End of training

.hydra/config.yaml

@@ -0,0 +1,17 @@
+model:
+  dataset_name: redwoodresearch/diamonds-seed0
+  model_type: codegen
+  pretrained_model_name: Salesforce/codegen-350M-mono
+  max_length: 1024
+hparams:
+  learning_rate: 2.0e-05
+  weight_decay: 0.02
+  lr_scheduler_type: cosine
+  warmup_steps: 64
+  effective_batch_size: 32
+  num_train_epochs: 5
+per_device_train_batch_size: 4
+per_device_eval_batch_size: 4
+fp16: true
+dataset_len: null
+push_to_hub: true

.hydra/hydra.yaml

@@ -0,0 +1,182 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+    timeout_min: 1440
+    cpus_per_task: null
+    gpus_per_node: null
+    tasks_per_node: 1
+    mem_gb: 16
+    nodes: 1
+    name: ${hydra.job.name}
+    stderr_to_stdout: false
+    _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.SlurmLauncher
+    partition: null
+    qos: high
+    comment: null
+    constraint: null
+    exclude: ddpg.ist.berkeley.edu,dqn.ist.berkeley.edu
+    gres: gpu:A6000:1
+    cpus_per_gpu: null
+    gpus_per_task: null
+    mem_per_gpu: null
+    mem_per_cpu: null
+    account: null
+    signal_delay_s: 120
+    max_num_timeout: 0
+    additional_parameters: {}
+    array_parallelism: 256
+    setup: null
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: MULTIRUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=MULTIRUN
+    task:
+    - model.dataset_name=redwoodresearch/diamonds-seed0
+  job:
+    name: train
+    chdir: null
+    override_dirname: model.dataset_name=redwoodresearch/diamonds-seed0
+    id: '747437'
+    num: 0
+    config_name: codegen_diamonds_slurm
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.1'
+    cwd: /nas/ucb/oliveradk/measurement-pred
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /nas/ucb/oliveradk/measurement-pred/conf
+      schema: file
+      provider: main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /nas/ucb/oliveradk/measurement-pred/multirun/2024-12-17/07-26-21/0
+    choices:
+      hparams: hparams
+      model: codegen_diamonds
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: slurm_chai
+      hydra/output: default
+  verbose: false

.hydra/overrides.yaml

@@ -0,0 +1 @@
+- model.dataset_name=redwoodresearch/diamonds-seed0

README.md

@@ -1,199 +1,76 @@
 ---
-library_name: transformers
-tags: []
+license: bsd-3-clause
+base_model: Salesforce/codegen-350M-mono
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: codegen-350M-mono-measurement_pred-diamonds-seed0
+  results: []
 ---
 
-# Model Card for Model ID
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+# codegen-350M-mono-measurement_pred-diamonds-seed0
 
+This model is a fine-tuned version of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3804
+- Accuracy: 0.9146
+- Accuracy Sensor 0: 0.9046
+- Auroc Sensor 0: 0.9551
+- Accuracy Sensor 1: 0.9170
+- Auroc Sensor 1: 0.9423
+- Accuracy Sensor 2: 0.9398
+- Auroc Sensor 2: 0.9764
+- Accuracy Aggregated: 0.8970
+- Auroc Aggregated: 0.9614
 
+## Model description
 
-## Model Details
+More information needed
 
-### Model Description
+## Intended uses & limitations
 
-<!-- Provide a longer summary of what this model is. -->
+More information needed
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Training and evaluation data
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+More information needed
 
-### Model Sources [optional]
+## Training procedure
 
-<!-- Provide the basic links for the model. -->
+### Training hyperparameters
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 64
+- num_epochs: 5
+- mixed_precision_training: Native AMP
 
-## Uses
+### Training results
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Accuracy Sensor 0 | Auroc Sensor 0 | Accuracy Sensor 1 | Auroc Sensor 1 | Accuracy Sensor 2 | Auroc Sensor 2 | Accuracy Aggregated | Auroc Aggregated |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-----------------:|:--------------:|:-------------------:|:----------------:|
+| 0.2892        | 0.9997 | 781  | 0.3250          | 0.8582   | 0.8459            | 0.8992         | 0.8448            | 0.8967         | 0.8836            | 0.9420         | 0.8584              | 0.9095           |
+| 0.1886        | 1.9994 | 1562 | 0.3029          | 0.8740   | 0.8822            | 0.9276         | 0.8798            | 0.9227         | 0.9057            | 0.9626         | 0.8284              | 0.9363           |
+| 0.1237        | 2.9990 | 2343 | 0.2722          | 0.9012   | 0.8803            | 0.9463         | 0.9087            | 0.9354         | 0.9390            | 0.9761         | 0.8767              | 0.9562           |
+| 0.0683        | 4.0    | 3125 | 0.3122          | 0.9088   | 0.8871            | 0.9520         | 0.9166            | 0.9417         | 0.9334            | 0.9757         | 0.8980              | 0.9590           |
+| 0.0322        | 4.9984 | 3905 | 0.3804          | 0.9146   | 0.9046            | 0.9551         | 0.9170            | 0.9423         | 0.9398            | 0.9764         | 0.8970              | 0.9614           |
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Framework versions
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- Transformers 4.41.0
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

added_tokens.json

@@ -0,0 +1,40 @@
+{
+  "\t\t": 50294,
+  "\t\t\t": 50293,
+  "\t\t\t\t": 50292,
+  "\t\t\t\t\t": 50291,
+  "\t\t\t\t\t\t": 50290,
+  "\t\t\t\t\t\t\t": 50289,
+  "\t\t\t\t\t\t\t\t": 50288,
+  "\t\t\t\t\t\t\t\t\t": 50287,
+  "  ": 50286,
+  "   ": 50285,
+  "    ": 50284,
+  "     ": 50283,
+  "      ": 50282,
+  "       ": 50281,
+  "        ": 50280,
+  "         ": 50279,
+  "          ": 50278,
+  "           ": 50277,
+  "            ": 50276,
+  "             ": 50275,
+  "              ": 50274,
+  "               ": 50273,
+  "                ": 50272,
+  "                 ": 50271,
+  "                  ": 50270,
+  "                   ": 50269,
+  "                    ": 50268,
+  "                     ": 50267,
+  "                      ": 50266,
+  "                       ": 50265,
+  "                        ": 50264,
+  "                         ": 50263,
+  "                          ": 50262,
+  "                           ": 50261,
+  "                            ": 50260,
+  "                             ": 50259,
+  "                              ": 50258,
+  "                               ": 50257
+}

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "multirun/2024-12-16/08-54-40/0/checkpoint-3905",
+  "_name_or_path": "Salesforce/codegen-350M-mono",
   "activation_function": "gelu_new",
   "aggregate_weight": 0.3,
   "architectures": [
@@ -28,6 +28,7 @@
   "resid_pdrop": 0.0,
   "rotary_dim": 32,
   "scale_attn_weights": true,
+  "sensor_loc_type": "locs_from_token",
   "sensor_token": " omit",
   "sensor_token_id": 42848,
   "sensors_weight": 0.7,

configuration_measurement_pred.py

@@ -1,12 +1,11 @@
 from abc import abstractmethod
 from transformers import PretrainedConfig
-
 class MeasurementPredictorConfig(PretrainedConfig):
     
     def __init__(
         self, 
         sensor_token=" omit",
-        sensor_token_id=None, # 35991
+        sensor_loc_type="locs_from_token",
         n_sensors=3,
         use_aggregated=True,
         sensors_weight = 0.7,
@@ -14,7 +13,7 @@ class MeasurementPredictorConfig(PretrainedConfig):
         **kwargs
     ):
         self.sensor_token = sensor_token 
-        self.sensor_token_id = sensor_token_id
+        self.sensor_loc_type = sensor_loc_type
         self.n_sensors = n_sensors
         self.use_aggregated = use_aggregated
         self.sensors_weight = sensors_weight

logs/events.out.tfevents.1734449259.gail.ist.berkeley.edu.2164203.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cab7f95345b22fed36c586e39d2b03ec5239eb4f542ebfb00a2b6c7f5da6929d
+size 16069

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:984c2c5f186c66ff269895180226e51f4e8b3dacf0d9786bcd7d9d9833b5c3b8
+oid sha256:85a7102fd45c558e330a211b7bb8dd04e0e62cfebe79d70a3cbf2dddb97f0f2f
 size 1216963976

modeling_measurement_pred.py

@@ -3,14 +3,19 @@ from typing import Optional, Tuple, Union
 import torch
 from torch.nn import BCEWithLogitsLoss
 from transformers import PreTrainedModel, PreTrainedTokenizer
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 from transformers.modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast
 
+
+from .sensor_loc_reg import SENSOR_LOC_REGISTRY
+from .sensor_loc_finder import SensorLocFinder
+
 class MeasurementPredictorMixin(PreTrainedModel):
     
     def __init__(self, config):
         super().__init__(config)
+        self.sensor_loc_type = config.sensor_loc_type
         self.sensor_token = config.sensor_token
-        self.sensor_token_id = config.sensor_token_id
         self.n_sensors = config.n_sensors
         self.sensor_probes = torch.nn.ModuleList([
             torch.nn.Linear(config.emb_dim, 1) for _ in range(config.n_sensors)
@@ -20,15 +25,13 @@ class MeasurementPredictorMixin(PreTrainedModel):
             self.aggregate_probe = torch.nn.Linear(config.emb_dim, 1)
         self.sensors_weight = config.sensors_weight
         self.aggregate_weight = config.aggregate_weight
+
+        self.get_sensor_locs: SensorLocFinder = None 
     
-    def check_tokenizer(self, tokenizer: PreTrainedTokenizer):
-        sensor_token_id = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(self.sensor_token))[0]
-        assert sensor_token_id == self.sensor_token_id
-    
-    def set_sensor_token(self, sensor_token: str, tokenizer: PreTrainedTokenizer):
-        sensor_token_id = tokenizer.tokenize(sensor_token)[0]
-        self.sensor_token = sensor_token
-        self.sensor_token_id = sensor_token_id
+    def init_sensor_loc_finder(self, tokenizer: PreTrainedTokenizerBase):
+        self.get_sensor_locs = SENSOR_LOC_REGISTRY[self.sensor_loc_type](
+            tokenizer, sensor_token=self.sensor_token, n_sensors=self.n_sensors
+        )
 
     def forward(
         self,
@@ -64,12 +67,11 @@ class MeasurementPredictorMixin(PreTrainedModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
         )
-        flat_tensor_token_idxs = (input_ids == self.sensor_token_id).nonzero(as_tuple=True)[1]
-        tensor_token_idxs = flat_tensor_token_idxs.view(-1, self.n_sensors)
+        sensor_locs = self.get_sensor_locs(input_ids)
         sensor_embs = base_model_output.last_hidden_state.gather(
-            1, tensor_token_idxs.unsqueeze(-1).expand(-1, -1, self.config.emb_dim)
+            1, sensor_locs.unsqueeze(-1).expand(-1, -1, self.config.emb_dim)
         )
-        assert sensor_embs.shape == (input_ids.shape[0], self.n_sensors, self.config.emb_dim)
+        assert sensor_embs.shape == (input_ids.shape[0], self.n_sensors, self.config.emb_dim), f"{sensor_embs.shape} != {(input_ids.shape[0], self.n_sensors, self.config.emb_dim)}"
         sensor_logits = torch.concat([self.sensor_probes[i](sensor_embs[:, i, :]) 
                                for i in range(self.n_sensors)], dim=-1)
         logits = sensor_logits

sensor_loc_finder.py

@@ -0,0 +1,17 @@
+from abc import ABC, abstractmethod
+import torch
+from transformers import PreTrainedTokenizerBase
+
+
+class SensorLocFinder(ABC):
+
+    @abstractmethod
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, **kwargs):
+        pass
+
+    @abstractmethod
+    def find_sensor_locs(self, input_ids: torch.Tensor) -> torch.Tensor:
+        pass
+
+    def __call__(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.find_sensor_locs(input_ids)

sensor_loc_reg.py

@@ -0,0 +1,10 @@
+from enum import Enum
+
+from .sensor_loc_stories import StoriesSensorLocFinder
+from .sensor_locs_from_token import SensorLocFinderFromToken
+
+
+SENSOR_LOC_REGISTRY = {
+    "stories": StoriesSensorLocFinder,
+    "locs_from_token": SensorLocFinderFromToken
+}

sensor_loc_stories.py

@@ -0,0 +1,46 @@
+import torch
+from transformers import PreTrainedTokenizerBase
+
+from .sensor_loc_finder import SensorLocFinder
+
+
+class StoriesSensorLocFinder(SensorLocFinder):
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, **kwargs):
+        self.questions_section_toks = tokenizer.encode("## Questions")
+        self.question_mark_tok = tokenizer.encode("?")[0]
+        self.other_question_mark_tok = tokenizer.encode(")?")[0]
+        assert len(self.questions_section_toks) == 2
+
+    def find_sensor_locs(self, input_ids: torch.Tensor) -> torch.Tensor:
+        device = input_ids.device
+        question_mark_locs = self._is_sensor_loc(input_ids)
+        total_locs = torch.cumsum(question_mark_locs, dim=-1)
+        total_overall = total_locs[:, -1]
+        assert (
+            total_overall == 3
+        ).all(), "can handle different cases, but assuming this is easiest"
+        eqs = total_locs[:, :, None] == torch.arange(1, 4)[None, None].to(device)
+        locs = torch.where(
+            eqs.any(dim=-2),
+            torch.argmax(eqs.to(torch.uint8), dim=-2),
+            input_ids.shape[-1] - 3,
+        ).clamp(max=input_ids.shape[-1] - 3)
+        return locs
+
+    
+    def _is_sensor_loc(self, input_ids: torch.Tensor):
+        questions_section_toks = self.questions_section_toks
+        question_mark_tok = self.question_mark_tok
+        other_question_mark_tok = self.other_question_mark_tok
+        eq_question_item = (input_ids[:, :-1] == questions_section_toks[0]) & (
+            input_ids[:, 1:] == questions_section_toks[1]
+        )
+        assert (eq_question_item.sum(dim=-1, dtype=torch.int) == 1).all(), "could relax"
+
+        summed = torch.cumsum(
+            torch.cat([eq_question_item, eq_question_item[:, -1:]], dim=-1), dim=-1
+        )
+        return (summed > 0) & (
+            (input_ids == question_mark_tok) | (input_ids == other_question_mark_tok)
+        )

sensor_locs_from_token.py

@@ -0,0 +1,16 @@
+import torch
+from transformers import PreTrainedTokenizerBase
+
+from .sensor_loc_finder import SensorLocFinder
+
+
+class SensorLocFinderFromToken(SensorLocFinder):
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, sensor_token: str, n_sensors: int):
+        self.sensor_token_id = tokenizer.encode(sensor_token)[0]
+        self.n_sensors = n_sensors
+
+    def find_sensor_locs(self, input_ids: torch.Tensor) -> torch.Tensor:
+        flat_sensor_token_idxs = (input_ids == self.sensor_token_id).nonzero(as_tuple=True)[1]
+        sensor_token_idxs = flat_sensor_token_idxs.view(-1, self.n_sensors)
+        return sensor_token_idxs

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,327 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50257": {
+      "content": "                               ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50258": {
+      "content": "                              ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50260": {
+      "content": "                            ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50261": {
+      "content": "                           ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50263": {
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+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
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+      "single_word": false,
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 2048,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "return_token_type_ids": false,
+  "tokenizer_class": "CodeGenTokenizer",
+  "truncation_side": "left",
+  "unk_token": "<|endoftext|>"
+}

train.log

@@ -0,0 +1 @@
+[2024-12-17 07:27:38,799][accelerate.utils.other][WARNING] - Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.

training_args.bin

@@ -0,0 +1,3 @@
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+oid sha256:8c6fd0a470c9f03c8acb06f4888eec8eac8dc77dd7e4efe3dcb775dc70689154
+size 5112