Upload CodeGenMeasurementPredictor

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[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]

config.json

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+{
+  "_name_or_path": "/nas/ucb/oliveradk/measurement-pred/multirun/2024-05-23/07-11-18/0/checkpoint-3905",
+  "activation_function": "gelu_new",
+  "aggregate_weight": 0.3,
+  "architectures": [
+    "CodeGenMeasurementPredictor"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_code_gen_measuremet_pred.CodeGenMeasurementPredictorConfig",
+    "AutoModelForSequenceClassification": "modeling_code_gen_measurement_pred.CodeGenMeasurementPredictor"
+  },
+  "bos_token_id": 1,
+  "emb_dim": 1024,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "codegen_mp",
+  "n_ctx": 2048,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 20,
+  "n_positions": 2048,
+  "n_sensors": 3,
+  "resid_pdrop": 0.0,
+  "rotary_dim": 32,
+  "scale_attn_weights": true,
+  "sensor_token": " omit",
+  "sensor_token_id": 42848,
+  "sensors_weight": 0.7,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50,
+      "temperature": 1.0
+    }
+  },
+  "tie_word_embeddings": false,
+  "tokenizer_class": "GPT2Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0",
+  "use_aggregated": true,
+  "use_cache": false,
+  "vocab_size": 51200
+}

configuration_code_gen_measuremet_pred.py

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+from transformers.models.codegen import CodeGenConfig
+from .configuration_measurement_pred import MeasurementPredictorConfig
+
+class CodeGenMeasurementPredictorConfig(MeasurementPredictorConfig, CodeGenConfig):
+    model_type = "codegen_mp"
+    def __init__(self, **kwargs):
+        kwargs["sensor_token_id"] = 42848
+        super().__init__(**kwargs)
+    
+    def get_emb_dim(self):
+        return self.n_embd

configuration_measurement_pred.py

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+from abc import abstractmethod
+from transformers import PretrainedConfig
+
+class MeasurementPredictorConfig(PretrainedConfig):
+    
+    def __init__(
+        self, 
+        sensor_token=" omit",
+        sensor_token_id=None, # 35991
+        n_sensors=3,
+        use_aggregated=True,
+        sensors_weight = 0.7,
+        aggregate_weight=0.3,
+        **kwargs
+    ):
+        self.sensor_token = sensor_token 
+        self.sensor_token_id = sensor_token_id
+        self.n_sensors = n_sensors
+        self.use_aggregated = use_aggregated
+        self.sensors_weight = sensors_weight
+        self.aggregate_weight = aggregate_weight
+        super().__init__(**kwargs)
+        self.emb_dim = self.get_emb_dim()
+    
+    @abstractmethod
+    def get_emb_dim(self):
+        raise NotImplementedError

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4c602a3fa565169091c0b8cee7bddc3e3d2a1be4fabdd566037282a764a6b7c2
+size 1216963976

modeling_code_gen_measurement_pred.py

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+from transformers.models.codegen import CodeGenPreTrainedModel, CodeGenModel
+
+from .modeling_measurement_pred import MeasurementPredictorMixin
+from .configuration_code_gen_measuremet_pred import CodeGenMeasurementPredictorConfig
+
+
+class CodeGenMeasurementPredictor(CodeGenPreTrainedModel, MeasurementPredictorMixin):
+    config_class = CodeGenMeasurementPredictorConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = CodeGenModel(config)
+        self.post_init()

modeling_measurement_pred.py

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+from typing import Optional, Tuple, Union
+
+import torch
+from torch.nn import BCEWithLogitsLoss
+from transformers import PreTrainedModel, PreTrainedTokenizer
+from transformers.modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast
+
+class MeasurementPredictorMixin(PreTrainedModel):
+    
+    def __init__(self, config):
+        super().__init__(config)
+        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)
+        ])
+        self.use_aggregated = config.use_aggregated
+        if config.use_aggregated:
+            self.aggregate_probe = torch.nn.Linear(config.emb_dim, 1)
+        self.sensors_weight = config.sensors_weight
+        self.aggregate_weight = config.aggregate_weight
+    
+    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 forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        base_model_output: BaseModelOutputWithPast = self.base_model(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        tensor_token_mask = torch.where(input_ids == self.sensor_token_id)[1]
+        sensor_embs = base_model_output.last_hidden_state[:, tensor_token_mask, :]
+        sensor_logits = torch.concat([self.sensor_probes[i](sensor_embs[:, i, :]) 
+                               for i in range(self.n_sensors)], dim=-1)
+        logits = sensor_logits
+
+        if self.use_aggregated:
+            last_emb = base_model_output.last_hidden_state[:, -1, :]
+            aggregate_logits = self.aggregate_probe(last_emb)
+            logits = torch.concat([logits, aggregate_logits], dim=-1)
+        
+        loss = None
+        if labels is not None:
+            loss_fct = BCEWithLogitsLoss()
+            sensor_loss = loss_fct(sensor_logits, labels[:, :self.n_sensors]) * self.sensors_weight
+            loss = sensor_loss
+            if self.use_aggregated: #TOOD: should be use aggregate
+                aggregate_loss = loss_fct(aggregate_logits, labels[:, -1:]) * self.aggregate_weight
+                loss += aggregate_loss
+
+        if not return_dict:
+            output = (logits, ) + base_model_output[1:]
+            return ((loss,) + output) if loss is not None else output 
+        
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=base_model_output.past_key_values,
+            hidden_states=base_model_output.hidden_states,
+            attentions=base_model_output.attentions,
+        )
+