Upload PureDebertaForSequenceClassification

config.json

@@ -1,8 +1,13 @@
 {
+  "_name_or_path": "microsoft/deberta-v3-large",
   "alpha": 1,
+  "architectures": [
+    "PureDebertaForSequenceClassification"
+  ],
   "attention_probs_dropout_prob": 0.1,
   "auto_map": {
-    "AutoConfig": "configuration_pure_deberta.PureDebertaConfig"
+    "AutoConfig": "configuration_pure_deberta.PureDebertaConfig",
+    "AutoModelForSequenceClassification": "modeling_pure_deberta.PureDebertaForSequenceClassification"
   },
   "center": false,
   "disable_covariance": true,
@@ -35,6 +40,7 @@
   "relative_attention": true,
   "share_att_key": true,
   "svd_rank": 5,
+  "torch_dtype": "float32",
   "transformers_version": "4.46.2",
   "type_vocab_size": 0,
   "vocab_size": 128100

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a506f94bfac95d49b2c7729c2ce3956233a9963c6e777dfbd17c4b314331d56b
+size 1736105856

modeling_pure_deberta.py

@@ -0,0 +1,337 @@
+import torch
+import torch.nn as nn
+from transformers import (
+    DebertaV2Model,
+    PreTrainedModel,
+)
+from transformers.models.deberta_v2.modeling_deberta_v2 import StableDropout
+from typing import Union, Tuple, Optional
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+from .configuration_pure_deberta import PureDebertaConfig
+
+
+class PFSA(torch.nn.Module):
+    """
+    https://openreview.net/pdf?id=isodM5jTA7h
+    """
+    def __init__(self, input_dim, alpha=1):
+        super(PFSA, self).__init__()
+        self.input_dim = input_dim
+        self.alpha = alpha
+
+    def forward_one_sample(self, x):
+        x = x.transpose(1, 2)[..., None]
+        k = torch.mean(x, dim=[-1, -2], keepdim=True)
+        kd = torch.sqrt((k - k.mean(dim=1, keepdim=True)).pow(2).sum(dim=1, keepdim=True)) # [B, 1, 1, 1]
+        qd = torch.sqrt((x - x.mean(dim=1, keepdim=True)).pow(2).sum(dim=1, keepdim=True)) # [B, 1, T, 1]
+        C_qk = (((x - x.mean(dim=1, keepdim=True)) * (k - k.mean(dim=1, keepdim=True))).sum(dim=1, keepdim=True)) / (qd * kd)
+        A = (1 - torch.sigmoid(C_qk)) ** self.alpha
+        out = x * A
+        out = out.squeeze(dim=-1).transpose(1, 2)
+        return out
+
+    def forward(self, input_values, attention_mask=None):
+        """
+        x: [B, T, F]
+        """
+        out = []
+        b, t, f = input_values.shape
+        for x, mask in zip(input_values, attention_mask):
+            x = x.view(1, t, f)
+            x_in = x[:, :sum(mask), :]
+            x_out = self.forward_one_sample(x_in)
+            x_expanded = torch.zeros_like(x, device=x.device)
+            x_expanded[:, :x_out.shape[-2], :x_out.shape[-1]] = x_out
+            out.append(x_expanded)
+        out = torch.vstack(out)
+        out = out.view(b, t, f)
+        return out
+
+
+class PURE(torch.nn.Module):
+
+    def __init__(
+        self, 
+        in_dim, 
+        target_rank=5, 
+        npc=1, 
+        center=False, 
+        num_iters=1, 
+        alpha=1, 
+        do_pcr=True, 
+        do_pfsa=True, 
+        *args, **kwargs
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.target_rank = target_rank
+        self.npc = npc
+        self.center = center
+        self.num_iters = num_iters
+        self.do_pcr = do_pcr
+        self.do_pfsa = do_pfsa
+        self.attention = PFSA(in_dim, alpha=alpha)
+    
+    def _compute_pc(self, X, attention_mask):
+        """
+        x: (B, T, F)
+        """
+        pcs = []
+        bs, seqlen, dim = X.shape
+        for x, mask in zip(X, attention_mask):
+            x_ = x[:sum(mask), :]
+            q = min(self.target_rank, sum(mask))
+            _, _, V = torch.pca_lowrank(x_, q=q, center=self.center, niter=self.num_iters)
+            pc = V.transpose(0, 1)[:self.npc, :] # pc: [K, F]
+            pcs.append(pc)
+        # pcs = torch.vstack(pcs)
+        # pcs = pcs.view(bs, self.num_pc_to_remove, dim)
+        return pcs
+
+    def _remove_pc(self, X, pcs):
+        """
+        [B, T, F], [B, ..., F]
+        """
+        b, t, f = X.shape
+        out = []
+        for i, (x, pc) in enumerate(zip(X, pcs)):
+            # v = []
+            # for j, t in enumerate(x):
+            #     t_ = t
+            #     for c_ in c:
+            #         t_ = t_.view(f, 1) - c_.view(f, 1) @ c_.view(1, f) @ t.view(f, 1)
+            #     v.append(t_.transpose(-1, -2))
+            # v = torch.vstack(v)
+            v = x - x @ pc.transpose(0, 1) @ pc
+            out.append(v[None, ...])
+        out = torch.vstack(out)
+        return out
+
+    def forward(self, input_values, attention_mask=None, *args, **kwargs):
+        """
+        PCR -> Attention
+        x: (B, T, F)
+        """
+        x = input_values
+        if self.do_pcr:
+            pc = self._compute_pc(x, attention_mask) # pc: [B, K, F]
+            xx = self._remove_pc(x, pc)
+            # xx = xt - xt @ pc.transpose(1, 2) @ pc # [B, T, F] * [B, F, K] * [B, K, F] = [B, T, F]
+        else:
+            xx = x
+        if self.do_pfsa:
+            xx = self.attention(xx, attention_mask)
+        return xx
+
+
+class StatisticsPooling(torch.nn.Module):
+
+    def __init__(self, return_mean=True, return_std=True):
+        super().__init__()
+
+        # Small value for GaussNoise
+        self.eps = 1e-5
+        self.return_mean = return_mean
+        self.return_std = return_std
+        if not (self.return_mean or self.return_std):
+            raise ValueError(
+                "both of statistics are equal to False \n"
+                "consider enabling mean and/or std statistic pooling"
+            )
+
+    def forward(self, input_values, attention_mask=None):
+        """Calculates mean and std for a batch (input tensor).
+
+        Arguments
+        ---------
+        x : torch.Tensor
+            It represents a tensor for a mini-batch.
+        """
+        x = input_values
+        if attention_mask is None:
+            if self.return_mean:
+                mean = x.mean(dim=1)
+            if self.return_std:
+                std = x.std(dim=1)
+        else:
+            mean = []
+            std = []
+            for snt_id in range(x.shape[0]):
+                # Avoiding padded time steps
+                lengths = torch.sum(attention_mask, dim=1)
+                relative_lengths = lengths / torch.max(lengths)
+                actual_size = torch.round(relative_lengths[snt_id] * x.shape[1]).int()
+                # actual_size = int(torch.round(lengths[snt_id] * x.shape[1]))
+
+                # computing statistics
+                if self.return_mean:
+                    mean.append(
+                        torch.mean(x[snt_id, 0:actual_size, ...], dim=0)
+                    )
+                if self.return_std:
+                    std.append(torch.std(x[snt_id, 0:actual_size, ...], dim=0))
+            if self.return_mean:
+                mean = torch.stack(mean)
+            if self.return_std:
+                std = torch.stack(std)
+
+        if self.return_mean:
+            gnoise = self._get_gauss_noise(mean.size(), device=mean.device)
+            gnoise = gnoise
+            mean += gnoise
+        if self.return_std:
+            std = std + self.eps
+
+        # Append mean and std of the batch
+        if self.return_mean and self.return_std:
+            pooled_stats = torch.cat((mean, std), dim=1)
+            pooled_stats = pooled_stats.unsqueeze(1)
+        elif self.return_mean:
+            pooled_stats = mean.unsqueeze(1)
+        elif self.return_std:
+            pooled_stats = std.unsqueeze(1)
+
+        return pooled_stats
+
+    def _get_gauss_noise(self, shape_of_tensor, device="cpu"):
+        """Returns a tensor of epsilon Gaussian noise.
+
+        Arguments
+        ---------
+        shape_of_tensor : tensor
+            It represents the size of tensor for generating Gaussian noise.
+        """
+        gnoise = torch.randn(shape_of_tensor, device=device)
+        gnoise -= torch.min(gnoise)
+        gnoise /= torch.max(gnoise)
+        gnoise = self.eps * ((1 - 9) * gnoise + 9)
+
+        return gnoise
+
+
+class DebertaV2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = PureDebertaConfig
+    base_model_prefix = "deberta"
+    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class PureDebertaForSequenceClassification(DebertaV2PreTrainedModel):
+
+    def __init__(
+        self, 
+        config, 
+    ):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.deberta = DebertaV2Model(config)
+        drop_out = getattr(config, "cls_dropout", None)
+        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
+        self.pure = PURE(
+            in_dim=config.hidden_size, 
+            svd_rank=config.svd_rank, 
+            num_pc_to_remove=config.num_pc_to_remove, 
+            center=config.center, 
+            num_iters=config.num_iters, 
+            alpha=config.alpha, 
+            disable_pcr=config.disable_pcr, 
+            disable_pfsa=config.disable_pfsa, 
+            disable_covariance=config.disable_covariance
+        )
+        self.mean = StatisticsPooling(return_mean=True, return_std=False)
+        self.dropout = StableDropout(drop_out)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.deberta(
+            input_ids,
+            token_type_ids=token_type_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        token_embeddings = outputs.last_hidden_state
+        token_embeddings = self.pure(token_embeddings, attention_mask)
+        pooled_output = self.mean(token_embeddings).squeeze(1)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )