Create modeling_avey.py

avey_model/modeling_avey.py

@@ -0,0 +1,396 @@
+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
+            )