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config.json

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+{
+  "architectures": [
+    "STEPFinetuningModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "step_finetune.STEPFinetuningModelConfig",
+    "AutoModel": "step_finetune.STEPFinetuningModel",
+    "AutoModelForSeq2SeqLM": "step_finetune.STEPFinetuningModel"
+  },
+  "classifier_dropout": 0.0,
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "STEP_finetuning",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 32128,
+  "num_precomputed_examples": 1000,
+  "num_examples": 512,
+  "prefix_length": 10,
+  "prefix_max_init_length": 20,
+  "random_selection": true
+}

step_finetune.py

@@ -0,0 +1,124 @@
+import torch
+from transformers import AutoTokenizer, PretrainedConfig, T5Config, PreTrainedModel, T5ForConditionalGeneration, \
+    AutoModelForSeq2SeqLM, Adafactor
+    
+from typing import Optional, List, Callable, Mapping, Any, Union
+import os
+
+class STEPFinetuningModelConfig(T5Config):
+    model_type = "STEP_finetune"
+    
+    def __init__(self,
+                 num_examples: int = 512,
+                 prefix_length: int = 10,
+                 random_selection: bool = True,
+                 # don't change these unless you change what the prefix of the model is initialized with:
+                 prefix_max_init_length: int = 20,
+                 num_precomputed_examples: int = 1000,
+                 **kwargs):
+        # These are all about the initialization of the prefix.
+        self.num_examples = num_examples
+        self.prefix_length = prefix_length
+        self.random_selection = random_selection
+        self.prefix_max_init_length = prefix_max_init_length
+        self.num_precomputed_examples = num_precomputed_examples
+        super().__init__(**kwargs)
+
+
+
+class STEPFinetuningModel(PreTrainedModel):
+    config_class = STEPFinetuningModelConfig
+
+    def __init__(self, config: STEPFinetuningModelConfig):
+        super().__init__(config)
+
+        self.model = T5ForConditionalGeneration(config)
+
+        # Initialize the prefix with NaNs.
+        self.register_buffer("prefix_init_tensor", torch.zeros(config.num_precomputed_examples, config.prefix_max_init_length, config.d_model))
+
+        # There are two cases: (1) we initialize the model after STEP-pretraining, i.e. the tunable prefix is not set
+        # and (2) the model has been fine-tuned on downstream data, and hence there is meaningful data in the tunable prefix
+
+        # Initialize the prefix with NaNs. If we initialize from STEP-pretraining, this will not be overwritten by a custom version of from_pretrained
+        # if we initialize after fine-tuning, the NaNs will be overwritten anyway.
+
+        self.prefix_embedding = torch.nn.Parameter(torch.nan + torch.zeros((1, self.config.prefix_length, self.config.d_model)))
+        self.prefix_has_been_initialized = False
+
+    def _initialize_prefix(self):
+        prefix_init_tensor = self.prefix_init_tensor
+        if self.config.random_selection:
+            # randomize selection of FSTs to average for initialization the prefix.
+            prefix_init_tensor = prefix_init_tensor[torch.randperm(prefix_init_tensor.shape[0]), :, :]
+
+        prefix_init_tensor = prefix_init_tensor[:self.config.num_examples, :self.config.prefix_length,
+                             :]  # shape (num ex, prefix length, d model)
+        self.prefix_embedding.data.copy_(prefix_init_tensor.mean(dim=0, keepdims=True))
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        **kwargs,
+    ):
+        model = super(STEPFinetuningModel, cls).from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        if torch.all(model.prefix_embedding.isnan()):
+            model._initialize_prefix()
+        return model
+
+
+    def prepare_input(self, kwargs):
+        """
+        Prepends the prefix to the given input.
+        :param kwargs:
+        :return:
+        """
+        input_ids = kwargs["input_ids"]
+
+        embedded_inputs = self.model.get_input_embeddings()(input_ids)
+
+        batch_size = input_ids.shape[0]
+
+        prefix = torch.repeat_interleave(self.prefix_embedding, batch_size, 0) #shape (batch, prefix length, embed dim)
+
+        kwargs = dict(kwargs)
+
+        embedded_inputs = torch.cat([prefix, embedded_inputs], dim=1)  # shape (batch, prefix + seq length, embed dim)
+
+        del kwargs["input_ids"]
+        kwargs["inputs_embeds"] = embedded_inputs
+
+        if "attention_mask" in kwargs:
+            ones = torch.ones((batch_size, self.config.prefix_length), device=embedded_inputs.device, dtype=kwargs["attention_mask"].dtype)
+            input_mask = torch.cat([ones, kwargs["attention_mask"]], dim=1)
+            kwargs["attention_mask"] = input_mask
+
+        return kwargs
+
+    def forward(self, **kwargs):
+        return self.model(**self.prepare_input(kwargs))
+
+    def generate(self, **kwargs):
+        return self.model.generate(**self.prepare_input(kwargs))
+
+
+    def get_optimizer(self, optimizer: Callable[..., torch.optim.Optimizer] = None, prefix_lr:float = 10.0, **kwargs) -> torch.optim.Optimizer:
+        """
+        Return an optimizer that uses a different learning rate (typically higher) for the prefix than for the rest of the model.
+        """
+
+        prefix_params = []
+        other_params = []
+        for name, param in self.named_parameters():
+            if name == "prefix_embedding":
+                prefix_params.append(param)
+            else:
+                other_params.append(param)
+        if optimizer is None:
+            # The optimizer used in the paper.
+            hparams = {"scale_parameter": False, "relative_step": False, "warmup_init": False, "lr": 1e-4}
+            return Adafactor(params=[{"params": prefix_params, "lr": prefix_lr}, {"params": other_params}], **(hparams | kwargs))
+        return optimizer(params=[{"params": prefix_params, "lr": prefix_lr}, {"params": other_params}], **kwargs)
+