Upload PureQwen2ForSequenceClassification

config.json

@@ -1,11 +1,13 @@
 {
+  "_name_or_path": "Qwen/Qwen2.5-0.5B",
   "alpha": 1,
   "architectures": [
-    "Qwen2ForCausalLM"
+    "PureQwen2ForSequenceClassification"
   ],
   "attention_dropout": 0.0,
   "auto_map": {
-    "AutoConfig": "configuration_pure_qwen2.PureQwen2Config"
+    "AutoConfig": "configuration_pure_qwen2.PureQwen2Config",
+    "AutoModelForSequenceClassification": "modeling_pure_qwen2.PureQwen2ForSequenceClassification"
   },
   "bos_token_id": 151643,
   "center": false,
@@ -31,7 +33,7 @@
   "sliding_window": null,
   "svd_rank": 5,
   "tie_word_embeddings": true,
-  "torch_dtype": "bfloat16",
+  "torch_dtype": "float32",
   "transformers_version": "4.46.2",
   "use_cache": true,
   "use_mrope": false,

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:baab20dc9577db688c4ada7822066b4be311fb9d765f1f9c4cfee7c9df6f8486
+size 1976170728

modeling_pure_qwen2.py

@@ -0,0 +1,605 @@
+import os
+from typing import Union, Tuple, Optional, List
+
+import torch
+import torch.nn as nn
+from torch.autograd import Function
+from transformers import PreTrainedModel
+from transformers.models.qwen2.modeling_qwen2 import (
+    Qwen2DecoderLayer, Qwen2RMSNorm, Qwen2RotaryEmbedding
+)
+from transformers.utils import logging
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import (
+    SequenceClassifierOutput, 
+    BaseModelOutputWithPast
+)
+from transformers.utils import ModelOutput
+
+from .configuration_pure_qwen2 import PureQwen2Config
+
+logger = logging.get_logger(__name__)
+
+
+class CovarianceFunction(Function):
+
+    @staticmethod
+    def forward(ctx, inputs):
+        x = inputs
+        b, c, h, w = x.data.shape
+        m = h * w
+        x = x.view(b, c, m)
+        I_hat = (-1.0 / m / m) * torch.ones(m, m, device=x.device) + (
+            1.0 / m
+        ) * torch.eye(m, m, device=x.device)
+        I_hat = I_hat.view(1, m, m).repeat(b, 1, 1).type(x.dtype)
+        y = x @ I_hat @ x.transpose(-1, -2)
+        ctx.save_for_backward(inputs, I_hat)
+        return y
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        inputs, I_hat = ctx.saved_tensors
+        x = inputs
+        b, c, h, w = x.data.shape
+        m = h * w
+        x = x.view(b, c, m)
+        grad_input = grad_output + grad_output.transpose(1, 2)
+        grad_input = grad_input @ x @ I_hat
+        grad_input = grad_input.reshape(b, c, h, w)
+        return grad_input
+
+
+class Covariance(nn.Module):
+
+    def __init__(self):
+        super(Covariance, self).__init__()
+        
+    def _covariance(self, x):
+        return CovarianceFunction.apply(x)
+
+    def forward(self, x):
+        # x should be [batch_size, seq_len, embed_dim]
+        if x.dim() == 2:
+            x = x.transpose(-1, -2)
+            C = self._covariance(x[None, :, :, None])
+        C = C.squeeze(dim=0)
+        return C
+
+
+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_in = x[:, :int(mask.sum().item()), :]
+            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, 
+        svd_rank=16, 
+        num_pc_to_remove=1, 
+        center=False, 
+        num_iters=2, 
+        alpha=1, 
+        disable_pcr=False, 
+        disable_pfsa=False, 
+        disable_covariance=True, 
+        *args, **kwargs
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.svd_rank = svd_rank
+        self.num_pc_to_remove = num_pc_to_remove
+        self.center = center
+        self.num_iters = num_iters
+        self.do_pcr = not disable_pcr
+        self.do_pfsa = not disable_pfsa
+        self.do_covariance = not disable_covariance
+        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):
+            rank = int(mask.sum().item())
+            x = x[:rank, :]
+            if self.do_covariance:
+                x = Covariance()(x)
+                q = self.svd_rank
+            else:
+                q = min(self.svd_rank, rank)
+            _, _, V = torch.pca_lowrank(x, q=q, center=self.center, niter=self.num_iters)
+            # _, _, Vh = torch.linalg.svd(x_, full_matrices=False)
+            # V = Vh.mH
+            pc = V.transpose(0, 1)[:self.num_pc_to_remove, :] # 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 PureQwen2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = PureQwen2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen2DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class PureQwen2Model(PureQwen2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
+
+    Args:
+        config: Qwen2Config
+    """
+
+    def __init__(self, config: PureQwen2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+        
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class PureQwen2ForSequenceClassification(PureQwen2PreTrainedModel):
+
+    def __init__(
+        self, 
+        config, 
+        label_smoothing=0.0, 
+    ):
+        super().__init__(config)
+        self.label_smoothing = label_smoothing
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.model = PureQwen2Model(config)
+        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.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward_pure_embeddings(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[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, ModelOutput]:
+        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
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        token_embeddings = transformer_outputs[0]
+        
+        token_embeddings = self.pure(token_embeddings, attention_mask)
+
+        return ModelOutput(
+            last_hidden_state=token_embeddings,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[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,
+        # position_ids: Optional[torch.LongTensor] = 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.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        token_embeddings = outputs[0]
+        
+        token_embeddings = self.pure(token_embeddings, attention_mask)
+        pooled_output = self.mean(token_embeddings).squeeze(1)
+        logits = self.score(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(label_smoothing=self.label_smoothing)
+                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,
+        )