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backpack_config.py

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+from transformers import GPT2Config
+
+
+class BackpackGPT2Config(GPT2Config):
+    model_type = "backpack-gpt2"
+
+    def __init__(
+        self,
+        num_senses: int = 16,
+        sense_intermediate_scale: int = 4,
+        vocab_size: int = 50264,
+        n_positions: int = 512,
+        scale_attn_by_inverse_layer_idx: bool = True,
+        **kwargs,
+    ):
+        self.num_senses = num_senses
+        self.sense_intermediate_scale = sense_intermediate_scale
+        super().__init__(
+            vocab_size=vocab_size,
+            n_positions=n_positions,
+            scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
+            **kwargs,
+        )

backpack_model.py

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+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.pytorch_utils import Conv1D
+from transformers.utils import ModelOutput
+from transformers import GPT2PreTrainedModel, GPT2Model
+from .backpack_config import BackpackGPT2Config
+
+
+### Backpack-Specific
+class BackpackGPT2PreTrainedModel(GPT2PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias"]
+
+    config_class = BackpackGPT2Config
+    base_model_prefix = "backpack"
+    is_parallelizable = True
+    supports_gradient_checkpointing = False
+    _no_split_modules = ["GPT2Block", "BackpackNoMixBlock"]
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+
+class BackpackMLP(nn.Module):
+    def __init__(self, embed_dim, intermediate_dim, out_dim, config):
+        super().__init__()
+        self.c_fc = Conv1D(intermediate_dim, embed_dim)
+        self.c_proj = Conv1D(out_dim, intermediate_dim)
+        self.act = ACT2FN[config.activation_function]
+        self.dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(
+        self, hidden_states: Optional[Tuple[torch.FloatTensor]]
+    ) -> torch.FloatTensor:
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.c_proj(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class BackpackNoMixBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.mlp = BackpackMLP(config.n_embd, config.n_embd * 4, config.n_embd, config)
+        self.resid_dropout1 = nn.Dropout(config.resid_pdrop)
+        self.resid_dropout2 = nn.Dropout(config.resid_pdrop)
+
+    def forward(self, hidden_states, residual):
+        residual = self.resid_dropout1(hidden_states) + residual
+        hidden_states = self.ln_1(residual)
+        mlp_out = self.mlp(hidden_states)
+        residual = self.resid_dropout2(mlp_out) + residual
+        hidden_states = self.ln_2(residual)
+        return hidden_states
+
+
+class BackpackSenseNetwork(nn.Module):
+    def __init__(self, config, num_senses, device=None, dtype=None):
+        super().__init__()
+        self.num_senses = num_senses
+        # self.embeddings = embeddings
+        self.n_embd = config.n_embd
+
+        self.dropout = nn.Dropout(config.embd_pdrop)
+        self.block = BackpackNoMixBlock(config)
+        self.ln = nn.LayerNorm(self.n_embd, eps=config.layer_norm_epsilon)
+        self.final_mlp = BackpackMLP(
+            embed_dim=config.n_embd,
+            intermediate_dim=config.sense_intermediate_scale * config.n_embd,
+            out_dim=config.n_embd * config.num_senses,
+            config=config,
+        )
+
+    def forward(self, input_embeds):
+        residual = self.dropout(input_embeds)
+        hidden_states = self.ln(residual)
+        hidden_states = self.block(hidden_states, residual)
+        senses = self.final_mlp(hidden_states)
+        bs, s, nvd = senses.shape
+        return senses.reshape(bs, s, self.num_senses, self.n_embd).transpose(
+            1, 2
+        )  # (bs, nv, s, d)
+
+
+class BackpackWeightNetwork(nn.Module):
+    def __init__(self, num_senses, embed_dim):
+        super().__init__()
+        self.n_embd = embed_dim
+        self.num_senses = num_senses
+        self.embed_per_sense = embed_dim // num_senses
+        self.c_attn = nn.Linear(embed_dim, 2 * num_senses * self.embed_per_sense)
+        self.softmax_scale = None
+
+    def forward(self, encoded):
+        b, s, d = encoded.shape
+        encoded = self.c_attn(encoded)  # (b, s, 2*d)
+        encoded = encoded.reshape(
+            b, s, 2, self.num_senses, self.embed_per_sense
+        )  # (b, s, 2, nv, d//nv)
+        batch_size, seqlen = encoded.shape[0], encoded.shape[1]
+
+        # compute scores & mask
+        q, k = encoded.unbind(dim=2)
+        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+        causal_mask = torch.triu(
+            torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1
+        )
+        scores = scores + causal_mask.to(dtype=scores.dtype)
+
+        return torch.softmax(scores, dim=-1, dtype=q.dtype)
+
+
+@dataclass
+class BackpackGPT2BaseModelOutput(ModelOutput):
+    hidden_states: torch.FloatTensor = None
+    contextualization: torch.FloatTensor = None
+
+
+class BackpackGPT2Model(BackpackGPT2PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r".*attn.masked_bias", r".*attn.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embed_dim = config.n_embd
+
+        self.num_senses = config.num_senses
+        self.gpt2_model = GPT2Model(config)
+        self.sense_network = BackpackSenseNetwork(
+            config, self.num_senses, self.gpt2_model.wte
+        )
+        self.word_embeddings = self.gpt2_model.wte
+        self.position_embeddings = self.gpt2_model.wpe
+        self.sense_weight_net = BackpackWeightNetwork(self.num_senses, self.embed_dim)
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.gradient_checkpointing = False
+
+    def get_num_senses(self):
+        return self.num_senses
+
+    def get_word_embeddings(self):
+        return self.word_embeddings
+
+    def get_sense_network(self):
+        return self.sense_network
+
+    def forward(self, input_ids, position_ids: Optional[torch.LongTensor] = None):
+        # Compute senses
+        sense_input_embeds = self.word_embeddings(input_ids)
+        senses = self.sense_network(sense_input_embeds)  # (bs, nv, s, d)
+
+        # Compute contextualization weights
+        contextl_hidden_states = self.gpt2_model(
+            input_ids, position_ids=position_ids
+        ).last_hidden_state  # (bs, s, d)
+        contextualization = self.sense_weight_net(
+            contextl_hidden_states
+        )  # (bs, nv, s, s)
+
+        # Compute resulting outputs
+        hidden_states = torch.sum(
+            contextualization @ senses, dim=1
+        )  # (bs, nv, s, d) -> (bs, s, d)
+
+        # divide hidden_states by 1 / num_senses
+        hidden_states = hidden_states / self.num_senses
+
+        return BackpackGPT2BaseModelOutput(
+            hidden_states=hidden_states,
+            contextualization=contextualization,
+        )
+
+    def run_with_custom_contextualization(self, input_ids, contextualization):
+        # Compute senses
+        sense_input_embeds = self.word_embeddings(input_ids)
+        senses = self.sense_network(sense_input_embeds)  # (bs, nv, s, d)
+
+        # Compute resulting outputs
+        hidden_states = torch.sum(
+            contextualization @ senses, dim=1
+        )  # (bs, nv, s, d) -> (bs, s, d)
+        return BackpackGPT2BaseModelOutput(
+            hidden_states=hidden_states,
+            contextualization=contextualization,
+        )
+
+
+@dataclass
+class BackpackGPT2LMHeadModelOutput(ModelOutput):
+    logits: torch.FloatTensor = None
+    contextualization: torch.FloatTensor = None
+
+
+class BackpackGPT2LMHeadModel(BackpackGPT2PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r".*attn.masked_bias", r".*attn.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.backpack = BackpackGPT2Model(config)
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+    def get_lm_head(self):
+        return self.lm_head
+
+    def forward(self, input_ids, position_ids=None):
+        outputs = self.backpack(input_ids, position_ids=position_ids)
+        hidden_states, contextualization = (
+            outputs.hidden_states,
+            outputs.contextualization,
+        )
+        # unembed the hidden_states
+        lm_logits = torch.einsum(
+            "bsd,nd->bsn", hidden_states, self.backpack.word_embeddings.weight
+        )
+        return BackpackGPT2LMHeadModelOutput(
+            logits=lm_logits,
+            contextualization=contextualization,
+        )
+
+    def run_with_custom_contextualization(self, input_ids, contextualization):
+        outputs = self.backpack.run_with_custom_contextualization(
+            input_ids, contextualization
+        )
+        hidden_states, contextualization = (
+            outputs.hidden_states,
+            outputs.contextualization,
+        )
+        lm_logits = self.lm_head(hidden_states)
+        return BackpackGPT2LMHeadModelOutput(
+            logits=lm_logits,
+            contextualization=contextualization,
+        )

config.json

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+{"return_dict": true, "output_hidden_states": false, "output_attentions": false, "torchscript": false, "torch_dtype": null, "use_bfloat16": false, "tf_legacy_loss": false, "pruned_heads": {}, "tie_word_embeddings": true, "is_encoder_decoder": false, "is_decoder": false, "cross_attention_hidden_size": null, "add_cross_attention": false, "tie_encoder_decoder": false, "max_length": 20, "min_length": 0, "do_sample": false, "early_stopping": false, "num_beams": 1, "num_beam_groups": 1, "diversity_penalty": 0.0, "temperature": 1.0, "top_k": 50, "top_p": 1.0, "typical_p": 1.0, "repetition_penalty": 1.0, "length_penalty": 1.0, "no_repeat_ngram_size": 0, "encoder_no_repeat_ngram_size": 0, "bad_words_ids": null, "num_return_sequences": 1, "chunk_size_feed_forward": 0, "output_scores": false, "return_dict_in_generate": false, "forced_bos_token_id": null, "forced_eos_token_id": null, "remove_invalid_values": false, "exponential_decay_length_penalty": null, "suppress_tokens": null, "begin_suppress_tokens": null, "architectures": null, "finetuning_task": null, "id2label": {"0": "LABEL_0", "1": "LABEL_1"}, "label2id": {"LABEL_0": 0, "LABEL_1": 1}, "tokenizer_class": null, "prefix": null, "bos_token_id": null, "pad_token_id": null, "eos_token_id": null, "sep_token_id": null, "decoder_start_token_id": null, "task_specific_params": null, "problem_type": null, "_name_or_path": "", "transformers_version": "4.29.2", "vocab_size": 50264, "n_positions": 512, "n_layer": 36, "n_head": 20, "n_embd": 1280, "initializer_range": 0.02, "attn_pdrop": 0.0, "embd_pdrop": 0.0, "layer_norm_epsilon": 1e-05, "activation_function": "gelu_new", "scale_attn_by_inverse_layer_idx": true, "reorder_and_upcast_attn": false, "num_senses": 48, "sense_intermediate_scale": 7, "auto_map": {"AutoConfig": "backpack_config.BackpackGPT2Config", "AutoModelForCausalLM": "backpack_model.BackpackGPT2LMHeadModel"}, "model_type": "backpack-gpt2"}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:95a0528c52856a01a9de3bb640e77beb40a478324423f71570de9f1557316247
+size 5666995936

special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1024,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}