\"][\"stage\"] == 3:\n dschf = HfDeepSpeedConfig(ds_config)\n else:\n dschf = None", "newString": " if ds_config is not None and ds_config[\"zero_optimization\"][\"stage\"] == 3:\n _dschf = HfDeepSpeedConfig(ds_config)", "originalFile": "from typing import Optional\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nfrom peft import LoraConfig, TaskType, get_peft_model\nfrom peft.tuners.lora import LoraLayer\nfrom transformers import AutoModelForCausalLM, BitsAndBytesConfig\nfrom transformers.integrations.deepspeed import HfDeepSpeedConfig\n\nfrom .ring_attn_utils import gather_and_pad_tensor, unpad_and_slice_tensor\nfrom .utils import compute_entropy, log_probs_from_logits\n\n\nclass Actor(nn.Module):\n \"\"\"\n Base class for Actor models in reinforcement learning.\n\n This class serves as a foundation for implementing various actor models, which are responsible for selecting actions based on the policy learned from the environment.\n\n Args:\n pretrain_or_model (nn.Module): A pretrained model or a new model instance to be used as the actor.\n use_flash_attention_2 (bool, optional): Whether to utilize Flash Attention 2.0 for improved performance. Defaults to False.\n bf16 (bool, optional): Enable bfloat16 precision for model computations. Defaults to True.\n load_in_4bit (bool, optional): Load the model in 4-bit precision. Defaults to False.\n lora_rank (int, optional): Rank for LoRA adaptation. Defaults to 0.\n lora_alpha (int, optional): Alpha parameter for LoRA. Defaults to 16.\n lora_dropout (float, optional): Dropout rate for LoRA layers. Defaults to 0.\n target_modules (list, optional): List of target modules for applying LoRA. Defaults to None.\n ds_config (dict, optional): Configuration for DeepSpeed, enabling model partitioning across multiple GPUs. Defaults to None.\n device_map (dict, optional): Device mapping for loading the model onto specific devices. Defaults to None.\n packing_samples (bool, optional): Whether to pack samples during training. Defaults to False.\n temperature (float, optional): Temperature for action selection. Defaults to 1.0.\n use_liger_kernel (bool, optional): Whether to use Liger Kernel for the model. Defaults to False.\n \"\"\"\n\n def __init__(\n self,\n pretrain_or_model,\n use_flash_attention_2=False,\n bf16=True,\n load_in_4bit=False,\n lora_rank=0,\n lora_alpha=16,\n lora_dropout=0,\n target_modules=None,\n ds_config=None,\n device_map=None,\n packing_samples=False,\n temperature=1.0,\n use_liger_kernel=False,\n **kwargs,\n ) -> None:\n super().__init__()\n self.temperature = temperature\n\n if isinstance(pretrain_or_model, str):\n attn_implementation = \"flash_attention_2\" if use_flash_attention_2 else \"eager\"\n\n # Note: dschf is defined in function scope to avoid global effects\n # https://huggingface.co/docs/transformers/deepspeed#non-trainer-deepspeed-integration\n if ds_config is not None and ds_config[\"zero_optimization\"][\"stage\"] == 3:\n dschf = HfDeepSpeedConfig(ds_config)\n else:\n dschf = None\n\n if load_in_4bit:\n assert bf16, \"we only support bnb_4bit_compute_dtype = bf16\"\n nf4_config = BitsAndBytesConfig(\n load_in_4bit=True,\n bnb_4bit_quant_type=\"nf4\",\n bnb_4bit_use_double_quant=True,\n bnb_4bit_compute_dtype=torch.bfloat16,\n )\n else:\n nf4_config = None\n\n if use_liger_kernel:\n from liger_kernel.transformers import AutoLigerKernelForCausalLM\n\n model_class = AutoLigerKernelForCausalLM\n else:\n model_class = AutoModelForCausalLM\n\n self.model = model_class.from_pretrained(\n pretrain_or_model,\n trust_remote_code=True,\n attn_implementation=attn_implementation,\n quantization_config=nf4_config,\n torch_dtype=torch.bfloat16 if bf16 else \"auto\",\n device_map=device_map,\n )\n\n # LoRA\n if lora_rank > 0:\n # https://github.com/huggingface/peft/issues/137\n self.model.enable_input_require_grads()\n lora_config = LoraConfig(\n task_type=TaskType.CAUSAL_LM,\n r=lora_rank,\n lora_alpha=lora_alpha,\n target_modules=target_modules,\n lora_dropout=lora_dropout,\n bias=\"none\",\n )\n self.model = get_peft_model(self.model, lora_config)\n\n if load_in_4bit:\n for name, module in self.model.named_modules():\n if isinstance(module, LoraLayer):\n module = module.to(torch.bfloat16)\n if \"norm\" in name:\n module = module.to(torch.float32)\n if \"lm_head\" in name or \"embed_tokens\" in name:\n if hasattr(module, \"weight\"):\n module = module.to(torch.bfloat16)\n\n # MoE - balancing loss\n model_config = self.model.config.to_dict()\n if \"output_router_logits\" in model_config:\n print(\"[MoE] set output_router_logits as True\")\n self.model.config.output_router_logits = True\n\n # https://github.com/huggingface/transformers/issues/26877\n # Use `model.generate(use_cache=True)` instead.`\n self.model.config.use_cache = False\n\n # packing samples using Flash Attention 2\n self.packing_samples = packing_samples\n else:\n self.model = pretrain_or_model\n\n def forward(\n self,\n sequences: torch.LongTensor,\n action_mask: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n return_output=False,\n allgather_logits=False,\n return_logprobs=False,\n ring_attn_group: Optional[dist.ProcessGroup] = None,\n packed_seq_lens: Optional[list[int]] = None,\n return_entropy=False,\n ) -> torch.Tensor:\n \"\"\"Returns action log probs\"\"\"\n batch, seqlen = sequences.size()\n foward_attention_mask = attention_mask\n if self.packing_samples:\n sequences, position_ids, rolled_sequences, ring_attn_pad_len, indices = unpad_and_slice_tensor(\n sequences, attention_mask, ring_attn_group\n )\n foward_attention_mask = None\n else:\n # https://github.com/OpenRLHF/OpenRLHF/issues/217\n rolled_sequences = torch.roll(sequences, shifts=-1, dims=1)\n position_ids = attention_mask.long().cumsum(-1) - 1\n position_ids.masked_fill_(attention_mask == 0, 1)\n\n output = self.model(sequences, attention_mask=foward_attention_mask, position_ids=position_ids)\n # https://github.com/OpenRLHF/OpenRLHF/pull/634\n output[\"logits\"] = output[\"logits\"].to(torch.float32)\n\n if return_entropy:\n assert return_output\n entropy = compute_entropy(output[\"logits\"])\n if self.packing_samples:\n entropy = gather_and_pad_tensor(entropy, ring_attn_group, ring_attn_pad_len, indices, batch, seqlen)\n setattr(output, \"entropy\", entropy[:, :-1])\n\n return_action_log_probs = action_mask is not None\n if not return_action_log_probs and not return_logprobs:\n assert return_output\n if allgather_logits and self.packing_samples:\n output[\"logits\"] = gather_and_pad_tensor(\n output[\"logits\"], ring_attn_group, ring_attn_pad_len, indices, batch, seqlen\n )\n return output\n\n log_probs = log_probs_from_logits(output[\"logits\"], rolled_sequences, temperature=self.temperature)\n\n if self.packing_samples:\n log_probs = gather_and_pad_tensor(log_probs, ring_attn_group, ring_attn_pad_len, indices, batch, seqlen)\n\n log_probs = log_probs[:, :-1]\n if not return_action_log_probs and return_logprobs:\n return (log_probs, output) if return_output else log_probs\n\n action_log_probs = log_probs[:, -action_mask.shape[1] :] * action_mask.float()\n\n return (action_log_probs, output) if return_output else action_log_probs\n\n def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs={\"use_reentrant\": False}):\n self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)\n\n def gradient_checkpointing_disable(self):\n self.model.gradient_checkpointing_disable()\n\n def print_trainable_parameters(self):\n self.model.print_trainable_parameters()\n", "structuredPatch": [ { "oldStart": 60, "oldLines": 9, "newStart": 60, "newLines": 7, "lines": [ " # Note: dschf is defined in function scope to avoid global effects", " # https://huggingface.co/docs/transformers/deepspeed#non-trainer-deepspeed-integration", " if ds_config is not None and ds_config[\"zero_optimization\"][\"stage\"] == 3:", "- dschf = HfDeepSpeedConfig(ds_config)", "- else:", "- dschf = None", "+ _dschf = HfDeepSpeedConfig(ds_config)", " ", " if load_in_4bit:", " assert bf16, \"we only support bnb_4bit_compute_dtype = bf16\"" ] } ], "userModified": false, "replaceAll": false }, "tool_use_id": "toolu_01JFGFeGKH4jBAm27SDkNRgM", "logged_at": "2025-12-23T14:15:10.718971" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Edit", "tool_input": { "file_path": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/modeling_clara.py", "old_string": " def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 reasoning training.\"\"\"\n B = batch[\"labels\"].shape[0]\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)", "new_string": " def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 reasoning training.\"\"\"\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)" }, "tool_response": { "filePath": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/models/modeling_clara.py", "oldString": " def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 reasoning training.\"\"\"\n B = batch[\"labels\"].shape[0]\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)", "newString": " def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 reasoning training.\"\"\"\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)", "originalFile": "#\n# For licensing see accompanying LICENSE file.\n# Copyright (C) 2025 Apple Inc. All Rights Reserved.\n#\n\nimport warnings\nimport os\nimport torch\nimport gc\nimport time\nimport random\nimport requests\n\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn.functional import gelu\nfrom jinja2.exceptions import TemplateError\nfrom peft import LoraConfig\nfrom transformers import (\n AutoModelForCausalLM, \n AutoTokenizer, \n BitsAndBytesConfig, \n PreTrainedModel, \n PretrainedConfig, \n StoppingCriteria\n)\nfrom huggingface_hub import hf_hub_download\nfrom typing import List, Dict, Tuple\n\n# Environment setup\ntorch.set_printoptions(threshold=float(\"inf\"))\nos.environ[\"NCCL_TIMEOUT\"] = \"5400\"\nos.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n\n# Constants\nIGNORE_INDEX = -100\nPARAPHRASE_INSTRUCTIONS = [\n 'Background: {docs} means the same as',\n \"Background: {docs} Can you put the above sentences in your own terms?\",\n \"Background: {docs} Please provide a reinterpretation of the preceding background text.\",\n \"These two expressions are equivalent in essence:\\n(1) {docs}\\n(2)\",\n \"Background: {docs} is a paraphrase of what?\",\n \"Background: {docs} Could you give me a different version of the background sentences above?\",\n \"In other words, background: {docs} is just another way of saying:\",\n \"You're getting across the same point whether you say background: {docs} or\",\n \"Background: {docs} After unpacking the ideas in the background information above, we got:\",\n \"Background: {docs} Please offer a restatement of the background sentences I've just read.\",\n \"Background: {docs}, which also means:\",\n \"Strip away the mystery, and you'll find background: {docs} is simply another rendition of:\",\n \"The essence of background: {docs} is captured again in the following statement:\",\n]\n\n\nclass StopOnCriteria(StoppingCriteria):\n \"\"\"Custom stopping criteria for generation.\"\"\"\n \n def __init__(self, tokenizer, stop_strings: List[str] = None, stop_token_ids: List[int] = None):\n self.tokenizer = tokenizer\n self.stop_strings = stop_strings or []\n self.stop_token_ids = stop_token_ids or []\n self.reason = None\n\n def __call__(self, input_ids, scores, **kwargs):\n # Check if last token is in stop_token_ids\n last_token = input_ids[0, -1].item()\n if last_token in self.stop_token_ids:\n self.reason = f\"stop_token_{last_token}\"\n return True\n\n # Check if any stop_strings appear in generated text\n text = self.tokenizer.decode(input_ids[0], skip_special_tokens=False)\n for stop_str in self.stop_strings:\n if stop_str in text:\n self.reason = f\"stop_string_{stop_str}\"\n return True\n\n return False\n\n\nclass LlamaRMSNorm(nn.Module):\n \"\"\"Llama-style RMS normalization layer.\"\"\"\n \n def __init__(self, hidden_size: int, eps: float = 1e-6):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.variance_epsilon = eps\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n input_dtype = hidden_states.dtype\n hidden_states = hidden_states.to(torch.float32)\n variance = hidden_states.pow(2).mean(-1, keepdim=True)\n hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)\n return self.weight * hidden_states.to(input_dtype)\n\n\nclass Converter(nn.Module):\n \"\"\"Converter module for dimension transformation.\"\"\"\n \n def __init__(self, input_dim: int, output_dim: int):\n super().__init__()\n self.input_dim = input_dim\n self.output_dim = output_dim\n \n self.rms_norm = LlamaRMSNorm(input_dim)\n self.dense_in = nn.Linear(input_dim, output_dim)\n self.dense_out = nn.Linear(output_dim, output_dim)\n \n self._print_trainable_parameters()\n \n def _print_trainable_parameters(self):\n \"\"\"Print parameter statistics.\"\"\"\n trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)\n total_params = sum(p.numel() for p in self.parameters())\n print(f\"Converter trainable parameters: {trainable_params}, Total parameters: {total_params}\")\n \n def forward(self, embeddings: torch.Tensor) -> torch.Tensor:\n embeddings = self.rms_norm(embeddings)\n x = self.dense_in(embeddings)\n x = self.dense_out(gelu(x))\n return x.to(torch.float32)\n\n\nclass CLaRaConfig(PretrainedConfig):\n \"\"\"Configuration class for CLaRa model.\"\"\"\n \n model_type = \"CLaRa\"\n\n def __init__(self,\n decoder_model_name: str = \"meta-llama/Llama-2-7b-chat-hf\",\n doc_max_length: int = 128,\n quantization: str = 'no',\n sep: bool = False,\n compr_model_name: str = \"google-bert/bert-base-uncased\",\n compr_rate: int = 64,\n compr_n_layers: int = None,\n compr_every_n_layer: int = None,\n compr_base_model_name: str = 'mistralai/Mistral-7B-Instruct-v0.2',\n compr_rms_norm: bool = False,\n compr_mlp_hidden_dim: int = 8096,\n compr_use_mlp: bool = True,\n compr_linear_type: str = \"concat\",\n lora: bool = False,\n lora_compressor: bool = False,\n training_form: str = \"both\",\n training_stage: str = \"stage1\",\n generation_top_k: int = 1,\n lora_r: int = 16,\n lora_r_compressor: int = None,\n load_adapters: bool = True,\n kbtc_training: bool = False,\n optimize_mem_tokens: bool = False,\n different_mem_tokens: bool = False,\n attn_implementation: str = None,\n _attn_implementation_autoset: bool = True,\n ae_mode: str = \"token\",\n max_new_tokens: int = 128,\n stage2_retrieval_top_n: int = 1,\n load_pretrained_checkpoint: bool = False,\n device_map=None,\n auto_map: dict = {\n \"AutoConfig\": \"modeling_clara.CLaRaConfig\",\n \"AutoModel\": \"modeling_clara.CLaRa\"\n },\n **kwargs):\n super().__init__(**kwargs)\n\n self.decoder_model_name = decoder_model_name\n self.doc_max_length = doc_max_length\n self.quantization = quantization\n self.sep = sep\n\n self.compr_model_name = compr_model_name\n self.compr_rate = compr_rate\n self.compr_use_mlp = compr_use_mlp\n self.compr_mlp_hidden_dim = compr_mlp_hidden_dim\n self.compr_n_layers = compr_n_layers\n self.compr_every_n_layer = compr_every_n_layer\n self.compr_base_model_name = compr_base_model_name\n self.compr_rms_norm = compr_rms_norm\n self.compr_linear_type = compr_linear_type\n\n self.lora = lora\n self.lora_compressor = lora_compressor\n self.training_form = training_form\n self.lora_r = lora_r\n self.lora_r_compressor = lora_r_compressor or lora_r\n self.load_adapters = load_adapters\n self.optimize_mem_tokens = optimize_mem_tokens\n self.different_mem_tokens = different_mem_tokens\n self.kbtc_training = kbtc_training\n self.training_stage = training_stage\n self.device_map = device_map\n self.attn_implementation = attn_implementation\n self._attn_implementation_autoset = _attn_implementation_autoset\n self.ae_mode = ae_mode\n self.max_new_tokens = max_new_tokens\n self.auto_map = auto_map\n self.load_pretrained_checkpoint = load_pretrained_checkpoint\n\n self.generation_top_k = generation_top_k\n self.stage2_retrieval_top_n = stage2_retrieval_top_n\n \n if training_form == 'compressor':\n assert compr_model_name is not None and not self.lora\n\n\n# Utility functions\ndef remote_generate(docs: List[str], questions: List[str], api_url: str) -> List[str]:\n \"\"\"Generate responses using remote API.\"\"\"\n response = requests.post(\n f\"{api_url}/generate\",\n json={\"docs\": docs, \"questions\": questions}\n )\n return response.json()[\"texts\"]\n\n\ndef add_memory_tokens_to_inputs(input_ids: torch.Tensor, \n attention_mask: torch.Tensor, \n n_mem_tokens: int, \n tokenizer) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Add memory tokens to input sequences.\"\"\"\n assert len(tokenizer.mem_tokens) == n_mem_tokens\n \n mem_tokens = torch.stack([tokenizer.mem_token_ids_pt] * input_ids.size(0), 0)\n assert len(mem_tokens) == input_ids.size(0)\n assert len(mem_tokens[0]) == n_mem_tokens\n \n input_ids = torch.cat([input_ids, mem_tokens], dim=1)\n attention_mask = torch.cat([attention_mask, torch.ones(input_ids.size(0), n_mem_tokens)], dim=1)\n \n return input_ids, attention_mask\n\n\ndef build_pos_mask(pos_index: List[List[int]], N: int, device: torch.device) -> torch.Tensor:\n \"\"\"Build positive mask for retrieval training.\"\"\"\n if isinstance(pos_index, (list, tuple)):\n B = len(pos_index)\n mask = torch.zeros(B, N, dtype=torch.bool, device=device)\n for b, idxs in enumerate(pos_index):\n if len(idxs) > 0:\n mask[b, torch.as_tensor(idxs, device=device, dtype=torch.long)] = True\n return mask\n else: # tensor [B, M]\n B, M = pos_index.shape\n mask = torch.zeros(B, N, dtype=torch.bool, device=device)\n for m in range(M):\n col = pos_index[:, m]\n v = col >= 0\n if v.any():\n mask[v, col[v]] = True\n return mask\n\n\ndef differentiable_topk_top_1(logits: torch.Tensor, k: int, temperature: float = 1.0) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Implements differentiable top-1 selection using Gumbel-Softmax.\"\"\"\n y = logits / temperature\n y_soft = F.softmax(y, dim=-1).float()\n \n # Hard one-hot version\n index = y_soft.argmax(dim=-1, keepdim=True)\n y_hard = torch.zeros_like(y_soft).scatter_(-1, index, 1.0)\n \n # Straight-through estimator\n z = y_hard + y_soft - y_soft.detach()\n z = z.unsqueeze(1).to(logits.dtype)\n \n return z, index\n\n\ndef differentiable_topk(logits: torch.Tensor, k: int, temperature: float = 1.0) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Differentiable top-k selection.\"\"\"\n B, N = logits.shape\n perturbed = logits / max(temperature, 1e-6)\n \n # Hard top-k indices\n topk_vals, topk_idx = perturbed.topk(k, dim=-1)\n K_hard = torch.zeros(B, k, N, device=logits.device, dtype=logits.dtype)\n K_hard.scatter_(2, topk_idx.unsqueeze(-1), 1.0)\n \n # Soft distributions for each slot\n K_soft = torch.zeros_like(K_hard)\n taken = torch.zeros(B, N, device=logits.device, dtype=logits.dtype)\n \n for j in range(k):\n mask = (1.0 - taken.detach())\n masked = perturbed + (mask + 1e-8).log()\n pj = F.softmax(masked, dim=-1).float()\n K_soft[:, j, :] = pj\n taken = torch.clamp(taken + K_hard[:, j, :], max=1.0)\n \n # Straight-through estimator\n W = K_hard + (K_soft - K_soft.detach())\n return W, topk_idx\n\n\nclass CLaRa(PreTrainedModel):\n \"\"\"CLaRa: Unified Retrieval-Augmented Generation Model.\"\"\"\n \n config_class = CLaRaConfig\n \n def __init__(self, cfg: CLaRaConfig):\n super().__init__(cfg)\n self.decoder_model_name = cfg.decoder_model_name\n self.decoder = self._create_decoder(cfg)\n self.doc_max_length = cfg.doc_max_length\n \n print(f'Base decoder parameters: {self.decoder.num_parameters()}')\n \n # Model configuration\n self.compr_model_name = cfg.compr_model_name\n self.training_form = cfg.training_form\n self.lora = cfg.lora\n self.adapter_keys = []\n self.compr = None\n \n # Initialize LoRA adapters if needed\n if cfg.lora and not getattr(cfg, 'pure_inference', False):\n self._setup_lora_adapters(cfg)\n \n print(f'Model adapter keys: {self.adapter_keys}')\n \n # Initialize tokenizer and resize embeddings\n self.decoder_tokenizer = self._create_decoder_tokenizer(cfg)\n self.decoder.resize_token_embeddings(len(self.decoder_tokenizer))\n self._configure_generation_config()\n \n # Model parameters\n self.generation_top_k = cfg.generation_top_k\n self.training_stage = cfg.training_stage\n self.stage2_retrieval_top_n = cfg.stage2_retrieval_top_n\n self.sep = cfg.sep\n self.compr_rate = cfg.compr_rate\n self.local_rank = os.getenv('LOCAL_RANK', '0')\n \n self.n_mem_tokens = self.doc_max_length // self.compr_rate\n self.hidden_size = self.decoder.config.hidden_size\n \n # Setup adapters and memory token optimization\n if self.lora:\n self._setup_adapter_training()\n else:\n print(f'Total trainable parameters: {self.num_parameters(only_trainable=True)}')\n \n self._prepare_mem_tokens_optimization()\n \n # Retrieval configuration\n self.url_retrieval = \"http://127.0.0.1:5004/queries\"\n \n def _create_decoder(self, cfg: CLaRaConfig) -> AutoModelForCausalLM:\n \"\"\"Create and configure the decoder model.\"\"\"\n if not torch.cuda.is_available():\n return AutoModelForCausalLM.from_pretrained(\n cfg.decoder_model_name,\n torch_dtype=torch.bfloat16,\n resume_download=True,\n trust_remote_code=True,\n device_map=cfg.device_map\n )\n \n if cfg.quantization == \"no\":\n return AutoModelForCausalLM.from_pretrained(\n cfg.decoder_model_name,\n torch_dtype=torch.bfloat16,\n attn_implementation=cfg.attn_implementation,\n device_map=cfg.device_map\n )\n elif cfg.quantization == \"int4\":\n quant_config = BitsAndBytesConfig(\n load_in_4bit=True,\n bnb_4bit_quant_type='nf4',\n bnb_4bit_compute_dtype='bfloat16',\n )\n return AutoModelForCausalLM.from_pretrained(\n cfg.decoder_model_name,\n quantization_config=quant_config,\n attn_implementation=cfg.attn_implementation,\n torch_dtype=torch.bfloat16,\n resume_download=True,\n trust_remote_code=True,\n device_map=cfg.device_map\n )\n elif cfg.quantization == \"int8\":\n quant_config = BitsAndBytesConfig(\n load_in_8bit=True,\n llm_int8_enable_fp32_cpu_offload=True,\n bnb_4bit_compute_dtype='bfloat16',\n )\n return AutoModelForCausalLM.from_pretrained(\n cfg.decoder_model_name,\n quantization_config=quant_config,\n attn_implementation=cfg.attn_implementation,\n torch_dtype=torch.bfloat16,\n resume_download=True,\n trust_remote_code=True,\n device_map=cfg.device_map\n )\n else:\n raise NotImplementedError(f\"Quantization {cfg.quantization} not supported\")\n \n def _setup_lora_adapters(self, cfg: CLaRaConfig):\n \"\"\"Setup LoRA adapters based on training stage.\"\"\"\n peft_config = self._get_peft_config(lora_r=cfg.lora_r)\n \n if cfg.training_stage == \"stage1\" and cfg.load_adapters:\n print('Loading encoder and decoder adapter for stage1')\n self.decoder.add_adapter(peft_config, 'decoder_adapter')\n self.adapter_keys.append('decoder_adapter')\n self.decoder.add_adapter(peft_config, 'encoder_adapter')\n self.adapter_keys.append('encoder_adapter')\n elif cfg.training_stage == \"stage2\" and cfg.load_adapters:\n if 'decoder_adapter' not in self.adapter_keys:\n self.decoder.add_adapter(peft_config, 'decoder_adapter')\n self.adapter_keys.append('decoder_adapter')\n if 'query_reasoner_adapter' not in self.adapter_keys:\n self.decoder.add_adapter(peft_config, 'query_reasoner_adapter')\n self.adapter_keys.append('query_reasoner_adapter')\n elif cfg.training_stage == 'stage1_2':\n if not cfg.load_adapters:\n print('Loading decoder adapter for stage1_2')\n self.decoder.add_adapter(peft_config, 'decoder_adapter')\n self.adapter_keys.append('decoder_adapter')\n elif cfg.load_adapters:\n print('Loading encoder and decoder adapter for stage1_2')\n self.decoder.add_adapter(peft_config, 'encoder_adapter')\n self.adapter_keys.append('encoder_adapter')\n self.decoder.add_adapter(peft_config, 'decoder_adapter')\n self.adapter_keys.append('decoder_adapter')\n elif cfg.training_stage == 'stage2_reasoning':\n if not cfg.load_adapters:\n print('Loading decoder adapter for stage2_reasoning')\n self.decoder.add_adapter(peft_config, 'decoder_adapter')\n self.adapter_keys.append('decoder_adapter')\n \n def _setup_adapter_training(self):\n \"\"\"Setup adapters for training.\"\"\"\n for adapter_key in self.adapter_keys:\n self.decoder.set_adapter(adapter_key)\n print(f'Adapter {adapter_key} trainable parameters: {self.num_parameters(only_trainable=True)}')\n self._set_all_adapters()\n \n def _configure_generation_config(self):\n \"\"\"Configure generation parameters.\"\"\"\n self.decoder.generation_config.top_p = None\n self.decoder.generation_config.temperature = None\n self.decoder.generation_config.pad_token_id = self.decoder_tokenizer.pad_token_id\n \n @staticmethod\n def _create_decoder_tokenizer(cfg: CLaRaConfig) -> AutoTokenizer:\n \"\"\"Create and configure the decoder tokenizer.\"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\n cfg.decoder_model_name, \n use_fast=True, \n padding_side='left'\n )\n\n # Define special tokens\n n_mem_tokens = cfg.doc_max_length // cfg.compr_rate\n existing_special_tokens = tokenizer.special_tokens_map.get(\"additional_special_tokens\", [])\n\n if cfg.different_mem_tokens:\n mem_tokens = [f'' for i in range(n_mem_tokens)]\n tokenizer.add_special_tokens({\n 'additional_special_tokens': existing_special_tokens + mem_tokens + ['', '', '']\n })\n tokenizer.mem_tokens = mem_tokens\n else:\n tokenizer.add_special_tokens({\n 'additional_special_tokens': existing_special_tokens + ['', '', '', '']\n })\n tokenizer.mem_tokens = [''] * n_mem_tokens\n \n tokenizer.mem_token_ids = [tokenizer.convert_tokens_to_ids(token) for token in tokenizer.mem_tokens]\n tokenizer.mem_token_ids_pt = torch.LongTensor(tokenizer.mem_token_ids)\n \n # Additional special tokens\n tokenizer.ae_token = ''\n tokenizer.ae_token_id = tokenizer.convert_tokens_to_ids('')\n tokenizer.enc_token = ''\n tokenizer.sep_token = ''\n tokenizer.sep_token_id = tokenizer.convert_tokens_to_ids('')\n \n # Handle model-specific tokens\n if tokenizer.bos_token is None and 'qwen' in cfg.decoder_model_name.lower():\n tokenizer.bos_token = tokenizer.special_tokens_map['additional_special_tokens'][0]\n tokenizer.bos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.bos_token)\n \n if tokenizer.eos_token is None and \"qwen\" in cfg.decoder_model_name.lower():\n tokenizer.eos_token = tokenizer.special_tokens_map['additional_special_tokens'][1]\n tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)\n\n # KBTC training tokens\n if cfg.kbtc_training:\n tokenizer.add_special_tokens({'additional_special_tokens': ['']})\n tokenizer.kbtc_token = ''\n tokenizer.kbtc_token_id = tokenizer.convert_tokens_to_ids('')\n\n # Set pad token\n if tokenizer.pad_token_id is None:\n tokenizer.pad_token_id = tokenizer.bos_token_id\n \n print(f'Memory token count: {n_mem_tokens}')\n return tokenizer\n\n def _get_peft_config(self, lora_r: int) -> LoraConfig:\n \"\"\"Build the PEFT configuration.\"\"\"\n return LoraConfig(\n task_type=\"CAUSAL_LM\", \n r=lora_r, \n lora_alpha=2*lora_r, \n target_modules='all-linear', \n lora_dropout=0.1\n )\n\n def _prepare_mem_tokens_optimization(self):\n \"\"\"Setup memory token optimization if enabled.\"\"\"\n if self.config.optimize_mem_tokens and self.compr is None:\n # Enable gradients for input embeddings\n self.decoder.get_input_embeddings().weight.requires_grad = True\n \n # Apply hook to zero gradients except for memory tokens\n def hook(grad):\n mask = torch.zeros_like(grad)\n mask[self.decoder_tokenizer.mem_token_ids] = 1.0\n return grad * mask\n \n self.decoder.get_input_embeddings().weight.register_hook(hook)\n \n def _set_all_adapters(self):\n \"\"\"Activate all adapters for training.\"\"\"\n if len(self.adapter_keys) > 0:\n self.decoder.set_adapter(self.adapter_keys)\n\n # Core compression and generation methods\n def compress(self, enc_input_ids: torch.Tensor, enc_attention_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Compress input documents.\"\"\"\n if self.compr:\n return self.compr(enc_input_ids, enc_attention_mask)\n else:\n return self._compr_decoder(enc_input_ids, enc_attention_mask)\n \n def _compr_decoder(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Use decoder as compressor.\"\"\"\n assert input_ids.size() == attention_mask.size()\n \n if 'encoder_adapter' in self.adapter_keys:\n self.decoder.set_adapter('encoder_adapter')\n else:\n raise ValueError(f\"encoder_adapter not in adapter_keys: {self.adapter_keys}\")\n\n # Get embeddings from decoder\n emb = self.decoder(\n input_ids=input_ids,\n attention_mask=attention_mask,\n output_hidden_states=True\n ).hidden_states[-1]\n\n # Create mask for memory tokens\n mask = torch.isin(\n input_ids, \n self.decoder_tokenizer.mem_token_ids_pt.to(input_ids.device)\n )\n\n # Calculate MSE loss between memory and non-memory regions\n attn = attention_mask.bool()\n mem_mask = mask & attn\n non_mem_mask = (~mask) & attn\n\n mem_len = mem_mask.sum(dim=1)\n non_mem_len = non_mem_mask.sum(dim=1)\n\n if (mem_len == 0).any():\n raise ValueError(\"Some samples have no memory tokens\")\n if (non_mem_len == 0).any():\n raise ValueError(\"Some samples have no non-memory tokens\")\n\n mem_sum = (emb * mem_mask.unsqueeze(-1)).sum(dim=1)\n non_mem_sum = (emb * non_mem_mask.unsqueeze(-1)).sum(dim=1)\n\n mem_mean = mem_sum / mem_len.unsqueeze(-1)\n non_mem_mean = non_mem_sum / non_mem_len.unsqueeze(-1)\n\n mse_loss = F.mse_loss(non_mem_mean, mem_mean, reduction='mean')\n\n return emb[mask].reshape(emb.size(0), -1, emb.size(-1)), mse_loss\n\n def _compr_query_reasoner_stage2(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Query reasoning compression for stage 2.\"\"\"\n assert input_ids.size() == attention_mask.size()\n \n if 'query_reasoner_adapter' in self.adapter_keys:\n self.decoder.set_adapter('query_reasoner_adapter')\n else:\n raise ValueError(f\"query_reasoner_adapter not in adapter_keys: {self.adapter_keys}\")\n\n emb = self.decoder(\n input_ids=input_ids,\n attention_mask=attention_mask,\n output_hidden_states=True\n ).hidden_states[-1]\n\n mask = torch.isin(\n input_ids, \n self.decoder_tokenizer.mem_token_ids_pt.to(input_ids.device)\n )\n\n return emb[mask].reshape(emb.size(0), -1)\n\n # Generation methods\n def generate_from_questions(self, \n questions: List[str], \n max_new_tokens: int = 128, \n temperature: float = 0.5, \n documents: List[List[str]] = None, \n stage2_mips: bool = False, \n stage2_retrieval_top_n: int = None,\n time_count: bool = False) -> Tuple[List[str], torch.Tensor]:\n \"\"\"Generate answers from questions using query reasoning.\"\"\"\n if \"query_reasoner_adapter\" not in self.adapter_keys:\n raise ValueError(\"Query reasoner adapter not found\")\n \n self.eval()\n \n with torch.no_grad():\n # Encode questions\n self.decoder.set_adapter('query_reasoner_adapter')\n flat_questions = [q for q in questions]\n \n if time_count:\n start_time = time.time()\n \n q_tok = self._prepare_encoder_inputs(flat_questions, max_length=self.doc_max_length)\n query_reps = self._compr_query_reasoner_stage2(\n q_tok[\"input_ids\"].to(self.decoder.device), \n q_tok[\"attention_mask\"].to(self.decoder.device)\n )\n \n # Document retrieval and selection\n if stage2_mips:\n retrieved_doc_embeddings = self._retrieve_embeddings(\n query_reps, stage2_retrieval_top_n=stage2_retrieval_top_n\n )\n scores = torch.bmm(\n query_reps.unsqueeze(1), \n retrieved_doc_embeddings.transpose(1, 2)\n ).squeeze(1)\n z, topk_idx = differentiable_topk(scores, self.generation_top_k, temperature=0.5)\n selected_doc_embeddings = torch.einsum('bkn,bnd->bkd', z, retrieved_doc_embeddings)\n selected_doc_embeddings = selected_doc_embeddings.view(\n selected_doc_embeddings.size(0) * selected_doc_embeddings.size(1), \n -1, self.hidden_size\n )\n else:\n # Use provided documents\n flat_documents = sum(documents, [])\n \n if time_count:\n start_time1 = time.time()\n \n input_encoder = self._prepare_encoder_inputs(flat_documents, max_length=self.doc_max_length)\n device = self.decoder.device\n enc_input_ids = input_encoder['input_ids'].to(device)\n enc_attention_mask = input_encoder['attention_mask'].to(device)\n retrieved_doc_embeddings, _ = self.compress(enc_input_ids, enc_attention_mask)\n \n if time_count:\n start_time2 = time.time()\n compress_time = start_time2 - start_time1\n \n B = len(questions)\n stage2_retrieval_top_n = retrieved_doc_embeddings.shape[0] // B\n retrieved_doc_embeddings = retrieved_doc_embeddings.reshape(B, stage2_retrieval_top_n, -1)\n query_reps = query_reps.to(retrieved_doc_embeddings.dtype)\n\n if time_count:\n start_time3 = time.time()\n \n scores = torch.bmm(\n F.normalize(query_reps, dim=-1, p=2).unsqueeze(1).float(),\n F.normalize(retrieved_doc_embeddings, dim=-1, p=2).float().transpose(1, 2)\n ).squeeze(1)\n \n z, topk_idx = differentiable_topk(scores, self.generation_top_k, temperature=0.02)\n selected_doc_embeddings = torch.einsum('bkn,bnd->bkd', z.to(retrieved_doc_embeddings.dtype), retrieved_doc_embeddings)\n selected_doc_embeddings = selected_doc_embeddings.view(\n selected_doc_embeddings.size(0) * selected_doc_embeddings.size(1), \n -1, self.hidden_size\n )\n \n if time_count:\n start_time4 = time.time()\n query_time = start_time4 - start_time3 + start_time1 - start_time\n\n # Generate instructions and decode\n if time_count:\n start_time5 = time.time()\n \n instructions = [\n self._blend_prompt_and_selected_memory_tokens(query=q)[1] \n for q in questions\n ]\n \n decoder_inputs = self.decoder_tokenizer(\n instructions,\n return_tensors='pt',\n padding=\"longest\",\n add_special_tokens=False,\n truncation=True,\n max_length=1024,\n )\n \n dec_input_ids = decoder_inputs['input_ids'].to(self.decoder.device)\n dec_attention_mask = decoder_inputs['attention_mask'].to(self.decoder.device)\n \n # Replace memory token embeddings\n inputs_embeds = self._replace_emb_stage2(selected_doc_embeddings, dec_input_ids)\n \n # Switch to decoder adapter for generation\n if 'decoder_adapter' in self.adapter_keys:\n self.decoder.set_adapter('decoder_adapter')\n \n # Generate answers\n output_ids = self.decoder.generate(\n inputs_embeds=inputs_embeds,\n attention_mask=dec_attention_mask,\n do_sample=False,\n top_p=None,\n temperature=None,\n max_new_tokens=max_new_tokens,\n pad_token_id=self.decoder_tokenizer.pad_token_id\n )\n \n if time_count:\n start_time6 = time.time()\n generate_time = start_time6 - start_time5\n \n # Decode generated tokens\n decoded = self.decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True)\n \n if time_count:\n return decoded, topk_idx, compress_time, query_time, generate_time, compress_time + query_time + generate_time\n else:\n return decoded, topk_idx\n def generate_from_paraphrase(self, questions: list[str], documents: list[list[str]], max_new_tokens: int = 128) -> list[str]:\n \"\"\"\n Generates answers from documents (via compression then decoding)\n questions: list of string\n documents: list of list of strings (they should all be of equal length: the nb of doc for each question)\n \"\"\"\n self.generation_top_k = len(documents[0])\n assert len(documents) == len(questions)\n assert all([len(context) == len(documents[0]) for context in documents])\n flat_documents = sum(documents, [])\n \n model_input = {}\n \n # Creating encoder inputs:\n input_encoder = self._prepare_encoder_inputs(flat_documents, max_length=self.doc_max_length)\n device = self.decoder.device\n model_input['enc_input_ids'], model_input['enc_attention_mask'] = input_encoder['input_ids'].to(device), input_encoder['attention_mask'].to(device)\n \n # Creating decoder inputs\n instr = [self._blend_prompt_and_memory_tokens(query=\"\", stage = \"stage1\", paraphrase_loss = True) for q in questions]\n inp_dec = self.decoder_tokenizer(instr, return_tensors='pt', padding=\"longest\", add_special_tokens=False, truncation=True, max_length=1024)\n model_input['dec_input_ids'], model_input['dec_attention_mask'] = inp_dec['input_ids'].to(device), inp_dec['attention_mask'].to(device)\n \n # Generation\n return self._generate(model_input, max_new_tokens=max_new_tokens)\n\n\n def generate_from_text(self, \n questions: List[str], \n documents: List[List[str]], \n max_new_tokens: int = 128) -> List[str]:\n \"\"\"Generate answers from documents via compression then decoding.\"\"\"\n self.generation_top_k = len(documents[0])\n assert len(documents) == len(questions)\n assert all(len(context) == len(documents[0]) for context in documents)\n \n flat_documents = sum(documents, [])\n \n # Create encoder inputs\n input_encoder = self._prepare_encoder_inputs(flat_documents, max_length=self.doc_max_length)\n device = self.decoder.device\n enc_input_ids = input_encoder['input_ids'].to(device)\n enc_attention_mask = input_encoder['attention_mask'].to(device)\n \n # Create decoder inputs\n instructions = [self._blend_prompt_and_memory_tokens(query=q, stage=\"stage1_2\") for q in questions]\n inp_dec = self.decoder_tokenizer(\n instructions, \n return_tensors='pt', \n padding=\"longest\", \n add_special_tokens=False, \n truncation=True, \n max_length=1024\n )\n dec_input_ids = inp_dec['input_ids'].to(device)\n dec_attention_mask = inp_dec['attention_mask'].to(device)\n \n # Generate\n return self._generate({\n 'enc_input_ids': enc_input_ids,\n 'enc_attention_mask': enc_attention_mask,\n 'dec_input_ids': dec_input_ids,\n 'dec_attention_mask': dec_attention_mask\n }, max_new_tokens=max_new_tokens)\n\n def generate_from_compressed_documents_and_questions(self, \n questions: List[str], \n compressed_documents: torch.Tensor, \n max_new_tokens: int = 128) -> List[str]:\n \"\"\"Generate answers from compressed documents.\"\"\"\n self.generation_top_k = compressed_documents.size(0) // len(questions)\n assert compressed_documents.size(0) % self.generation_top_k == 0\n \n # Create decoder inputs\n instructions = [self._blend_prompt_and_memory_tokens(query=q, stage=\"stage1_2\") for q in questions]\n inp_dec = self.decoder_tokenizer(\n instructions, \n return_tensors='pt', \n padding=\"longest\", \n add_special_tokens=False, \n truncation=True, \n max_length=1024\n )\n device = self.decoder.device\n dec_input_ids = inp_dec['input_ids'].to(device)\n dec_attention_mask = inp_dec['attention_mask'].to(device)\n\n # Create input decoder embeddings from prompt + compressed documents\n inputs_embeds = self._replace_emb(compressed_documents, dec_input_ids)\n \n # Activate decoder generator\n if 'decoder_adapter' in self.adapter_keys:\n self.decoder.set_adapter('decoder_adapter')\n \n output_ids = self.decoder.generate(\n inputs_embeds=inputs_embeds,\n attention_mask=dec_attention_mask,\n max_new_tokens=max_new_tokens\n )\n \n return self.decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True)\n\n def compress_documents(self, documents: List[str]) -> torch.Tensor:\n \"\"\"Compress a list of documents.\"\"\"\n input_encoder = self._prepare_encoder_inputs(documents, max_length=self.doc_max_length)\n enc_input_ids = input_encoder['input_ids'].to(self.decoder.device)\n attention_mask = input_encoder['attention_mask'].to(self.decoder.device)\n return self.compress(enc_input_ids=enc_input_ids, enc_attention_mask=attention_mask)\n\n # Helper methods\n def _prepare_encoder_inputs(self, texts: List[str], max_length: int, q_texts: List[str] = None) -> Dict[str, torch.Tensor]:\n \"\"\"Create inputs for the encoder.\"\"\"\n if q_texts is not None:\n assert len(texts) == len(q_texts)\n\n if self.compr is None:\n return self._prepare_encoder_inputs_to_decoder(texts, max_length, q_texts)\n else:\n return self.compr.prepare_inputs(texts, max_length, q_texts)\n\n def _prepare_encoder_inputs_to_decoder(self, texts: List[str], max_length: int, q_texts: List[str] = None) -> Dict[str, torch.Tensor]:\n \"\"\"Prepare encoder inputs when using decoder as compressor.\"\"\"\n if q_texts is not None:\n texts_to_encode = [\n self.decoder_tokenizer.enc_token + \n self.decoder_tokenizer.bos_token + \n '\\nQuery:\\n' + query + \n 'Document:\\n' + text + \n self.decoder_tokenizer.eos_token \n for text, query in zip(texts, q_texts)\n ]\n inp_enc = self.decoder_tokenizer(\n texts_to_encode, \n return_tensors='pt', \n padding='max_length', \n max_length=max_length + 8,\n truncation=True, \n add_special_tokens=False\n )\n else:\n inp_enc = [\n self.decoder_tokenizer.enc_token + \n self.decoder_tokenizer.bos_token + \n text + \n self.decoder_tokenizer.eos_token \n for text in texts\n ]\n inp_enc = self.decoder_tokenizer(\n inp_enc, \n return_tensors='pt', \n padding=\"max_length\", \n max_length=max_length + 3,\n truncation=True, \n add_special_tokens=False\n )\n\n num_mem_tokens = self.doc_max_length // self.compr_rate\n assert num_mem_tokens == len(self.decoder_tokenizer.mem_tokens)\n\n inp_enc['input_ids'], inp_enc['attention_mask'] = add_memory_tokens_to_inputs(\n inp_enc['input_ids'], \n inp_enc['attention_mask'], \n num_mem_tokens, \n tokenizer=self.decoder_tokenizer\n )\n\n return inp_enc\n\n def _replace_emb(self, compressed_embs: torch.Tensor, dec_input_ids: torch.Tensor) -> torch.Tensor:\n \"\"\"Replace memory tokens in decoder input with compressed embeddings.\"\"\"\n indices = range(0, compressed_embs.size(0) + 1, self.generation_top_k) \n return self._replace_embeddings(compressed_embs, dec_input_ids, indices)\n\n def _replace_emb_stage2(self, compressed_embs: torch.Tensor, dec_input_ids: torch.Tensor) -> torch.Tensor:\n \"\"\"Replace memory tokens for stage 2.\"\"\"\n indices = range(0, compressed_embs.size(0) + 1, self.generation_top_k) \n return self._replace_embeddings(compressed_embs, dec_input_ids, indices)\n\n def _replace_embeddings(self, compressed_embs: torch.Tensor, dec_input_ids: torch.Tensor, indices: range) -> torch.Tensor:\n \"\"\"Replace memory tokens with compressed embeddings.\"\"\"\n inputs_embeds = self.decoder.get_input_embeddings()(dec_input_ids)\n num_embs = compressed_embs.size(1)\n slot_len = num_embs + (1 if self.sep else 0)\n \n # Get first memory token indices\n first_mem_token_indices = torch.argmax(\n (dec_input_ids == self.decoder_tokenizer.mem_token_ids[0]).int(), dim=1\n )\n batch_size = inputs_embeds.size(0)\n \n # Replace with compressed embeddings\n for i in range(batch_size):\n for j in range(indices[i], indices[i + 1]):\n start_idx = first_mem_token_indices[i].item() + (j - indices[i]) * slot_len\n assert inputs_embeds[i, start_idx:start_idx + num_embs, :].size() == compressed_embs[j].size()\n inputs_embeds[i, start_idx:start_idx + num_embs, :] = compressed_embs[j]\n \n return inputs_embeds\n\n def _retrieve_embeddings(self, questions: torch.Tensor, stage2_retrieval_top_n: int = 1) -> torch.Tensor:\n \"\"\"Retrieve embeddings of documents.\"\"\"\n response = requests.post(\n self.url_retrieval, \n json={\n \"queries\": questions.detach().cpu().float().numpy().tolist(), \n 'k': self.generation_top_k\n }\n )\n \n if response.status_code != 200:\n raise Exception(f\"Error: {response.status_code} - {response.text}\")\n \n results = response.json()\n retrieval_embeddings = results['retrieved_embeddings']\n retrieval_embeddings = torch.tensor(\n retrieval_embeddings, \n dtype=torch.bfloat16, \n device=questions.device\n )\n \n if len(retrieval_embeddings.shape) == 4:\n retrieval_embeddings = retrieval_embeddings.reshape(\n retrieval_embeddings.shape[0] * retrieval_embeddings.shape[1], \n retrieval_embeddings.shape[2], -1\n )\n \n return retrieval_embeddings\n\n def _blend_prompt_and_memory_tokens(self, query: str, answer: str = None, qa_loss: bool = False, \n paraphrase_loss: bool = False, stage: str = \"stage1\") -> Tuple[int, str]:\n \"\"\"Blend prompt with memory tokens for different training stages.\"\"\"\n mem_tokens_str = ''.join(self.decoder_tokenizer.mem_tokens) + self.decoder_tokenizer.sep_token\n docs = mem_tokens_str * self.generation_top_k\n \n if stage == \"stage1\":\n if qa_loss:\n return self._blend_qa_prompt(docs, query, answer)\n elif paraphrase_loss:\n return self._blend_paraphrase_prompt(docs, answer)\n elif stage == \"stage1_2\":\n return self._blend_standard_prompt(docs, query, answer)\n \n raise ValueError(f\"Unknown stage: {stage}\")\n\n def _blend_qa_prompt(self, docs: str, query: List[str], answer: List[str]) -> Tuple[int, str]:\n \"\"\"Create QA prompt for stage 1.\"\"\"\n prompt_system = 'You are a helpful assistant. Given a document, your task is to generate some single questions to cover all key information of the document and answer them sequentially.'\n prompt_user = f\"Background:\\n{docs}\"\n \n sys_prompt = [{\"role\": \"system\", \"content\": prompt_system}]\n user_prompt = [{\"role\": \"user\", \"content\": prompt_user.replace(':\\ ', ': ')}]\n\n qa_lines = [f\"Question: {q}\\nAnswer: {a}\" for q, a in zip(query, answer)]\n query_answer = \"\\n\".join(qa_lines)\n assistant_prompt = [{\"role\": \"assistant\", \"content\": query_answer}]\n \n try:\n prompt = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt, \n tokenize=False, \n add_generation_prompt=True, \n enable_thinking=False\n )\n response = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt + assistant_prompt, \n tokenize=False, \n add_generation_prompt=False, \n enable_thinking=False\n )\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n except TemplateError as e:\n if \"System role not supported\" in str(e):\n messages = [{\"role\": \"user\", \"content\": sys_prompt[0]['content'] + '\\n' + user_prompt[0]['content']}]\n prompt = self.decoder_tokenizer.apply_chat_template(\n messages, tokenize=False, add_generation_prompt=True, enable_thinking=False\n )\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n # Handle response for unsupported system role\n messages_with_answer = messages + assistant_prompt\n response = self.decoder_tokenizer.apply_chat_template(\n messages_with_answer, tokenize=False, add_generation_prompt=False, enable_thinking=False\n )\n else:\n raise e\n \n return prompt_len, response\n\n def _blend_paraphrase_prompt(self, docs: str, answer: str) -> Tuple[int, str]:\n \"\"\"Create paraphrase prompt for stage 1.\"\"\"\n prompt_system = 'You are a helpful assistant. Your task is follow the instructions to paraphrase the background information.'\n prompt_user = random.choice(PARAPHRASE_INSTRUCTIONS).format(docs=docs)\n\n sys_prompt = [{\"role\": \"system\", \"content\": prompt_system}]\n user_prompt = [{\"role\": \"user\", \"content\": prompt_user.replace(':\\ ', ': ')}]\n \n try:\n prompt = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt, \n tokenize=False, \n add_generation_prompt=True, \n enable_thinking=False\n )\n if answer is None:\n return prompt\n \n assistant_prompt = [{\"role\": \"assistant\", \"content\": answer}]\n response = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt + assistant_prompt, \n tokenize=False, \n add_generation_prompt=False, \n enable_thinking=False\n )\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n except TemplateError as e:\n if \"System role not supported\" in str(e):\n combined_content = prompt_system + '\\n' + prompt_user.replace(':\\ ', ': ')\n messages = [{\"role\": \"user\", \"content\": combined_content}]\n prompt = self.decoder_tokenizer.apply_chat_template(\n messages, tokenize=False, add_generation_prompt=True, enable_thinking=False\n )\n if answer is None:\n return prompt\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n messages_with_answer = messages + [{\"role\": \"assistant\", \"content\": answer}]\n response = self.decoder_tokenizer.apply_chat_template(\n messages_with_answer, tokenize=False, add_generation_prompt=False, enable_thinking=False\n )\n else:\n raise e\n \n return prompt_len, response\n\n def _blend_standard_prompt(self, docs: str, query: str, answer: str) -> Tuple[int, str]:\n \"\"\"Create standard prompt for stage 1_2.\"\"\"\n prompt_system = 'You are a helpful assistant. Your task is to extract relevant information from provided documents and to answer to questions as briefly as possible.'\n prompt_user = f\"Background:\\n{docs}\\n\\nQuestion:{query}\"\n \n sys_prompt = [{\"role\": \"system\", \"content\": prompt_system}]\n user_prompt = [{\"role\": \"user\", \"content\": prompt_user.replace(':\\ ', ': ')}]\n \n try:\n prompt = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt, \n tokenize=False, \n add_generation_prompt=True, \n enable_thinking=False\n )\n if answer is None:\n return prompt\n \n assistant_prompt = [{\"role\": \"assistant\", \"content\": answer}]\n response = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt + assistant_prompt, \n tokenize=False, \n add_generation_prompt=False, \n enable_thinking=False\n )\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n except TemplateError as e:\n if \"System role not supported\" in str(e):\n combined_content = prompt_system + '\\n' + prompt_user.replace(':\\ ', ': ')\n messages = [{\"role\": \"user\", \"content\": combined_content}]\n prompt = self.decoder_tokenizer.apply_chat_template(\n messages, tokenize=False, add_generation_prompt=True, enable_thinking=False\n )\n if answer is None:\n return prompt\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n messages_with_answer = messages + [{\"role\": \"assistant\", \"content\": answer}]\n response = self.decoder_tokenizer.apply_chat_template(\n messages_with_answer, tokenize=False, add_generation_prompt=False, enable_thinking=False\n )\n else:\n raise e\n \n return prompt_len, response\n\n def _blend_prompt_and_selected_memory_tokens(self, query: str, answer: str = None) -> Tuple[int, str]:\n \"\"\"Create prompt for stage 2 with selected memory tokens.\"\"\"\n mem_tokens_str = ''.join(self.decoder_tokenizer.mem_tokens) + self.decoder_tokenizer.sep_token\n docs = mem_tokens_str * self.generation_top_k\n \n prompt_system = 'You are a helpful assistant. Your task is to extract relevant information from provided documents and to answer to questions as briefly as possible.'\n prompt_user = f\"Background:\\n{docs}\\n\\nQuestion:{query}\"\n \n sys_prompt = [{\"role\": \"system\", \"content\": prompt_system}]\n user_prompt = [{\"role\": \"user\", \"content\": prompt_user.replace(':\\ ', ': ')}]\n \n try:\n prompt = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt, \n tokenize=False, \n add_generation_prompt=True, \n enable_thinking=False\n )\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n \n if answer is not None:\n assistant_prompt = [{\"role\": \"assistant\", \"content\": answer}]\n response = self.decoder_tokenizer.apply_chat_template(\n sys_prompt + user_prompt + assistant_prompt, \n tokenize=False, \n add_generation_prompt=False,\n enable_thinking=False\n )\n else:\n response = prompt\n \n except TemplateError as e:\n if \"System role not supported\" in str(e):\n combined_content = prompt_system + '\\n' + prompt_user.replace(':\\ ', ': ')\n messages = [{\"role\": \"user\", \"content\": combined_content}]\n \n prompt = self.decoder_tokenizer.apply_chat_template(\n messages, \n tokenize=False, \n add_generation_prompt=True, \n enable_thinking=False\n )\n prompt_len = len(self.decoder_tokenizer.encode(prompt, add_special_tokens=False))\n \n if answer is not None:\n messages_with_answer = messages + [{\"role\": \"assistant\", \"content\": answer}]\n response = self.decoder_tokenizer.apply_chat_template(\n messages_with_answer, \n tokenize=False, \n add_generation_prompt=False, \n enable_thinking=False\n )\n else:\n response = prompt\n else:\n raise e\n \n return prompt_len, response\n\n # Model saving and loading methods\n def save_pretrained(self, save_directory: str, **kwargs):\n \"\"\"Save only the LoRA adapters and their configurations.\"\"\"\n if self.lora:\n if not os.path.exists(save_directory):\n os.makedirs(save_directory) \n\n # Save LoRA adapter weights\n torch.save(\n self._get_all_adapters_state_dict(), \n os.path.join(save_directory, \"adapters.pth\")\n )\n \n # Save first and last layers of decoder\n torch.save(\n self._get_decoder_first_and_last_layer_state_dict(), \n os.path.join(save_directory, \"decoder_first_last_layers.pth\")\n )\n \n # Save configuration\n self.config.save_pretrained(save_directory)\n else:\n super().save_pretrained(save_directory, **kwargs)\n\n def _get_all_adapters_state_dict(self) -> Dict[str, Dict[str, torch.Tensor]]:\n \"\"\"Return the state dicts of all adapters.\"\"\"\n return {\n key: {k: v.cpu() for k, v in self.decoder.get_adapter_state_dict(key).items()} \n for key in self.adapter_keys\n }\n\n def _get_decoder_first_and_last_layer_state_dict(self) -> Dict[str, torch.Tensor]:\n \"\"\"Get first and last layers that change when adding tokens.\"\"\"\n out = {}\n for k, v in self.decoder.named_parameters():\n if 'lm_head.weight' in k or 'embed_tokens.weight' in k:\n out[k] = v.cpu()\n return out\n\n @classmethod\n def from_pretrained(cls, pretrained_model_name_or_path: str, *args, **kwargs):\n \"\"\"Load model from pretrained checkpoint.\"\"\"\n # Load configuration\n config = CLaRaConfig.from_pretrained(pretrained_model_name_or_path)\n \n # Update config with kwargs\n for key, value in kwargs.items():\n if hasattr(config, key):\n setattr(config, key, value)\n \n map_location = torch.device(\"cpu\") if not torch.cuda.is_available() else None\n\n if config.lora:\n # Delay adapter construction\n config.load_adapters = False\n if 'device_map' in kwargs:\n config.device_map = kwargs['device_map']\n\n # Initialize model\n print(f\"Initializing model from trained checkpoint: {config}\")\n model = cls(config)\n\n # Load first and last layers\n try:\n first_and_last_layers_path = hf_hub_download(\n repo_id=pretrained_model_name_or_path, \n filename=\"decoder_first_last_layers.pth\"\n )\n except Exception:\n first_and_last_layers_path = os.path.join(\n pretrained_model_name_or_path, \"decoder_first_last_layers.pth\"\n )\n\n if os.path.exists(first_and_last_layers_path):\n first_and_last_decoder_state_dict = torch.load(\n first_and_last_layers_path, map_location=map_location, weights_only=True\n )\n for key in first_and_last_decoder_state_dict:\n assert key in model.decoder.state_dict()\n model.decoder.load_state_dict(first_and_last_decoder_state_dict, strict=False)\n else:\n print(f'First and last layer not found: {first_and_last_layers_path}')\n\n peft_config = model._get_peft_config(lora_r=config.lora_r)\n \n # Load LoRA adapters\n try:\n adapters_path = hf_hub_download(\n repo_id=pretrained_model_name_or_path, \n filename=\"adapters.pth\"\n )\n except Exception:\n adapters_path = os.path.join(pretrained_model_name_or_path, \"adapters.pth\")\n \n if os.path.exists(adapters_path):\n adapters_state_dict = torch.load(adapters_path, map_location=map_location, weights_only=True)\n model._load_adapters_from_state_dict(adapters_state_dict, peft_config, config)\n else:\n warnings.warn(f'Adapters not found at {adapters_path}')\n\n model._set_all_adapters()\n config.load_adapters = True\n return model\n else:\n return super().from_pretrained(pretrained_model_name_or_path, **kwargs)\n def _load_adapters_from_state_dict(self, adapters_state_dict: Dict, peft_config: LoraConfig, config: CLaRaConfig):\n \"\"\"Load adapters from state dict based on training stage.\"\"\"\n if not getattr(config, 'pure_inference', False):\n for key, val in adapters_state_dict.items():\n # Skip certain adapters based on training stage\n if config.training_stage == 'stage1' and key == 'query_reasoner_adapter':\n continue\n elif config.training_stage == 'stage1_2' and key in ['query_reasoner_adapter', 'decoder_adapter']:\n continue\n elif config.training_stage == 'stage2_reasoning' and key == 'decoder_adapter':\n continue\n\n self._load_adapter_from_state_dict(\n peft_config=peft_config, \n adapter_name=key, \n adapter_state_dict=val\n )\n else:\n # Load all adapters for pure inference\n for key, val in adapters_state_dict.items():\n self._load_adapter_from_state_dict(\n peft_config=peft_config, \n adapter_name=key, \n adapter_state_dict=val\n )\n\n # Handle special cases for stage 2 training\n if config.training_stage == 'stage2' and 'query_reasoner_adapter' not in adapters_state_dict:\n self._handle_query_reasoner_adapter_loading(adapters_state_dict, peft_config)\n\n def _load_adapter_from_state_dict(self, peft_config: LoraConfig, adapter_name: str, adapter_state_dict: Dict):\n \"\"\"Create adapter from state dict.\"\"\"\n print(f'Loading checkpoint adapter: {adapter_name}')\n self.decoder.load_adapter(\n peft_config=peft_config, \n adapter_name=adapter_name, \n adapter_state_dict=adapter_state_dict\n )\n self.adapter_keys.append(adapter_name)\n\n def _handle_query_reasoner_adapter_loading(self, adapters_state_dict: Dict, peft_config: LoraConfig):\n \"\"\"Handle special loading logic for query reasoner adapter.\"\"\"\n if 'encoder_adapter' in adapters_state_dict and 'query_reasoner_adapter' not in adapters_state_dict:\n # Rename encoder adapter to query reasoner adapter\n renamed = {}\n for k, v in adapters_state_dict['encoder_adapter'].items():\n new_k = k.replace('encoder_adapter', 'query_reasoner_adapter')\n renamed[new_k] = v.detach().clone()\n \n self._load_adapter_from_state_dict(\n peft_config=peft_config,\n adapter_name='query_reasoner_adapter',\n adapter_state_dict=renamed\n )\n print('Loaded query_reasoner_adapter from stage 1 compressor checkpoint')\n else:\n # Create new adapter randomly\n self.decoder.add_adapter(peft_config, 'query_reasoner_adapter')\n self.adapter_keys.append('query_reasoner_adapter')\n print('Loaded query_reasoner_adapter randomly for stage 2 training')\n\n # Forward pass methods\n def forward(self, \n batch: Dict = None,\n questions: List[str] = None,\n documents: List[List[str]] = None,\n answers: List[str] = None,\n original_answer_gen_api: str = None,\n stage2_mips: bool = False,\n stage2_retrieval_top_n: int = None) -> Tuple[torch.Tensor, Dict]:\n \"\"\"\n Forward pass with support for both batch and legacy interfaces.\n \n Args:\n batch: Preprocessed batch dict (new interface)\n questions: List of questions (legacy interface) \n documents: List of document lists (legacy interface)\n answers: List of answers (legacy interface)\n original_answer_gen_api: API URL for generation (legacy interface)\n stage2_mips: Whether to use MIPS for stage2\n stage2_retrieval_top_n: Top-n for stage2 retrieval\n \n Returns:\n Tuple of (loss, additional_outputs_dict)\n \"\"\"\n if batch is not None:\n return self._forward_batch(batch, stage2_mips, stage2_retrieval_top_n)\n else:\n return self._forward_legacy(questions, documents, answers, original_answer_gen_api)\n\n def _forward_batch(self, batch: Dict, stage2_mips: bool, stage2_retrieval_top_n: int) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Handle batch-based forward pass.\"\"\"\n stage = batch.get(\"stage\", None)\n \n if stage in [\"stage1\", \"stage1_2\"]:\n return self._forward_stage1_batch(batch)\n elif stage == \"stage2\":\n return self._forward_stage2_batch(batch, stage2_mips, stage2_retrieval_top_n)\n elif stage == \"stage2_pretrain_retrieval\":\n return self._forward_stage2_pretrain_batch(batch, stage2_mips, stage2_retrieval_top_n)\n elif stage == \"stage2_reasoning\":\n return self._forward_stage2_reasoning_batch(batch)\n else:\n raise ValueError(f\"Unknown stage: {stage}\")\n\n def _forward_stage1_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 1 training.\"\"\"\n # Move tensors to device\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)\n enc_attention_mask = batch[\"enc_attention_mask\"].to(self.decoder.device)\n dec_input_ids = batch[\"dec_input_ids\"].to(self.decoder.device)\n dec_attention_mask = batch[\"dec_attention_mask\"].to(self.decoder.device)\n labels = batch[\"labels\"].to(self.decoder.device)\n \n out = self._forward_stage_1(\n enc_input_ids=enc_input_ids,\n enc_attention_mask=enc_attention_mask,\n dec_input_ids=dec_input_ids,\n dec_attention_mask=dec_attention_mask,\n labels=labels,\n )\n return out[\"loss\"], {\"logits\": out[\"logits\"], \"mse_loss\": out[\"mse_loss\"]}\n\n def _forward_stage2_batch(self, batch: Dict, stage2_mips: bool, stage2_retrieval_top_n: int) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 training.\"\"\"\n self.decoder.set_adapter('query_reasoner_adapter')\n \n B = batch[\"labels\"].shape[0]\n query_reps = self._compr_query_reasoner_stage2(\n batch[\"query_input_ids\"].to(self.decoder.device), \n batch[\"query_attention_mask\"].to(self.decoder.device)\n )\n\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)\n enc_attention_mask = batch[\"enc_attention_mask\"].to(self.decoder.device)\n dec_input_ids = batch[\"dec_input_ids\"].to(self.decoder.device)\n dec_attention_mask = batch[\"dec_attention_mask\"].to(self.decoder.device)\n labels = batch[\"labels\"].to(self.decoder.device)\n\n # Document retrieval and selection\n if stage2_mips:\n retrieved_doc_embeddings = self._retrieve_embeddings(\n query_reps, stage2_retrieval_top_n=stage2_retrieval_top_n\n )\n scores = torch.bmm(\n query_reps.unsqueeze(1), \n retrieved_doc_embeddings.transpose(1, 2)\n ).squeeze(1)\n z, topk_idx = differentiable_topk(scores, self.generation_top_k, temperature=1)\n selected = torch.einsum('bkn,bnd->bkd', z, retrieved_doc_embeddings)\n selected = selected.view(selected.size(0) * selected.size(1), -1, self.hidden_size)\n else:\n with torch.no_grad():\n retrieved_doc_embeddings, mse_loss = self.compress(enc_input_ids, enc_attention_mask)\n \n stage2_retrieval_top_n = retrieved_doc_embeddings.shape[0] // B\n retrieved_doc_embeddings = retrieved_doc_embeddings.reshape(B, stage2_retrieval_top_n, -1)\n query_reps = query_reps.to(retrieved_doc_embeddings.dtype)\n \n scores = torch.bmm(\n F.normalize(query_reps, dim=-1, p=2).unsqueeze(1).float(),\n F.normalize(retrieved_doc_embeddings, dim=-1, p=2).float().transpose(1, 2)\n ).squeeze(1)\n \n z, topk_idx = differentiable_topk(scores, self.generation_top_k, temperature=0.02)\n selected = torch.einsum('bkn,bnd->bkd', z.to(retrieved_doc_embeddings.dtype), retrieved_doc_embeddings)\n selected = selected.view(selected.size(0) * selected.size(1), -1, self.hidden_size)\n\n inputs_embeds = self._replace_emb_stage2(selected, dec_input_ids)\n \n if 'decoder_adapter' in self.adapter_keys:\n self.decoder.set_adapter('decoder_adapter')\n \n dec_out = self.decoder(\n inputs_embeds=inputs_embeds,\n attention_mask=dec_attention_mask,\n labels=labels,\n )\n \n self.decoder.set_adapter(['decoder_adapter', 'query_reasoner_adapter'])\n return dec_out.loss, {\"logits\": dec_out.logits, \"topk_idx\": topk_idx, \"mse_loss\": mse_loss}\n\n def _forward_stage2_pretrain_batch(self, batch: Dict, stage2_mips: bool, stage2_retrieval_top_n: int) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 pretraining with retrieval.\"\"\"\n self.decoder.set_adapter('query_reasoner_adapter')\n \n B = batch[\"labels\"].shape[0]\n N = batch[\"enc_input_ids\"].shape[0] // B\n device = self.decoder.device\n \n query_reps = self._compr_query_reasoner_stage2(\n batch[\"query_input_ids\"].to(device), \n batch[\"query_attention_mask\"].to(device)\n )\n\n enc_input_ids = batch[\"enc_input_ids\"].to(device)\n enc_attention_mask = batch[\"enc_attention_mask\"].to(device)\n\n with torch.no_grad():\n retrieved_doc_embeddings, mse_loss = self.compress(enc_input_ids, enc_attention_mask)\n \n stage2_retrieval_top_n = retrieved_doc_embeddings.shape[0] // B\n retrieved_doc_embeddings = retrieved_doc_embeddings.reshape(B, stage2_retrieval_top_n, -1)\n query_reps = query_reps.to(retrieved_doc_embeddings.dtype)\n \n scores = torch.bmm(\n F.normalize(query_reps, dim=-1, p=2).unsqueeze(1).float(),\n F.normalize(retrieved_doc_embeddings, dim=-1, p=2).float().transpose(1, 2)\n ).squeeze(1)\n \n pos_index = batch[\"pos_index\"]\n pos_mask = build_pos_mask(pos_index, N, device)\n tau = 0.02\n logits = scores / tau\n \n pos_logits = logits.masked_fill(~pos_mask, float('-inf'))\n num = torch.logsumexp(pos_logits, dim=-1)\n den = torch.logsumexp(logits, dim=-1)\n loss_vec = -(num - den)\n valid = pos_mask.any(dim=-1)\n loss = loss_vec[valid].mean()\n\n topk = self.generation_top_k\n topk_idx = logits.topk(k=min(topk, N), dim=-1).indices\n \n return loss, {\"logits\": [[]], \"topk_idx\": topk_idx, \"mse_loss\": mse_loss}\n\n def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:\n \"\"\"Forward pass for stage 2 reasoning training.\"\"\"\n B = batch[\"labels\"].shape[0]\n enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)\n enc_attention_mask = batch[\"enc_attention_mask\"].to(self.decoder.device)\n dec_input_ids = batch[\"dec_input_ids\"].to(self.decoder.device)\n dec_attention_mask = batch[\"dec_attention_mask\"].to(self.decoder.device)\n labels = batch[\"labels\"].to(self.decoder.device)\n\n if sum(batch[\"docs_num\"]) != 0:\n with torch.no_grad():\n selected, mse_loss = self.compress(enc_input_ids, enc_attention_mask)\n indices = batch[\"docs_num\"]\n inputs_embeds = self._replace_reasoning_embeddings(selected, dec_input_ids, indices)\n else:\n inputs_embeds = self.decoder.get_input_embeddings()(dec_input_ids)\n mse_loss = 0\n\n if 'decoder_adapter' in self.adapter_keys:\n self.decoder.set_adapter('decoder_adapter')\n \n dec_out = self.decoder(\n inputs_embeds=inputs_embeds,\n attention_mask=dec_attention_mask,\n labels=labels,\n )\n \n self.decoder.set_adapter(['decoder_adapter'])\n return dec_out.loss, {\"logits\": dec_out.logits, \"mse_loss\": mse_loss}\n\n def _forward_stage_1(self,\n enc_input_ids: torch.LongTensor = None,\n enc_attention_mask: torch.LongTensor = None,\n dec_input_ids: torch.LongTensor = None,\n dec_attention_mask: torch.LongTensor = None,\n labels: torch.LongTensor = None) -> Dict[str, torch.Tensor]:\n \"\"\"Stage 1 forward pass for document compression and QA.\"\"\"\n assert enc_input_ids.size() == enc_attention_mask.size()\n \n # Flatten 3D inputs to 2D if needed\n if len(enc_input_ids.size()) == 3:\n batch_size, top_k, seq_length = enc_input_ids.size()\n enc_input_ids = enc_input_ids.view(batch_size * top_k, seq_length)\n enc_attention_mask = enc_attention_mask.view(batch_size * top_k, seq_length)\n \n assert enc_input_ids.size(0) == dec_input_ids.size(0) * self.generation_top_k\n \n # Compress documents\n compressed_embs, mse_loss = self.compress(enc_input_ids, enc_attention_mask)\n \n # Replace memory tokens with compressed embeddings\n inputs_embeds = self._replace_emb(compressed_embs, dec_input_ids)\n\n # Detach if compressor-only training\n if (self.training_form == \"compressor\") and (self.compr is None):\n inputs_embeds = inputs_embeds.detach()\n\n # Set decoder adapter\n if 'decoder_adapter' in self.adapter_keys:\n self.decoder.set_adapter('decoder_adapter')\n\n # Forward through decoder\n decoder_outputs = self.decoder(\n inputs_embeds=inputs_embeds,\n attention_mask=dec_attention_mask,\n labels=labels\n )\n\n # Reactivate all adapters\n self.decoder.set_adapter(['decoder_adapter', 'encoder_adapter'])\n \n return {\n \"loss\": decoder_outputs.loss, \n \"logits\": decoder_outputs.logits, \n \"mse_loss\": mse_loss\n }\n\n def _replace_reasoning_embeddings(self,\n compressed_embs: torch.Tensor,\n dec_input_ids: torch.LongTensor,\n docs_per_example: List[int]) -> torch.Tensor:\n \"\"\"Replace memory slots with compressed embeddings for reasoning.\"\"\"\n device = dec_input_ids.device\n inputs_embeds = self.decoder.get_input_embeddings()(dec_input_ids)\n\n num_embs = compressed_embs.size(1)\n slot_len = num_embs + (1 if getattr(self, \"sep\", False) else 0)\n\n if not isinstance(docs_per_example, torch.Tensor):\n docs_per_example = torch.tensor(docs_per_example, device=device, dtype=torch.long)\n else:\n docs_per_example = docs_per_example.to(device=device, dtype=torch.long)\n\n offsets = torch.zeros(docs_per_example.size(0) + 1, device=device, dtype=torch.long)\n offsets[1:] = torch.cumsum(docs_per_example, dim=0)\n total_docs = int(offsets[-1].item())\n assert total_docs == compressed_embs.size(0)\n\n mem_id = self.decoder_tokenizer.mem_token_ids[0]\n B, L, H = inputs_embeds.size()\n\n for i in range(B):\n # Find first memory token position\n mem_pos = (dec_input_ids[i] == mem_id).nonzero(as_tuple=True)[0]\n if mem_pos.numel() == 0:\n continue\n first_mem_idx = int(mem_pos[0].item())\n\n n_docs_i = int(docs_per_example[i].item())\n base = int(offsets[i].item())\n\n needed_len = first_mem_idx + n_docs_i * slot_len\n assert needed_len <= L\n\n for local_j in range(n_docs_i):\n global_j = base + local_j\n start_idx = first_mem_idx + local_j * slot_len\n target_slice = inputs_embeds[i, start_idx:start_idx + num_embs, :]\n src = compressed_embs[global_j]\n assert target_slice.size() == src.size()\n inputs_embeds[i, start_idx:start_idx + num_embs, :] = src\n\n return inputs_embeds\n\n def _generate(self, model_input: Dict[str, torch.Tensor], max_new_tokens: int = 128, \n return_doc_embeddings: bool = False) -> List[str]:\n \"\"\"Generate text from model inputs.\"\"\"\n enc_input_ids = model_input['enc_input_ids']\n enc_attention_mask = model_input['enc_attention_mask']\n dec_input_ids = model_input['dec_input_ids']\n dec_attention_mask = model_input['dec_attention_mask']\n \n assert enc_input_ids.size() == enc_attention_mask.size()\n \n if len(enc_input_ids.size()) == 3:\n batch_size, top_k, seq_length = enc_input_ids.size()\n enc_input_ids = enc_input_ids.view(batch_size * top_k, seq_length)\n enc_attention_mask = enc_attention_mask.view(batch_size * top_k, seq_length)\n \n assert enc_input_ids.size(0) == dec_input_ids.size(0) * self.generation_top_k\n \n compressed_embs, _ = self.compress(enc_input_ids.to('cuda'), enc_attention_mask.to('cuda'))\n inputs_embeds = self._replace_emb(compressed_embs, dec_input_ids.to('cuda'))\n \n if 'decoder_adapter' in self.adapter_keys:\n self.decoder.set_adapter('decoder_adapter') \n\n output_ids = self.decoder.generate(\n inputs_embeds=inputs_embeds.to(\"cuda\"),\n attention_mask=dec_attention_mask.to(\"cuda\"),\n do_sample=False,\n top_p=None,\n max_new_tokens=max_new_tokens\n )\n\n decoded = self.decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True)\n \n if return_doc_embeddings:\n assert 'batch_size' in locals() and 'top_k' in locals()\n compressed_embs = compressed_embs.view(batch_size, top_k, compressed_embs.size(1), compressed_embs.size(2))\n return decoded, compressed_embs\n else:\n return decoded\n\n\n# Example usage and testing\nif __name__ == '__main__':\n # Example configuration\n cfg = CLaRaConfig(\n decoder_model_name='/mnt/ceph_rbd/model/Mistral-7B-Instruct-v0.2',\n compr_model_name=\"mistral_trimmed\",\n compr_rate=64,\n compr_n_layers=5,\n compr_mlp_hidden_dim=8096,\n compr_use_mlp=False, \n lora=True,\n lora_compressor=True,\n training_form=\"both\",\n load_adapters=True,\n kbtc_training=False,\n optimize_mem_tokens=True,\n different_mem_tokens=True,\n attn_implementation='flash_attention_2'\n )\n \n # Initialize model\n clara = CLaRa(cfg)\n \n # Save and reload test\n clara.save_pretrained('test_ckpt')\n \n del clara\n torch.cuda.empty_cache()\n gc.collect()\n \n # Reload model\n clara = CLaRa.from_pretrained('test_ckpt')\n print(\"Model successfully loaded!\")", "structuredPatch": [ { "oldStart": 1510, "oldLines": 7, "newStart": 1510, "newLines": 6, "lines": [ " ", " def _forward_stage2_reasoning_batch(self, batch: Dict) -> Tuple[torch.Tensor, Dict]:", " \"\"\"Forward pass for stage 2 reasoning training.\"\"\"", "- B = batch[\"labels\"].shape[0]", " enc_input_ids = batch[\"enc_input_ids\"].to(self.decoder.device)", " enc_attention_mask = batch[\"enc_attention_mask\"].to(self.decoder.device)", " dec_input_ids = batch[\"dec_input_ids\"].to(self.decoder.device)" ] } ], "userModified": false, "replaceAll": false }, "tool_use_id": "toolu_017meuLudkyBprHFNNcD1ne7", "logged_at": "2025-12-23T14:15:11.737309" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Read", "tool_input": { "file_path": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/datasets/sft_dataset.py", "offset": 175, "limit": 15 }, "tool_response": { "type": "text", "file": { "filePath": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/datasets/sft_dataset.py", "content": " \"answers\": answers,\n \"docs\": docs_list,\n }\n \n def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 2 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n B = len(questions)\n \n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]\n \n # Prepare encoder inputs\n enc_inputs = clara_model._prepare_encoder_inputs(flat_docs, max_length=enc_max_len)\n enc_input_ids = enc_inputs[\"input_ids\"]", "numLines": 15, "startLine": 175, "totalLines": 568 } }, "tool_use_id": "toolu_01GBjJ6zBBSywfYcXBV6RDvs", "logged_at": "2025-12-23T14:15:18.197084" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": "/Users/danielletterio/.claude/projects/-Users-danielletterio-Documents-GitHub-sonik-dev-sonik-os/686f0296-e115-4596-81e5-c65fba6ab2ef.jsonl", "cwd": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara", "permission_mode": "acceptEdits", "hook_event_name": "PostToolUse", "tool_name": "Edit", "tool_input": { "file_path": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/datasets/sft_dataset.py", "old_string": " def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 2 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n B = len(questions)\n \n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]", "new_string": " def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 2 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n\n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]" }, "tool_response": { "filePath": "/Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara/openrlhf/datasets/sft_dataset.py", "oldString": " def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 2 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n B = len(questions)\n \n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]", "newString": " def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 2 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n\n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]", "originalFile": "#!/usr/bin/env python3\n#\n# For licensing see accompanying LICENSE file.\n# Copyright (C) 2025 Apple Inc. All Rights Reserved.\n#\n\n\"\"\"\nCLaRa Dataset and Collate Functions\n\nThis module provides dataset handling and batch collation for CLaRa training.\n\"\"\"\n\nimport re\nimport torch\nfrom typing import Callable, List, Tuple, Dict, Any, Optional\nfrom collections import defaultdict\nfrom torch.utils.data import Dataset\n\n\ndef make_collate_fn(clara_model, enc_max_len: int = 256, dec_max_len: int = 1024, qa_loss: bool = False):\n \"\"\"\n Create a collate function for CLaRa training.\n \n Args:\n clara_model: CLaRa model instance\n enc_max_len: Maximum encoder sequence length\n dec_max_len: Maximum decoder sequence length\n qa_loss: Whether to use QA loss for joint training\n \n Returns:\n Collate function that processes batches for training\n \"\"\"\n tokenizer = clara_model.decoder_tokenizer\n generation_top_k = clara_model.generation_top_k\n \n def _mask_prompt(labels: torch.Tensor, \n attention_mask: torch.Tensor, \n prompt_lengths: List[int], \n pad_token_id: int) -> torch.Tensor:\n \"\"\"Mask prompt tokens in labels to only compute loss on answer tokens.\"\"\"\n for i, prompt_len in enumerate(prompt_lengths):\n attn = attention_mask[i]\n valid_positions = attn.nonzero(as_tuple=True)[0]\n \n if len(valid_positions) == 0:\n continue\n \n first_valid = valid_positions[0].item()\n last_valid_plus1 = valid_positions[-1].item() + 1\n end_pos = min(first_valid + prompt_len, last_valid_plus1)\n labels[i, :end_pos] = -100\n \n return labels\n \n def _find_subsequence(sequence: List[int], pattern: List[int], start: int = 0) -> int:\n \"\"\"Find subsequence pattern in sequence starting from start index.\"\"\"\n if not pattern:\n return -1\n \n n, m = len(sequence), len(pattern)\n for i in range(start, n - m + 1):\n if sequence[i:i+m] == pattern:\n return i\n return -1\n \n def _mask_information_spans(labels: torch.Tensor, \n input_ids: torch.Tensor, \n attention_mask: torch.Tensor, \n tokenizer) -> torch.Tensor:\n \"\"\"Mask ... spans in labels.\"\"\"\n open_pattern = tokenizer.encode(\"\", add_special_tokens=False)\n close_pattern = tokenizer.encode(\"\", add_special_tokens=False)\n \n B, L = input_ids.size()\n \n for i in range(B):\n ids = input_ids[i].tolist()\n \n # Find valid token range\n valid_positions = attention_mask[i].nonzero(as_tuple=True)[0]\n if len(valid_positions) == 0:\n continue\n last_valid = valid_positions[-1].item()\n \n # Find first non-masked position\n non_masked_positions = (labels[i] != -100).nonzero(as_tuple=True)\n if len(non_masked_positions[0]) == 0:\n continue\n pos = non_masked_positions[0][0].item()\n \n # Find and mask information spans\n while True:\n start = _find_subsequence(ids, open_pattern, pos)\n if start == -1 or start > last_valid:\n break\n \n end = _find_subsequence(ids, close_pattern, start + len(open_pattern))\n if end == -1 or end > last_valid:\n # No closing tag found, mask to end\n labels[i, start:last_valid+1] = -100\n break\n else:\n # Mask the entire span including tags\n end_inclusive = end + len(close_pattern) - 1\n end_inclusive = min(end_inclusive, last_valid)\n labels[i, start:end_inclusive+1] = -100\n pos = end_inclusive + 1\n \n return labels\n \n def _process_stage1_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 1 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n B = len(questions)\n \n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]\n \n # Prepare encoder inputs\n enc_inputs = clara_model._prepare_encoder_inputs(flat_docs, max_length=enc_max_len)\n enc_input_ids = enc_inputs[\"input_ids\"]\n enc_attention_mask = enc_inputs[\"attention_mask\"]\n \n assert enc_input_ids.size(0) == B * generation_top_k\n \n # Prepare decoder inputs\n prompt_responses = []\n for q, a, data_type in zip(questions, answers, data_types):\n if data_type == \"paraphrase\":\n # Handle paraphrase data (answer is a list)\n prompt_responses.append(\n clara_model._blend_prompt_and_memory_tokens(\n query=q, answer=a[0], paraphrase_loss=True, stage=clara_model.training_stage\n )\n )\n else:\n prompt_responses.append(\n clara_model._blend_prompt_and_memory_tokens(\n query=q, answer=a, qa_loss=qa_loss, stage=clara_model.training_stage\n )\n )\n \n prompt_lengths = [pr[0] for pr in prompt_responses]\n instructions = [pr[1] for pr in prompt_responses]\n \n # Tokenize decoder inputs\n dec_inputs = tokenizer(\n instructions,\n return_tensors=\"pt\",\n padding=\"longest\",\n add_special_tokens=False,\n truncation=True,\n max_length=dec_max_len,\n )\n \n dec_input_ids = dec_inputs[\"input_ids\"]\n dec_attention_mask = dec_inputs[\"attention_mask\"]\n \n # Create labels\n labels = torch.where(\n dec_attention_mask.bool(),\n dec_input_ids.clone(),\n torch.tensor(tokenizer.pad_token_id, dtype=dec_input_ids.dtype),\n )\n labels = _mask_prompt(labels, dec_attention_mask, prompt_lengths, tokenizer.pad_token_id)\n \n return {\n \"stage\": clara_model.training_stage,\n \"enc_input_ids\": enc_input_ids,\n \"enc_attention_mask\": enc_attention_mask,\n \"dec_input_ids\": dec_input_ids,\n \"dec_attention_mask\": dec_attention_mask,\n \"labels\": labels,\n \"questions\": questions,\n \"answers\": answers,\n \"docs\": docs_list,\n }\n \n def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for stage 2 training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n B = len(questions)\n \n # Flatten documents for encoding\n flat_docs = [doc for doc_list in docs_list for doc in doc_list]\n \n # Prepare encoder inputs\n enc_inputs = clara_model._prepare_encoder_inputs(flat_docs, max_length=enc_max_len)\n enc_input_ids = enc_inputs[\"input_ids\"]\n enc_attention_mask = enc_inputs[\"attention_mask\"]\n \n # Prepare query inputs\n query_inputs = clara_model._prepare_encoder_inputs(questions, max_length=dec_max_len)\n \n # Prepare decoder inputs with selected memory tokens\n prompt_responses = [\n clara_model._blend_prompt_and_selected_memory_tokens(query=q, answer=a)\n for q, a in zip(questions, answers)\n ]\n \n prompt_lengths = [pr[0] for pr in prompt_responses]\n instructions = [pr[1] for pr in prompt_responses]\n \n # Tokenize decoder inputs\n dec_inputs = tokenizer(\n instructions,\n return_tensors=\"pt\",\n padding=\"longest\",\n add_special_tokens=False,\n truncation=True,\n max_length=dec_max_len,\n )\n \n dec_input_ids = dec_inputs[\"input_ids\"]\n dec_attention_mask = dec_inputs[\"attention_mask\"]\n \n # Create labels\n labels = torch.where(\n dec_attention_mask.bool(),\n dec_input_ids.clone(),\n torch.tensor(tokenizer.pad_token_id, dtype=dec_input_ids.dtype),\n )\n labels = _mask_prompt(labels, dec_attention_mask, prompt_lengths, tokenizer.pad_token_id)\n \n return {\n \"stage\": clara_model.training_stage,\n \"enc_input_ids\": enc_input_ids,\n \"enc_attention_mask\": enc_attention_mask,\n \"query_input_ids\": query_inputs[\"input_ids\"],\n \"query_attention_mask\": query_inputs[\"attention_mask\"],\n \"dec_input_ids\": dec_input_ids,\n \"dec_attention_mask\": dec_attention_mask,\n \"labels\": labels,\n \"questions\": questions,\n \"answers\": answers,\n \"docs\": docs_list,\n \"pos_index\": pos_indices,\n }\n \n def _process_reasoning_batch(batch_data: Tuple) -> Dict[str, Any]:\n \"\"\"Process batch for reasoning training.\"\"\"\n docs_list, questions, answers, data_types, pos_indices = batch_data\n \n # Parse reasoning paths from answers\n thinking_paths = []\n for answer in answers:\n # Extract structured reasoning components\n pattern_full = r\"<(?:information|think|answer|search)>.*?\"\n tags = re.findall(r\"<(information|think|answer|search)>.*?\", answer, flags=re.DOTALL)\n fulls = re.findall(pattern_full, answer, flags=re.DOTALL)\n \n counter = defaultdict(int)\n result = {}\n for tag, full in zip(tags, fulls):\n counter[tag] += 1\n key = f\"<{tag}>{counter[tag]}\"\n result[key] = full.strip()\n \n thinking_paths.append(result)\n \n # Extract documents from information tags\n flat_docs = []\n docs_counts = []\n \n for thinking_path in thinking_paths:\n doc_count = 0\n for key, value in thinking_path.items():\n if 'information' in key:\n # Extract information content\n info_match = re.search(r\"(.*?)\", value, flags=re.DOTALL)\n if info_match:\n info_content = info_match.group(1)\n # Split by document markers\n temp_docs = re.split(r\"(?m)^\\(\\d+\\)\", info_content)\n temp_docs = [doc.strip() for doc in temp_docs if doc.strip()]\n flat_docs.extend(temp_docs)\n thinking_path[key] = \"\".join(temp_docs)\n doc_count += 5 # Assuming 5 docs per information tag\n \n docs_counts.append(doc_count)\n \n # Prepare encoder inputs\n enc_inputs = clara_model._prepare_encoder_inputs(flat_docs, max_length=enc_max_len)\n enc_input_ids = enc_inputs[\"input_ids\"]\n enc_attention_mask = enc_inputs[\"attention_mask\"]\n \n # Prepare decoder inputs with reasoning\n prompt_responses = [\n clara_model._blend_prompt_and_selected_memory_tokens_for_reasoning(\n query=q, answer=tp\n )\n for q, tp in zip(questions, thinking_paths)\n ]\n \n prompt_lengths = [pr[0] for pr in prompt_responses]\n instructions = [pr[1] for pr in prompt_responses]\n \n # Tokenize decoder inputs\n dec_inputs = tokenizer(\n instructions,\n return_tensors=\"pt\",\n padding=\"longest\",\n add_special_tokens=False,\n truncation=True,\n max_length=dec_max_len,\n )\n \n dec_input_ids = dec_inputs[\"input_ids\"]\n dec_attention_mask = dec_inputs[\"attention_mask\"]\n \n # Create labels and mask information spans\n labels = torch.where(\n dec_attention_mask.bool(),\n dec_input_ids.clone(),\n torch.tensor(tokenizer.pad_token_id, dtype=dec_input_ids.dtype),\n )\n labels = _mask_prompt(labels, dec_attention_mask, prompt_lengths, tokenizer.pad_token_id)\n labels = _mask_information_spans(labels, dec_input_ids, dec_attention_mask, tokenizer)\n \n return {\n \"stage\": clara_model.training_stage,\n \"enc_input_ids\": enc_input_ids,\n \"enc_attention_mask\": enc_attention_mask,\n \"dec_input_ids\": dec_input_ids,\n \"dec_attention_mask\": dec_attention_mask,\n \"labels\": labels,\n \"questions\": questions,\n \"answers\": answers,\n \"docs\": docs_list,\n \"pos_index\": pos_indices,\n \"docs_num\": docs_counts\n }\n \n def collate(batch: List[Tuple]) -> Dict[str, Any]:\n \"\"\"Main collate function that routes to appropriate stage processor.\"\"\"\n # Unpack batch\n docs_list, questions, answers, data_types, pos_indices = zip(*batch)\n \n # Convert to lists\n docs_list = list(docs_list)\n questions = list(questions)\n answers = list(answers)\n data_types = list(data_types)\n pos_indices = list(pos_indices)\n \n # Validate batch for non-stage2 training\n if clara_model.training_stage not in [\"stage2\", \"stage2_pretrain_retrieval\", \"stage2_reasoning\"]:\n assert len(docs_list[0]) == generation_top_k, \\\n f\"Expected {generation_top_k} documents, got {len(docs_list[0])}\"\n \n batch_data = (docs_list, questions, answers, data_types, pos_indices)\n \n # Route to appropriate processor\n if clara_model.training_stage in [\"stage1\", \"stage1_2\"]:\n return _process_stage1_batch(batch_data)\n elif clara_model.training_stage in [\"stage2\", \"stage2_pretrain_retrieval\"]:\n return _process_stage2_batch(batch_data)\n elif clara_model.training_stage == \"stage2_reasoning\":\n return _process_reasoning_batch(batch_data)\n else:\n raise ValueError(f\"Unknown training stage: {clara_model.training_stage}\")\n \n return collate\n\n\ndef preprocess_data(data: Dict[str, Any], \n input_template: Optional[str] = None,\n input_key: str = \"input\",\n output_key: Optional[str] = None,\n apply_chat_template: Optional[Callable] = None,\n multiturn: bool = False) -> Tuple[List[str], str, str, str, List[int]]:\n \"\"\"\n Preprocess raw data into format expected by CLaRa dataset.\n \n Args:\n data: Raw data dictionary\n input_template: Template for input formatting\n input_key: Key for input data\n output_key: Key for output data\n apply_chat_template: Chat template function\n multiturn: Whether this is multiturn data\n \n Returns:\n Tuple of (docs, question, answer, data_type, pos_index)\n \"\"\"\n # Extract documents\n if \"docs\" in data and isinstance(data['docs'], list):\n docs = data['docs']\n elif \"context\" in data and isinstance(data['context'], list):\n docs = data['context']\n elif \"content\" in data and isinstance(data['content'], list):\n docs = data['content']\n else:\n raise ValueError(f\"No valid document field found in data: {list(data.keys())}\")\n \n # Extract answers\n if \"answer\" in data and isinstance(data['answer'], str):\n answers = data['answer']\n elif \"answers\" in data and isinstance(data['answers'], list):\n answers = data['answers']\n elif \"golden_answers\" in data and isinstance(data['golden_answers'], list):\n answers = data['golden_answers'][0]\n else:\n raise ValueError(f\"No valid answer field found in data: {list(data.keys())}\")\n \n # Extract data type\n data_type = data.get('data_type', 'qa')\n \n # Extract question\n if data_type != \"paraphrase\":\n questions = data['question']\n else:\n questions = \"\"\n \n # Extract positive indices\n pos_index = data.get('pos_index', [])\n \n return docs, questions, answers, data_type, pos_index\n\n\nclass SFTDataset(Dataset):\n \"\"\"\n Dataset for CLaRa Supervised Fine-Tuning.\n \n This dataset handles data preprocessing and loading for different CLaRa training stages.\n \"\"\"\n \n def __init__(self,\n dataset,\n tokenizer: Callable,\n max_length: int,\n strategy,\n input_template: Optional[str] = None,\n pretrain_mode: bool = False,\n num_processors: int = 8,\n multiturn: bool = False) -> None:\n \"\"\"\n Initialize the SFT dataset.\n \n Args:\n dataset: HuggingFace dataset object\n tokenizer: Tokenizer function\n max_length: Maximum sequence length\n strategy: Training strategy object\n input_template: Template for input formatting\n pretrain_mode: Whether in pretraining mode\n num_processors: Number of processors for data processing\n multiturn: Whether to handle multiturn conversations\n \"\"\"\n super().__init__()\n \n self.tokenizer = tokenizer\n self.strategy = strategy\n self.pretrain_mode = pretrain_mode\n self.max_length = max_length\n self.multiturn = multiturn\n \n # Chat template configuration\n self.input_template = input_template\n self.input_key = getattr(self.strategy.args, \"input_key\", None)\n self.output_key = getattr(self.strategy.args, \"output_key\", None)\n self.apply_chat_template = getattr(self.strategy.args, \"apply_chat_template\", False)\n \n if self.apply_chat_template:\n self.apply_chat_template = self.tokenizer.apply_chat_template\n tokenizer_chat_template = getattr(self.strategy.args, \"tokenizer_chat_template\", None)\n if tokenizer_chat_template:\n self.tokenizer.chat_template = tokenizer_chat_template\n \n # Process dataset in parallel\n processed_dataset = dataset.map(\n self._process_data,\n remove_columns=dataset.column_names,\n num_proc=num_processors,\n )\n \n # Store processed data\n self.docs = processed_dataset[\"docs\"]\n self.questions = processed_dataset[\"questions\"]\n self.answers = processed_dataset[\"answers\"]\n self.data_type = processed_dataset[\"data_type\"]\n self.pos_index = processed_dataset[\"pos_index\"]\n \n def _process_data(self, data: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Process a single data sample.\"\"\"\n if self.multiturn and self.output_key:\n data[self.input_key].append(data[self.output_key])\n data[self.output_key] = None\n \n if self.multiturn:\n assert (\n not self.output_key or not data[self.output_key]\n ), \"For multiturn data, put the whole trajectory in input_key and don't set output_key\"\n \n # Process multiturn conversation\n input_key = self.input_key\n apply_chat_template = self.apply_chat_template\n response_ranges = []\n \n for idx, message in enumerate(data[input_key]):\n if message[\"role\"] == \"assistant\":\n prompt = apply_chat_template(\n data[input_key][:idx], \n tokenize=False, \n add_generation_prompt=True\n )\n response = apply_chat_template(\n data[input_key][:idx + 1], \n tokenize=False\n )[len(prompt):]\n \n # Calculate token ranges\n start_idx = (\n self.tokenizer(\n prompt,\n max_length=self.max_length,\n padding=False,\n truncation=True,\n return_tensors=\"pt\",\n add_special_tokens=False,\n )[\"attention_mask\"]\n .int()\n .sum()\n .item()\n )\n \n end_idx = (\n start_idx\n + self.tokenizer(\n response,\n max_length=self.max_length,\n padding=False,\n truncation=True,\n return_tensors=\"pt\",\n add_special_tokens=False,\n )[\"attention_mask\"]\n .int()\n .sum()\n .item()\n - 1\n )\n \n response_ranges.append((start_idx, end_idx))\n \n # Preprocess the data\n docs, questions, answers, data_type, pos_index = preprocess_data(data)\n \n return {\n \"docs\": docs,\n \"questions\": questions,\n \"answers\": answers,\n \"data_type\": data_type,\n \"pos_index\": pos_index,\n }\n \n def __len__(self) -> int:\n \"\"\"Return the length of the dataset.\"\"\"\n return len(self.questions)\n \n def __getitem__(self, idx: int) -> Tuple[List[str], str, str, str, List[int]]:\n \"\"\"Get a single item from the dataset.\"\"\"\n return (\n self.docs[idx],\n self.questions[idx],\n self.answers[idx],\n self.data_type[idx],\n self.pos_index[idx]\n )", "structuredPatch": [ { "oldStart": 179, "oldLines": 8, "newStart": 179, "newLines": 7, "lines": [ " def _process_stage2_batch(batch_data: Tuple) -> Dict[str, Any]:", " \"\"\"Process batch for stage 2 training.\"\"\"", " docs_list, questions, answers, data_types, pos_indices = batch_data", "- B = len(questions)", "- ", "+", " # Flatten documents for encoding", " flat_docs = [doc for doc_list in docs_list for doc in doc_list]", " " ] } ], "userModified": false, "replaceAll": false }, "tool_use_id": "toolu_01KmysftH7MsGu1a7sh7XGmh", "logged_at": "2025-12-23T14:15:26.237218" }, { "session_id": 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"python -m pytest tests/ -v 2>&1 | tail -10", "description": "Run tests to verify placeholder works" }, "tool_response": { "stdout": "============================= test session starts ==============================\nplatform darwin -- Python 3.14.2, pytest-9.0.2, pluggy-1.6.0 -- /Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-echo/offline-voice-ai/venv/bin/python\ncachedir: .pytest_cache\nrootdir: /Users/danielletterio/Documents/GitHub/sonik-dev/sonik-os/sonik-infra/models/clara/ml-clara\nplugins: anyio-4.12.0, bdd-8.1.0\ncollecting ... collected 1 item\n\ntests/test_placeholder.py::test_placeholder PASSED [100%]\n\n============================== 1 passed in 0.01s ===============================", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_01CFjNDjcP6EEkGMwd2uPNqd", "logged_at": "2025-12-23T14:17:48.579277" }, { "session_id": "686f0296-e115-4596-81e5-c65fba6ab2ef", "transcript_path": 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[100%]\n1 passed in 0.00s", "stderr": "", "interrupted": false, "isImage": false }, "tool_use_id": "toolu_01WUbb15ybX19nMFuET9oU9k", "logged_at": "2025-12-23T14:48:18.933387" } ]