Delete avey_model/modellin_avey.py

avey_model/modellin_avey.py

@@ -1,396 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import PreTrainedModel
-from transformers.modeling_outputs import (
-    BaseModelOutput,
-    MaskedLMOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput
-)
-from configuration_avey import AveyConfig
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from torch.utils.checkpoint import checkpoint
-
-class Contextualizer(nn.Module):
-    def __init__(self, config: AveyConfig, layer_idx):
-        super().__init__()
-        self.eps = config.eps
-        self.layer_idx = layer_idx
-        if self.layer_idx % 2 == 0:
-            self.spatial_proj = nn.Parameter(torch.empty(config.chunk_size, config.chunk_size))
-            nn.init.xavier_normal_(self.spatial_proj)
-
-    def cosim(self, embeddings: torch.Tensor) -> torch.Tensor:
-        norm = torch.sqrt(torch.sum(embeddings ** 2, dim=-1, keepdim=True) + self.eps)
-        normalized = embeddings / norm
-        cosim = torch.matmul(normalized, normalized.transpose(-1, -2))
-        return cosim
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        _, T, _ = x.shape
-        x0, x1 = x.chunk(2, dim=-1)
-        if self.layer_idx % 2 == 0:
-            x0 = self.spatial_proj[:T, :T] @ x0
-        else:
-            sim_scores = self.cosim(x0)
-            row_sums = sim_scores.sum(dim=-1, keepdim=True)
-            sim_scores = sim_scores / (row_sums + self.eps)
-            x0 = sim_scores @ x0
-        output = x0 * x1
-        return output
-
-
-class ContextualizerLayer(nn.Module):
-    def __init__(self, config: AveyConfig, layer_idx):
-        super().__init__()
-        expanded_dim = config.d_embed * config.expansion_factor
-        self.split_factor = [
-            int(expanded_dim * config.context_proportion),
-            int(expanded_dim * (1-config.context_proportion))
-        ]
-        diff = expanded_dim - (self.split_factor[0] + self.split_factor[1])
-        self.split_factor[1] += diff
-        if self.split_factor[0] % 2 != 0:
-            self.split_factor[0] += 1
-            self.split_factor[1] -= 1
-
-        self.enricher = nn.Linear(config.d_embed, expanded_dim)
-        self.contextualizer = Contextualizer(config, layer_idx)
-        proj_in_features = int(self.split_factor[0] / 2 + self.split_factor[1])
-        self.fuser = nn.Linear(proj_in_features, config.d_embed)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x_proj = F.relu(self.enricher(x)).square()
-        x0, x1 = x_proj.split(self.split_factor, dim=-1)
-        x0 = self.contextualizer(x0)
-        out = self.fuser(torch.cat([x0, x1], dim=-1))
-        return out
-
-
-class AveyLayer(nn.Module):
-    def __init__(self, config: AveyConfig, layer_idx):
-        super().__init__()
-        self.rms_norm = nn.RMSNorm(config.d_embed, eps=config.eps)
-        self.ctxt = ContextualizerLayer(config, layer_idx)
-
-    @torch.compile()
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return x + self.ctxt(self.rms_norm(x))
-
-
-class Ranker(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.chunk_size = config.chunk_size
-        self.k = config.k + 1
-        self.extended_len = self.k * config.chunk_size
-        self.eps = config.eps
-        self.down_proj = nn.Parameter(torch.empty(self.chunk_size, self.extended_len))
-        nn.init.xavier_normal_(self.down_proj)
-
-    def preprocess(self, x):
-        B, T, E = x.shape
-        cs, L = self.chunk_size, self.extended_len
-
-        padded = False
-        orig_T = T
-        if T % cs != 0:
-            pad_len = cs - (T % cs)
-            pad = torch.zeros(B, pad_len, E, device=x.device, dtype=x.dtype)
-            x = torch.cat([x, pad], dim=1)
-            T += pad_len
-            padded = True
-
-        N = T // cs
-        x_chunks = x.view(B, N, cs, E)
-
-        extended = []
-        for i in range(0, N):
-            cur = x_chunks[:, i]
-            others = x_chunks[:, :i]
-            cat = self._extend(others, cur)      # (B, ≤k⋅cs+cs, E)
-
-            # pad or truncate to length L
-            cur_len = cat.size(1)
-            if cur_len < L:
-                pad2 = torch.zeros(B, L - cur_len, E, device=x.device, dtype=x.dtype)
-                cat = torch.cat([pad2, cat], dim=1)
-            else:
-                cat = cat[:, -L:]
-
-            extended.append(cat)
-
-        ext = torch.stack(extended, dim=1)     # (B, N, L, E)
-        ext = (self.down_proj @ ext) + x_chunks
-        h = ext.view(B * N, cs, E)
-
-        state = {
-            "B": B,
-            "N": N,
-            "orig_T": orig_T,
-            "padded": padded,
-        }
-        return h, state
-
-    def contract(self, h, st):
-        B, cs = st["B"], self.chunk_size
-        N = st["N"]
-        padded = st["padded"]
-        orig_T = st["orig_T"]
-
-        E = h.size(-1)
-        final_chunks = h.view(B, N, cs, E)
-
-        out = final_chunks.reshape(B, N * cs, E)
-
-        if padded:
-            out = out[:, :orig_T, :]
-
-        return out
-
-    def _extend(self, other_chunks, cur_chunk):
-        B, cs, E = cur_chunk.shape
-        if other_chunks is None or other_chunks.size(1) == 0:
-            return cur_chunk
-
-        i = other_chunks.size(1)
-        num_sel = min(i, self.k - 1)
-        if num_sel <= 0:
-            return cur_chunk
-
-        # l2 normalize
-        cn = other_chunks / (other_chunks.norm(dim=-1, keepdim=True) + self.eps)
-        cm = cur_chunk / (cur_chunk.norm(dim=-1, keepdim=True) + self.eps)
-
-        # cosine sim
-        cm_e = cm.unsqueeze(1)                  # (B, 1, cs, E)
-        ct = cn.transpose(-1, -2)             # (B, i, E, cs)
-        sims = torch.matmul(cm_e, ct)           # (B, i, cs, cs)
-        mx, _ = sims.max(dim=-1)              # (B, i, cs)
-        scores = mx.sum(dim=-1)                # (B, i)
-
-        # topk
-        topk_vals, topk_idx = scores.topk(num_sel, dim=1)
-
-        # normalize weights
-        v_min = topk_vals.min(dim=-1, keepdim=True)[0]  # (B, 1)
-        w = topk_vals / (v_min + self.eps)              # (B, num_sel)
-        w = w.unsqueeze(-1).unsqueeze(-1)               # (B, num_sel, 1, 1)
-
-        # gather
-        idx_e = topk_idx.unsqueeze(-1).unsqueeze(-1).expand(-1, -1, cs, E)
-        sel = other_chunks.gather(1, idx_e)              # (B, num_sel, cs, E)
-
-        # weight & flatten
-        wt = (sel * w).reshape(B, num_sel * cs, E)
-
-        return torch.cat([wt, cur_chunk], dim=1)        # (B, ≤k⋅cs+cs, E)
-
-
-class AveyPreTrainedModel(PreTrainedModel):
-    config_class = AveyConfig
-
-    def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module):
-        if isinstance(module, nn.Linear):
-            nn.init.xavier_normal_(module.weight)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            nn.init.xavier_normal_(module.weight)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
-class AveyModel(AveyPreTrainedModel):
-    def __init__(self, config: AveyConfig):
-        super().__init__(config)
-        self.config = config
-        self.embeddings = nn.Embedding(config.vocab_size, config.d_embed)
-        self.layers = nn.ModuleList([AveyLayer(config, i) for i in range(config.n_layers)])
-        self.ranker = Ranker(config)
-        self.post_init()
-
-    def forward(self, input_ids: torch.Tensor, attention_mask=None, **kwargs):
-        h = self.embeddings(input_ids)
-        if attention_mask is not None:
-            h = h * attention_mask.unsqueeze(-1)
-
-        B, T, E = h.shape
-        padded = False
-        orig_T = T
-        if T % self.config.chunk_size != 0:
-            pad_len = self.config.chunk_size - (T % self.config.chunk_size)
-            pad_tensor = torch.zeros(
-                B, pad_len, E, device=h.device, dtype=h.dtype)
-            h = torch.cat([h, pad_tensor], dim=1)
-            T = h.shape[1]
-            padded = True
-
-        h, state = self.ranker.preprocess(h)
-        for (i, layer) in enumerate(self.layers):
-            # if i < self.config.n_layers - 2:
-            #     h = checkpoint(layer,h,use_reentrant=False)
-            # else:
-            #     h = layer(h)
-            h = layer(h)
-        h = self.ranker.contract(h, state)
-        if padded:
-            h = h[:, :orig_T, :]
-        
-        out = BaseModelOutput(last_hidden_state=h)
-
-        return out
-
-
-class AveyForMaskedLM(AveyPreTrainedModel):
-    def __init__(self, config: AveyConfig):
-        super().__init__(config)
-        self.config = config
-
-        self.base_avey_model = AveyModel(config)
-        self.ln_f = nn.RMSNorm(config.d_embed, eps=config.eps)
-
-        self.post_init()
-
-    def forward(self, input_ids: torch.Tensor, labels: torch.Tensor = None, **kwargs):
-        h = self.base_avey_model(input_ids, **kwargs).last_hidden_state
-        logits = F.linear(self.ln_f(h), self.base_avey_model.embeddings.weight)
-
-        if labels is not None:
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-100)
-            return MaskedLMOutput(logits=logits, loss=loss)
-
-        return MaskedLMOutput(logits=logits)
-
-
-class AveyForSequenceClassification(AveyPreTrainedModel):
-    def __init__(self, config: AveyConfig, avey_model: AveyForMaskedLM = None):
-        super().__init__(config)
-        self.config = config
-        self.num_labels = config.num_labels
-
-        if avey_model is None:
-            self.avey_model = AveyForMaskedLM(config)
-        else:
-            self.avey_model = avey_model
-
-        self.classifier = nn.Linear(config.d_embed, config.num_labels)
-        self.dense = nn.Sequential(
-            nn.Linear(self.config.d_embed, self.config.d_embed*2),
-            nn.GELU(),
-            nn.Linear(self.config.d_embed*2, self.config.d_embed*2),
-            nn.GELU(),
-            nn.Linear(self.config.d_embed*2, self.config.d_embed)
-        )
-        self.post_init()
-
-    def forward(self, input_ids: torch.Tensor, labels: torch.Tensor = None, **kwargs):
-        h = self.avey_model.base_avey_model(input_ids, **kwargs).last_hidden_state
-        h = h.mean(dim=1)
-        h = self.avey_model.ln_f(h)
-        h = self.dense(h)
-        h = F.gelu(h)
-        logits = self.classifier(h)
-        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 = 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 = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        return SequenceClassifierOutput(logits=logits, loss=loss)
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: str, *model_args, **kwargs):
-        config = kwargs.pop("config", None)
-        if config is None:
-            config = AveyConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        archs = getattr(config, "architectures", [])
-        is_mlm = any("MaskedLM" in a for a in archs)
-
-        if is_mlm:
-            mlm_model = AveyForMaskedLM.from_pretrained(pretrained_model_name_or_path,  **kwargs)
-            return cls(config, avey_model=mlm_model)
-        else:
-            return super().from_pretrained(
-                pretrained_model_name_or_path,
-                *model_args,
-                config=config,
-                **kwargs
-            )
-
-
-class AveyForTokenClassification(AveyPreTrainedModel):
-    def __init__(self, config: AveyConfig, avey_model: AveyForMaskedLM = None):
-        super().__init__(config)
-        self.config = config
-        self.num_labels = config.num_labels
-
-        if avey_model is None:
-            self.avey_model = AveyForMaskedLM(config)
-        else:
-            self.avey_model = avey_model
-
-        self.classifier = nn.Linear(config.d_embed, config.num_labels)
-        self.dense = nn.Sequential(
-            nn.Linear(config.d_embed, config.d_embed),
-            nn.Tanh()
-        )
-        self.post_init()
-
-    def forward(self, input_ids: torch.Tensor, labels: torch.Tensor = None, **kwargs):
-        outputs = self.avey_model.base_avey_model(input_ids, **kwargs)
-
-        h = outputs.last_hidden_state
-        h = self.avey_model.ln_f(h)
-        h = self.dense(h)
-        logits = self.classifier(h)
-        loss = None
-
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        return TokenClassifierOutput(loss=loss, logits=logits)
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: str, *model_args, **kwargs):
-        config = kwargs.pop("config", None)
-        if config is None:
-            config = AveyConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        archs = getattr(config, "architectures", [])
-        is_mlm = any("MaskedLM" in a for a in archs)
-
-        if is_mlm:
-            mlm_model = AveyForMaskedLM.from_pretrained(pretrained_model_name_or_path,  **kwargs)
-            return cls(config, avey_model=mlm_model)
-        else:
-            return super().from_pretrained(
-                pretrained_model_name_or_path,
-                *model_args,
-                config=config,
-                **kwargs
-            )